Merge branch 'development' into main

This merge adds the following major features:

* Support for image variations.

* Security fix for webGUI (binds to localhost by default, use
--host=0.0.0.0 to allow access from external interface.

* Scalable configs/models.yaml configuration file for adding more
models as they become available.

* More tuning and exception handling for M1 hardware running MPS.

* Various documentation fixes.

.dev_scripts/diff_images.py

@@ -0,0 +1,32 @@
+import argparse
+
+import numpy as np
+from PIL import Image
+
+
+def read_image_int16(image_path):
+    image = Image.open(image_path)
+    return np.array(image).astype(np.int16)
+
+
+def calc_images_mean_L1(image1_path, image2_path):
+    image1 = read_image_int16(image1_path)
+    image2 = read_image_int16(image2_path)
+    assert image1.shape == image2.shape
+
+    mean_L1 = np.abs(image1 - image2).mean()
+    return mean_L1
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('image1_path')
+    parser.add_argument('image2_path')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    mean_L1 = calc_images_mean_L1(args.image1_path, args.image2_path)
+    print(mean_L1)

.dev_scripts/sample_command.txt

@@ -0,0 +1 @@
+"a photograph of an astronaut riding a horse" -s50 -S42

.dev_scripts/test_regression_txt2img_dream_v1_4.sh

@@ -0,0 +1,20 @@
+# generate an image
+PROMPT_FILE=".dev_scripts/sample_command.txt"
+OUT_DIR="outputs/img-samples/test_regression_txt2img_v1_4"
+SAMPLES_DIR=${OUT_DIR}
+python scripts/dream.py \
+    --from_file ${PROMPT_FILE} \
+    --outdir ${OUT_DIR} \
+    --sampler plms \
+    --full_precision
+
+# original output by CompVis/stable-diffusion
+IMAGE1=".dev_scripts/images/v1_4_astronaut_rides_horse_plms_step50_seed42.png"
+# new output
+IMAGE2=`ls -A ${SAMPLES_DIR}/*.png | sort | tail -n 1`
+
+echo ""
+echo "comparing the following two images"
+echo "IMAGE1: ${IMAGE1}"
+echo "IMAGE2: ${IMAGE2}"
+python .dev_scripts/diff_images.py ${IMAGE1} ${IMAGE2}

.dev_scripts/test_regression_txt2img_v1_4.sh

@@ -0,0 +1,23 @@
+# generate an image
+PROMPT="a photograph of an astronaut riding a horse"
+OUT_DIR="outputs/txt2img-samples/test_regression_txt2img_v1_4"
+SAMPLES_DIR="outputs/txt2img-samples/test_regression_txt2img_v1_4/samples"
+python scripts/orig_scripts/txt2img.py \
+    --prompt "${PROMPT}" \
+    --outdir ${OUT_DIR} \
+    --plms \
+    --ddim_steps 50 \
+    --n_samples 1 \
+    --n_iter 1 \
+    --seed 42
+
+# original output by CompVis/stable-diffusion
+IMAGE1=".dev_scripts/images/v1_4_astronaut_rides_horse_plms_step50_seed42.png"
+# new output
+IMAGE2=`ls -A ${SAMPLES_DIR}/*.png | sort | tail -n 1`
+
+echo ""
+echo "comparing the following two images"
+echo "IMAGE1: ${IMAGE1}"
+echo "IMAGE2: ${IMAGE2}"
+python .dev_scripts/diff_images.py ${IMAGE1} ${IMAGE2}

.gitattributes

@@ -0,0 +1,4 @@
+# Auto normalizes line endings on commit so devs don't need to change local settings.
+# Only affects text files and ignores other file types. 
+# For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
+* text=auto

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,36 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe your environment**
+- GPU: [cuda/amd/mps/cpu]
+- VRAM: [if known]
+- CPU arch: [x86/arm]
+- OS: [Linux/Windows/macOS]
+- Python: [Anaconda/miniconda/miniforge/pyenv/other (explain)]
+- Branch: [if `git status` says anything other than "On branch main" paste it here]
+- Commit: [run `git show` and paste the line that starts with "Merge" here]
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.gitignore

@@ -2,6 +2,9 @@
 outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
 
+# ignore a directory which serves as a place for initial images
+inputs/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -170,6 +173,13 @@ cython_debug/
 #.idea/
 
 src
-logs/
 **/__pycache__/
 outputs
+
+# Logs and associated folders 
+# created from generated embeddings.
+logs
+testtube
+checkpoints
+# If it's a Mac
+.DS_Store

.gitmodules

@@ -1,13 +0,0 @@
-[submodule "taming-transformers"]
-	   path = src/taming-transformers
-	   url  = https://github.com/CompVis/taming-transformers.git
-	   ignore = dirty
-[submodule "clip"]
-	   path = src/clip
-	   url  = https://github.com/openai/CLIP.git
-	   ignore = dirty
-[submodule "k-diffusion"]
-	   path = src/k-diffusion
-	   url  = https://github.com/lstein/k-diffusion.git
-	   ignore = dirty
-	   

CHANGELOG.md

@@ -0,0 +1,137 @@
+# **Changelog**
+
+## v1.13 (in process)
+
+- Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)
+- WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling [Kevin Gibbons](https://github.com/bakkot)
+- WebUI supports incremental display of in-progress images during generation [Kevin Gibbons](https://github.com/bakkot)
+- Output directory can be specified on the dream> command line.
+- The grid was displaying duplicated images when not enough images to fill the final row [Muhammad Usama](https://github.com/SMUsamaShah)
+- Can specify --grid on dream.py command line as the default.
+- Miscellaneous internal bug and stability fixes.
+
+---
+
+## v1.12 (28 August 2022)
+
+- Improved file handling, including ability to read prompts from standard input.
+  (kudos to [Yunsaki](https://github.com/yunsaki)
+- The web server is now integrated with the dream.py script. Invoke by adding --web to
+  the dream.py command arguments.
+- Face restoration and upscaling via GFPGAN and Real-ESGAN are now automatically
+  enabled if the GFPGAN directory is located as a sibling to Stable Diffusion.
+  VRAM requirements are modestly reduced. Thanks to both [Blessedcoolant](https://github.com/blessedcoolant) and
+  [Oceanswave](https://github.com/oceanswave) for their work on this.
+- You can now swap samplers on the dream> command line. [Blessedcoolant](https://github.com/blessedcoolant)
+
+---
+
+## v1.11 (26 August 2022)
+
+- NEW FEATURE: Support upscaling and face enhancement using the GFPGAN module. (kudos to [Oceanswave](https://github.com/Oceanswave)
+- You now can specify a seed of -1 to use the previous image's seed, -2 to use the seed for the image generated before that, etc.
+  Seed memory only extends back to the previous command, but will work on all images generated with the -n# switch.
+- Variant generation support temporarily disabled pending more general solution.
+- Created a feature branch named **yunsaki-morphing-dream** which adds experimental support for
+  iteratively modifying the prompt and its parameters. Please see[ Pull Request #86](https://github.com/lstein/stable-diffusion/pull/86)
+  for a synopsis of how this works. Note that when this feature is eventually added to the main branch, it will may be modified
+  significantly.
+
+---
+
+## v1.10 (25 August 2022)
+
+- A barebones but fully functional interactive web server for online generation of txt2img and img2img.
+
+---
+
+## v1.09 (24 August 2022)
+
+- A new -v option allows you to generate multiple variants of an initial image
+  in img2img mode. (kudos to [Oceanswave](https://github.com/Oceanswave). [
+  See this discussion in the PR for examples and details on use](https://github.com/lstein/stable-diffusion/pull/71#issuecomment-1226700810))
+- Added ability to personalize text to image generation (kudos to [Oceanswave](https://github.com/Oceanswave) and [nicolai256](https://github.com/nicolai256))
+- Enabled all of the samplers from k_diffusion
+
+---
+
+## v1.08 (24 August 2022)
+
+- Escape single quotes on the dream> command before trying to parse. This avoids
+  parse errors.
+- Removed instruction to get Python3.8 as first step in Windows install.
+  Anaconda3 does it for you.
+- Added bounds checks for numeric arguments that could cause crashes.
+- Cleaned up the copyright and license agreement files.
+
+---
+
+## v1.07 (23 August 2022)
+
+- Image filenames will now never fill gaps in the sequence, but will be assigned the
+  next higher name in the chosen directory. This ensures that the alphabetic and chronological
+  sort orders are the same.
+
+---
+
+## v1.06 (23 August 2022)
+
+- Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
+- Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
+
+---
+
+## v1.05 (22 August 2022 - after the drop)
+
+- Filenames now use the following formats:
+  000010.95183149.png -- Two files produced by the same command (e.g. -n2),
+  000010.26742632.png -- distinguished by a different seed.
+
+  000011.455191342.01.png -- Two files produced by the same command using
+  000011.455191342.02.png -- a batch size>1 (e.g. -b2). They have the same seed.
+
+  000011.4160627868.grid#1-4.png -- a grid of four images (-g); the whole grid can
+  be regenerated with the indicated key
+
+- It should no longer be possible for one image to overwrite another
+- You can use the "cd" and "pwd" commands at the dream> prompt to set and retrieve
+  the path of the output directory.
+
+---
+
+## v1.04 (22 August 2022 - after the drop)
+
+- Updated README to reflect installation of the released weights.
+- Suppressed very noisy and inconsequential warning when loading the frozen CLIP
+  tokenizer.
+
+---
+
+## v1.03 (22 August 2022)
+
+- The original txt2img and img2img scripts from the CompViz repository have been moved into
+  a subfolder named "orig_scripts", to reduce confusion.
+
+---
+
+## v1.02 (21 August 2022)
+
+- A copy of the prompt and all of its switches and options is now stored in the corresponding
+  image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
+  or an image editor that allows you to explore the full metadata.
+  **Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
+
+---
+
+## v1.01 (21 August 2022)
+
+- added k_lms sampling.
+  **Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
+- use half precision arithmetic by default, resulting in faster execution and lower memory requirements
+  Pass argument --full_precision to dream.py to get slower but more accurate image generation
+
+---
+
+## Links
+
+- **[Read Me](readme.md)**

README-Mac-MPS.md

@@ -0,0 +1,322 @@
+# macOS Instructions
+
+Requirements
+
+- macOS 12.3 Monterey or later
+- Python
+- Patience
+- Apple Silicon*
+
+*I haven't tested any of this on Intel Macs but I have read that one person got
+it to work, so Apple Silicon might not be requried.
+
+Things have moved really fast and so these instructions change often and are
+often out-of-date. One of the problems is that there are so many different ways to
+run this.
+
+We are trying to build a testing setup so that when we make changes it doesn't
+always break.
+
+How to (this hasn't been 100% tested yet):
+
+First get the weights checkpoint download started - it's big:
+
+1. Sign up at https://huggingface.co
+2. Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
+3. Accept the terms and click Access Repository: 
+4. Download [sd-v1-4.ckpt (4.27 GB)](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt) and note where you have saved it (probably the Downloads folder)
+
+While that is downloading, open Terminal and run the following commands one at a time.
+
+```
+# install brew (and Xcode command line tools):
+/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+
+# install python 3, git, cmake, protobuf:
+brew install cmake protobuf rust
+
+# install miniconda (M1 arm64 version):
+curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o Miniconda3-latest-MacOSX-arm64.sh
+/bin/bash Miniconda3-latest-MacOSX-arm64.sh
+
+# clone the repo
+git clone https://github.com/lstein/stable-diffusion.git
+cd stable-diffusion
+
+#
+# wait until the checkpoint file has downloaded, then proceed
+#
+
+# create symlink to checkpoint
+mkdir -p models/ldm/stable-diffusion-v1/
+
+PATH_TO_CKPT="$HOME/Downloads"  # or wherever you saved sd-v1-4.ckpt
+
+ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
+
+# install packages
+PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yaml
+conda activate ldm
+
+# only need to do this once
+python scripts/preload_models.py
+
+# run SD!
+python scripts/dream.py --full_precision  # half-precision requires autocast and won't work
+```
+
+The original scripts should work as well.
+
+```
+python scripts/orig_scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
+```
+
+Note, `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env create -f environment-mac.yaml`
+never finishing in some situations. So it isn't required but wont hurt.
+
+After you follow all the instructions and run dream.py you might get several
+errors. Here's the errors I've seen and found solutions for.
+
+### Is it slow?
+
+Be sure to specify 1 sample and 1 iteration.
+
+	python ./scripts/orig_scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
+
+### Doesn't work anymore?
+
+PyTorch nightly includes support for MPS. Because of this, this setup is
+inherently unstable. One morning I woke up and it no longer worked no matter
+what I did until I switched to miniforge. However, I have another Mac that works
+just fine with Anaconda. If you can't get it to work, please search a little
+first because many of the errors will get posted and solved. If you can't find
+a solution please [create an issue](https://github.com/lstein/stable-diffusion/issues).
+
+One debugging step is to update to the latest version of PyTorch nightly.
+
+	conda install pytorch torchvision torchaudio -c pytorch-nightly
+
+If `conda env create -f environment-mac.yaml` takes forever run this.
+
+	git clean -f
+
+And run this.
+
+	conda clean --yes --all
+
+Or you could reset Anaconda.
+
+	conda update --force-reinstall -y -n base -c defaults conda
+
+### "No module named cv2", torch, 'ldm', 'transformers', 'taming', etc.
+
+There are several causes of these errors.
+
+First, did you remember to `conda activate ldm`? If your terminal prompt
+begins with "(ldm)" then you activated it. If it begins with "(base)"
+or something else you haven't.
+
+Second, you might've run `./scripts/preload_models.py` or `./scripts/dream.py`
+instead of `python ./scripts/preload_models.py` or `python ./scripts/dream.py`.
+The cause of this error is long so it's below.
+
+Third, if it says you're missing taming you need to rebuild your virtual
+environment.
+
+	conda env remove -n ldm
+	conda env create -f environment-mac.yaml
+
+Fourth, If you have activated the ldm virtual environment and tried rebuilding
+it, maybe the problem could be that I have something installed that
+you don't and you'll just need to manually install it. Make sure you
+activate the virtual environment so it installs there instead of
+globally.
+
+	conda activate ldm
+	pip install *name*
+
+You might also need to install Rust (I mention this again below).
+
+### How many snakes are living in your computer?
+
+Here's the reason why you have to specify which python to use.
+There are several versions of python on macOS and the computer is
+picking the wrong one. More specifically, preload_models.py and dream.py says to
+find the first `python3` in the path environment variable. You can see which one
+it is picking with `which python3`. These are the mostly likely paths you'll see.
+
+	% which python3
+	/usr/bin/python3
+
+The above path is part of the OS. However, that path is a stub that asks you if
+you want to install Xcode. If you have Xcode installed already,
+/usr/bin/python3 will execute /Library/Developer/CommandLineTools/usr/bin/python3 or
+/Applications/Xcode.app/Contents/Developer/usr/bin/python3 (depending on which
+Xcode you've selected with `xcode-select`).
+
+	% which python3
+	/opt/homebrew/bin/python3
+
+If you installed python3 with Homebrew and you've modified your path to search
+for Homebrew binaries before system ones, you'll see the above path.
+
+	% which python
+	/opt/anaconda3/bin/python
+
+If you drop the "3" you get an entirely different python. Note: starting in
+macOS 12.3, /usr/bin/python no longer exists (it was python 2 anyway).
+
+If you have Anaconda installed, this is what you'll see. There is a
+/opt/anaconda3/bin/python3 also.
+
+	(ldm) % which python
+	/Users/name/miniforge3/envs/ldm/bin/python
+
+This is what you'll see if you have miniforge and you've correctly activated
+the ldm environment. This is the goal.
+
+It's all a mess and you should know [how to modify the path environment variable](https://support.apple.com/guide/terminal/use-environment-variables-apd382cc5fa-4f58-4449-b20a-41c53c006f8f/mac)
+if you want to fix it. Here's a brief hint of all the ways you can modify it
+(don't really have the time to explain it all here).
+
+- ~/.zshrc
+- ~/.bash_profile
+- ~/.bashrc
+- /etc/paths.d
+- /etc/path
+
+Which one you use will depend on what you have installed except putting a file
+in /etc/paths.d is what I prefer to do.
+
+### Debugging?
+
+Tired of waiting for your renders to finish before you can see if it
+works? Reduce the steps! The image quality will be horrible but at least you'll
+get quick feedback.
+
+	python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
+
+### OSError: Can't load tokenizer for 'openai/clip-vit-large-patch14'...
+
+	python scripts/preload_models.py
+
+### "The operator [name] is not current implemented for the MPS device." (sic)
+
+Example error.
+
+```
+...
+NotImplementedError: The operator 'aten::_index_put_impl_' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on [https://github.com/pytorch/pytorch/issues/77764](https://github.com/pytorch/pytorch/issues/77764). As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.
+```
+
+The lstein branch includes this fix in [environment-mac.yaml](https://github.com/lstein/stable-diffusion/blob/main/environment-mac.yaml).
+
+### "Could not build wheels for tokenizers"
+
+I have not seen this error because I had Rust installed on my computer before I started playing with Stable Diffusion. The fix is to install Rust.
+
+	curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
+
+### How come `--seed` doesn't work?
+
+First this:
+
+> Completely reproducible results are not guaranteed across PyTorch
+releases, individual commits, or different platforms. Furthermore,
+results may not be reproducible between CPU and GPU executions, even
+when using identical seeds.
+
+[PyTorch docs](https://pytorch.org/docs/stable/notes/randomness.html)
+
+Second, we might have a fix that at least gets a consistent seed sort of. We're
+still working on it.
+
+### libiomp5.dylib error?
+
+	OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized.
+
+You are likely using an Intel package by mistake. Be sure to run conda with
+the environment variable `CONDA_SUBDIR=osx-arm64`, like so:
+
+`CONDA_SUBDIR=osx-arm64 conda install ...`
+
+This error happens with Anaconda on Macs when the Intel-only `mkl` is pulled in by
+a dependency. [nomkl](https://stackoverflow.com/questions/66224879/what-is-the-nomkl-python-package-used-for)
+is a metapackage designed to prevent this, by making it impossible to install
+`mkl`, but if your environment is already broken it may not work.
+
+Do *not* use `os.environ['KMP_DUPLICATE_LIB_OK']='True'` or equivalents as this
+masks the underlying issue of using Intel packages.
+
+### Not enough memory.
+
+This seems to be a common problem and is probably the underlying
+problem for a lot of symptoms (listed below). The fix is to lower your
+image size or to add `model.half()` right after the model is loaded. I
+should probably test it out. I've read that the reason this fixes
+problems is because it converts the model from 32-bit to 16-bit and
+that leaves more RAM for other things. I have no idea how that would
+affect the quality of the images though.
+
+See [this issue](https://github.com/CompVis/stable-diffusion/issues/71).
+
+### "Error: product of dimension sizes > 2**31'"
+
+This error happens with img2img, which I haven't played with too much
+yet. But I know it's because your image is too big or the resolution
+isn't a multiple of 32x32. Because the stable-diffusion model was
+trained on images that were 512 x 512, it's always best to use that
+output size (which is the default). However, if you're using that size
+and you get the above error, try 256 x 256 or 512 x 256 or something
+as the source image.
+
+BTW, 2**31-1 = [2,147,483,647](https://en.wikipedia.org/wiki/2,147,483,647#In_computing), which is also 32-bit signed [LONG_MAX](https://en.wikipedia.org/wiki/C_data_types) in C.
+
+### I just got Rickrolled! Do I have a virus?
+
+You don't have a virus. It's part of the project. Here's
+[Rick](https://github.com/lstein/stable-diffusion/blob/main/assets/rick.jpeg)
+and here's [the
+code](https://github.com/lstein/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
+that swaps him in. It's a NSFW filter, which IMO, doesn't work very
+good (and we call this "computer vision", sheesh).
+
+Actually, this could be happening because there's not enough RAM. You could try the `model.half()` suggestion or specify smaller output images.
+
+### My images come out black
+
+We might have this fixed, we are still testing.
+
+There's a [similar issue](https://github.com/CompVis/stable-diffusion/issues/69)
+on CUDA GPU's where the images come out green. Maybe it's the same issue?
+Someone in that issue says to use "--precision full", but this fork
+actually disables that flag. I don't know why, someone else provided
+that code and I don't know what it does. Maybe the `model.half()`
+suggestion above would fix this issue too. I should probably test it.
+
+### "view size is not compatible with input tensor's size and stride"
+
+```
+  File "/opt/anaconda3/envs/ldm/lib/python3.10/site-packages/torch/nn/functional.py", line 2511, in layer_norm
+    return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
+RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
+```
+
+Update to the latest version of lstein/stable-diffusion. We were
+patching pytorch but we found a file in stable-diffusion that we could
+change instead. This is a 32-bit vs 16-bit problem.
+
+### The processor must support the Intel bla bla bla
+
+What? Intel? On an Apple Silicon?
+
+	Intel MKL FATAL ERROR: This system does not meet the minimum requirements for use of the Intel(R) Math Kernel Library.
+	The processor must support the Intel(R) Supplemental Streaming SIMD Extensions 3 (Intel(R) SSSE3) instructions.
+	The processor must support the Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) instructions.
+	The processor must support the Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
+
+This is due to the Intel `mkl` package getting picked up when you try to install
+something that depends on it-- Rosetta can translate some Intel instructions but
+not the specialized ones here. To avoid this, make sure to use the environment
+variable `CONDA_SUBDIR=osx-arm64`, which restricts the Conda environment to only
+use ARM packages, and use `nomkl` as described above.

README.md

@@ -1,19 +1,71 @@
-# Stable Diffusion Dream Script
+<h1 align='center'><b>Stable Diffusion Dream Script</b></h1>
+
+<p align='center'>
+<img src="static/logo_temp.png"/>
+</p>
+
+<p align="center">
+    <img src="https://img.shields.io/github/last-commit/lstein/stable-diffusion?logo=Python&logoColor=green&style=for-the-badge" alt="last-commit"/>
+    <img src="https://img.shields.io/github/stars/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="stars"/>
+    <br>
+    <img src="https://img.shields.io/github/issues/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="issues"/>
+    <img src="https://img.shields.io/github/issues-pr/lstein/stable-diffusion?logo=GitHub&style=for-the-badge" alt="pull-requests"/>
+</p>
 
 This is a fork of CompVis/stable-diffusion, the wonderful open source
-text-to-image generator.
+text-to-image generator. This fork supports:
 
-The original has been modified in several ways:
+1. An interactive command-line interface that accepts the same prompt
+   and switches as the Discord bot.
+
+2. A basic Web interface that allows you to run a local web server for
+   generating images in your browser.
+
+3. Support for img2img in which you provide a seed image to guide the
+      image creation. (inpainting & masking coming soon)
+
+4. A notebook for running the code on Google Colab.
+
+5. Upscaling and face fixing using the optional ESRGAN and GFPGAN
+   packages.
+
+6. Weighted subprompts for prompt tuning.
+
+7. [Image variations](VARIATIONS.md) which allow you to systematically
+generate variations of an image you like and combine two or more
+images together to combine the best features of both.
+
+8. Textual inversion for customization of the prompt language and images.
+
+8. ...and more!
+
+This fork is rapidly evolving, so use the Issues panel to report bugs
+and make feature requests, and check back periodically for
+improvements and bug fixes.
+
+# Table of Contents
+
+1. [Major Features](#features)
+2. [Changelog](#latest-changes)
+3. [Installation](#installation)
+   1. [Linux](#linux)
+   1. [Windows](#windows)
+   1. [MacOS](README-Mac-MPS.md)
+4. [Troubleshooting](#troubleshooting)
+5. [Contributing](#contributing)
+6. [Support](#support)
+
+# Features
 
 ## Interactive command-line interface similar to the Discord bot
 
-The *dream.py* script, located in scripts/dream.py, 
+The _dream.py_ script, located in scripts/dream.py,
 provides an interactive interface to image generation similar to
 the "dream mothership" bot that Stable AI provided on its Discord
 server. Unlike the txt2img.py and img2img.py scripts provided in the
 original CompViz/stable-diffusion source code repository, the
 time-consuming initialization of the AI model
-initialization only happens once. After that image generation 
+initialization only happens once. After that image generation
 from the command-line interface is very fast.
 
 The script uses the readline library to allow for in-line editing,
@@ -27,17 +79,11 @@ The script is confirmed to work on Linux and Windows systems. It should
 work on MacOSX as well, but this is not confirmed. Note that this script
 runs from the command-line (CMD or Terminal window), and does not have a GUI.
 
-~~~~
+```
 (ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
 * Initializing, be patient...
 Loading model from models/ldm/text2img-large/model.ckpt
-LatentDiffusion: Running in eps-prediction mode
-DiffusionWrapper has 872.30 M params.
-making attention of type 'vanilla' with 512 in_channels
-Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
-making attention of type 'vanilla' with 512 in_channels
-Loading Bert tokenizer from "models/bert"
-setting sampler to plms
+(...more initialization messages...)
 
 * Initialization done! Awaiting your command...
 dream> ashley judd riding a camel -n2 -s150
@@ -55,13 +101,17 @@ dream> q
 00009.png: "ashley judd riding a camel" -s150 -S 416354203
 00010.png: "ashley judd riding a camel" -s150 -S 1362479620
 00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
-~~~~
+```
+
+<p align='center'>
+<img src="static/dream-py-demo.png"/>
+</p>
 
 The dream> prompt's arguments are pretty much identical to those used
 in the Discord bot, except you don't need to type "!dream" (it doesn't
 hurt if you do). A significant change is that creation of individual
 images is now the default unless --grid (-g) is given. For backward
-compatibility, the -i switch is recognized.  For command-line help
+compatibility, the -i switch is recognized. For command-line help
 type -h (or --help) at the dream> prompt.
 
 The script itself also recognizes a series of command-line switches
@@ -73,26 +123,207 @@ image outputs and the location of the model weight files.
 This script also provides an img2img feature that lets you seed your
 creations with a drawing or photo. This is a really cool feature that tells
 stable diffusion to build the prompt on top of the image you provide, preserving
-the original's basic shape and layout. To use it, provide the --init_img 
+the original's basic shape and layout. To use it, provide the --init_img
 option as shown here:
 
-~~~~
+```
 dream> "waterfall and rainbow" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
-~~~~
+```
 
 The --init_img (-I) option gives the path to the seed picture. --strength (-f) controls how much
 the original will be modified, ranging from 0.0 (keep the original intact), to 1.0 (ignore the original
 completely). The default is 0.75, and ranges from 0.25-0.75 give interesting results.
 
+You may also pass a -v<count> option to generate count variants on the original image. This is done by
+passing the first generated image back into img2img the requested number of times. It generates interesting
+variants.
+
+## GFPGAN and Real-ESRGAN Support
+
+The script also provides the ability to do face restoration and
+upscaling with the help of GFPGAN and Real-ESRGAN respectively.
+
+To use the ability, clone the **[GFPGAN
+repository](https://github.com/TencentARC/GFPGAN)** and follow their
+installation instructions. By default, we expect GFPGAN to be
+installed in a 'GFPGAN' sibling directory. Be sure that the `"ldm"`
+conda environment is active as you install GFPGAN.
+
+You can use the `--gfpgan_dir` argument with `dream.py` to set a
+custom path to your GFPGAN directory. _There are other GFPGAN related
+boot arguments if you wish to customize further._
+
+You can install **Real-ESRGAN** by typing the following command.
+
+```
+pip install realesrgan
+```
+
+**Note: Internet connection needed:**
+Users whose GPU machines are isolated from the Internet (e.g. on a
+University cluster) should be aware that the first time you run
+dream.py with GFPGAN and Real-ESRGAN turned on, it will try to
+download model files from the Internet. To rectify this, you may run
+`python3 scripts/preload_models.py` after you have installed GFPGAN
+and all its dependencies.
+
+**Usage**
+
+You will now have access to two new prompt arguments.
+
+**Upscaling**
+
+`-U : <upscaling_factor> <upscaling_strength>`
+
+The upscaling prompt argument takes two values. The first value is a
+scaling factor and should be set to either `2` or `4` only. This will
+either scale the image 2x or 4x respectively using different models.
+
+You can set the scaling stength between `0` and `1.0` to control
+intensity of the of the scaling. This is handy because AI upscalers
+generally tend to smooth out texture details. If you wish to retain
+some of those for natural looking results, we recommend using values
+between `0.5 to 0.8`.
+
+If you do not explicitly specify an upscaling_strength, it will
+default to 0.75.
+
+**Face Restoration**
+
+`-G : <gfpgan_strength>`
+
+This prompt argument controls the strength of the face restoration
+that is being applied. Similar to upscaling, values between `0.5 to 0.8` are recommended.
+
+You can use either one or both without any conflicts. In cases where
+you use both, the image will be first upscaled and then the face
+restoration process will be executed to ensure you get the highest
+quality facial features.
+
+`--save_orig`
+
+When you use either `-U` or `-G`, the final result you get is upscaled
+or face modified. If you want to save the original Stable Diffusion
+generation, you can use the `-save_orig` prompt argument to save the
+original unaffected version too.
+
+**Example Usage**
+
+```
+dream > superman dancing with a panda bear -U 2 0.6 -G 0.4
+```
+
+This also works with img2img:
+
+```
+dream> a man wearing a pineapple hat -I path/to/your/file.png -U 2 0.5 -G 0.6
+```
+
+**Note**
+
+GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid
+crashes and memory overloads during the Stable Diffusion process,
+these effects are applied after Stable Diffusion has completed its
+work.
+
+In single image generations, you will see the output right away but
+when you are using multiple iterations, the images will first be
+generated and then upscaled and face restored after that process is
+complete. While the image generation is taking place, you will still
+be able to preview the base images.
+
+If you wish to stop during the image generation but want to upscale or
+face restore a particular generated image, pass it again with the same
+prompt and generated seed along with the `-U` and `-G` prompt
+arguments to perform those actions.
+
+## Google Colab
+
+Stable Diffusion AI Notebook: <a href="https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/Stable_Diffusion_AI_Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> <br>
+Open and follow instructions to use an isolated environment running Dream.<br>
+
+Output example:
+![Colab Notebook](static/colab_notebook.png)
+
+## Barebones Web Server
+
+As of version 1.10, this distribution comes with a bare bones web
+server (see screenshot). To use it, run the _dream.py_ script by
+adding the **--web** option.
+
+```
+(ldm) ~/stable-diffusion$ python3 scripts/dream.py --web
+```
+
+You can then connect to the server by pointing your web browser at
+http://localhost:9090, or to the network name or IP address of the server.
+
+Kudos to [Tesseract Cat](https://github.com/TesseractCat) for
+contributing this code, and to [dagf2101](https://github.com/dagf2101)
+for refining it.
+
+![Dream Web Server](static/dream_web_server.png)
+
+## Reading Prompts from a File
+
+You can automate dream.py by providing a text file with the prompts
+you want to run, one line per prompt. The text file must be composed
+with a text editor (e.g. Notepad) and not a word processor. Each line
+should look like what you would type at the dream> prompt:
+
+```
+a beautiful sunny day in the park, children playing -n4 -C10
+stormy weather on a mountain top, goats grazing     -s100
+innovative packaging for a squid's dinner           -S137038382
+```
+
+Then pass this file's name to dream.py when you invoke it:
+
+```
+(ldm) ~/stable-diffusion$ python3 scripts/dream.py --from_file "path/to/prompts.txt"
+```
+
+You may read a series of prompts from standard input by providing a filename of "-":
+
+```
+(ldm) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/dream.py --from_file -
+```
+
+## Shortcut for reusing seeds from the previous command
+
+Since it is so common to reuse seeds while refining a prompt, there is
+now a shortcut as of version 1.11. Provide a **-S** (or **--seed**)
+switch of -1 to use the seed of the most recent image generated. If
+you produced multiple images with the **-n** switch, then you can go
+back further using -2, -3, etc. up to the first image generated by the
+previous command. Sorry, but you can't go back further than one
+command.
+
+Here's an example of using this to do a quick refinement. It also
+illustrates using the new **-G** switch to turn on upscaling and
+face enhancement (see previous section):
+
+```
+dream> a cute child playing hopscotch -G0.5
+[...]
+outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304
+
+# I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
+dream> a cute child playing hopscotch -G1.0 -s100 -S -1
+reusing previous seed 3498014304
+[...]
+outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
+```
+
 ## Weighted Prompts
 
 You may weight different sections of the prompt to tell the sampler to attach different levels of
 priority to them, by adding :(number) to the end of the section you wish to up- or downweight.
 For example consider this prompt:
 
-~~~~
+```
     tabby cat:0.25 white duck:0.75 hybrid
-~~~~
+```
 
 This will tell the sampler to invest 25% of its effort on the tabby
 cat aspect of the image and 75% on the white duck aspect
@@ -107,8 +338,10 @@ and introducing a new vocabulary to the fixed model.
 
 To train, prepare a folder that contains images sized at 512x512 and execute the following:
 
-~~~~
-# As the default backend is not available on Windows, if you're using that platform, execute SET PL_TORCH_DISTRIBUTED_BACKEND=gloo
+
+WINDOWS: As the default backend is not available on Windows, if you're using that platform, set the environment variable `PL_TORCH_DISTRIBUTED_BACKEND=gloo`
+    
+```
 (ldm) ~/stable-diffusion$ python3 ./main.py --base ./configs/stable-diffusion/v1-finetune.yaml \
                                             -t \
                                             --actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
@@ -116,151 +349,118 @@ To train, prepare a folder that contains images sized at 512x512 and execute the
                                             --gpus 0, \
                                             --data_root D:/textual-inversion/my_cat \
                                             --init_word 'cat'
-~~~~
+```
 
 During the training process, files will be created in /logs/[project][time][project]/
 where you can see the process.
 
-conditioning* contains the training prompts
+conditioning\* contains the training prompts
 inputs, reconstruction the input images for the training epoch
-samples, samples scaled for a sample of the prompt and one with the init word provided 
+samples, samples scaled for a sample of the prompt and one with the init word provided
 
 On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.
 
 Note: According to the associated paper, the optimal number of images
-is 3-5 any more images than that and your model might not converge.
+is 3-5. Your model may not converge if you use more images than that.
 
 Training will run indefinately, but you may wish to stop it before the
-heat death of the universe, when you fine a low loss epoch or around
+heat death of the universe, when you find a low loss epoch or around
 ~5000 iterations.
 
 Once the model is trained, specify the trained .pt file when starting
 dream using
 
-~~~~
+```
 (ldm) ~/stable-diffusion$ python3 ./scripts/dream.py --embedding_path /path/to/embedding.pt --full_precision
-~~~~
+```
 
 Then, to utilize your subject at the dream prompt
 
-~~~
+```
 dream> "a photo of *"
-~~~
+```
 
 this also works with image2image
-~~~~
+
+```
 dream> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
-~~~~
+```
 
 It's also possible to train multiple tokens (modify the placeholder string in configs/stable-diffusion/v1-finetune.yaml) and combine LDM checkpoints using:
 
-~~~~
+```
 (ldm) ~/stable-diffusion$ python3 ./scripts/merge_embeddings.py \
                                             --manager_ckpts /path/to/first/embedding.pt /path/to/second/embedding.pt [...] \
                                             --output_path /path/to/output/embedding.pt
-~~~~
+```
 
 Credit goes to @rinongal and the repository located at
 https://github.com/rinongal/textual_inversion Please see the
 repository and associated paper for details and limitations.
 
-## Changes
+# Latest Changes
 
-* v1.08 (24 August 2022)
-   * Escape single quotes on the dream> command before trying to parse. This avoids
-     parse errors.
-   * A new -v option allows you to generate multiple variants of an initial image
-     in img2img mode. (kudos to Oceanswave)
-   * Removed instruction to get Python3.8 as first step in Windows install.
-     Anaconda3 does it for you.
-   * Added bounds checks for numeric arguments that could cause crashes.
-   * Cleaned up the copyright and license agreement files.
+- v1.13 (in process)
 
-* v1.07 (23 August 2022)
-   * Image filenames will now never fill gaps in the sequence, but will be assigned the
-     next higher name in the chosen directory. This ensures that the alphabetic and chronological
-     sort orders are the same.
+  - Supports a Google Colab notebook for a standalone server running on Google hardware [Arturo Mendivil](https://github.com/artmen1516)
+  - WebUI supports GFPGAN/ESRGAN facial reconstruction and upscaling [Kevin Gibbons](https://github.com/bakkot)
+  - WebUI supports incremental display of in-progress images during generation [Kevin Gibbons](https://github.com/bakkot)
+  - Can specify --grid on dream.py command line as the default.
+  - Miscellaneous internal bug and stability fixes.
+  - Works on M1 Apple hardware.
+  - Multiple bug fixes.
 
-* v1.06 (23 August 2022)
-   * Added weighted prompt support contributed by [xraxra](https://github.com/xraxra)
-   * Example of using weighted prompts to tweak a demonic figure contributed by [bmaltais](https://github.com/bmaltais)
+For older changelogs, please visit **[CHANGELOGS](CHANGELOG.md)**.
 
-* v1.05 (22 August 2022 - after the drop)
-   * Filenames now use the following formats:
-       000010.95183149.png      -- Two files produced by the same command (e.g. -n2),
-       000010.26742632.png      -- distinguished by a different seed.
+# Installation
 
-       000011.455191342.01.png  -- Two files produced by the same command using
-       000011.455191342.02.png  -- a batch size>1 (e.g. -b2). They have the same seed.
+There are separate installation walkthroughs for [Linux](#linux), [Windows](#windows) and [Macintosh](#Macintosh)
 
-       000011.4160627868.grid#1-4.png  -- a grid of four images (-g); the whole grid can
-                                          be regenerated with the indicated key
-
-    * It should no longer be possible for one image to overwrite another
-    * You can use the "cd" and "pwd" commands at the dream> prompt to set and retrieve
-      the path of the output directory.
-     
-* v1.04 (22 August 2022 - after the drop)
-   * Updated README to reflect installation of the released weights.
-   * Suppressed very noisy and inconsequential warning when loading the frozen CLIP
-   tokenizer.
-
-* v1.03 (22 August 2022)
-   * The original txt2img and img2img scripts from the CompViz repository have been moved into
-     a subfolder named "orig_scripts", to reduce confusion.
-   
-* v1.02 (21 August 2022)
-    * A copy of the prompt and all of its switches and options is now stored in the corresponding
-    image in a tEXt metadata field named "Dream". You can read the prompt using scripts/images2prompt.py,
-    or an image editor that allows you to explore the full metadata.
-        **Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
-
-* v1.01 (21 August 2022)
-    * added k_lms sampling. 
-      **Please run "conda env update -f environment.yaml" to load the k_lms dependencies!!**
-    * use half precision arithmetic by default, resulting in faster execution and lower memory requirements
-    Pass argument --full_precision to dream.py to get slower but more accurate image generation
-
-
-## Installation
-
-There are separate installation walkthroughs for [Linux/Mac](#linuxmac) and [Windows](#windows).
-
-### Linux/Mac
+## Linux
 
 1. You will need to install the following prerequisites if they are not already available. Use your
-operating system's preferred installer
-* Python (version 3.8.5 recommended; higher may work)
-* git
+   operating system's preferred installer
+
+- Python (version 3.8.5 recommended; higher may work)
+- git
 
 2. Install the Python Anaconda environment manager using pip3.
+
 ```
 ~$ pip3 install anaconda
 ```
+
 After installing anaconda, you should log out of your system and log back in. If the installation
 worked, your command prompt will be prefixed by the name of the current anaconda environment, "(base)".
 
 3. Copy the stable-diffusion source code from GitHub:
+
 ```
 (base) ~$ git clone https://github.com/lstein/stable-diffusion.git
 ```
+
 This will create stable-diffusion folder where you will follow the rest of the steps.
 
 4. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
+
 ```
 (base) ~$ cd stable-diffusion
 (base) ~/stable-diffusion$
 ```
+
 5. Use anaconda to copy necessary python packages, create a new python environment named "ldm",
-and activate the environment. 
+   and activate the environment.
+
 ```
 (base) ~/stable-diffusion$ conda env create -f environment.yaml
 (base) ~/stable-diffusion$ conda activate ldm
 (ldm) ~/stable-diffusion$
 ```
+
 After these steps, your command prompt will be prefixed by "(ldm)" as shown above.
 
 6. Load a couple of small machine-learning models required by stable diffusion:
+
 ```
 (ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
 ```
@@ -280,13 +480,14 @@ to a page that prompts you to click the "download" link. Save the file somewhere
 
 Now run the following commands from within the stable-diffusion directory. This will create a symbolic
 link from the stable-diffusion model.ckpt file, to the true location of the sd-v1-4.ckpt file.
-   
+
 ```
 (ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1
 (ldm) ~/stable-diffusion$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
 ```
 
 8. Start generating images!
+
 ```
 # for the pre-release weights use the -l or --liaon400m switch
 (ldm) ~/stable-diffusion$ python3 scripts/dream.py -l
@@ -297,18 +498,45 @@ link from the stable-diffusion model.ckpt file, to the true location of the sd-v
 # for additional configuration switches and arguments, use -h or --help
 (ldm) ~/stable-diffusion$ python3 scripts/dream.py -h
 ```
-9. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the "stable-diffusion" 
-directory, and then launch the dream script (step 8). If you forget to activate the ldm environment, the script will fail with multiple ModuleNotFound errors.
 
-#### Updating to newer versions of the script
+9. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the "stable-diffusion"
+   directory, and then launch the dream script (step 8). If you forget to activate the ldm environment, the script will fail with multiple ModuleNotFound errors.
+
+### Updating to newer versions of the script
 
 This distribution is changing rapidly. If you used the "git clone" method (step 5) to download the stable-diffusion directory, then to update to the latest and greatest version, launch the Anaconda window, enter "stable-diffusion", and type:
+
 ```
 (ldm) ~/stable-diffusion$ git pull
 ```
+
 This will bring your local copy into sync with the remote one.
 
-### Windows
+## Windows
+
+### Notebook install (semi-automated)
+
+We have a
+[Jupyter notebook](https://github.com/lstein/stable-diffusion/blob/main/Stable-Diffusion-local-Windows.ipynb)
+with cell-by-cell installation steps. It will download the code in this repo as
+one of the steps, so instead of cloning this repo, simply download the notebook
+from the link above and load it up in VSCode (with the
+appropriate extensions installed)/Jupyter/JupyterLab and start running the cells one-by-one.
+
+Note that you will need NVIDIA drivers, Python 3.10, and Git installed
+beforehand - simplified
+[step-by-step instructions](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)
+are available in the wiki (you'll only need steps 1, 2, & 3 ).
+
+### Manual installs
+
+#### pip
+
+See
+[Easy-peasy Windows install](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)
+in the wiki
+
+#### Conda
 
 1. Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/
 
@@ -317,24 +545,30 @@ This will bring your local copy into sync with the remote one.
 3. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
 
 4. Run the command:
+
 ```
 git clone https://github.com/lstein/stable-diffusion.git
 ```
+
 This will create stable-diffusion folder where you will follow the rest of the steps.
 
 5. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
+
 ```
 cd stable-diffusion
 ```
 
 6. Run the following two commands:
+
 ```
 conda env create -f environment.yaml    (step 6a)
 conda activate ldm                      (step 6b)
 ```
+
 This will install all python requirements and activate the "ldm" environment which sets PATH and other environment variables properly.
 
 7. Run the command:
+
 ```
 python scripts\preload_models.py
 ```
@@ -347,30 +581,32 @@ downloaded just-in-time)
 8. Now you need to install the weights for the big stable diffusion model.
 
 For running with the released weights, you will first need to set up
-an acount with Hugging Face (https://huggingface.co).  Use your
+an acount with Hugging Face (https://huggingface.co). Use your
 credentials to log in, and then point your browser at
-https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.  You
+https://huggingface.co/CompVis/stable-diffusion-v-1-4-original. You
 may be asked to sign a license agreement at this point.
 
 Click on "Files and versions" near the top of the page, and then click
 on the file named "sd-v1-4.ckpt". You'll be taken to a page that
 prompts you to click the "download" link. Now save the file somewhere
-safe on your local machine.  The weight file is >4 GB in size, so
+safe on your local machine. The weight file is >4 GB in size, so
 downloading may take a while.
 
 Now run the following commands from **within the stable-diffusion
 directory** to copy the weights file to the right place:
-   
+
 ```
 mkdir -p models\ldm\stable-diffusion-v1
 copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
 ```
+
 Please replace "C:\path\to\sd-v1.4.ckpt" with the correct path to wherever
-you stashed this file. If you prefer not to copy or move the .ckpt file, 
+you stashed this file. If you prefer not to copy or move the .ckpt file,
 you may instead create a shortcut to it from within
 "models\ldm\stable-diffusion-v1\".
 
 9. Start generating images!
+
 ```
 # for the pre-release weights
 python scripts\dream.py -l
@@ -378,38 +614,57 @@ python scripts\dream.py -l
 # for the post-release weights
 python scripts\dream.py
 ```
-10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3), enter the stable-diffusion directory (step 5, "cd \path\to\stable-diffusion"), run "conda activate ldm" (step 6b), and then launch the dream script (step 9).
 
-#### Updating to newer versions of the script
+10. Subsequently, to relaunch the script, first activate the Anaconda
+command window (step 3), enter the stable-diffusion directory (step 5,
+"cd \path\to\stable-diffusion"), run "conda activate ldm" (step 6b),
+and then launch the dream script (step 9).
+
+**Note:** Tildebyte has written an alternative ["Easy peasy Windows
+install"](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)
+which uses the Windows Powershell and pew. If you are having trouble
+with Anaconda on Windows, give this a try (or try it first!)
+
+### Updating to newer versions of the script
+
+This distribution is changing rapidly. If you used the "git clone"
+method (step 5) to download the stable-diffusion directory, then to
+update to the latest and greatest version, launch the Anaconda window,
+enter "stable-diffusion", and type:
 
-This distribution is changing rapidly. If you used the "git clone" method (step 5) to download the stable-diffusion directory, then to update to the latest and greatest version, launch the Anaconda window, enter "stable-diffusion", and type:
 ```
 git pull
 ```
+
 This will bring your local copy into sync with the remote one.
 
-## Simplified API for text to image generation
+## Macintosh
+
+See [README-Mac-MPS](README-Mac-MPS.md) for instructions.
+
+# Simplified API for text to image generation
 
 For programmers who wish to incorporate stable-diffusion into other
-products, this repository includes a simplified API for text to image generation, which
-lets you create images from a prompt in just three lines of code:
+products, this repository includes a simplified API for text to image
+generation, which lets you create images from a prompt in just three
+lines of code:
 
-~~~~
+```
 from ldm.simplet2i import T2I
 model   = T2I()
 outputs = model.txt2img("a unicorn in manhattan")
-~~~~
+```
 
 Outputs is a list of lists in the format [[filename1,seed1],[filename2,seed2]...]
-Please see ldm/simplet2i.py for more information.
+Please see ldm/simplet2i.py for more information. A set of example scripts is
+coming RSN.
 
-
-## Workaround for machines with limited internet connectivity
+# Workaround for machines with limited internet connectivity
 
 My development machine is a GPU node in a high-performance compute
 cluster which has no connection to the internet. During model
 initialization, stable-diffusion tries to download the Bert tokenizer
-and a file needed by the kornia library.  This obviously didn't work
+and a file needed by the kornia library. This obviously didn't work
 for me.
 
 To work around this, I have modified ldm/modules/encoders/modules.py
@@ -420,7 +675,7 @@ prior to running the code on an isolated one. This assumes that both
 machines share a common network-mounted filesystem with a common
 .cache directory.
 
-~~~~
+```
 (ldm) ~/stable-diffusion$ python3 ./scripts/preload_models.py
 preloading bert tokenizer...
 Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
@@ -432,30 +687,113 @@ preloading kornia requirements...
 Downloading: "https://github.com/DagnyT/hardnet/raw/master/pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth" to /u/lstein/.cache/torch/hub/checkpoints/checkpoint_liberty_with_aug.pth
 100%|███████████████████████████████████████████████| 5.10M/5.10M [00:00<00:00, 101MB/s]
 ...success
-~~~~
+```
 
-If you don't need this change and want to download the files just in
-time, copy over the file ldm/modules/encoders/modules.py from the
-CompVis/stable-diffusion repository. Or you can run preload_models.py
-on the target machine.
+# Troubleshooting
 
-## Support
+Here are a few common installation problems and their solutions. Often
+these are caused by incomplete installations or crashes during the
+install process.
+
+- PROBLEM: During "conda env create -f environment.yaml", conda
+  hangs indefinitely.
+
+- SOLUTION: Enter the stable-diffusion directory and completely
+  remove the "src" directory and all its contents. The safest way
+  to do this is to enter the stable-diffusion directory and
+  give the command "git clean -f". If this still doesn't fix
+  the problem, try "conda clean -all" and then restart at the
+  "conda env create" step.
+
+---
+
+- PROBLEM: dream.py crashes with the complaint that it can't find
+  ldm.simplet2i.py. Or it complains that function is being passed
+  incorrect parameters.
+
+- SOLUTION: Reinstall the stable diffusion modules. Enter the
+  stable-diffusion directory and give the command "pip install -e ."
+
+---
+
+- PROBLEM: dream.py dies, complaining of various missing modules, none
+  of which starts with "ldm".
+
+- SOLUTION: From within the stable-diffusion directory, run "conda env
+  update -f environment.yaml" This is also frequently the solution to
+  complaints about an unknown function in a module.
+
+---
+
+- PROBLEM: There's a feature or bugfix in the Stable Diffusion GitHub
+  that you want to try out.
+
+- SOLUTION: If the fix/feature is on the "main" branch, enter the stable-diffusion
+  directory and do a "git pull". Usually this will be sufficient, but if
+  you start to see errors about missing or incorrect modules, use the
+  command "pip install -e ." and/or "conda env update -f environment.yaml"
+  (These commands won't break anything.)
+
+- If the feature/fix is on a branch (e.g. "foo-bugfix"), the recipe is similar, but
+  do a "git pull <name of branch>".
+
+- If the feature/fix is in a pull request that has not yet been made
+  part of the main branch or a feature/bugfix branch, then from the page
+  for the desired pull request, look for the line at the top that reads
+  "xxxx wants to merge xx commits into lstein:main from YYYYYY". Copy
+  the URL in YYYY. It should have the format
+  https://github.com/<name of contributor>/stable-diffusion/tree/<name
+  of branch>
+
+- Then **go to the directory above stable-diffusion**, and rename the
+  directory to "stable-diffusion.lstein", "stable-diffusion.old", or
+  whatever. You can then git clone the branch that contains the
+  pull request:
+
+```
+git clone https://github.com/<name of contributor>/stable-diffusion/tree/<name
+of branch>
+```
+
+You will need to go through the install procedure again, but it should
+be fast because all the dependencies are already loaded.
+
+# Contributing
+
+Anyone who wishes to contribute to this project, whether
+documentation, features, bug fixes, code cleanup, testing, or code
+reviews, is very much encouraged to do so. If you are unfamiliar with
+how to contribute to GitHub projects, here is a [Getting Started
+Guide](https://opensource.com/article/19/7/create-pull-request-github).
+
+A full set of contribution guidelines, along with templates, are in
+progress, but for now the most important thing is to **make your pull
+request against the "development" branch**, and not against
+"main". This will help keep public breakage to a minimum and will
+allow you to propose more radical changes.
+
+# Support
 
 For support,
 please use this repository's GitHub Issues tracking service. Feel free
 to send me an email if you use and like the script.
 
-*Original Author:* Lincoln D. Stein <lincoln.stein@gmail.com>
+_Original Author:_ Lincoln D. Stein <lincoln.stein@gmail.com>
 
-*Contributions by:* 
+_Contributions by:_
 [Peter Kowalczyk](https://github.com/slix), [Henry Harrison](https://github.com/hwharrison),
-[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan] (https://github.com/Oceanswave],
-[nicolai256](https://github.com/nicolai256], [Benjamin Warner](https://github.com/warner-benjamin),
-and [tildebyte](https://github.com/tildebyte)
+[xraxra](https://github.com/xraxra), [bmaltais](https://github.com/bmaltais), [Sean McLellan](https://github.com/Oceanswave),
+[nicolai256](https://github.com/nicolai256), [Benjamin Warner](https://github.com/warner-benjamin),
+[tildebyte](https://github.com/tildebyte),[yunsaki](https://github.com/yunsaki), [James Reynolds][https://github.com/magnusviri],
+[Tesseract Cat](https://github.com/TesseractCat), and many more!
+
+(If you have contributed and don't see your name on the list of
+contributors, please let lstein know about the omission, or make a
+pull request)
 
 Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)
 
-#Further Reading
+# Further Reading
 
 Please see the original README for more information on this software
-and underlying algorithm, located in the file README-CompViz.md.
+and underlying algorithm, located in the file [README-CompViz.md](README-CompViz.md).

Stable-Diffusion-local-Windows.ipynb

@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Easy-peasy Windows install"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that you will need NVIDIA drivers, Python 3.10, and Git installed\n",
+    "beforehand - simplified\n",
+    "[step-by-step instructions](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)\n",
+    "are available in the wiki (you'll only need steps 1, 2, & 3 )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run each cell in turn. In VSCode, either hit SHIFT-ENTER, or click on the little ▶️ to the left of the cell. In Jupyter/JupyterLab, you **must** hit SHIFT-ENTER"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install pew"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%cmd\n",
+    "git clone https://github.com/lstein/stable-diffusion.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cd stable-diffusion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile requirements.txt\n",
+    "albumentations==0.4.3\n",
+    "einops==0.3.0\n",
+    "huggingface-hub==0.8.1\n",
+    "imageio-ffmpeg==0.4.2\n",
+    "imageio==2.9.0\n",
+    "kornia==0.6.0\n",
+    "omegaconf==2.1.1\n",
+    "opencv-python==4.6.0.66\n",
+    "pillow==9.2.0\n",
+    "pudb==2019.2\n",
+    "pytorch-lightning==1.4.2\n",
+    "streamlit==1.12.0\n",
+    "# Regular \"taming-transformers\" doesn't seem to work\n",
+    "taming-transformers-rom1504==0.0.6\n",
+    "test-tube>=0.7.5\n",
+    "torch-fidelity==0.3.0\n",
+    "torchmetrics==0.6.0\n",
+    "torchvision==0.12.0\n",
+    "transformers==4.19.2\n",
+    "git+https://github.com/openai/CLIP.git@main#egg=clip\n",
+    "git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion\n",
+    "# No CUDA in PyPi builds\n",
+    "torch@https://download.pytorch.org/whl/cu113/torch-1.11.0%2Bcu113-cp310-cp310-win_amd64.whl\n",
+    "# No MKL in PyPi builds (faster, more robust than OpenBLAS)\n",
+    "numpy@https://download.lfd.uci.edu/pythonlibs/archived/numpy-1.22.4+mkl-cp310-cp310-win_amd64.whl\n",
+    "-e .\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%cmd\n",
+    "pew new --python 3.10 -r requirements.txt --dont-activate ldm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Switch the notebook kernel to the new 'ldm' environment!\n",
+    "\n",
+    "## VSCode: restart VSCode and come back to this cell\n",
+    "\n",
+    "1. Ctrl+Shift+P\n",
+    "1. Type \"Select Interpreter\" and select \"Jupyter: Select Interpreter to Start Jupyter Server\"\n",
+    "1. VSCode will say that it needs to install packages. Click the \"Install\" button.\n",
+    "1. Once the install is finished, do 1 & 2 again\n",
+    "1. Pick 'ldm'\n",
+    "1. Run the following cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%cd stable-diffusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Jupyter/JupyterLab\n",
+    "\n",
+    "1. Run the cell below\n",
+    "1. Click on the toolbar where it says \"(ipyknel)\" ↗️. You should get a pop-up asking you to \"Select Kernel\". Pick 'ldm' from the drop-down.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### DO NOT RUN THE FOLLOWING CELL IF YOU ARE USING VSCODE!!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DO NOT RUN THIS CELL IF YOU ARE USING VSCODE!!\n",
+    "%%cmd\n",
+    "pew workon ldm\n",
+    "pip3 install ipykernel\n",
+    "python -m ipykernel install --name=ldm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### When running the next cell, Jupyter/JupyterLab users might get a warning saying \"IProgress not found\". This can be ignored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run \"scripts/preload_models.py\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%cmd\n",
+    "mkdir \"models/ldm/stable-diffusion-v1\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now copy the SD model you downloaded from Hugging Face into the above new directory, and (if necessary) rename it to 'model.ckpt'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now go create some magic!\n",
+    "\n",
+    "VSCode\n",
+    "\n",
+    "- The actual input box for the 'dream' prompt will appear at the very top of the VSCode window. Type in your commands and hit 'ENTER'.\n",
+    "- To quit, hit the 'Interrupt' button in the toolbar up there ⬆️ a couple of times, then hit ENTER (you'll probably see a terrifying traceback from Python - just ignore it).\n",
+    "\n",
+    "Jupyter/JupyterLab\n",
+    "\n",
+    "- The input box for the 'dream' prompt will appear below. Type in your commands and hit 'ENTER'.\n",
+    "- To quit, hit the interrupt button (⏹️) in the toolbar up there ⬆️ a couple of times, then hit ENTER (you'll probably see a terrifying traceback from Python - just ignore it)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run \"scripts/dream.py\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Once this seems to be working well, you can try opening a terminal\n",
+    "\n",
+    "- VSCode: type ('CTRL+`')\n",
+    "- Jupyter/JupyterLab: File|New Terminal\n",
+    "- Or jump out of the notebook entirely, and open Powershell/Command Prompt\n",
+    "\n",
+    "Now:\n",
+    "\n",
+    "1. `cd` to wherever the 'stable-diffusion' directory is\n",
+    "1. Run `pew workon ldm`\n",
+    "1. Run `winpty python scripts\\dream.py`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.6 ('ldm')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "a05e4574567b7bc2c98f7f9aa579f9ea5b8739b54844ab610ac85881c4be2659"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Stable_Diffusion_AI_Notebook.ipynb

@@ -0,0 +1,256 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Stable_Diffusion_AI_Notebook.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "private_outputs": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Stable Diffusion AI Notebook\n",
+        "\n",
+        "<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
+        "#### Instructions:\n",
+        "1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
+        "2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll to enter `pipenv run scripts/dream.py` command to run Dream bot.<br> \n",
+        "3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
+        "3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also display images in cell #10.\n",
+        "4. To quit Dream bot use `q` command. <br> \n",
+        "---\n",
+        "<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
+        "<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #6\n",
+        "##### For more details visit Github repository: [lstein/stable-diffusion](https://github.com/lstein/stable-diffusion)\n",
+        "---\n"
+      ],
+      "metadata": {
+        "id": "ycYWcsEKc6w7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 1. Check current GPU assigned\n",
+        "!nvidia-smi -L\n",
+        "!nvidia-smi"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "a2Z5Qu_o8VtQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "vbI9ZsQHzjqF"
+      },
+      "outputs": [],
+      "source": [
+        "#@title 2. Download stable-diffusion Repository\n",
+        "from os.path import exists\n",
+        "\n",
+        "if exists(\"/content/stable-diffusion/\")==True:\n",
+        "  print(\"Already downloaded repo\")\n",
+        "else:\n",
+        "  !git clone --quiet https://github.com/lstein/stable-diffusion.git # Original repo\n",
+        "  %cd stable-diffusion/\n",
+        "  !git checkout --quiet tags/release-1.09\n",
+        "  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 3. Install Python 3.8 \n",
+        "%%capture --no-stderr\n",
+        "import gc\n",
+        "!apt-get -qq install python3.8\n",
+        "gc.collect()"
+      ],
+      "metadata": {
+        "id": "daHlozvwKesj",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 4. Install dependencies from file in a VirtualEnv\n",
+        "#@markdown Be patient, it takes ~ 5 - 7min <br>\n",
+        "%%capture --no-stderr\n",
+        "#Virtual environment\n",
+        "!pip install pipenv -q\n",
+        "!pip install colab-xterm\n",
+        "%load_ext colabxterm\n",
+        "!pipenv --python 3.8\n",
+        "!pipenv install -r requirements.txt --skip-lock\n",
+        "gc.collect()\n"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "QbXcGXYEFSNB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 5. Mount google Drive\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "YEWPV-sF1RDM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 6. Drive Path to model\n",
+        "#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
+        "#@markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
+        "#@markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
+        "from os.path import exists\n",
+        "\n",
+        "model_path = \"\" #@param {type:\"string\"}\n",
+        "if exists(model_path)==True:\n",
+        "  print(\"✅ Valid directory\")\n",
+        "else: \n",
+        "  print(\"❌ File doesn't exist\")"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "zRTJeZ461WGu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 7. Symlink to model\n",
+        "\n",
+        "from os.path import exists\n",
+        "import os \n",
+        "\n",
+        "# Folder creation if it doesn't exist\n",
+        "if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1\")==True:\n",
+        "  print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
+        "else:\n",
+        "  %mkdir /content/stable-diffusion/models/ldm/stable-diffusion-v1\n",
+        "  print(\"✅ Dir stable-diffusion-v1 created\")\n",
+        "\n",
+        "# Symbolic link if it doesn't exist\n",
+        "if exists(\"/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt\")==True:\n",
+        "  print(\"❗ Symlink already created\")\n",
+        "else: \n",
+        "  src = model_path\n",
+        "  dst = '/content/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt'\n",
+        "  os.symlink(src, dst) \n",
+        "  print(\"✅ Symbolic link created successfully\")"
+      ],
+      "metadata": {
+        "id": "UY-NNz4I8_aG",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 8. Load small ML models required\n",
+        "%%capture --no-stderr\n",
+        "!pipenv run scripts/preload_models.py\n",
+        "gc.collect()"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "ChIDWxLVHGGJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 9. Run Terminal and Execute Dream bot\n",
+        "#@markdown <font color=\"blue\">Steps:</font> <br>\n",
+        "#@markdown 1. Execute command `pipenv run scripts/dream.py` to run dream bot.<br>\n",
+        "#@markdown 2. After initialized you'll see `Dream>` line.<br>\n",
+        "#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
+        "#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
+        "\n",
+        "#Run from virtual env\n",
+        "\n",
+        "%xterm\n",
+        "gc.collect()"
+      ],
+      "metadata": {
+        "id": "ir4hCrMIuUpl",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title 10. Show generated images\n",
+        "\n",
+        "import glob\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.image as mpimg\n",
+        "%matplotlib inline\n",
+        "\n",
+        "images = []\n",
+        "for img_path in glob.glob('/content/stable-diffusion/outputs/img-samples/*.png'):\n",
+        "    images.append(mpimg.imread(img_path))\n",
+        "\n",
+        "# Remove ticks and labels on x-axis and y-axis both\n",
+        "\n",
+        "plt.figure(figsize=(20,10))\n",
+        "\n",
+        "columns = 5\n",
+        "for i, image in enumerate(images):\n",
+        "    ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
+        "    ax.axes.xaxis.set_visible(False)\n",
+        "    ax.axes.yaxis.set_visible(False)\n",
+        "    ax.axis('off')\n",
+        "    plt.imshow(image)\n",
+        "    gc.collect()\n",
+        "\n"
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "qnLohSHmKoGk"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

TODO.txt

@@ -1,31 +0,0 @@
-Feature requests:
-
-1. "gobig" mode - split image into strips, scale up, add detail using
-   img2img and reassemble with feathering. Issue #66.
-
-2. Port basujindal low VRAM optimizations. Issue #62
-
-3. Store images under folders named after the prompt. Issue #27.
-
-4. Some sort of automation for generating variations. Issues #32 and #47.
-
-5. Support for inpainting masks #68.
-
-6. Support for loading variations of the stable-diffusion
-   weights #49
-
-7. Support for klms and other non-ddim samplers in img2img() #36
-
-8. Pass a shell command to open up an image viewer on the last
-   batch of images generated #29.
-
-Code Refactorization:
-
-1. Move the PNG file generation code out of simplet2i and into
-   separate module. txt2img() and img2img() should return Image
-   objects, and parent code is responsible for filenaming logic.
-
-2. Refactor redundant code that is shared between txt2img() and
-   img2img().
-
-3. Experiment with replacing CompViz code with HuggingFace.

VARIATIONS.md

@@ -0,0 +1,113 @@
+# Cheat Sheat for Generating Variations
+
+Release 1.13 of SD-Dream adds support for image variations. There are two things that you can do:
+
+1. Generate a series of systematic variations of an image, given a
+prompt. The amount of variation from one image to the next can be
+controlled.
+
+2. Given two or more variations that you like, you can combine them in
+a weighted fashion
+
+This cheat sheet provides a quick guide for how this works in
+practice, using variations to create the desired image of Xena,
+Warrior Princess.
+
+## Step 1 -- find a base image that you like
+
+The prompt we will use throughout is "lucy lawless as xena, warrior
+princess, character portrait, high resolution." This will be indicated
+as "prompt" in the examples below.
+
+First we let SD create a series of images in the usual way, in this case
+requesting six iterations:
+
+~~~
+dream> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
+...
+Outputs:
+./outputs/Xena/000001.1579445059.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1579445059
+./outputs/Xena/000001.1880768722.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1880768722
+./outputs/Xena/000001.332057179.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S332057179
+./outputs/Xena/000001.2224800325.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S2224800325
+./outputs/Xena/000001.465250761.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S465250761
+./outputs/Xena/000001.3357757885.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S3357757885
+~~~
+
+The one with seed 3357757885 looks nice:
+
+<img src="static/variation_walkthru/000001.3357757885.png"/>
+
+Let's try to generate some variations. Using the same seed, we pass
+the argument -v0.1 (or --variant_amount), which generates a series of
+variations each differing by a variation amount of 0.2. This number
+ranges from 0 to 1.0, with higher numbers being larger amounts of
+variation.
+
+~~~
+dream> "prompt" -n6 -S3357757885 -v0.2
+...
+Outputs:
+./outputs/Xena/000002.784039624.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 784039624:0.2 -S3357757885
+./outputs/Xena/000002.3647897225.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.2 -S3357757885
+./outputs/Xena/000002.917731034.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 917731034:0.2 -S3357757885
+./outputs/Xena/000002.4116285959.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 4116285959:0.2 -S3357757885
+./outputs/Xena/000002.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1614299449:0.2 -S3357757885
+./outputs/Xena/000002.1335553075.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1335553075:0.2 -S3357757885
+~~~
+
+Note that the output for each image has a -V option giving the
+"variant subseed" for that image, consisting of a seed followed by the
+variation amount used to generate it.
+
+This gives us a series of closely-related variations, including the
+two shown here.
+
+<img src="static/variation_walkthru/000002.3647897225.png">
+<img src="static/variation_walkthru/000002.1614299449.png">
+
+
+I like the expression on Xena's face in the first one (subseed
+3647897225), and the armor on her shoulder in the second one (subseed
+1614299449). Can we combine them to get the best of both worlds?
+
+We combine the two variations using -V (--with_variations). Again, we
+must provide the seed for the originally-chosen image in order for
+this to work.
+
+~~~
+dream> "prompt"  -S3357757885 -V3647897225,0.1;1614299449,0.1
+Outputs:
+./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
+~~~
+
+Here we are providing equal weights (0.1 and 0.1) for both the
+subseeds. The resulting image is close, but not exactly what I
+wanted:
+
+<img src="static/variation_walkthru/000003.1614299449.png">
+
+We could either try combining the images with different weights, or we
+can generate more variations around the almost-but-not-quite image. We
+do the latter, using both the -V (combining) and -v (variation
+strength) options. Note that we use -n6 to generate 6 variations:
+
+~~~~
+dream> "prompt" -S3357757885 -V3647897225,0.1;1614299449,0.1 -v0.05 -n6
+Outputs:
+./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885
+./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2853129515:0.05 -S3357757885
+./outputs/Xena/000004.3747154981.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3747154981:0.05 -S3357757885
+./outputs/Xena/000004.2664260391.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2664260391:0.05 -S3357757885
+./outputs/Xena/000004.1642517170.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,1642517170:0.05 -S3357757885
+./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2183375608:0.05 -S3357757885
+~~~~
+
+This produces six images, all slight variations on the combination of
+the chosen two images. Here's the one I like best:
+
+<img src="static/variation_walkthru/000004.3747154981.png">
+
+As you can see, this is a very powerful too, which when combined with
+subprompt weighting, gives you great control over the content and
+quality of your generated images.

configs/models.yaml

@@ -0,0 +1,18 @@
+# This file describes the alternative machine learning models
+#  available to the dream script.
+#
+# To add a new model, follow the examples below. Each
+# model requires a model config file, a weights file,
+# and the width and height of the images it
+# was trained on.
+
+laion400m:
+    config:  configs/latent-diffusion/txt2img-1p4B-eval.yaml
+    weights: models/ldm/text2img-large/model.ckpt
+    width: 256
+    height: 256
+stable-diffusion-1.4:
+    config:  configs/stable-diffusion/v1-inference.yaml
+    weights: models/ldm/stable-diffusion-v1/model.ckpt
+    width: 512
+    height: 512

configs/stable-diffusion/v1-finetune.yaml

@@ -73,8 +73,8 @@ model:
 data:
   target: main.DataModuleFromConfig
   params:
-    batch_size: 2
-    num_workers: 16
+    batch_size: 1
+    num_workers: 2
     wrap: false
     train:
       target: ldm.data.personalized.PersonalizedBase
@@ -92,6 +92,9 @@ data:
         repeats: 10
 
 lightning:
+  modelcheckpoint:
+    params:
+      every_n_train_steps: 500
   callbacks:
     image_logger:
       target: main.ImageLogger
@@ -102,4 +105,5 @@ lightning:
 
   trainer:
     benchmark: True
-    max_steps: 6100
+    max_steps: 4000
+    

environment-mac.yaml

@@ -0,0 +1,58 @@
+name: ldm
+channels:
+  - pytorch-nightly
+  - conda-forge
+dependencies:
+  - python==3.9.13
+  - pip==22.2.2
+ 
+  # pytorch-nightly, left unpinned
+  - pytorch
+  - torchmetrics
+  - torchvision
+
+  # I suggest to keep the other deps sorted for convenience.
+  # If you wish to upgrade to 3.10, try to run this:
+  #
+  # ```shell
+  # CONDA_CMD=conda
+  # sed -E 's/python==3.9.13/python==3.10.5/;s/ldm/ldm-3.10/;21,99s/- ([^=]+)==.+/- \1/' environment-mac.yaml > /tmp/environment-mac-updated.yml
+  # CONDA_SUBDIR=osx-arm64 $CONDA_CMD env create -f /tmp/environment-mac-updated.yml && $CONDA_CMD list -n ldm-3.10 | awk ' {print "  - " $1 "==" $2;} '
+  # ```
+  #
+  # Unfortunately, as of 2022-08-31, this fails at the pip stage.
+  - albumentations==1.2.1
+  - coloredlogs==15.0.1
+  - einops==0.4.1
+  - grpcio==1.46.4
+  - humanfriendly
+  - imageio-ffmpeg==0.4.7
+  - imageio==2.21.2
+  - imgaug==0.4.0
+  - kornia==0.6.7
+  - mpmath==1.2.1
+  - nomkl
+  - numpy==1.23.2
+  - omegaconf==2.1.1
+  - onnx==1.12.0
+  - onnxruntime==1.12.1
+  - opencv==4.6.0
+  - pudb==2022.1
+  - pytorch-lightning==1.6.5
+  - scipy==1.9.1
+  - streamlit==1.12.2
+  - sympy==1.10.1
+  - tensorboard==2.9.0
+  - transformers==4.21.2
+  - pip:
+    - invisible-watermark
+    - test-tube
+    - tokenizers
+    - torch-fidelity
+    - -e git+https://github.com/huggingface/diffusers.git@v0.2.4#egg=diffusers
+    - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
+    - -e git+https://github.com/openai/CLIP.git@main#egg=clip
+    - -e git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
+    - -e .
+variables:
+  PYTORCH_ENABLE_MPS_FALLBACK: 1

environment.yaml

@@ -18,14 +18,13 @@ dependencies:
     - pytorch-lightning==1.4.2
     - omegaconf==2.1.1
     - test-tube>=0.7.5
-    - streamlit>=0.73.1
-    - pillow==9.0.1
+    - streamlit==1.12.0
+    - pillow==9.2.0
     - einops==0.3.0
     - torch-fidelity==0.3.0
     - transformers==4.19.2
     - torchmetrics==0.6.0
-    - kornia==0.6
-    - accelerate==0.12.0
+    - kornia==0.6.0
     - -e git+https://github.com/openai/CLIP.git@main#egg=clip
     - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
     - -e git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion

ldm/data/base.py

@@ -1,11 +1,17 @@
 from abc import abstractmethod
-from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
+from torch.utils.data import (
+    Dataset,
+    ConcatDataset,
+    ChainDataset,
+    IterableDataset,
+)
 
 
 class Txt2ImgIterableBaseDataset(IterableDataset):
-    '''
+    """
     Define an interface to make the IterableDatasets for text2img data chainable
-    '''
+    """
+
     def __init__(self, num_records=0, valid_ids=None, size=256):
         super().__init__()
         self.num_records = num_records
@@ -13,11 +19,13 @@ class Txt2ImgIterableBaseDataset(IterableDataset):
         self.sample_ids = valid_ids
         self.size = size
 
-        print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
+        print(
+            f'{self.__class__.__name__} dataset contains {self.__len__()} examples.'
+        )
 
     def __len__(self):
         return self.num_records
 
     @abstractmethod
     def __iter__(self):
-        pass
+        pass

ldm/data/imagenet.py

@@ -11,24 +11,34 @@ from tqdm import tqdm
 from torch.utils.data import Dataset, Subset
 
 import taming.data.utils as tdu
-from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
+from taming.data.imagenet import (
+    str_to_indices,
+    give_synsets_from_indices,
+    download,
+    retrieve,
+)
 from taming.data.imagenet import ImagePaths
 
-from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
+from ldm.modules.image_degradation import (
+    degradation_fn_bsr,
+    degradation_fn_bsr_light,
+)
 
 
-def synset2idx(path_to_yaml="data/index_synset.yaml"):
+def synset2idx(path_to_yaml='data/index_synset.yaml'):
     with open(path_to_yaml) as f:
         di2s = yaml.load(f)
-    return dict((v,k) for k,v in di2s.items())
+    return dict((v, k) for k, v in di2s.items())
 
 
 class ImageNetBase(Dataset):
     def __init__(self, config=None):
         self.config = config or OmegaConf.create()
-        if not type(self.config)==dict:
+        if not type(self.config) == dict:
             self.config = OmegaConf.to_container(self.config)
-        self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
+        self.keep_orig_class_label = self.config.get(
+            'keep_orig_class_label', False
+        )
         self.process_images = True  # if False we skip loading & processing images and self.data contains filepaths
         self._prepare()
         self._prepare_synset_to_human()
@@ -46,17 +56,23 @@ class ImageNetBase(Dataset):
         raise NotImplementedError()
 
     def _filter_relpaths(self, relpaths):
-        ignore = set([
-            "n06596364_9591.JPEG",
-        ])
-        relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
-        if "sub_indices" in self.config:
-            indices = str_to_indices(self.config["sub_indices"])
-            synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn)  # returns a list of strings
+        ignore = set(
+            [
+                'n06596364_9591.JPEG',
+            ]
+        )
+        relpaths = [
+            rpath for rpath in relpaths if not rpath.split('/')[-1] in ignore
+        ]
+        if 'sub_indices' in self.config:
+            indices = str_to_indices(self.config['sub_indices'])
+            synsets = give_synsets_from_indices(
+                indices, path_to_yaml=self.idx2syn
+            )  # returns a list of strings
             self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
             files = []
             for rpath in relpaths:
-                syn = rpath.split("/")[0]
+                syn = rpath.split('/')[0]
                 if syn in synsets:
                     files.append(rpath)
             return files
@@ -65,78 +81,89 @@ class ImageNetBase(Dataset):
 
     def _prepare_synset_to_human(self):
         SIZE = 2655750
-        URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
-        self.human_dict = os.path.join(self.root, "synset_human.txt")
-        if (not os.path.exists(self.human_dict) or
-                not os.path.getsize(self.human_dict)==SIZE):
+        URL = 'https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1'
+        self.human_dict = os.path.join(self.root, 'synset_human.txt')
+        if (
+            not os.path.exists(self.human_dict)
+            or not os.path.getsize(self.human_dict) == SIZE
+        ):
             download(URL, self.human_dict)
 
     def _prepare_idx_to_synset(self):
-        URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
-        self.idx2syn = os.path.join(self.root, "index_synset.yaml")
-        if (not os.path.exists(self.idx2syn)):
+        URL = 'https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1'
+        self.idx2syn = os.path.join(self.root, 'index_synset.yaml')
+        if not os.path.exists(self.idx2syn):
             download(URL, self.idx2syn)
 
     def _prepare_human_to_integer_label(self):
-        URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
-        self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
-        if (not os.path.exists(self.human2integer)):
+        URL = 'https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1'
+        self.human2integer = os.path.join(
+            self.root, 'imagenet1000_clsidx_to_labels.txt'
+        )
+        if not os.path.exists(self.human2integer):
             download(URL, self.human2integer)
-        with open(self.human2integer, "r") as f:
+        with open(self.human2integer, 'r') as f:
             lines = f.read().splitlines()
             assert len(lines) == 1000
             self.human2integer_dict = dict()
             for line in lines:
-                value, key = line.split(":")
+                value, key = line.split(':')
                 self.human2integer_dict[key] = int(value)
 
     def _load(self):
-        with open(self.txt_filelist, "r") as f:
+        with open(self.txt_filelist, 'r') as f:
             self.relpaths = f.read().splitlines()
             l1 = len(self.relpaths)
             self.relpaths = self._filter_relpaths(self.relpaths)
-            print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
+            print(
+                'Removed {} files from filelist during filtering.'.format(
+                    l1 - len(self.relpaths)
+                )
+            )
 
-        self.synsets = [p.split("/")[0] for p in self.relpaths]
+        self.synsets = [p.split('/')[0] for p in self.relpaths]
         self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
 
         unique_synsets = np.unique(self.synsets)
-        class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
+        class_dict = dict(
+            (synset, i) for i, synset in enumerate(unique_synsets)
+        )
         if not self.keep_orig_class_label:
             self.class_labels = [class_dict[s] for s in self.synsets]
         else:
             self.class_labels = [self.synset2idx[s] for s in self.synsets]
 
-        with open(self.human_dict, "r") as f:
+        with open(self.human_dict, 'r') as f:
             human_dict = f.read().splitlines()
             human_dict = dict(line.split(maxsplit=1) for line in human_dict)
 
         self.human_labels = [human_dict[s] for s in self.synsets]
 
         labels = {
-            "relpath": np.array(self.relpaths),
-            "synsets": np.array(self.synsets),
-            "class_label": np.array(self.class_labels),
-            "human_label": np.array(self.human_labels),
+            'relpath': np.array(self.relpaths),
+            'synsets': np.array(self.synsets),
+            'class_label': np.array(self.class_labels),
+            'human_label': np.array(self.human_labels),
         }
 
         if self.process_images:
-            self.size = retrieve(self.config, "size", default=256)
-            self.data = ImagePaths(self.abspaths,
-                                   labels=labels,
-                                   size=self.size,
-                                   random_crop=self.random_crop,
-                                   )
+            self.size = retrieve(self.config, 'size', default=256)
+            self.data = ImagePaths(
+                self.abspaths,
+                labels=labels,
+                size=self.size,
+                random_crop=self.random_crop,
+            )
         else:
             self.data = self.abspaths
 
 
 class ImageNetTrain(ImageNetBase):
-    NAME = "ILSVRC2012_train"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
+    NAME = 'ILSVRC2012_train'
+    URL = 'http://www.image-net.org/challenges/LSVRC/2012/'
+    AT_HASH = 'a306397ccf9c2ead27155983c254227c0fd938e2'
     FILES = [
-        "ILSVRC2012_img_train.tar",
+        'ILSVRC2012_img_train.tar',
     ]
     SIZES = [
         147897477120,
@@ -151,57 +178,64 @@ class ImageNetTrain(ImageNetBase):
         if self.data_root:
             self.root = os.path.join(self.data_root, self.NAME)
         else:
-            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
+            cachedir = os.environ.get(
+                'XDG_CACHE_HOME', os.path.expanduser('~/.cache')
+            )
+            self.root = os.path.join(cachedir, 'autoencoders/data', self.NAME)
 
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
+        self.datadir = os.path.join(self.root, 'data')
+        self.txt_filelist = os.path.join(self.root, 'filelist.txt')
         self.expected_length = 1281167
-        self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
-                                    default=True)
+        self.random_crop = retrieve(
+            self.config, 'ImageNetTrain/random_crop', default=True
+        )
         if not tdu.is_prepared(self.root):
             # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
+            print('Preparing dataset {} in {}'.format(self.NAME, self.root))
 
             datadir = self.datadir
             if not os.path.exists(datadir):
                 path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
+                if (
+                    not os.path.exists(path)
+                    or not os.path.getsize(path) == self.SIZES[0]
+                ):
                     import academictorrents as at
+
                     atpath = at.get(self.AT_HASH, datastore=self.root)
                     assert atpath == path
 
-                print("Extracting {} to {}".format(path, datadir))
+                print('Extracting {} to {}'.format(path, datadir))
                 os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
+                with tarfile.open(path, 'r:') as tar:
                     tar.extractall(path=datadir)
 
-                print("Extracting sub-tars.")
-                subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
+                print('Extracting sub-tars.')
+                subpaths = sorted(glob.glob(os.path.join(datadir, '*.tar')))
                 for subpath in tqdm(subpaths):
-                    subdir = subpath[:-len(".tar")]
+                    subdir = subpath[: -len('.tar')]
                     os.makedirs(subdir, exist_ok=True)
-                    with tarfile.open(subpath, "r:") as tar:
+                    with tarfile.open(subpath, 'r:') as tar:
                         tar.extractall(path=subdir)
 
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
+            filelist = glob.glob(os.path.join(datadir, '**', '*.JPEG'))
             filelist = [os.path.relpath(p, start=datadir) for p in filelist]
             filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
+            filelist = '\n'.join(filelist) + '\n'
+            with open(self.txt_filelist, 'w') as f:
                 f.write(filelist)
 
             tdu.mark_prepared(self.root)
 
 
 class ImageNetValidation(ImageNetBase):
-    NAME = "ILSVRC2012_validation"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
-    VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
+    NAME = 'ILSVRC2012_validation'
+    URL = 'http://www.image-net.org/challenges/LSVRC/2012/'
+    AT_HASH = '5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5'
+    VS_URL = 'https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1'
     FILES = [
-        "ILSVRC2012_img_val.tar",
-        "validation_synset.txt",
+        'ILSVRC2012_img_val.tar',
+        'validation_synset.txt',
     ]
     SIZES = [
         6744924160,
@@ -217,39 +251,49 @@ class ImageNetValidation(ImageNetBase):
         if self.data_root:
             self.root = os.path.join(self.data_root, self.NAME)
         else:
-            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
+            cachedir = os.environ.get(
+                'XDG_CACHE_HOME', os.path.expanduser('~/.cache')
+            )
+            self.root = os.path.join(cachedir, 'autoencoders/data', self.NAME)
+        self.datadir = os.path.join(self.root, 'data')
+        self.txt_filelist = os.path.join(self.root, 'filelist.txt')
         self.expected_length = 50000
-        self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
-                                    default=False)
+        self.random_crop = retrieve(
+            self.config, 'ImageNetValidation/random_crop', default=False
+        )
         if not tdu.is_prepared(self.root):
             # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
+            print('Preparing dataset {} in {}'.format(self.NAME, self.root))
 
             datadir = self.datadir
             if not os.path.exists(datadir):
                 path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
+                if (
+                    not os.path.exists(path)
+                    or not os.path.getsize(path) == self.SIZES[0]
+                ):
                     import academictorrents as at
+
                     atpath = at.get(self.AT_HASH, datastore=self.root)
                     assert atpath == path
 
-                print("Extracting {} to {}".format(path, datadir))
+                print('Extracting {} to {}'.format(path, datadir))
                 os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
+                with tarfile.open(path, 'r:') as tar:
                     tar.extractall(path=datadir)
 
                 vspath = os.path.join(self.root, self.FILES[1])
-                if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
+                if (
+                    not os.path.exists(vspath)
+                    or not os.path.getsize(vspath) == self.SIZES[1]
+                ):
                     download(self.VS_URL, vspath)
 
-                with open(vspath, "r") as f:
+                with open(vspath, 'r') as f:
                     synset_dict = f.read().splitlines()
                     synset_dict = dict(line.split() for line in synset_dict)
 
-                print("Reorganizing into synset folders")
+                print('Reorganizing into synset folders')
                 synsets = np.unique(list(synset_dict.values()))
                 for s in synsets:
                     os.makedirs(os.path.join(datadir, s), exist_ok=True)
@@ -258,21 +302,26 @@ class ImageNetValidation(ImageNetBase):
                     dst = os.path.join(datadir, v)
                     shutil.move(src, dst)
 
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
+            filelist = glob.glob(os.path.join(datadir, '**', '*.JPEG'))
             filelist = [os.path.relpath(p, start=datadir) for p in filelist]
             filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
+            filelist = '\n'.join(filelist) + '\n'
+            with open(self.txt_filelist, 'w') as f:
                 f.write(filelist)
 
             tdu.mark_prepared(self.root)
 
 
-
 class ImageNetSR(Dataset):
-    def __init__(self, size=None,
-                 degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
-                 random_crop=True):
+    def __init__(
+        self,
+        size=None,
+        degradation=None,
+        downscale_f=4,
+        min_crop_f=0.5,
+        max_crop_f=1.0,
+        random_crop=True,
+    ):
         """
         Imagenet Superresolution Dataloader
         Performs following ops in order:
@@ -296,67 +345,86 @@ class ImageNetSR(Dataset):
         self.LR_size = int(size / downscale_f)
         self.min_crop_f = min_crop_f
         self.max_crop_f = max_crop_f
-        assert(max_crop_f <= 1.)
+        assert max_crop_f <= 1.0
         self.center_crop = not random_crop
 
-        self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
+        self.image_rescaler = albumentations.SmallestMaxSize(
+            max_size=size, interpolation=cv2.INTER_AREA
+        )
 
-        self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
+        self.pil_interpolation = (
+            False  # gets reset later if incase interp_op is from pillow
+        )
 
-        if degradation == "bsrgan":
-            self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
+        if degradation == 'bsrgan':
+            self.degradation_process = partial(
+                degradation_fn_bsr, sf=downscale_f
+            )
 
-        elif degradation == "bsrgan_light":
-            self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
+        elif degradation == 'bsrgan_light':
+            self.degradation_process = partial(
+                degradation_fn_bsr_light, sf=downscale_f
+            )
 
         else:
             interpolation_fn = {
-            "cv_nearest": cv2.INTER_NEAREST,
-            "cv_bilinear": cv2.INTER_LINEAR,
-            "cv_bicubic": cv2.INTER_CUBIC,
-            "cv_area": cv2.INTER_AREA,
-            "cv_lanczos": cv2.INTER_LANCZOS4,
-            "pil_nearest": PIL.Image.NEAREST,
-            "pil_bilinear": PIL.Image.BILINEAR,
-            "pil_bicubic": PIL.Image.BICUBIC,
-            "pil_box": PIL.Image.BOX,
-            "pil_hamming": PIL.Image.HAMMING,
-            "pil_lanczos": PIL.Image.LANCZOS,
+                'cv_nearest': cv2.INTER_NEAREST,
+                'cv_bilinear': cv2.INTER_LINEAR,
+                'cv_bicubic': cv2.INTER_CUBIC,
+                'cv_area': cv2.INTER_AREA,
+                'cv_lanczos': cv2.INTER_LANCZOS4,
+                'pil_nearest': PIL.Image.NEAREST,
+                'pil_bilinear': PIL.Image.BILINEAR,
+                'pil_bicubic': PIL.Image.BICUBIC,
+                'pil_box': PIL.Image.BOX,
+                'pil_hamming': PIL.Image.HAMMING,
+                'pil_lanczos': PIL.Image.LANCZOS,
             }[degradation]
 
-            self.pil_interpolation = degradation.startswith("pil_")
+            self.pil_interpolation = degradation.startswith('pil_')
 
             if self.pil_interpolation:
-                self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
+                self.degradation_process = partial(
+                    TF.resize,
+                    size=self.LR_size,
+                    interpolation=interpolation_fn,
+                )
 
             else:
-                self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
-                                                                          interpolation=interpolation_fn)
+                self.degradation_process = albumentations.SmallestMaxSize(
+                    max_size=self.LR_size, interpolation=interpolation_fn
+                )
 
     def __len__(self):
         return len(self.base)
 
     def __getitem__(self, i):
         example = self.base[i]
-        image = Image.open(example["file_path_"])
+        image = Image.open(example['file_path_'])
 
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
+        if not image.mode == 'RGB':
+            image = image.convert('RGB')
 
         image = np.array(image).astype(np.uint8)
 
         min_side_len = min(image.shape[:2])
-        crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
+        crop_side_len = min_side_len * np.random.uniform(
+            self.min_crop_f, self.max_crop_f, size=None
+        )
         crop_side_len = int(crop_side_len)
 
         if self.center_crop:
-            self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
+            self.cropper = albumentations.CenterCrop(
+                height=crop_side_len, width=crop_side_len
+            )
 
         else:
-            self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
+            self.cropper = albumentations.RandomCrop(
+                height=crop_side_len, width=crop_side_len
+            )
 
-        image = self.cropper(image=image)["image"]
-        image = self.image_rescaler(image=image)["image"]
+        image = self.cropper(image=image)['image']
+        image = self.image_rescaler(image=image)['image']
 
         if self.pil_interpolation:
             image_pil = PIL.Image.fromarray(image)
@@ -364,10 +432,10 @@ class ImageNetSR(Dataset):
             LR_image = np.array(LR_image).astype(np.uint8)
 
         else:
-            LR_image = self.degradation_process(image=image)["image"]
+            LR_image = self.degradation_process(image=image)['image']
 
-        example["image"] = (image/127.5 - 1.0).astype(np.float32)
-        example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
+        example['image'] = (image / 127.5 - 1.0).astype(np.float32)
+        example['LR_image'] = (LR_image / 127.5 - 1.0).astype(np.float32)
 
         return example
 
@@ -377,9 +445,11 @@ class ImageNetSRTrain(ImageNetSR):
         super().__init__(**kwargs)
 
     def get_base(self):
-        with open("data/imagenet_train_hr_indices.p", "rb") as f:
+        with open('data/imagenet_train_hr_indices.p', 'rb') as f:
             indices = pickle.load(f)
-        dset = ImageNetTrain(process_images=False,)
+        dset = ImageNetTrain(
+            process_images=False,
+        )
         return Subset(dset, indices)
 
 
@@ -388,7 +458,9 @@ class ImageNetSRValidation(ImageNetSR):
         super().__init__(**kwargs)
 
     def get_base(self):
-        with open("data/imagenet_val_hr_indices.p", "rb") as f:
+        with open('data/imagenet_val_hr_indices.p', 'rb') as f:
             indices = pickle.load(f)
-        dset = ImageNetValidation(process_images=False,)
+        dset = ImageNetValidation(
+            process_images=False,
+        )
         return Subset(dset, indices)

ldm/data/lsun.py

@@ -7,30 +7,33 @@ from torchvision import transforms
 
 
 class LSUNBase(Dataset):
-    def __init__(self,
-                 txt_file,
-                 data_root,
-                 size=None,
-                 interpolation="bicubic",
-                 flip_p=0.5
-                 ):
+    def __init__(
+        self,
+        txt_file,
+        data_root,
+        size=None,
+        interpolation='bicubic',
+        flip_p=0.5,
+    ):
         self.data_paths = txt_file
         self.data_root = data_root
-        with open(self.data_paths, "r") as f:
+        with open(self.data_paths, 'r') as f:
             self.image_paths = f.read().splitlines()
         self._length = len(self.image_paths)
         self.labels = {
-            "relative_file_path_": [l for l in self.image_paths],
-            "file_path_": [os.path.join(self.data_root, l)
-                           for l in self.image_paths],
+            'relative_file_path_': [l for l in self.image_paths],
+            'file_path_': [
+                os.path.join(self.data_root, l) for l in self.image_paths
+            ],
         }
 
         self.size = size
-        self.interpolation = {"linear": PIL.Image.LINEAR,
-                              "bilinear": PIL.Image.BILINEAR,
-                              "bicubic": PIL.Image.BICUBIC,
-                              "lanczos": PIL.Image.LANCZOS,
-                              }[interpolation]
+        self.interpolation = {
+            'linear': PIL.Image.LINEAR,
+            'bilinear': PIL.Image.BILINEAR,
+            'bicubic': PIL.Image.BICUBIC,
+            'lanczos': PIL.Image.LANCZOS,
+        }[interpolation]
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
     def __len__(self):
@@ -38,55 +41,86 @@ class LSUNBase(Dataset):
 
     def __getitem__(self, i):
         example = dict((k, self.labels[k][i]) for k in self.labels)
-        image = Image.open(example["file_path_"])
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
+        image = Image.open(example['file_path_'])
+        if not image.mode == 'RGB':
+            image = image.convert('RGB')
 
         # default to score-sde preprocessing
         img = np.array(image).astype(np.uint8)
         crop = min(img.shape[0], img.shape[1])
-        h, w, = img.shape[0], img.shape[1]
-        img = img[(h - crop) // 2:(h + crop) // 2,
-              (w - crop) // 2:(w + crop) // 2]
+        h, w, = (
+            img.shape[0],
+            img.shape[1],
+        )
+        img = img[
+            (h - crop) // 2 : (h + crop) // 2,
+            (w - crop) // 2 : (w + crop) // 2,
+        ]
 
         image = Image.fromarray(img)
         if self.size is not None:
-            image = image.resize((self.size, self.size), resample=self.interpolation)
+            image = image.resize(
+                (self.size, self.size), resample=self.interpolation
+            )
 
         image = self.flip(image)
         image = np.array(image).astype(np.uint8)
-        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
+        example['image'] = (image / 127.5 - 1.0).astype(np.float32)
         return example
 
 
 class LSUNChurchesTrain(LSUNBase):
     def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
+        super().__init__(
+            txt_file='data/lsun/church_outdoor_train.txt',
+            data_root='data/lsun/churches',
+            **kwargs
+        )
 
 
 class LSUNChurchesValidation(LSUNBase):
-    def __init__(self, flip_p=0., **kwargs):
-        super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
-                         flip_p=flip_p, **kwargs)
+    def __init__(self, flip_p=0.0, **kwargs):
+        super().__init__(
+            txt_file='data/lsun/church_outdoor_val.txt',
+            data_root='data/lsun/churches',
+            flip_p=flip_p,
+            **kwargs
+        )
 
 
 class LSUNBedroomsTrain(LSUNBase):
     def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
+        super().__init__(
+            txt_file='data/lsun/bedrooms_train.txt',
+            data_root='data/lsun/bedrooms',
+            **kwargs
+        )
 
 
 class LSUNBedroomsValidation(LSUNBase):
     def __init__(self, flip_p=0.0, **kwargs):
-        super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
-                         flip_p=flip_p, **kwargs)
+        super().__init__(
+            txt_file='data/lsun/bedrooms_val.txt',
+            data_root='data/lsun/bedrooms',
+            flip_p=flip_p,
+            **kwargs
+        )
 
 
 class LSUNCatsTrain(LSUNBase):
     def __init__(self, **kwargs):
-        super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
+        super().__init__(
+            txt_file='data/lsun/cat_train.txt',
+            data_root='data/lsun/cats',
+            **kwargs
+        )
 
 
 class LSUNCatsValidation(LSUNBase):
-    def __init__(self, flip_p=0., **kwargs):
-        super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",
-                         flip_p=flip_p, **kwargs)
+    def __init__(self, flip_p=0.0, **kwargs):
+        super().__init__(
+            txt_file='data/lsun/cat_val.txt',
+            data_root='data/lsun/cats',
+            flip_p=flip_p,
+            **kwargs
+        )

ldm/data/personalized.py

@@ -72,31 +72,57 @@ imagenet_dual_templates_small = [
 ]
 
 per_img_token_list = [
-    'א', 'ב', 'ג', 'ד', 'ה', 'ו', 'ז', 'ח', 'ט', 'י', 'כ', 'ל', 'מ', 'נ', 'ס', 'ע', 'פ', 'צ', 'ק', 'ר', 'ש', 'ת',
+    'א',
+    'ב',
+    'ג',
+    'ד',
+    'ה',
+    'ו',
+    'ז',
+    'ח',
+    'ט',
+    'י',
+    'כ',
+    'ל',
+    'מ',
+    'נ',
+    'ס',
+    'ע',
+    'פ',
+    'צ',
+    'ק',
+    'ר',
+    'ש',
+    'ת',
 ]
 
+
 class PersonalizedBase(Dataset):
-    def __init__(self,
-                 data_root,
-                 size=None,
-                 repeats=100,
-                 interpolation="bicubic",
-                 flip_p=0.5,
-                 set="train",
-                 placeholder_token="*",
-                 per_image_tokens=False,
-                 center_crop=False,
-                 mixing_prob=0.25,
-                 coarse_class_text=None,
-                 ):
+    def __init__(
+        self,
+        data_root,
+        size=None,
+        repeats=100,
+        interpolation='bicubic',
+        flip_p=0.5,
+        set='train',
+        placeholder_token='*',
+        per_image_tokens=False,
+        center_crop=False,
+        mixing_prob=0.25,
+        coarse_class_text=None,
+    ):
 
         self.data_root = data_root
 
-        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
+        self.image_paths = [
+            os.path.join(self.data_root, file_path)
+            for file_path in os.listdir(self.data_root)
+        ]
 
         # self._length = len(self.image_paths)
         self.num_images = len(self.image_paths)
-        self._length = self.num_images 
+        self._length = self.num_images
 
         self.placeholder_token = placeholder_token
 
@@ -107,17 +133,20 @@ class PersonalizedBase(Dataset):
         self.coarse_class_text = coarse_class_text
 
         if per_image_tokens:
-            assert self.num_images < len(per_img_token_list), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
+            assert self.num_images < len(
+                per_img_token_list
+            ), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
 
-        if set == "train":
+        if set == 'train':
             self._length = self.num_images * repeats
 
         self.size = size
-        self.interpolation = {"linear": PIL.Image.LINEAR,
-                              "bilinear": PIL.Image.BILINEAR,
-                              "bicubic": PIL.Image.BICUBIC,
-                              "lanczos": PIL.Image.LANCZOS,
-                              }[interpolation]
+        self.interpolation = {
+            'linear': PIL.Image.LINEAR,
+            'bilinear': PIL.Image.BILINEAR,
+            'bicubic': PIL.Image.BICUBIC,
+            'lanczos': PIL.Image.LANCZOS,
+        }[interpolation]
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
     def __len__(self):
@@ -127,34 +156,47 @@ class PersonalizedBase(Dataset):
         example = {}
         image = Image.open(self.image_paths[i % self.num_images])
 
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
+        if not image.mode == 'RGB':
+            image = image.convert('RGB')
 
         placeholder_string = self.placeholder_token
         if self.coarse_class_text:
-            placeholder_string = f"{self.coarse_class_text} {placeholder_string}"
+            placeholder_string = (
+                f'{self.coarse_class_text} {placeholder_string}'
+            )
 
         if self.per_image_tokens and np.random.uniform() < self.mixing_prob:
-            text = random.choice(imagenet_dual_templates_small).format(placeholder_string, per_img_token_list[i % self.num_images])
+            text = random.choice(imagenet_dual_templates_small).format(
+                placeholder_string, per_img_token_list[i % self.num_images]
+            )
         else:
-            text = random.choice(imagenet_templates_small).format(placeholder_string)
-            
-        example["caption"] = text
+            text = random.choice(imagenet_templates_small).format(
+                placeholder_string
+            )
+
+        example['caption'] = text
 
         # default to score-sde preprocessing
         img = np.array(image).astype(np.uint8)
-        
+
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            h, w, = img.shape[0], img.shape[1]
-            img = img[(h - crop) // 2:(h + crop) // 2,
-                (w - crop) // 2:(w + crop) // 2]
+            h, w, = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[
+                (h - crop) // 2 : (h + crop) // 2,
+                (w - crop) // 2 : (w + crop) // 2,
+            ]
 
         image = Image.fromarray(img)
         if self.size is not None:
-            image = image.resize((self.size, self.size), resample=self.interpolation)
+            image = image.resize(
+                (self.size, self.size), resample=self.interpolation
+            )
 
         image = self.flip(image)
         image = np.array(image).astype(np.uint8)
-        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
-        return example
+        example['image'] = (image / 127.5 - 1.0).astype(np.float32)
+        return example

ldm/data/personalized_style.py

@@ -50,29 +50,55 @@ imagenet_dual_templates_small = [
 ]
 
 per_img_token_list = [
-    'א', 'ב', 'ג', 'ד', 'ה', 'ו', 'ז', 'ח', 'ט', 'י', 'כ', 'ל', 'מ', 'נ', 'ס', 'ע', 'פ', 'צ', 'ק', 'ר', 'ש', 'ת',
+    'א',
+    'ב',
+    'ג',
+    'ד',
+    'ה',
+    'ו',
+    'ז',
+    'ח',
+    'ט',
+    'י',
+    'כ',
+    'ל',
+    'מ',
+    'נ',
+    'ס',
+    'ע',
+    'פ',
+    'צ',
+    'ק',
+    'ר',
+    'ש',
+    'ת',
 ]
 
+
 class PersonalizedBase(Dataset):
-    def __init__(self,
-                 data_root,
-                 size=None,
-                 repeats=100,
-                 interpolation="bicubic",
-                 flip_p=0.5,
-                 set="train",
-                 placeholder_token="*",
-                 per_image_tokens=False,
-                 center_crop=False,
-                 ):
+    def __init__(
+        self,
+        data_root,
+        size=None,
+        repeats=100,
+        interpolation='bicubic',
+        flip_p=0.5,
+        set='train',
+        placeholder_token='*',
+        per_image_tokens=False,
+        center_crop=False,
+    ):
 
         self.data_root = data_root
 
-        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
+        self.image_paths = [
+            os.path.join(self.data_root, file_path)
+            for file_path in os.listdir(self.data_root)
+        ]
 
         # self._length = len(self.image_paths)
         self.num_images = len(self.image_paths)
-        self._length = self.num_images 
+        self._length = self.num_images
 
         self.placeholder_token = placeholder_token
 
@@ -80,17 +106,20 @@ class PersonalizedBase(Dataset):
         self.center_crop = center_crop
 
         if per_image_tokens:
-            assert self.num_images < len(per_img_token_list), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
+            assert self.num_images < len(
+                per_img_token_list
+            ), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
 
-        if set == "train":
+        if set == 'train':
             self._length = self.num_images * repeats
 
         self.size = size
-        self.interpolation = {"linear": PIL.Image.LINEAR,
-                              "bilinear": PIL.Image.BILINEAR,
-                              "bicubic": PIL.Image.BICUBIC,
-                              "lanczos": PIL.Image.LANCZOS,
-                              }[interpolation]
+        self.interpolation = {
+            'linear': PIL.Image.LINEAR,
+            'bilinear': PIL.Image.BILINEAR,
+            'bicubic': PIL.Image.BICUBIC,
+            'lanczos': PIL.Image.LANCZOS,
+        }[interpolation]
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
     def __len__(self):
@@ -100,30 +129,41 @@ class PersonalizedBase(Dataset):
         example = {}
         image = Image.open(self.image_paths[i % self.num_images])
 
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
+        if not image.mode == 'RGB':
+            image = image.convert('RGB')
 
         if self.per_image_tokens and np.random.uniform() < 0.25:
-            text = random.choice(imagenet_dual_templates_small).format(self.placeholder_token, per_img_token_list[i % self.num_images])
+            text = random.choice(imagenet_dual_templates_small).format(
+                self.placeholder_token, per_img_token_list[i % self.num_images]
+            )
         else:
-            text = random.choice(imagenet_templates_small).format(self.placeholder_token)
-            
-        example["caption"] = text
+            text = random.choice(imagenet_templates_small).format(
+                self.placeholder_token
+            )
+
+        example['caption'] = text
 
         # default to score-sde preprocessing
         img = np.array(image).astype(np.uint8)
-        
+
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            h, w, = img.shape[0], img.shape[1]
-            img = img[(h - crop) // 2:(h + crop) // 2,
-                (w - crop) // 2:(w + crop) // 2]
+            h, w, = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[
+                (h - crop) // 2 : (h + crop) // 2,
+                (w - crop) // 2 : (w + crop) // 2,
+            ]
 
         image = Image.fromarray(img)
         if self.size is not None:
-            image = image.resize((self.size, self.size), resample=self.interpolation)
+            image = image.resize(
+                (self.size, self.size), resample=self.interpolation
+            )
 
         image = self.flip(image)
         image = np.array(image).astype(np.uint8)
-        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
-        return example
+        example['image'] = (image / 127.5 - 1.0).astype(np.float32)
+        return example

ldm/dream/devices.py

@@ -0,0 +1,17 @@
+import torch
+
+def choose_torch_device() -> str:
+    '''Convenience routine for guessing which GPU device to run model on'''
+    if torch.cuda.is_available():
+        return 'cuda'
+    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
+        return 'mps'
+    return 'cpu'
+
+def choose_autocast_device(device) -> str:
+    '''Returns an autocast compatible device from a torch device'''
+    device_type = device.type # this returns 'mps' on M1
+    # autocast only supports cuda or cpu
+    if device_type not in ('cuda','cpu'):
+        return 'cpu'
+    return device_type

ldm/dream/image_util.py

@@ -0,0 +1,70 @@
+from math import sqrt, floor, ceil
+from PIL import Image
+
+class InitImageResizer():
+    """Simple class to create resized copies of an Image while preserving the aspect ratio."""
+    def __init__(self,Image):
+        self.image = Image
+
+    def resize(self,width=None,height=None) -> Image:
+        """
+        Return a copy of the image resized to fit within
+        a box width x height. The aspect ratio is 
+        maintained. If neither width nor height are provided, 
+        then returns a copy of the original image. If one or the other is
+        provided, then the other will be calculated from the
+        aspect ratio.
+
+        Everything is floored to the nearest multiple of 64 so
+        that it can be passed to img2img()
+        """
+        im    = self.image
+        
+        ar = im.width/float(im.height)
+
+        # Infer missing values from aspect ratio
+        if not(width or height): # both missing
+            width  = im.width
+            height = im.height
+        elif not height:           # height missing
+            height = int(width/ar)
+        elif not width:            # width missing
+            width  = int(height*ar)
+
+        # rw and rh are the resizing width and height for the image
+        # they maintain the aspect ratio, but may not completelyl fill up
+        # the requested destination size
+        (rw,rh) = (width,int(width/ar)) if im.width>=im.height else (int(height*ar),height)
+
+        #round everything to multiples of 64
+        width,height,rw,rh = map(
+            lambda x: x-x%64, (width,height,rw,rh)
+        )
+
+        # no resize necessary, but return a copy
+        if im.width == width and im.height == height:
+            return im.copy()
+        
+        # otherwise resize the original image so that it fits inside the bounding box
+        resized_image = self.image.resize((rw,rh),resample=Image.Resampling.LANCZOS)
+        return resized_image
+
+def make_grid(image_list, rows=None, cols=None):
+    image_cnt = len(image_list)
+    if None in (rows, cols):
+        rows = floor(sqrt(image_cnt))  # try to make it square
+        cols = ceil(image_cnt / rows)
+    width = image_list[0].width
+    height = image_list[0].height
+
+    grid_img = Image.new('RGB', (width * cols, height * rows))
+    i = 0
+    for r in range(0, rows):
+        for c in range(0, cols):
+            if i >= len(image_list):
+                break
+            grid_img.paste(image_list[i], (c * width, r * height))
+            i = i + 1
+
+    return grid_img
+

ldm/dream/pngwriter.py

@@ -0,0 +1,79 @@
+"""
+Two helper classes for dealing with PNG images and their path names.
+PngWriter -- Converts Images generated by T2I into PNGs, finds
+             appropriate names for them, and writes prompt metadata
+             into the PNG.
+PromptFormatter -- Utility for converting a Namespace of prompt parameters
+             back into a formatted prompt string with command-line switches.
+"""
+import os
+import re
+from PIL import PngImagePlugin
+
+# -------------------image generation utils-----
+
+
+class PngWriter:
+    def __init__(self, outdir):
+        self.outdir = outdir
+        os.makedirs(outdir, exist_ok=True)
+
+    # gives the next unique prefix in outdir
+    def unique_prefix(self):
+        # sort reverse alphabetically until we find max+1
+        dirlist = sorted(os.listdir(self.outdir), reverse=True)
+        # find the first filename that matches our pattern or return 000000.0.png
+        existing_name = next(
+            (f for f in dirlist if re.match('^(\d+)\..*\.png', f)),
+            '0000000.0.png',
+        )
+        basecount = int(existing_name.split('.', 1)[0]) + 1
+        return f'{basecount:06}'
+
+    # saves image named _image_ to outdir/name, writing metadata from prompt
+    # returns full path of output
+    def save_image_and_prompt_to_png(self, image, prompt, name):
+        path = os.path.join(self.outdir, name)
+        info = PngImagePlugin.PngInfo()
+        info.add_text('Dream', prompt)
+        image.save(path, 'PNG', pnginfo=info)
+        return path
+
+
+class PromptFormatter:
+    def __init__(self, t2i, opt):
+        self.t2i = t2i
+        self.opt = opt
+
+    # note: the t2i object should provide all these values.
+    # there should be no need to or against opt values
+    def normalize_prompt(self):
+        """Normalize the prompt and switches"""
+        t2i = self.t2i
+        opt = self.opt
+
+        switches = list()
+        switches.append(f'"{opt.prompt}"')
+        switches.append(f'-s{opt.steps        or t2i.steps}')
+        switches.append(f'-W{opt.width        or t2i.width}')
+        switches.append(f'-H{opt.height       or t2i.height}')
+        switches.append(f'-C{opt.cfg_scale    or t2i.cfg_scale}')
+        switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
+        if opt.init_img:
+            switches.append(f'-I{opt.init_img}')
+        if opt.fit:
+            switches.append(f'--fit')
+        if opt.strength and opt.init_img is not None:
+            switches.append(f'-f{opt.strength or t2i.strength}')
+        if opt.gfpgan_strength:
+            switches.append(f'-G{opt.gfpgan_strength}')
+        if opt.upscale:
+            switches.append(f'-U {" ".join([str(u) for u in opt.upscale])}')
+        if opt.variation_amount > 0:
+            switches.append(f'-v{opt.variation_amount}')
+        if opt.with_variations:
+            formatted_variations = ','.join(f'{seed}:{weight}' for seed, weight in opt.with_variations)
+            switches.append(f'-V{formatted_variations}')
+        if t2i.full_precision:
+            switches.append('-F')
+        return ' '.join(switches)

ldm/dream/readline.py

@@ -0,0 +1,121 @@
+"""
+Readline helper functions for dream.py (linux and mac only).
+"""
+import os
+import re
+import atexit
+
+# ---------------readline utilities---------------------
+try:
+    import readline
+
+    readline_available = True
+except:
+    readline_available = False
+
+
+class Completer:
+    def __init__(self, options):
+        self.options = sorted(options)
+        return
+
+    def complete(self, text, state):
+        buffer = readline.get_line_buffer()
+
+        if text.startswith(('-I', '--init_img')):
+            return self._path_completions(text, state, ('.png','.jpg','.jpeg'))
+
+        if buffer.strip().endswith('cd') or text.startswith(('.', '/')):
+            return self._path_completions(text, state, ())
+
+        response = None
+        if state == 0:
+            # This is the first time for this text, so build a match list.
+            if text:
+                self.matches = [
+                    s for s in self.options if s and s.startswith(text)
+                ]
+            else:
+                self.matches = self.options[:]
+
+        # Return the state'th item from the match list,
+        # if we have that many.
+        try:
+            response = self.matches[state]
+        except IndexError:
+            response = None
+        return response
+
+    def _path_completions(self, text, state, extensions):
+        # get the path so far
+        if text.startswith('-I'):
+            path = text.replace('-I', '', 1).lstrip()
+        elif text.startswith('--init_img='):
+            path = text.replace('--init_img=', '', 1).lstrip()
+        else:
+            path = text
+
+        matches = list()
+
+        path = os.path.expanduser(path)
+        if len(path) == 0:
+            matches.append(text + './')
+        else:
+            dir = os.path.dirname(path)
+            dir_list = os.listdir(dir)
+            for n in dir_list:
+                if n.startswith('.') and len(n) > 1:
+                    continue
+                full_path = os.path.join(dir, n)
+                if full_path.startswith(path):
+                    if os.path.isdir(full_path):
+                        matches.append(
+                            os.path.join(os.path.dirname(text), n) + '/'
+                        )
+                    elif n.endswith(extensions):
+                        matches.append(os.path.join(os.path.dirname(text), n))
+
+        try:
+            response = matches[state]
+        except IndexError:
+            response = None
+        return response
+
+
+if readline_available:
+    readline.set_completer(
+        Completer(
+            [
+                '--steps','-s',
+                '--seed','-S',
+                '--iterations','-n',
+                '--width','-W','--height','-H',
+                '--cfg_scale','-C',
+                '--grid','-g',
+                '--individual','-i',
+                '--init_img','-I',
+                '--strength','-f',
+                '--variants','-v',
+                '--outdir','-o',
+                '--sampler','-A','-m',
+                '--embedding_path',
+                '--device',
+                '--grid','-g',
+                '--gfpgan_strength','-G',
+                '--upscale','-U',
+                '-save_orig','--save_original',
+                '--skip_normalize','-x',
+                '--log_tokenization','t',
+            ]
+        ).complete
+    )
+    readline.set_completer_delims(' ')
+    readline.parse_and_bind('tab: complete')
+
+    histfile = os.path.join(os.path.expanduser('~'), '.dream_history')
+    try:
+        readline.read_history_file(histfile)
+        readline.set_history_length(1000)
+    except FileNotFoundError:
+        pass
+    atexit.register(readline.write_history_file, histfile)

ldm/dream/server.py

@@ -0,0 +1,202 @@
+import json
+import base64
+import mimetypes
+import os
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from ldm.dream.pngwriter import PngWriter
+from threading import Event
+
+class CanceledException(Exception):
+    pass
+
+class DreamServer(BaseHTTPRequestHandler):
+    model = None
+    canceled = Event()
+
+    def do_GET(self):
+        if self.path == "/":
+            self.send_response(200)
+            self.send_header("Content-type", "text/html")
+            self.end_headers()
+            with open("./static/dream_web/index.html", "rb") as content:
+                self.wfile.write(content.read())
+        elif self.path == "/config.js":
+            # unfortunately this import can't be at the top level, since that would cause a circular import
+            from ldm.gfpgan.gfpgan_tools import gfpgan_model_exists
+            self.send_response(200)
+            self.send_header("Content-type", "application/javascript")
+            self.end_headers()
+            config = {
+                'gfpgan_model_exists': gfpgan_model_exists
+            }
+            self.wfile.write(bytes("let config = " + json.dumps(config) + ";\n", "utf-8"))
+        elif self.path == "/cancel":
+            self.canceled.set()
+            self.send_response(200)
+            self.send_header("Content-type", "application/json")
+            self.end_headers()
+            self.wfile.write(bytes('{}', 'utf8'))
+        else:
+            path = "." + self.path
+            cwd = os.path.realpath(os.getcwd())
+            is_in_cwd = os.path.commonprefix((os.path.realpath(path), cwd)) == cwd
+            if not (is_in_cwd and os.path.exists(path)):
+                self.send_response(404)
+                return
+            mime_type = mimetypes.guess_type(path)[0]
+            if mime_type is not None:
+                self.send_response(200)
+                self.send_header("Content-type", mime_type)
+                self.end_headers()
+                with open("." + self.path, "rb") as content:
+                    self.wfile.write(content.read())
+            else:
+                self.send_response(404)
+
+    def do_POST(self):
+        self.send_response(200)
+        self.send_header("Content-type", "application/json")
+        self.end_headers()
+
+        # unfortunately this import can't be at the top level, since that would cause a circular import
+        from ldm.gfpgan.gfpgan_tools import gfpgan_model_exists
+
+        content_length = int(self.headers['Content-Length'])
+        post_data = json.loads(self.rfile.read(content_length))
+        prompt = post_data['prompt']
+        initimg = post_data['initimg']
+        strength = float(post_data['strength'])
+        iterations = int(post_data['iterations'])
+        steps = int(post_data['steps'])
+        width = int(post_data['width'])
+        height = int(post_data['height'])
+        fit    = 'fit' in post_data
+        cfgscale = float(post_data['cfgscale'])
+        sampler_name  = post_data['sampler']
+        gfpgan_strength = float(post_data['gfpgan_strength']) if gfpgan_model_exists else 0
+        upscale_level    = post_data['upscale_level']
+        upscale_strength = post_data['upscale_strength']
+        upscale = [int(upscale_level),float(upscale_strength)] if upscale_level != '' else None
+        progress_images = 'progress_images' in post_data
+        seed = self.model.seed if int(post_data['seed']) == -1 else int(post_data['seed'])
+
+        self.canceled.clear()
+        print(f">> Request to generate with prompt: {prompt}")
+        # In order to handle upscaled images, the PngWriter needs to maintain state
+        # across images generated by each call to prompt2img(), so we define it in
+        # the outer scope of image_done()
+        config = post_data.copy() # Shallow copy
+        config['initimg'] = ''
+
+        images_generated = 0    # helps keep track of when upscaling is started
+        images_upscaled = 0     # helps keep track of when upscaling is completed
+        pngwriter = PngWriter("./outputs/img-samples/")
+
+        prefix = pngwriter.unique_prefix()
+        # if upscaling is requested, then this will be called twice, once when
+        # the images are first generated, and then again when after upscaling
+        # is complete. The upscaling replaces the original file, so the second
+        # entry should not be inserted into the image list.
+        def image_done(image, seed, upscaled=False):
+            name = f'{prefix}.{seed}.png'
+            path = pngwriter.save_image_and_prompt_to_png(image, f'{prompt} -S{seed}', name)
+
+            # Append post_data to log, but only once!
+            if not upscaled:
+                with open("./outputs/img-samples/dream_web_log.txt", "a") as log:
+                    log.write(f"{path}: {json.dumps(config)}\n")
+
+                self.wfile.write(bytes(json.dumps(
+                    {'event': 'result', 'url': path, 'seed': seed, 'config': config}
+                ) + '\n',"utf-8"))
+
+            # control state of the "postprocessing..." message
+            upscaling_requested = upscale or gfpgan_strength>0
+            nonlocal images_generated # NB: Is this bad python style? It is typical usage in a perl closure.
+            nonlocal images_upscaled  # NB: Is this bad python style? It is typical usage in a perl closure.
+            if upscaled:
+                images_upscaled += 1
+            else:
+                images_generated +=1
+            if upscaling_requested:
+                action = None
+                if images_generated >= iterations:
+                    if images_upscaled < iterations:
+                        action = 'upscaling-started'
+                    else:
+                        action = 'upscaling-done'
+                if action:
+                    x = images_upscaled+1
+                    self.wfile.write(bytes(json.dumps(
+                        {'event':action,'processed_file_cnt':f'{x}/{iterations}'}
+                    ) + '\n',"utf-8"))
+
+        step_writer = PngWriter('./outputs/intermediates/')
+        step_index = 1
+        def image_progress(sample, step):
+            if self.canceled.is_set():
+                self.wfile.write(bytes(json.dumps({'event':'canceled'}) + '\n', 'utf-8'))
+                raise CanceledException
+            path = None
+            # since rendering images is moderately expensive, only render every 5th image
+            # and don't bother with the last one, since it'll render anyway
+            nonlocal step_index
+            if progress_images and step % 5 == 0 and step < steps - 1:
+                image = self.model._sample_to_image(sample)
+                name = f'{prefix}.{seed}.{step_index}.png'
+                metadata = f'{prompt} -S{seed} [intermediate]'
+                path = step_writer.save_image_and_prompt_to_png(image, metadata, name)
+                step_index += 1
+            self.wfile.write(bytes(json.dumps(
+                {'event': 'step', 'step': step + 1, 'url': path}
+            ) + '\n',"utf-8"))
+
+        try:
+            if initimg is None:
+                # Run txt2img
+                self.model.prompt2image(prompt,
+                                        iterations=iterations,
+                                        cfg_scale = cfgscale,
+                                        width = width,
+                                        height = height,
+                                        seed = seed,
+                                        steps = steps,
+                                        gfpgan_strength = gfpgan_strength,
+                                        upscale         = upscale,
+                                        sampler_name    = sampler_name,
+                                        step_callback=image_progress,
+                                        image_callback=image_done)
+            else:
+                # Decode initimg as base64 to temp file
+                with open("./img2img-tmp.png", "wb") as f:
+                    initimg = initimg.split(",")[1] # Ignore mime type
+                    f.write(base64.b64decode(initimg))
+
+                try:
+                    # Run img2img
+                    self.model.prompt2image(prompt,
+                                            init_img = "./img2img-tmp.png",
+                                            strength = strength,
+                                            iterations = iterations,
+                                            cfg_scale  = cfgscale,
+                                            seed       = seed,
+                                            steps      = steps,
+                                            sampler_name    = sampler_name,
+                                            width      = width,
+                                            height     = height,
+                                            fit        = fit,
+                                            gfpgan_strength=gfpgan_strength,
+                                            upscale         = upscale,
+                                            step_callback=image_progress,
+                                            image_callback=image_done)
+                finally:
+                    # Remove the temp file
+                    os.remove("./img2img-tmp.png")
+        except CanceledException:
+            print(f"Canceled.")
+            return
+
+
+class ThreadingDreamServer(ThreadingHTTPServer):
+    def __init__(self, server_address):
+        super(ThreadingDreamServer, self).__init__(server_address, DreamServer)

ldm/gfpgan/gfpgan_tools.py

@@ -0,0 +1,167 @@
+import torch
+import warnings
+import os
+import sys
+import numpy as np
+
+from PIL import Image
+from scripts.dream import create_argv_parser
+
+arg_parser = create_argv_parser()
+opt = arg_parser.parse_args()
+
+model_path = os.path.join(opt.gfpgan_dir, opt.gfpgan_model_path)
+gfpgan_model_exists = os.path.isfile(model_path)
+
+def _run_gfpgan(image, strength, prompt, seed, upsampler_scale=4):
+    print(f'>> GFPGAN - Restoring Faces: {prompt} : seed:{seed}')
+    gfpgan = None
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', category=DeprecationWarning)
+        warnings.filterwarnings('ignore', category=UserWarning)
+
+        try:
+            if not gfpgan_model_exists:
+                raise Exception('GFPGAN model not found at path ' + model_path)
+
+            sys.path.append(os.path.abspath(opt.gfpgan_dir))
+            from gfpgan import GFPGANer
+
+            bg_upsampler = _load_gfpgan_bg_upsampler(
+                opt.gfpgan_bg_upsampler, upsampler_scale, opt.gfpgan_bg_tile
+            )
+
+            gfpgan = GFPGANer(
+                model_path=model_path,
+                upscale=upsampler_scale,
+                arch='clean',
+                channel_multiplier=2,
+                bg_upsampler=bg_upsampler,
+            )
+        except Exception:
+            import traceback
+
+            print('>> Error loading GFPGAN:', file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    if gfpgan is None:
+        print(
+            f'>> GFPGAN not initialized, it must be loaded via the --gfpgan argument'
+        )
+        return image
+
+    image = image.convert('RGB')
+
+    cropped_faces, restored_faces, restored_img = gfpgan.enhance(
+        np.array(image, dtype=np.uint8),
+        has_aligned=False,
+        only_center_face=False,
+        paste_back=True,
+    )
+    res = Image.fromarray(restored_img)
+
+    if strength < 1.0:
+        # Resize the image to the new image if the sizes have changed
+        if restored_img.size != image.size:
+            image = image.resize(res.size)
+        res = Image.blend(image, res, strength)
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    gfpgan = None
+
+    return res
+
+
+def _load_gfpgan_bg_upsampler(bg_upsampler, upsampler_scale, bg_tile=400):
+    if bg_upsampler == 'realesrgan':
+        if not torch.cuda.is_available():  # CPU
+            warnings.warn(
+                'The unoptimized RealESRGAN is slow on CPU. We do not use it. '
+                'If you really want to use it, please modify the corresponding codes.'
+            )
+            bg_upsampler = None
+        else:
+            model_path = {
+                2: 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
+                4: 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
+            }
+
+            if upsampler_scale not in model_path:
+                return None
+
+            from basicsr.archs.rrdbnet_arch import RRDBNet
+            from realesrgan import RealESRGANer
+
+            if upsampler_scale == 4:
+                model = RRDBNet(
+                    num_in_ch=3,
+                    num_out_ch=3,
+                    num_feat=64,
+                    num_block=23,
+                    num_grow_ch=32,
+                    scale=4,
+                )
+            if upsampler_scale == 2:
+                model = RRDBNet(
+                    num_in_ch=3,
+                    num_out_ch=3,
+                    num_feat=64,
+                    num_block=23,
+                    num_grow_ch=32,
+                    scale=2,
+                )
+
+            bg_upsampler = RealESRGANer(
+                scale=upsampler_scale,
+                model_path=model_path[upsampler_scale],
+                model=model,
+                tile=bg_tile,
+                tile_pad=10,
+                pre_pad=0,
+                half=True,
+            )  # need to set False in CPU mode
+    else:
+        bg_upsampler = None
+
+    return bg_upsampler
+
+
+def real_esrgan_upscale(image, strength, upsampler_scale, prompt, seed):
+    print(
+        f'>> Real-ESRGAN Upscaling: {prompt} : seed:{seed} : scale:{upsampler_scale}x'
+    )
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', category=DeprecationWarning)
+        warnings.filterwarnings('ignore', category=UserWarning)
+
+        try:
+            upsampler = _load_gfpgan_bg_upsampler(
+                opt.gfpgan_bg_upsampler, upsampler_scale, opt.gfpgan_bg_tile
+            )
+        except Exception:
+            import traceback
+
+            print('>> Error loading Real-ESRGAN:', file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+
+    output, img_mode = upsampler.enhance(
+        np.array(image, dtype=np.uint8),
+        outscale=upsampler_scale,
+        alpha_upsampler=opt.gfpgan_bg_upsampler,
+    )
+
+    res = Image.fromarray(output)
+
+    if strength < 1.0:
+        # Resize the image to the new image if the sizes have changed
+        if output.size != image.size:
+            image = image.resize(res.size)
+        res = Image.blend(image, res, strength)
+
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    upsampler = None
+
+    return res

ldm/lr_scheduler.py

@@ -5,32 +5,49 @@ class LambdaWarmUpCosineScheduler:
     """
     note: use with a base_lr of 1.0
     """
-    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
+
+    def __init__(
+        self,
+        warm_up_steps,
+        lr_min,
+        lr_max,
+        lr_start,
+        max_decay_steps,
+        verbosity_interval=0,
+    ):
         self.lr_warm_up_steps = warm_up_steps
         self.lr_start = lr_start
         self.lr_min = lr_min
         self.lr_max = lr_max
         self.lr_max_decay_steps = max_decay_steps
-        self.last_lr = 0.
+        self.last_lr = 0.0
         self.verbosity_interval = verbosity_interval
 
     def schedule(self, n, **kwargs):
         if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
+            if n % self.verbosity_interval == 0:
+                print(
+                    f'current step: {n}, recent lr-multiplier: {self.last_lr}'
+                )
         if n < self.lr_warm_up_steps:
-            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
+            lr = (
+                self.lr_max - self.lr_start
+            ) / self.lr_warm_up_steps * n + self.lr_start
             self.last_lr = lr
             return lr
         else:
-            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
+            t = (n - self.lr_warm_up_steps) / (
+                self.lr_max_decay_steps - self.lr_warm_up_steps
+            )
             t = min(t, 1.0)
             lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
-                    1 + np.cos(t * np.pi))
+                1 + np.cos(t * np.pi)
+            )
             self.last_lr = lr
             return lr
 
     def __call__(self, n, **kwargs):
-        return self.schedule(n,**kwargs)
+        return self.schedule(n, **kwargs)
 
 
 class LambdaWarmUpCosineScheduler2:
@@ -38,15 +55,30 @@ class LambdaWarmUpCosineScheduler2:
     supports repeated iterations, configurable via lists
     note: use with a base_lr of 1.0.
     """
-    def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
-        assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
+
+    def __init__(
+        self,
+        warm_up_steps,
+        f_min,
+        f_max,
+        f_start,
+        cycle_lengths,
+        verbosity_interval=0,
+    ):
+        assert (
+            len(warm_up_steps)
+            == len(f_min)
+            == len(f_max)
+            == len(f_start)
+            == len(cycle_lengths)
+        )
         self.lr_warm_up_steps = warm_up_steps
         self.f_start = f_start
         self.f_min = f_min
         self.f_max = f_max
         self.cycle_lengths = cycle_lengths
         self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
-        self.last_f = 0.
+        self.last_f = 0.0
         self.verbosity_interval = verbosity_interval
 
     def find_in_interval(self, n):
@@ -60,17 +92,25 @@ class LambdaWarmUpCosineScheduler2:
         cycle = self.find_in_interval(n)
         n = n - self.cum_cycles[cycle]
         if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
-                                                       f"current cycle {cycle}")
+            if n % self.verbosity_interval == 0:
+                print(
+                    f'current step: {n}, recent lr-multiplier: {self.last_f}, '
+                    f'current cycle {cycle}'
+                )
         if n < self.lr_warm_up_steps[cycle]:
-            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
+            f = (
+                self.f_max[cycle] - self.f_start[cycle]
+            ) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
             self.last_f = f
             return f
         else:
-            t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
+            t = (n - self.lr_warm_up_steps[cycle]) / (
+                self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
+            )
             t = min(t, 1.0)
-            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
-                    1 + np.cos(t * np.pi))
+            f = self.f_min[cycle] + 0.5 * (
+                self.f_max[cycle] - self.f_min[cycle]
+            ) * (1 + np.cos(t * np.pi))
             self.last_f = f
             return f
 
@@ -79,20 +119,25 @@ class LambdaWarmUpCosineScheduler2:
 
 
 class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
-
     def schedule(self, n, **kwargs):
         cycle = self.find_in_interval(n)
         n = n - self.cum_cycles[cycle]
         if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
-                                                       f"current cycle {cycle}")
+            if n % self.verbosity_interval == 0:
+                print(
+                    f'current step: {n}, recent lr-multiplier: {self.last_f}, '
+                    f'current cycle {cycle}'
+                )
 
         if n < self.lr_warm_up_steps[cycle]:
-            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
+            f = (
+                self.f_max[cycle] - self.f_start[cycle]
+            ) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
             self.last_f = f
             return f
         else:
-            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
+            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
+                self.cycle_lengths[cycle] - n
+            ) / (self.cycle_lengths[cycle])
             self.last_f = f
             return f
-

ldm/models/autoencoder.py

@@ -6,29 +6,32 @@ from contextlib import contextmanager
 from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
 
 from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+from ldm.modules.distributions.distributions import (
+    DiagonalGaussianDistribution,
+)
 
 from ldm.util import instantiate_from_config
 
 
 class VQModel(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 n_embed,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 batch_resize_range=None,
-                 scheduler_config=None,
-                 lr_g_factor=1.0,
-                 remap=None,
-                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
-                 use_ema=False
-                 ):
+    def __init__(
+        self,
+        ddconfig,
+        lossconfig,
+        n_embed,
+        embed_dim,
+        ckpt_path=None,
+        ignore_keys=[],
+        image_key='image',
+        colorize_nlabels=None,
+        monitor=None,
+        batch_resize_range=None,
+        scheduler_config=None,
+        lr_g_factor=1.0,
+        remap=None,
+        sane_index_shape=False,  # tell vector quantizer to return indices as bhw
+        use_ema=False,
+    ):
         super().__init__()
         self.embed_dim = embed_dim
         self.n_embed = n_embed
@@ -36,24 +39,34 @@ class VQModel(pl.LightningModule):
         self.encoder = Encoder(**ddconfig)
         self.decoder = Decoder(**ddconfig)
         self.loss = instantiate_from_config(lossconfig)
-        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
-                                        remap=remap,
-                                        sane_index_shape=sane_index_shape)
-        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.quantize = VectorQuantizer(
+            n_embed,
+            embed_dim,
+            beta=0.25,
+            remap=remap,
+            sane_index_shape=sane_index_shape,
+        )
+        self.quant_conv = torch.nn.Conv2d(ddconfig['z_channels'], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(
+            embed_dim, ddconfig['z_channels'], 1
+        )
         if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+            assert type(colorize_nlabels) == int
+            self.register_buffer(
+                'colorize', torch.randn(3, colorize_nlabels, 1, 1)
+            )
         if monitor is not None:
             self.monitor = monitor
         self.batch_resize_range = batch_resize_range
         if self.batch_resize_range is not None:
-            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+            print(
+                f'{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.'
+            )
 
         self.use_ema = use_ema
         if self.use_ema:
             self.model_ema = LitEma(self)
-            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+            print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
 
         if ckpt_path is not None:
             self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
@@ -66,28 +79,30 @@ class VQModel(pl.LightningModule):
             self.model_ema.store(self.parameters())
             self.model_ema.copy_to(self)
             if context is not None:
-                print(f"{context}: Switched to EMA weights")
+                print(f'{context}: Switched to EMA weights')
         try:
             yield None
         finally:
             if self.use_ema:
                 self.model_ema.restore(self.parameters())
                 if context is not None:
-                    print(f"{context}: Restored training weights")
+                    print(f'{context}: Restored training weights')
 
     def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
+        sd = torch.load(path, map_location='cpu')['state_dict']
         keys = list(sd.keys())
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
+                    print('Deleting key {} from state_dict.'.format(k))
                     del sd[k]
         missing, unexpected = self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        print(
+            f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+        )
         if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
-            print(f"Unexpected Keys: {unexpected}")
+            print(f'Missing Keys: {missing}')
+            print(f'Unexpected Keys: {unexpected}')
 
     def on_train_batch_end(self, *args, **kwargs):
         if self.use_ema:
@@ -115,7 +130,7 @@ class VQModel(pl.LightningModule):
         return dec
 
     def forward(self, input, return_pred_indices=False):
-        quant, diff, (_,_,ind) = self.encode(input)
+        quant, diff, (_, _, ind) = self.encode(input)
         dec = self.decode(quant)
         if return_pred_indices:
             return dec, diff, ind
@@ -125,7 +140,11 @@ class VQModel(pl.LightningModule):
         x = batch[k]
         if len(x.shape) == 3:
             x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        x = (
+            x.permute(0, 3, 1, 2)
+            .to(memory_format=torch.contiguous_format)
+            .float()
+        )
         if self.batch_resize_range is not None:
             lower_size = self.batch_resize_range[0]
             upper_size = self.batch_resize_range[1]
@@ -133,9 +152,11 @@ class VQModel(pl.LightningModule):
                 # do the first few batches with max size to avoid later oom
                 new_resize = upper_size
             else:
-                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+                new_resize = np.random.choice(
+                    np.arange(lower_size, upper_size + 16, 16)
+                )
             if new_resize != x.shape[2]:
-                x = F.interpolate(x, size=new_resize, mode="bicubic")
+                x = F.interpolate(x, size=new_resize, mode='bicubic')
             x = x.detach()
         return x
 
@@ -147,81 +168,139 @@ class VQModel(pl.LightningModule):
 
         if optimizer_idx == 0:
             # autoencode
-            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train",
-                                            predicted_indices=ind)
+            aeloss, log_dict_ae = self.loss(
+                qloss,
+                x,
+                xrec,
+                optimizer_idx,
+                self.global_step,
+                last_layer=self.get_last_layer(),
+                split='train',
+                predicted_indices=ind,
+            )
 
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            self.log_dict(
+                log_dict_ae,
+                prog_bar=False,
+                logger=True,
+                on_step=True,
+                on_epoch=True,
+            )
             return aeloss
 
         if optimizer_idx == 1:
             # discriminator
-            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            discloss, log_dict_disc = self.loss(
+                qloss,
+                x,
+                xrec,
+                optimizer_idx,
+                self.global_step,
+                last_layer=self.get_last_layer(),
+                split='train',
+            )
+            self.log_dict(
+                log_dict_disc,
+                prog_bar=False,
+                logger=True,
+                on_step=True,
+                on_epoch=True,
+            )
             return discloss
 
     def validation_step(self, batch, batch_idx):
         log_dict = self._validation_step(batch, batch_idx)
         with self.ema_scope():
-            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+            log_dict_ema = self._validation_step(
+                batch, batch_idx, suffix='_ema'
+            )
         return log_dict
 
-    def _validation_step(self, batch, batch_idx, suffix=""):
+    def _validation_step(self, batch, batch_idx, suffix=''):
         x = self.get_input(batch, self.image_key)
         xrec, qloss, ind = self(x, return_pred_indices=True)
-        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
-                                        self.global_step,
-                                        last_layer=self.get_last_layer(),
-                                        split="val"+suffix,
-                                        predicted_indices=ind
-                                        )
+        aeloss, log_dict_ae = self.loss(
+            qloss,
+            x,
+            xrec,
+            0,
+            self.global_step,
+            last_layer=self.get_last_layer(),
+            split='val' + suffix,
+            predicted_indices=ind,
+        )
 
-        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
-                                            self.global_step,
-                                            last_layer=self.get_last_layer(),
-                                            split="val"+suffix,
-                                            predicted_indices=ind
-                                            )
-        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
-        self.log(f"val{suffix}/rec_loss", rec_loss,
-                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
-        self.log(f"val{suffix}/aeloss", aeloss,
-                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        discloss, log_dict_disc = self.loss(
+            qloss,
+            x,
+            xrec,
+            1,
+            self.global_step,
+            last_layer=self.get_last_layer(),
+            split='val' + suffix,
+            predicted_indices=ind,
+        )
+        rec_loss = log_dict_ae[f'val{suffix}/rec_loss']
+        self.log(
+            f'val{suffix}/rec_loss',
+            rec_loss,
+            prog_bar=True,
+            logger=True,
+            on_step=False,
+            on_epoch=True,
+            sync_dist=True,
+        )
+        self.log(
+            f'val{suffix}/aeloss',
+            aeloss,
+            prog_bar=True,
+            logger=True,
+            on_step=False,
+            on_epoch=True,
+            sync_dist=True,
+        )
         if version.parse(pl.__version__) >= version.parse('1.4.0'):
-            del log_dict_ae[f"val{suffix}/rec_loss"]
+            del log_dict_ae[f'val{suffix}/rec_loss']
         self.log_dict(log_dict_ae)
         self.log_dict(log_dict_disc)
         return self.log_dict
 
     def configure_optimizers(self):
         lr_d = self.learning_rate
-        lr_g = self.lr_g_factor*self.learning_rate
-        print("lr_d", lr_d)
-        print("lr_g", lr_g)
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quantize.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr_g, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr_d, betas=(0.5, 0.9))
+        lr_g = self.lr_g_factor * self.learning_rate
+        print('lr_d', lr_d)
+        print('lr_g', lr_g)
+        opt_ae = torch.optim.Adam(
+            list(self.encoder.parameters())
+            + list(self.decoder.parameters())
+            + list(self.quantize.parameters())
+            + list(self.quant_conv.parameters())
+            + list(self.post_quant_conv.parameters()),
+            lr=lr_g,
+            betas=(0.5, 0.9),
+        )
+        opt_disc = torch.optim.Adam(
+            self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)
+        )
 
         if self.scheduler_config is not None:
             scheduler = instantiate_from_config(self.scheduler_config)
 
-            print("Setting up LambdaLR scheduler...")
+            print('Setting up LambdaLR scheduler...')
             scheduler = [
                 {
-                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+                    'scheduler': LambdaLR(
+                        opt_ae, lr_lambda=scheduler.schedule
+                    ),
                     'interval': 'step',
-                    'frequency': 1
+                    'frequency': 1,
                 },
                 {
-                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+                    'scheduler': LambdaLR(
+                        opt_disc, lr_lambda=scheduler.schedule
+                    ),
                     'interval': 'step',
-                    'frequency': 1
+                    'frequency': 1,
                 },
             ]
             return [opt_ae, opt_disc], scheduler
@@ -235,7 +314,7 @@ class VQModel(pl.LightningModule):
         x = self.get_input(batch, self.image_key)
         x = x.to(self.device)
         if only_inputs:
-            log["inputs"] = x
+            log['inputs'] = x
             return log
         xrec, _ = self(x)
         if x.shape[1] > 3:
@@ -243,21 +322,24 @@ class VQModel(pl.LightningModule):
             assert xrec.shape[1] > 3
             x = self.to_rgb(x)
             xrec = self.to_rgb(xrec)
-        log["inputs"] = x
-        log["reconstructions"] = xrec
+        log['inputs'] = x
+        log['reconstructions'] = xrec
         if plot_ema:
             with self.ema_scope():
                 xrec_ema, _ = self(x)
-                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
-                log["reconstructions_ema"] = xrec_ema
+                if x.shape[1] > 3:
+                    xrec_ema = self.to_rgb(xrec_ema)
+                log['reconstructions_ema'] = xrec_ema
         return log
 
     def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        assert self.image_key == 'segmentation'
+        if not hasattr(self, 'colorize'):
+            self.register_buffer(
+                'colorize', torch.randn(3, x.shape[1], 1, 1).to(x)
+            )
         x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
         return x
 
 
@@ -283,43 +365,50 @@ class VQModelInterface(VQModel):
 
 
 class AutoencoderKL(pl.LightningModule):
-    def __init__(self,
-                 ddconfig,
-                 lossconfig,
-                 embed_dim,
-                 ckpt_path=None,
-                 ignore_keys=[],
-                 image_key="image",
-                 colorize_nlabels=None,
-                 monitor=None,
-                 ):
+    def __init__(
+        self,
+        ddconfig,
+        lossconfig,
+        embed_dim,
+        ckpt_path=None,
+        ignore_keys=[],
+        image_key='image',
+        colorize_nlabels=None,
+        monitor=None,
+    ):
         super().__init__()
         self.image_key = image_key
         self.encoder = Encoder(**ddconfig)
         self.decoder = Decoder(**ddconfig)
         self.loss = instantiate_from_config(lossconfig)
-        assert ddconfig["double_z"]
-        self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        assert ddconfig['double_z']
+        self.quant_conv = torch.nn.Conv2d(
+            2 * ddconfig['z_channels'], 2 * embed_dim, 1
+        )
+        self.post_quant_conv = torch.nn.Conv2d(
+            embed_dim, ddconfig['z_channels'], 1
+        )
         self.embed_dim = embed_dim
         if colorize_nlabels is not None:
-            assert type(colorize_nlabels)==int
-            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+            assert type(colorize_nlabels) == int
+            self.register_buffer(
+                'colorize', torch.randn(3, colorize_nlabels, 1, 1)
+            )
         if monitor is not None:
             self.monitor = monitor
         if ckpt_path is not None:
             self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
 
     def init_from_ckpt(self, path, ignore_keys=list()):
-        sd = torch.load(path, map_location="cpu")["state_dict"]
+        sd = torch.load(path, map_location='cpu')['state_dict']
         keys = list(sd.keys())
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
+                    print('Deleting key {} from state_dict.'.format(k))
                     del sd[k]
         self.load_state_dict(sd, strict=False)
-        print(f"Restored from {path}")
+        print(f'Restored from {path}')
 
     def encode(self, x):
         h = self.encoder(x)
@@ -345,7 +434,11 @@ class AutoencoderKL(pl.LightningModule):
         x = batch[k]
         if len(x.shape) == 3:
             x = x[..., None]
-        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        x = (
+            x.permute(0, 3, 1, 2)
+            .to(memory_format=torch.contiguous_format)
+            .float()
+        )
         return x
 
     def training_step(self, batch, batch_idx, optimizer_idx):
@@ -354,44 +447,102 @@ class AutoencoderKL(pl.LightningModule):
 
         if optimizer_idx == 0:
             # train encoder+decoder+logvar
-            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
-                                            last_layer=self.get_last_layer(), split="train")
-            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            aeloss, log_dict_ae = self.loss(
+                inputs,
+                reconstructions,
+                posterior,
+                optimizer_idx,
+                self.global_step,
+                last_layer=self.get_last_layer(),
+                split='train',
+            )
+            self.log(
+                'aeloss',
+                aeloss,
+                prog_bar=True,
+                logger=True,
+                on_step=True,
+                on_epoch=True,
+            )
+            self.log_dict(
+                log_dict_ae,
+                prog_bar=False,
+                logger=True,
+                on_step=True,
+                on_epoch=False,
+            )
             return aeloss
 
         if optimizer_idx == 1:
             # train the discriminator
-            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
-                                                last_layer=self.get_last_layer(), split="train")
+            discloss, log_dict_disc = self.loss(
+                inputs,
+                reconstructions,
+                posterior,
+                optimizer_idx,
+                self.global_step,
+                last_layer=self.get_last_layer(),
+                split='train',
+            )
 
-            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+            self.log(
+                'discloss',
+                discloss,
+                prog_bar=True,
+                logger=True,
+                on_step=True,
+                on_epoch=True,
+            )
+            self.log_dict(
+                log_dict_disc,
+                prog_bar=False,
+                logger=True,
+                on_step=True,
+                on_epoch=False,
+            )
             return discloss
 
     def validation_step(self, batch, batch_idx):
         inputs = self.get_input(batch, self.image_key)
         reconstructions, posterior = self(inputs)
-        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
-                                        last_layer=self.get_last_layer(), split="val")
+        aeloss, log_dict_ae = self.loss(
+            inputs,
+            reconstructions,
+            posterior,
+            0,
+            self.global_step,
+            last_layer=self.get_last_layer(),
+            split='val',
+        )
 
-        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
-                                            last_layer=self.get_last_layer(), split="val")
+        discloss, log_dict_disc = self.loss(
+            inputs,
+            reconstructions,
+            posterior,
+            1,
+            self.global_step,
+            last_layer=self.get_last_layer(),
+            split='val',
+        )
 
-        self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
+        self.log('val/rec_loss', log_dict_ae['val/rec_loss'])
         self.log_dict(log_dict_ae)
         self.log_dict(log_dict_disc)
         return self.log_dict
 
     def configure_optimizers(self):
         lr = self.learning_rate
-        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
-                                  list(self.decoder.parameters())+
-                                  list(self.quant_conv.parameters())+
-                                  list(self.post_quant_conv.parameters()),
-                                  lr=lr, betas=(0.5, 0.9))
-        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
-                                    lr=lr, betas=(0.5, 0.9))
+        opt_ae = torch.optim.Adam(
+            list(self.encoder.parameters())
+            + list(self.decoder.parameters())
+            + list(self.quant_conv.parameters())
+            + list(self.post_quant_conv.parameters()),
+            lr=lr,
+            betas=(0.5, 0.9),
+        )
+        opt_disc = torch.optim.Adam(
+            self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)
+        )
         return [opt_ae, opt_disc], []
 
     def get_last_layer(self):
@@ -409,17 +560,19 @@ class AutoencoderKL(pl.LightningModule):
                 assert xrec.shape[1] > 3
                 x = self.to_rgb(x)
                 xrec = self.to_rgb(xrec)
-            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
-            log["reconstructions"] = xrec
-        log["inputs"] = x
+            log['samples'] = self.decode(torch.randn_like(posterior.sample()))
+            log['reconstructions'] = xrec
+        log['inputs'] = x
         return log
 
     def to_rgb(self, x):
-        assert self.image_key == "segmentation"
-        if not hasattr(self, "colorize"):
-            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        assert self.image_key == 'segmentation'
+        if not hasattr(self, 'colorize'):
+            self.register_buffer(
+                'colorize', torch.randn(3, x.shape[1], 1, 1).to(x)
+            )
         x = F.conv2d(x, weight=self.colorize)
-        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
         return x
 
 

ldm/models/diffusion/classifier.py

@@ -10,13 +10,13 @@ from einops import rearrange
 from glob import glob
 from natsort import natsorted
 
-from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
+from ldm.modules.diffusionmodules.openaimodel import (
+    EncoderUNetModel,
+    UNetModel,
+)
 from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
 
-__models__ = {
-    'class_label': EncoderUNetModel,
-    'segmentation': UNetModel
-}
+__models__ = {'class_label': EncoderUNetModel, 'segmentation': UNetModel}
 
 
 def disabled_train(self, mode=True):
@@ -26,37 +26,49 @@ def disabled_train(self, mode=True):
 
 
 class NoisyLatentImageClassifier(pl.LightningModule):
-
-    def __init__(self,
-                 diffusion_path,
-                 num_classes,
-                 ckpt_path=None,
-                 pool='attention',
-                 label_key=None,
-                 diffusion_ckpt_path=None,
-                 scheduler_config=None,
-                 weight_decay=1.e-2,
-                 log_steps=10,
-                 monitor='val/loss',
-                 *args,
-                 **kwargs):
+    def __init__(
+        self,
+        diffusion_path,
+        num_classes,
+        ckpt_path=None,
+        pool='attention',
+        label_key=None,
+        diffusion_ckpt_path=None,
+        scheduler_config=None,
+        weight_decay=1.0e-2,
+        log_steps=10,
+        monitor='val/loss',
+        *args,
+        **kwargs,
+    ):
         super().__init__(*args, **kwargs)
         self.num_classes = num_classes
         # get latest config of diffusion model
-        diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
+        diffusion_config = natsorted(
+            glob(os.path.join(diffusion_path, 'configs', '*-project.yaml'))
+        )[-1]
         self.diffusion_config = OmegaConf.load(diffusion_config).model
         self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
         self.load_diffusion()
 
         self.monitor = monitor
-        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
-        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
+        self.numd = (
+            self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
+        )
+        self.log_time_interval = (
+            self.diffusion_model.num_timesteps // log_steps
+        )
         self.log_steps = log_steps
 
-        self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
+        self.label_key = (
+            label_key
+            if not hasattr(self.diffusion_model, 'cond_stage_key')
             else self.diffusion_model.cond_stage_key
+        )
 
-        assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
+        assert (
+            self.label_key is not None
+        ), 'label_key neither in diffusion model nor in model.params'
 
         if self.label_key not in __models__:
             raise NotImplementedError()
@@ -68,22 +80,27 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         self.weight_decay = weight_decay
 
     def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
-        sd = torch.load(path, map_location="cpu")
-        if "state_dict" in list(sd.keys()):
-            sd = sd["state_dict"]
+        sd = torch.load(path, map_location='cpu')
+        if 'state_dict' in list(sd.keys()):
+            sd = sd['state_dict']
         keys = list(sd.keys())
         for k in keys:
             for ik in ignore_keys:
                 if k.startswith(ik):
-                    print("Deleting key {} from state_dict.".format(k))
+                    print('Deleting key {} from state_dict.'.format(k))
                     del sd[k]
-        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
-            sd, strict=False)
-        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        missing, unexpected = (
+            self.load_state_dict(sd, strict=False)
+            if not only_model
+            else self.model.load_state_dict(sd, strict=False)
+        )
+        print(
+            f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
+        )
         if len(missing) > 0:
-            print(f"Missing Keys: {missing}")
+            print(f'Missing Keys: {missing}')
         if len(unexpected) > 0:
-            print(f"Unexpected Keys: {unexpected}")
+            print(f'Unexpected Keys: {unexpected}')
 
     def load_diffusion(self):
         model = instantiate_from_config(self.diffusion_config)
@@ -93,17 +110,25 @@ class NoisyLatentImageClassifier(pl.LightningModule):
             param.requires_grad = False
 
     def load_classifier(self, ckpt_path, pool):
-        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
-        model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
+        model_config = deepcopy(
+            self.diffusion_config.params.unet_config.params
+        )
+        model_config.in_channels = (
+            self.diffusion_config.params.unet_config.params.out_channels
+        )
         model_config.out_channels = self.num_classes
         if self.label_key == 'class_label':
             model_config.pool = pool
 
         self.model = __models__[self.label_key](**model_config)
         if ckpt_path is not None:
-            print('#####################################################################')
+            print(
+                '#####################################################################'
+            )
             print(f'load from ckpt "{ckpt_path}"')
-            print('#####################################################################')
+            print(
+                '#####################################################################'
+            )
             self.init_from_ckpt(ckpt_path)
 
     @torch.no_grad()
@@ -111,11 +136,19 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         noise = default(noise, lambda: torch.randn_like(x))
         continuous_sqrt_alpha_cumprod = None
         if self.diffusion_model.use_continuous_noise:
-            continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
+            continuous_sqrt_alpha_cumprod = (
+                self.diffusion_model.sample_continuous_noise_level(
+                    x.shape[0], t + 1
+                )
+            )
             # todo: make sure t+1 is correct here
 
-        return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
-                                             continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
+        return self.diffusion_model.q_sample(
+            x_start=x,
+            t=t,
+            noise=noise,
+            continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod,
+        )
 
     def forward(self, x_noisy, t, *args, **kwargs):
         return self.model(x_noisy, t)
@@ -141,17 +174,21 @@ class NoisyLatentImageClassifier(pl.LightningModule):
             targets = rearrange(targets, 'b h w c -> b c h w')
             for down in range(self.numd):
                 h, w = targets.shape[-2:]
-                targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
+                targets = F.interpolate(
+                    targets, size=(h // 2, w // 2), mode='nearest'
+                )
 
             # targets = rearrange(targets,'b c h w -> b h w c')
 
         return targets
 
-    def compute_top_k(self, logits, labels, k, reduction="mean"):
+    def compute_top_k(self, logits, labels, k, reduction='mean'):
         _, top_ks = torch.topk(logits, k, dim=1)
-        if reduction == "mean":
-            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
-        elif reduction == "none":
+        if reduction == 'mean':
+            return (
+                (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
+            )
+        elif reduction == 'none':
             return (top_ks == labels[:, None]).float().sum(dim=-1)
 
     def on_train_epoch_start(self):
@@ -162,29 +199,59 @@ class NoisyLatentImageClassifier(pl.LightningModule):
     def write_logs(self, loss, logits, targets):
         log_prefix = 'train' if self.training else 'val'
         log = {}
-        log[f"{log_prefix}/loss"] = loss.mean()
-        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
-            logits, targets, k=1, reduction="mean"
+        log[f'{log_prefix}/loss'] = loss.mean()
+        log[f'{log_prefix}/acc@1'] = self.compute_top_k(
+            logits, targets, k=1, reduction='mean'
         )
-        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
-            logits, targets, k=5, reduction="mean"
+        log[f'{log_prefix}/acc@5'] = self.compute_top_k(
+            logits, targets, k=5, reduction='mean'
         )
 
-        self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
-        self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
-        self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
+        self.log_dict(
+            log,
+            prog_bar=False,
+            logger=True,
+            on_step=self.training,
+            on_epoch=True,
+        )
+        self.log(
+            'loss', log[f'{log_prefix}/loss'], prog_bar=True, logger=False
+        )
+        self.log(
+            'global_step',
+            self.global_step,
+            logger=False,
+            on_epoch=False,
+            prog_bar=True,
+        )
         lr = self.optimizers().param_groups[0]['lr']
-        self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
+        self.log(
+            'lr_abs',
+            lr,
+            on_step=True,
+            logger=True,
+            on_epoch=False,
+            prog_bar=True,
+        )
 
     def shared_step(self, batch, t=None):
-        x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
+        x, *_ = self.diffusion_model.get_input(
+            batch, k=self.diffusion_model.first_stage_key
+        )
         targets = self.get_conditioning(batch)
         if targets.dim() == 4:
             targets = targets.argmax(dim=1)
         if t is None:
-            t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
+            t = torch.randint(
+                0,
+                self.diffusion_model.num_timesteps,
+                (x.shape[0],),
+                device=self.device,
+            ).long()
         else:
-            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
+            t = torch.full(
+                size=(x.shape[0],), fill_value=t, device=self.device
+            ).long()
         x_noisy = self.get_x_noisy(x, t)
         logits = self(x_noisy, t)
 
@@ -200,8 +267,14 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         return loss
 
     def reset_noise_accs(self):
-        self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
-                          range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
+        self.noisy_acc = {
+            t: {'acc@1': [], 'acc@5': []}
+            for t in range(
+                0,
+                self.diffusion_model.num_timesteps,
+                self.diffusion_model.log_every_t,
+            )
+        }
 
     def on_validation_start(self):
         self.reset_noise_accs()
@@ -212,24 +285,35 @@ class NoisyLatentImageClassifier(pl.LightningModule):
 
         for t in self.noisy_acc:
             _, logits, _, targets = self.shared_step(batch, t)
-            self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
-            self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
+            self.noisy_acc[t]['acc@1'].append(
+                self.compute_top_k(logits, targets, k=1, reduction='mean')
+            )
+            self.noisy_acc[t]['acc@5'].append(
+                self.compute_top_k(logits, targets, k=5, reduction='mean')
+            )
 
         return loss
 
     def configure_optimizers(self):
-        optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
+        optimizer = AdamW(
+            self.model.parameters(),
+            lr=self.learning_rate,
+            weight_decay=self.weight_decay,
+        )
 
         if self.use_scheduler:
             scheduler = instantiate_from_config(self.scheduler_config)
 
-            print("Setting up LambdaLR scheduler...")
+            print('Setting up LambdaLR scheduler...')
             scheduler = [
                 {
-                    'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
+                    'scheduler': LambdaLR(
+                        optimizer, lr_lambda=scheduler.schedule
+                    ),
                     'interval': 'step',
-                    'frequency': 1
-                }]
+                    'frequency': 1,
+                }
+            ]
             return [optimizer], scheduler
 
         return optimizer
@@ -243,7 +327,7 @@ class NoisyLatentImageClassifier(pl.LightningModule):
         y = self.get_conditioning(batch)
 
         if self.label_key == 'class_label':
-            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+            y = log_txt_as_img((x.shape[2], x.shape[3]), batch['human_label'])
             log['labels'] = y
 
         if ismap(y):
@@ -256,10 +340,14 @@ class NoisyLatentImageClassifier(pl.LightningModule):
 
                 log[f'inputs@t{current_time}'] = x_noisy
 
-                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
+                pred = F.one_hot(
+                    logits.argmax(dim=1), num_classes=self.num_classes
+                )
                 pred = rearrange(pred, 'b h w c -> b c h w')
 
-                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
+                log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(
+                    pred
+                )
 
         for key in log:
             log[key] = log[key][:N]

ldm/models/diffusion/ddim.py

@@ -4,89 +4,146 @@ import torch
 import numpy as np
 from tqdm import tqdm
 from functools import partial
+from ldm.dream.devices import choose_torch_device
 
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
-    extract_into_tensor
+from ldm.modules.diffusionmodules.util import (
+    make_ddim_sampling_parameters,
+    make_ddim_timesteps,
+    noise_like,
+    extract_into_tensor,
+)
 
 
 class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", device="cuda", **kwargs):
+    def __init__(self, model, schedule='linear', device=None, **kwargs):
         super().__init__()
         self.model = model
         self.ddpm_num_timesteps = model.num_timesteps
         self.schedule = schedule
-        self.device = device
+        self.device   = device or choose_torch_device()
 
     def register_buffer(self, name, attr):
         if type(attr) == torch.Tensor:
             if attr.device != torch.device(self.device):
-                attr = attr.to(torch.device(self.device))
+                attr = attr.to(dtype=torch.float32, device=self.device)
         setattr(self, name, attr)
 
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+    def make_schedule(
+        self,
+        ddim_num_steps,
+        ddim_discretize='uniform',
+        ddim_eta=0.0,
+        verbose=True,
+    ):
+        self.ddim_timesteps = make_ddim_timesteps(
+            ddim_discr_method=ddim_discretize,
+            num_ddim_timesteps=ddim_num_steps,
+            num_ddpm_timesteps=self.ddpm_num_timesteps,
+            verbose=verbose,
+        )
         alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+        assert (
+            alphas_cumprod.shape[0] == self.ddpm_num_timesteps
+        ), 'alphas have to be defined for each timestep'
+        to_torch = (
+            lambda x: x.clone()
+            .detach()
+            .to(torch.float32)
+            .to(self.model.device)
+        )
 
         self.register_buffer('betas', to_torch(self.model.betas))
         self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+        self.register_buffer(
+            'alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)
+        )
 
         # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+        self.register_buffer(
+            'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))
+        )
+        self.register_buffer(
+            'sqrt_one_minus_alphas_cumprod',
+            to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
+        )
+        self.register_buffer(
+            'log_one_minus_alphas_cumprod',
+            to_torch(np.log(1.0 - alphas_cumprod.cpu())),
+        )
+        self.register_buffer(
+            'sqrt_recip_alphas_cumprod',
+            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())),
+        )
+        self.register_buffer(
+            'sqrt_recipm1_alphas_cumprod',
+            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
+        )
 
         # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
+        (
+            ddim_sigmas,
+            ddim_alphas,
+            ddim_alphas_prev,
+        ) = make_ddim_sampling_parameters(
+            alphacums=alphas_cumprod.cpu(),
+            ddim_timesteps=self.ddim_timesteps,
+            eta=ddim_eta,
+            verbose=verbose,
+        )
         self.register_buffer('ddim_sigmas', ddim_sigmas)
         self.register_buffer('ddim_alphas', ddim_alphas)
         self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        self.register_buffer(
+            'ddim_sqrt_one_minus_alphas', np.sqrt(1.0 - ddim_alphas)
+        )
         sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+            (1 - self.alphas_cumprod_prev)
+            / (1 - self.alphas_cumprod)
+            * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
+        )
+        self.register_buffer(
+            'ddim_sigmas_for_original_num_steps',
+            sigmas_for_original_sampling_steps,
+        )
 
     @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
+    def sample(
+        self,
+        S,
+        batch_size,
+        shape,
+        conditioning=None,
+        callback=None,
+        normals_sequence=None,
+        img_callback=None,
+        quantize_x0=False,
+        eta=0.0,
+        mask=None,
+        x0=None,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        verbose=True,
+        x_T=None,
+        log_every_t=100,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+        **kwargs,
+    ):
         if conditioning is not None:
             if isinstance(conditioning, dict):
                 cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                 if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+                    print(
+                        f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'
+                    )
             else:
                 if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+                    print(
+                        f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'
+                    )
 
         self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
         # sampling
@@ -94,30 +151,47 @@ class DDIMSampler(object):
         size = (batch_size, C, H, W)
         print(f'Data shape for DDIM sampling is {size}, eta {eta}')
 
-        samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    )
+        samples, intermediates = self.ddim_sampling(
+            conditioning,
+            size,
+            callback=callback,
+            img_callback=img_callback,
+            quantize_denoised=quantize_x0,
+            mask=mask,
+            x0=x0,
+            ddim_use_original_steps=False,
+            noise_dropout=noise_dropout,
+            temperature=temperature,
+            score_corrector=score_corrector,
+            corrector_kwargs=corrector_kwargs,
+            x_T=x_T,
+            log_every_t=log_every_t,
+            unconditional_guidance_scale=unconditional_guidance_scale,
+            unconditional_conditioning=unconditional_conditioning,
+        )
         return samples, intermediates
 
     @torch.no_grad()
-    def ddim_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
+    def ddim_sampling(
+        self,
+        cond,
+        shape,
+        x_T=None,
+        ddim_use_original_steps=False,
+        callback=None,
+        timesteps=None,
+        quantize_denoised=False,
+        mask=None,
+        x0=None,
+        img_callback=None,
+        log_every_t=100,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+    ):
         device = self.model.betas.device
         b = shape[0]
         if x_T is None:
@@ -126,17 +200,38 @@ class DDIMSampler(object):
             img = x_T
 
         if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+            timesteps = (
+                self.ddpm_num_timesteps
+                if ddim_use_original_steps
+                else self.ddim_timesteps
+            )
         elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            subset_end = (
+                int(
+                    min(timesteps / self.ddim_timesteps.shape[0], 1)
+                    * self.ddim_timesteps.shape[0]
+                )
+                - 1
+            )
             timesteps = self.ddim_timesteps[:subset_end]
 
         intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
+        time_range = (
+            reversed(range(0, timesteps))
+            if ddim_use_original_steps
+            else np.flip(timesteps)
+        )
+        total_steps = (
+            timesteps if ddim_use_original_steps else timesteps.shape[0]
+        )
+        print(f'Running DDIM Sampling with {total_steps} timesteps')
 
-        iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps, dynamic_ncols=True)
+        iterator = tqdm(
+            time_range,
+            desc='DDIM Sampler',
+            total=total_steps,
+            dynamic_ncols=True,
+        )
 
         for i, step in enumerate(iterator):
             index = total_steps - i - 1
@@ -144,18 +239,30 @@ class DDIMSampler(object):
 
             if mask is not None:
                 assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
+                img_orig = self.model.q_sample(
+                    x0, ts
+                )  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1.0 - mask) * img
 
-            outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning)
+            outs = self.p_sample_ddim(
+                img,
+                cond,
+                ts,
+                index=index,
+                use_original_steps=ddim_use_original_steps,
+                quantize_denoised=quantize_denoised,
+                temperature=temperature,
+                noise_dropout=noise_dropout,
+                score_corrector=score_corrector,
+                corrector_kwargs=corrector_kwargs,
+                unconditional_guidance_scale=unconditional_guidance_scale,
+                unconditional_conditioning=unconditional_conditioning,
+            )
             img, pred_x0 = outs
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(pred_x0, i)
 
             if index % log_every_t == 0 or index == total_steps - 1:
                 intermediates['x_inter'].append(img)
@@ -164,42 +271,82 @@ class DDIMSampler(object):
         return img, intermediates
 
     @torch.no_grad()
-    def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None):
+    def p_sample_ddim(
+        self,
+        x,
+        c,
+        t,
+        index,
+        repeat_noise=False,
+        use_original_steps=False,
+        quantize_denoised=False,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+    ):
         b, *_, device = *x.shape, x.device
 
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+        if (
+            unconditional_conditioning is None
+            or unconditional_guidance_scale == 1.0
+        ):
             e_t = self.model.apply_model(x, t, c)
         else:
             x_in = torch.cat([x] * 2)
             t_in = torch.cat([t] * 2)
             c_in = torch.cat([unconditional_conditioning, c])
             e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+            e_t = e_t_uncond + unconditional_guidance_scale * (
+                e_t - e_t_uncond
+            )
 
         if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+            assert self.model.parameterization == 'eps'
+            e_t = score_corrector.modify_score(
+                self.model, e_t, x, t, c, **corrector_kwargs
+            )
 
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        alphas = (
+            self.model.alphas_cumprod
+            if use_original_steps
+            else self.ddim_alphas
+        )
+        alphas_prev = (
+            self.model.alphas_cumprod_prev
+            if use_original_steps
+            else self.ddim_alphas_prev
+        )
+        sqrt_one_minus_alphas = (
+            self.model.sqrt_one_minus_alphas_cumprod
+            if use_original_steps
+            else self.ddim_sqrt_one_minus_alphas
+        )
+        sigmas = (
+            self.model.ddim_sigmas_for_original_num_steps
+            if use_original_steps
+            else self.ddim_sigmas
+        )
         # select parameters corresponding to the currently considered timestep
         a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
         a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
         sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+        sqrt_one_minus_at = torch.full(
+            (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
+        )
 
         # current prediction for x_0
         pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
         if quantize_denoised:
             pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
         # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
+        dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
+        noise = (
+            sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        )
+        if noise_dropout > 0.0:
             noise = torch.nn.functional.dropout(noise, p=noise_dropout)
         x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
         return x_prev, pred_x0
@@ -217,26 +364,55 @@ class DDIMSampler(object):
 
         if noise is None:
             noise = torch.randn_like(x0)
-        return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
-                extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+        return (
+            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
+            + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)
+            * noise
+        )
 
     @torch.no_grad()
-    def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               use_original_steps=False):
+    def decode(
+        self,
+        x_latent,
+        cond,
+        t_start,
+        img_callback=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        use_original_steps=False,
+    ):
 
-        timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+        timesteps = (
+            np.arange(self.ddpm_num_timesteps)
+            if use_original_steps
+            else self.ddim_timesteps
+        )
         timesteps = timesteps[:t_start]
 
         time_range = np.flip(timesteps)
         total_steps = timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
+        print(f'Running DDIM Sampling with {total_steps} timesteps')
 
         iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
         x_dec = x_latent
         for i, step in enumerate(iterator):
             index = total_steps - i - 1
-            ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
-            x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
-                                          unconditional_guidance_scale=unconditional_guidance_scale,
-                                          unconditional_conditioning=unconditional_conditioning)
+            ts = torch.full(
+                (x_latent.shape[0],),
+                step,
+                device=x_latent.device,
+                dtype=torch.long,
+            )
+            x_dec, _ = self.p_sample_ddim(
+                x_dec,
+                cond,
+                ts,
+                index=index,
+                use_original_steps=use_original_steps,
+                unconditional_guidance_scale=unconditional_guidance_scale,
+                unconditional_conditioning=unconditional_conditioning,
+            )
+            if img_callback:
+                img_callback(x_dec, i)
+
         return x_dec

ldm/models/diffusion/ksampler.py

@@ -1,8 +1,8 @@
-'''wrapper around part of Karen Crownson's k-duffsion library, making it call compatible with other Samplers'''
+"""wrapper around part of Katherine Crowson's k-diffusion library, making it call compatible with other Samplers"""
 import k_diffusion as K
 import torch
 import torch.nn as nn
-import accelerate
+from ldm.dream.devices import choose_torch_device
 
 class CFGDenoiser(nn.Module):
     def __init__(self, model):
@@ -16,59 +16,73 @@ class CFGDenoiser(nn.Module):
         uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
         return uncond + (cond - uncond) * cond_scale
 
+
 class KSampler(object):
-    def __init__(self,model,schedule="lms", **kwargs):
+    def __init__(self, model, schedule='lms', device=None, **kwargs):
         super().__init__()
-        self.model        = K.external.CompVisDenoiser(model)
-        self.accelerator  = accelerate.Accelerator()
-        self.device       = self.accelerator.device
+        self.model = K.external.CompVisDenoiser(model)
         self.schedule = schedule
+        self.device   = device or choose_torch_device()
 
         def forward(self, x, sigma, uncond, cond, cond_scale):
             x_in = torch.cat([x] * 2)
             sigma_in = torch.cat([sigma] * 2)
             cond_in = torch.cat([uncond, cond])
-            uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+            uncond, cond = self.inner_model(
+                x_in, sigma_in, cond=cond_in
+            ).chunk(2)
             return uncond + (cond - uncond) * cond_scale
 
-
     # most of these arguments are ignored and are only present for compatibility with
     # other samples
     @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
+    def sample(
+        self,
+        S,
+        batch_size,
+        shape,
+        conditioning=None,
+        callback=None,
+        normals_sequence=None,
+        img_callback=None,
+        quantize_x0=False,
+        eta=0.0,
+        mask=None,
+        x0=None,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        verbose=True,
+        x_T=None,
+        log_every_t=100,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+        **kwargs,
+    ):
+        def route_callback(k_callback_values):
+            if img_callback is not None:
+                img_callback(k_callback_values['x'], k_callback_values['i'])
 
         sigmas = self.model.get_sigmas(S)
-        if x_T:
-            x = x_T
+        if x_T is not None:
+            x = x_T * sigmas[0]
         else:
-            x = torch.randn([batch_size, *shape], device=self.device) * sigmas[0] # for GPU draw
+            x = (
+                torch.randn([batch_size, *shape], device=self.device)
+                * sigmas[0]
+            )   # for GPU draw
         model_wrap_cfg = CFGDenoiser(self.model)
-        extra_args = {'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}
-        return (K.sampling.__dict__[f'sample_{self.schedule}'](model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not self.accelerator.is_main_process),
-                None)
-
-    def gather(samples_ddim):
-        return self.accelerator.gather(samples_ddim)
+        extra_args = {
+            'cond': conditioning,
+            'uncond': unconditional_conditioning,
+            'cond_scale': unconditional_guidance_scale,
+        }
+        return (
+            K.sampling.__dict__[f'sample_{self.schedule}'](
+                model_wrap_cfg, x, sigmas, extra_args=extra_args,
+                callback=route_callback
+            ),
+            None,
+        )

ldm/models/diffusion/plms.py

@@ -4,122 +4,195 @@ import torch
 import numpy as np
 from tqdm import tqdm
 from functools import partial
+from ldm.dream.devices import choose_torch_device
 
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+from ldm.modules.diffusionmodules.util import (
+    make_ddim_sampling_parameters,
+    make_ddim_timesteps,
+    noise_like,
+)
 
 
 class PLMSSampler(object):
-    def __init__(self, model, schedule="linear", device="cuda", **kwargs):
+    def __init__(self, model, schedule='linear', device=None, **kwargs):
         super().__init__()
         self.model = model
         self.ddpm_num_timesteps = model.num_timesteps
         self.schedule = schedule
-        self.device = device
+        self.device   = device if device else choose_torch_device()
 
     def register_buffer(self, name, attr):
         if type(attr) == torch.Tensor:
             if attr.device != torch.device(self.device):
-                attr = attr.to(torch.device(self.device))
-
+                attr = attr.to(torch.float32).to(torch.device(self.device))
         setattr(self, name, attr)
 
-    def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+    def make_schedule(
+        self,
+        ddim_num_steps,
+        ddim_discretize='uniform',
+        ddim_eta=0.0,
+        verbose=True,
+    ):
         if ddim_eta != 0:
             raise ValueError('ddim_eta must be 0 for PLMS')
-        self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
-                                                  num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+        self.ddim_timesteps = make_ddim_timesteps(
+            ddim_discr_method=ddim_discretize,
+            num_ddim_timesteps=ddim_num_steps,
+            num_ddpm_timesteps=self.ddpm_num_timesteps,
+            verbose=verbose,
+        )
         alphas_cumprod = self.model.alphas_cumprod
-        assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
-        to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+        assert (
+            alphas_cumprod.shape[0] == self.ddpm_num_timesteps
+        ), 'alphas have to be defined for each timestep'
+        to_torch = (
+            lambda x: x.clone()
+            .detach()
+            .to(torch.float32)
+            .to(self.model.device)
+        )
 
         self.register_buffer('betas', to_torch(self.model.betas))
         self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
-        self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+        self.register_buffer(
+            'alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)
+        )
 
         # calculations for diffusion q(x_t | x_{t-1}) and others
-        self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
-        self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
-        self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+        self.register_buffer(
+            'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))
+        )
+        self.register_buffer(
+            'sqrt_one_minus_alphas_cumprod',
+            to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
+        )
+        self.register_buffer(
+            'log_one_minus_alphas_cumprod',
+            to_torch(np.log(1.0 - alphas_cumprod.cpu())),
+        )
+        self.register_buffer(
+            'sqrt_recip_alphas_cumprod',
+            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())),
+        )
+        self.register_buffer(
+            'sqrt_recipm1_alphas_cumprod',
+            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
+        )
 
         # ddim sampling parameters
-        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
-                                                                                   ddim_timesteps=self.ddim_timesteps,
-                                                                                   eta=ddim_eta,verbose=verbose)
+        (
+            ddim_sigmas,
+            ddim_alphas,
+            ddim_alphas_prev,
+        ) = make_ddim_sampling_parameters(
+            alphacums=alphas_cumprod.cpu(),
+            ddim_timesteps=self.ddim_timesteps,
+            eta=ddim_eta,
+            verbose=verbose,
+        )
         self.register_buffer('ddim_sigmas', ddim_sigmas)
         self.register_buffer('ddim_alphas', ddim_alphas)
         self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
-        self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+        self.register_buffer(
+            'ddim_sqrt_one_minus_alphas', np.sqrt(1.0 - ddim_alphas)
+        )
         sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
-            (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
-                        1 - self.alphas_cumprod / self.alphas_cumprod_prev))
-        self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+            (1 - self.alphas_cumprod_prev)
+            / (1 - self.alphas_cumprod)
+            * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
+        )
+        self.register_buffer(
+            'ddim_sigmas_for_original_num_steps',
+            sigmas_for_original_sampling_steps,
+        )
 
     @torch.no_grad()
-    def sample(self,
-               S,
-               batch_size,
-               shape,
-               conditioning=None,
-               callback=None,
-               normals_sequence=None,
-               img_callback=None,
-               quantize_x0=False,
-               eta=0.,
-               mask=None,
-               x0=None,
-               temperature=1.,
-               noise_dropout=0.,
-               score_corrector=None,
-               corrector_kwargs=None,
-               verbose=True,
-               x_T=None,
-               log_every_t=100,
-               unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               **kwargs
-               ):
+    def sample(
+        self,
+        S,
+        batch_size,
+        shape,
+        conditioning=None,
+        callback=None,
+        normals_sequence=None,
+        img_callback=None,
+        quantize_x0=False,
+        eta=0.0,
+        mask=None,
+        x0=None,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        verbose=True,
+        x_T=None,
+        log_every_t=100,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+        **kwargs,
+    ):
         if conditioning is not None:
             if isinstance(conditioning, dict):
                 cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                 if cbs != batch_size:
-                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+                    print(
+                        f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'
+                    )
             else:
                 if conditioning.shape[0] != batch_size:
-                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+                    print(
+                        f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'
+                    )
 
         self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
         # sampling
         C, H, W = shape
         size = (batch_size, C, H, W)
-#        print(f'Data shape for PLMS sampling is {size}')
+        #        print(f'Data shape for PLMS sampling is {size}')
 
-        samples, intermediates = self.plms_sampling(conditioning, size,
-                                                    callback=callback,
-                                                    img_callback=img_callback,
-                                                    quantize_denoised=quantize_x0,
-                                                    mask=mask, x0=x0,
-                                                    ddim_use_original_steps=False,
-                                                    noise_dropout=noise_dropout,
-                                                    temperature=temperature,
-                                                    score_corrector=score_corrector,
-                                                    corrector_kwargs=corrector_kwargs,
-                                                    x_T=x_T,
-                                                    log_every_t=log_every_t,
-                                                    unconditional_guidance_scale=unconditional_guidance_scale,
-                                                    unconditional_conditioning=unconditional_conditioning,
-                                                    )
+        samples, intermediates = self.plms_sampling(
+            conditioning,
+            size,
+            callback=callback,
+            img_callback=img_callback,
+            quantize_denoised=quantize_x0,
+            mask=mask,
+            x0=x0,
+            ddim_use_original_steps=False,
+            noise_dropout=noise_dropout,
+            temperature=temperature,
+            score_corrector=score_corrector,
+            corrector_kwargs=corrector_kwargs,
+            x_T=x_T,
+            log_every_t=log_every_t,
+            unconditional_guidance_scale=unconditional_guidance_scale,
+            unconditional_conditioning=unconditional_conditioning,
+        )
         return samples, intermediates
 
     @torch.no_grad()
-    def plms_sampling(self, cond, shape,
-                      x_T=None, ddim_use_original_steps=False,
-                      callback=None, timesteps=None, quantize_denoised=False,
-                      mask=None, x0=None, img_callback=None, log_every_t=100,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
+    def plms_sampling(
+        self,
+        cond,
+        shape,
+        x_T=None,
+        ddim_use_original_steps=False,
+        callback=None,
+        timesteps=None,
+        quantize_denoised=False,
+        mask=None,
+        x0=None,
+        img_callback=None,
+        log_every_t=100,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+    ):
         device = self.model.betas.device
         b = shape[0]
         if x_T is None:
@@ -128,42 +201,81 @@ class PLMSSampler(object):
             img = x_T
 
         if timesteps is None:
-            timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+            timesteps = (
+                self.ddpm_num_timesteps
+                if ddim_use_original_steps
+                else self.ddim_timesteps
+            )
         elif timesteps is not None and not ddim_use_original_steps:
-            subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+            subset_end = (
+                int(
+                    min(timesteps / self.ddim_timesteps.shape[0], 1)
+                    * self.ddim_timesteps.shape[0]
+                )
+                - 1
+            )
             timesteps = self.ddim_timesteps[:subset_end]
 
         intermediates = {'x_inter': [img], 'pred_x0': [img]}
-        time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
-        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-#        print(f"Running PLMS Sampling with {total_steps} timesteps")
+        time_range = (
+            list(reversed(range(0, timesteps)))
+            if ddim_use_original_steps
+            else np.flip(timesteps)
+        )
+        total_steps = (
+            timesteps if ddim_use_original_steps else timesteps.shape[0]
+        )
+        #        print(f"Running PLMS Sampling with {total_steps} timesteps")
 
-        iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps, dynamic_ncols=True)
+        iterator = tqdm(
+            time_range,
+            desc='PLMS Sampler',
+            total=total_steps,
+            dynamic_ncols=True,
+        )
         old_eps = []
 
         for i, step in enumerate(iterator):
             index = total_steps - i - 1
             ts = torch.full((b,), step, device=device, dtype=torch.long)
-            ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+            ts_next = torch.full(
+                (b,),
+                time_range[min(i + 1, len(time_range) - 1)],
+                device=device,
+                dtype=torch.long,
+            )
 
             if mask is not None:
                 assert x0 is not None
-                img_orig = self.model.q_sample(x0, ts)  # TODO: deterministic forward pass?
-                img = img_orig * mask + (1. - mask) * img
+                img_orig = self.model.q_sample(
+                    x0, ts
+                )  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1.0 - mask) * img
 
-            outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
-                                      quantize_denoised=quantize_denoised, temperature=temperature,
-                                      noise_dropout=noise_dropout, score_corrector=score_corrector,
-                                      corrector_kwargs=corrector_kwargs,
-                                      unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning,
-                                      old_eps=old_eps, t_next=ts_next)
+            outs = self.p_sample_plms(
+                img,
+                cond,
+                ts,
+                index=index,
+                use_original_steps=ddim_use_original_steps,
+                quantize_denoised=quantize_denoised,
+                temperature=temperature,
+                noise_dropout=noise_dropout,
+                score_corrector=score_corrector,
+                corrector_kwargs=corrector_kwargs,
+                unconditional_guidance_scale=unconditional_guidance_scale,
+                unconditional_conditioning=unconditional_conditioning,
+                old_eps=old_eps,
+                t_next=ts_next,
+            )
             img, pred_x0, e_t = outs
             old_eps.append(e_t)
             if len(old_eps) >= 4:
                 old_eps.pop(0)
-            if callback: callback(i)
-            if img_callback: img_callback(pred_x0, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(pred_x0, i)
 
             if index % log_every_t == 0 or index == total_steps - 1:
                 intermediates['x_inter'].append(img)
@@ -172,47 +284,95 @@ class PLMSSampler(object):
         return img, intermediates
 
     @torch.no_grad()
-    def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                      temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
+    def p_sample_plms(
+        self,
+        x,
+        c,
+        t,
+        index,
+        repeat_noise=False,
+        use_original_steps=False,
+        quantize_denoised=False,
+        temperature=1.0,
+        noise_dropout=0.0,
+        score_corrector=None,
+        corrector_kwargs=None,
+        unconditional_guidance_scale=1.0,
+        unconditional_conditioning=None,
+        old_eps=None,
+        t_next=None,
+    ):
         b, *_, device = *x.shape, x.device
 
         def get_model_output(x, t):
-            if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            if (
+                unconditional_conditioning is None
+                or unconditional_guidance_scale == 1.0
+            ):
                 e_t = self.model.apply_model(x, t, c)
             else:
                 x_in = torch.cat([x] * 2)
                 t_in = torch.cat([t] * 2)
                 c_in = torch.cat([unconditional_conditioning, c])
-                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+                e_t_uncond, e_t = self.model.apply_model(
+                    x_in, t_in, c_in
+                ).chunk(2)
+                e_t = e_t_uncond + unconditional_guidance_scale * (
+                    e_t - e_t_uncond
+                )
 
             if score_corrector is not None:
-                assert self.model.parameterization == "eps"
-                e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+                assert self.model.parameterization == 'eps'
+                e_t = score_corrector.modify_score(
+                    self.model, e_t, x, t, c, **corrector_kwargs
+                )
 
             return e_t
 
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-        sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+        alphas = (
+            self.model.alphas_cumprod
+            if use_original_steps
+            else self.ddim_alphas
+        )
+        alphas_prev = (
+            self.model.alphas_cumprod_prev
+            if use_original_steps
+            else self.ddim_alphas_prev
+        )
+        sqrt_one_minus_alphas = (
+            self.model.sqrt_one_minus_alphas_cumprod
+            if use_original_steps
+            else self.ddim_sqrt_one_minus_alphas
+        )
+        sigmas = (
+            self.model.ddim_sigmas_for_original_num_steps
+            if use_original_steps
+            else self.ddim_sigmas
+        )
 
         def get_x_prev_and_pred_x0(e_t, index):
             # select parameters corresponding to the currently considered timestep
             a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+            a_prev = torch.full(
+                (b, 1, 1, 1), alphas_prev[index], device=device
+            )
             sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-            sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+            sqrt_one_minus_at = torch.full(
+                (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
+            )
 
             # current prediction for x_0
             pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
             if quantize_denoised:
                 pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
             # direction pointing to x_t
-            dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-            if noise_dropout > 0.:
+            dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
+            noise = (
+                sigma_t
+                * noise_like(x.shape, device, repeat_noise)
+                * temperature
+            )
+            if noise_dropout > 0.0:
                 noise = torch.nn.functional.dropout(noise, p=noise_dropout)
             x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
             return x_prev, pred_x0
@@ -231,7 +391,12 @@ class PLMSSampler(object):
             e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
         elif len(old_eps) >= 3:
             # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+            e_t_prime = (
+                55 * e_t
+                - 59 * old_eps[-1]
+                + 37 * old_eps[-2]
+                - 9 * old_eps[-3]
+            ) / 24
 
         x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
 

ldm/modules/attention.py

@@ -13,7 +13,7 @@ def exists(val):
 
 
 def uniq(arr):
-    return{el: True for el in arr}.keys()
+    return {el: True for el in arr}.keys()
 
 
 def default(val, d):
@@ -45,19 +45,18 @@ class GEGLU(nn.Module):
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
+        project_in = (
+            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+            if not glu
+            else GEGLU(dim, inner_dim)
+        )
 
         self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
+            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
         )
 
     def forward(self, x):
@@ -74,7 +73,9 @@ def zero_module(module):
 
 
 def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+    return torch.nn.GroupNorm(
+        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+    )
 
 
 class LinearAttention(nn.Module):
@@ -82,17 +83,28 @@ class LinearAttention(nn.Module):
         super().__init__()
         self.heads = heads
         hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
         self.to_out = nn.Conv2d(hidden_dim, dim, 1)
 
     def forward(self, x):
         b, c, h, w = x.shape
         qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
-        k = k.softmax(dim=-1)  
+        q, k, v = rearrange(
+            qkv,
+            'b (qkv heads c) h w -> qkv b heads c (h w)',
+            heads=self.heads,
+            qkv=3,
+        )
+        k = k.softmax(dim=-1)
         context = torch.einsum('bhdn,bhen->bhde', k, v)
         out = torch.einsum('bhde,bhdn->bhen', context, q)
-        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
+        out = rearrange(
+            out,
+            'b heads c (h w) -> b (heads c) h w',
+            heads=self.heads,
+            h=h,
+            w=w,
+        )
         return self.to_out(out)
 
 
@@ -102,26 +114,18 @@ class SpatialSelfAttention(nn.Module):
         self.in_channels = in_channels
 
         self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
 
     def forward(self, x):
         h_ = x
@@ -131,12 +135,12 @@ class SpatialSelfAttention(nn.Module):
         v = self.v(h_)
 
         # compute attention
-        b,c,h,w = q.shape
+        b, c, h, w = q.shape
         q = rearrange(q, 'b c h w -> b (h w) c')
         k = rearrange(k, 'b c h w -> b c (h w)')
         w_ = torch.einsum('bij,bjk->bik', q, k)
 
-        w_ = w_ * (int(c)**(-0.5))
+        w_ = w_ * (int(c) ** (-0.5))
         w_ = torch.nn.functional.softmax(w_, dim=2)
 
         # attend to values
@@ -146,16 +150,18 @@ class SpatialSelfAttention(nn.Module):
         h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
         h_ = self.proj_out(h_)
 
-        return x+h_
+        return x + h_
 
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+    def __init__(
+        self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0
+    ):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
 
-        self.scale = dim_head ** -0.5
+        self.scale = dim_head**-0.5
         self.heads = heads
 
         self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
@@ -163,8 +169,7 @@ class CrossAttention(nn.Module):
         self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
 
         self.to_out = nn.Sequential(
-            nn.Linear(inner_dim, query_dim),
-            nn.Dropout(dropout)
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
         )
 
     def forward(self, x, context=None, mask=None):
@@ -175,7 +180,9 @@ class CrossAttention(nn.Module):
         k = self.to_k(context)
         v = self.to_v(context)
 
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+        q, k, v = map(
+            lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)
+        )
 
         sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
 
@@ -194,21 +201,40 @@ class CrossAttention(nn.Module):
 
 
 class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+    ):
         super().__init__()
-        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
+        self.attn1 = CrossAttention(
+            query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
+        )  # is a self-attention
         self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
-        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
-                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
+        self.attn2 = CrossAttention(
+            query_dim=dim,
+            context_dim=context_dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+        )  # is self-attn if context is none
         self.norm1 = nn.LayerNorm(dim)
         self.norm2 = nn.LayerNorm(dim)
         self.norm3 = nn.LayerNorm(dim)
         self.checkpoint = checkpoint
 
     def forward(self, x, context=None):
-        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+        return checkpoint(
+            self._forward, (x, context), self.parameters(), self.checkpoint
+        )
 
     def _forward(self, x, context=None):
+        x = x.contiguous() if x.device.type == 'mps' else x
         x = self.attn1(self.norm1(x)) + x
         x = self.attn2(self.norm2(x), context=context) + x
         x = self.ff(self.norm3(x)) + x
@@ -223,29 +249,43 @@ class SpatialTransformer(nn.Module):
     Then apply standard transformer action.
     Finally, reshape to image
     """
-    def __init__(self, in_channels, n_heads, d_head,
-                 depth=1, dropout=0., context_dim=None):
+
+    def __init__(
+        self,
+        in_channels,
+        n_heads,
+        d_head,
+        depth=1,
+        dropout=0.0,
+        context_dim=None,
+    ):
         super().__init__()
         self.in_channels = in_channels
         inner_dim = n_heads * d_head
         self.norm = Normalize(in_channels)
 
-        self.proj_in = nn.Conv2d(in_channels,
-                                 inner_dim,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
-                for d in range(depth)]
+        self.proj_in = nn.Conv2d(
+            in_channels, inner_dim, kernel_size=1, stride=1, padding=0
         )
 
-        self.proj_out = zero_module(nn.Conv2d(inner_dim,
-                                              in_channels,
-                                              kernel_size=1,
-                                              stride=1,
-                                              padding=0))
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    n_heads,
+                    d_head,
+                    dropout=dropout,
+                    context_dim=context_dim,
+                )
+                for d in range(depth)
+            ]
+        )
+
+        self.proj_out = zero_module(
+            nn.Conv2d(
+                inner_dim, in_channels, kernel_size=1, stride=1, padding=0
+            )
+        )
 
     def forward(self, x, context=None):
         # note: if no context is given, cross-attention defaults to self-attention
@@ -258,4 +298,4 @@ class SpatialTransformer(nn.Module):
             x = block(x, context=context)
         x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
         x = self.proj_out(x)
-        return x + x_in
+        return x + x_in

ldm/modules/diffusionmodules/openaimodel.py

@@ -24,6 +24,7 @@ from ldm.modules.attention import SpatialTransformer
 def convert_module_to_f16(x):
     pass
 
+
 def convert_module_to_f32(x):
     pass
 
@@ -42,7 +43,9 @@ class AttentionPool2d(nn.Module):
         output_dim: int = None,
     ):
         super().__init__()
-        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+        self.positional_embedding = nn.Parameter(
+            th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
+        )
         self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
         self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
         self.num_heads = embed_dim // num_heads_channels
@@ -97,37 +100,45 @@ class Upsample(nn.Module):
                  upsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+    def __init__(
+        self, channels, use_conv, dims=2, out_channels=None, padding=1
+    ):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
         self.use_conv = use_conv
         self.dims = dims
         if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+            self.conv = conv_nd(
+                dims, self.channels, self.out_channels, 3, padding=padding
+            )
 
     def forward(self, x):
         assert x.shape[1] == self.channels
         if self.dims == 3:
             x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest'
             )
         else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
+            x = F.interpolate(x, scale_factor=2, mode='nearest')
         if self.use_conv:
             x = self.conv(x)
         return x
 
+
 class TransposedUpsample(nn.Module):
-    'Learned 2x upsampling without padding'
+    """Learned 2x upsampling without padding"""
+
     def __init__(self, channels, out_channels=None, ks=5):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
 
-        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+        self.up = nn.ConvTranspose2d(
+            self.channels, self.out_channels, kernel_size=ks, stride=2
+        )
 
-    def forward(self,x):
+    def forward(self, x):
         return self.up(x)
 
 
@@ -140,7 +151,9 @@ class Downsample(nn.Module):
                  downsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+    def __init__(
+        self, channels, use_conv, dims=2, out_channels=None, padding=1
+    ):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -149,7 +162,12 @@ class Downsample(nn.Module):
         stride = 2 if dims != 3 else (1, 2, 2)
         if use_conv:
             self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+                dims,
+                self.channels,
+                self.out_channels,
+                3,
+                stride=stride,
+                padding=padding,
             )
         else:
             assert self.channels == self.out_channels
@@ -219,7 +237,9 @@ class ResBlock(TimestepBlock):
             nn.SiLU(),
             linear(
                 emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+                2 * self.out_channels
+                if use_scale_shift_norm
+                else self.out_channels,
             ),
         )
         self.out_layers = nn.Sequential(
@@ -227,7 +247,9 @@ class ResBlock(TimestepBlock):
             nn.SiLU(),
             nn.Dropout(p=dropout),
             zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+                conv_nd(
+                    dims, self.out_channels, self.out_channels, 3, padding=1
+                )
             ),
         )
 
@@ -238,7 +260,9 @@ class ResBlock(TimestepBlock):
                 dims, channels, self.out_channels, 3, padding=1
             )
         else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 1
+            )
 
     def forward(self, x, emb):
         """
@@ -251,7 +275,6 @@ class ResBlock(TimestepBlock):
             self._forward, (x, emb), self.parameters(), self.use_checkpoint
         )
 
-
     def _forward(self, x, emb):
         if self.updown:
             in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
@@ -297,7 +320,7 @@ class AttentionBlock(nn.Module):
         else:
             assert (
                 channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            ), f'q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}'
             self.num_heads = channels // num_head_channels
         self.use_checkpoint = use_checkpoint
         self.norm = normalization(channels)
@@ -312,8 +335,10 @@ class AttentionBlock(nn.Module):
         self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
 
     def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
-        #return pt_checkpoint(self._forward, x)  # pytorch
+        return checkpoint(
+            self._forward, (x,), self.parameters(), True
+        )   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+        # return pt_checkpoint(self._forward, x)  # pytorch
 
     def _forward(self, x):
         b, c, *spatial = x.shape
@@ -340,7 +365,7 @@ def count_flops_attn(model, _x, y):
     # We perform two matmuls with the same number of ops.
     # The first computes the weight matrix, the second computes
     # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
+    matmul_ops = 2 * b * (num_spatial**2) * c
     model.total_ops += th.DoubleTensor([matmul_ops])
 
 
@@ -362,13 +387,15 @@ class QKVAttentionLegacy(nn.Module):
         bs, width, length = qkv.shape
         assert width % (3 * self.n_heads) == 0
         ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(
+            ch, dim=1
+        )
         scale = 1 / math.sqrt(math.sqrt(ch))
         weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
+            'bct,bcs->bts', q * scale, k * scale
         )  # More stable with f16 than dividing afterwards
         weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
+        a = th.einsum('bts,bcs->bct', weight, v)
         return a.reshape(bs, -1, length)
 
     @staticmethod
@@ -397,12 +424,14 @@ class QKVAttention(nn.Module):
         q, k, v = qkv.chunk(3, dim=1)
         scale = 1 / math.sqrt(math.sqrt(ch))
         weight = th.einsum(
-            "bct,bcs->bts",
+            'bct,bcs->bts',
             (q * scale).view(bs * self.n_heads, ch, length),
             (k * scale).view(bs * self.n_heads, ch, length),
         )  # More stable with f16 than dividing afterwards
         weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        a = th.einsum(
+            'bts,bcs->bct', weight, v.reshape(bs * self.n_heads, ch, length)
+        )
         return a.reshape(bs, -1, length)
 
     @staticmethod
@@ -461,19 +490,24 @@ class UNetModel(nn.Module):
         use_scale_shift_norm=False,
         resblock_updown=False,
         use_new_attention_order=False,
-        use_spatial_transformer=False,    # custom transformer support
-        transformer_depth=1,              # custom transformer support
-        context_dim=None,                 # custom transformer support
-        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
+        use_spatial_transformer=False,  # custom transformer support
+        transformer_depth=1,  # custom transformer support
+        context_dim=None,  # custom transformer support
+        n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
         legacy=True,
     ):
         super().__init__()
         if use_spatial_transformer:
-            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+            assert (
+                context_dim is not None
+            ), 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
 
         if context_dim is not None:
-            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            assert (
+                use_spatial_transformer
+            ), 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
             from omegaconf.listconfig import ListConfig
+
             if type(context_dim) == ListConfig:
                 context_dim = list(context_dim)
 
@@ -481,10 +515,14 @@ class UNetModel(nn.Module):
             num_heads_upsample = num_heads
 
         if num_heads == -1:
-            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+            assert (
+                num_head_channels != -1
+            ), 'Either num_heads or num_head_channels has to be set'
 
         if num_head_channels == -1:
-            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+            assert (
+                num_heads != -1
+            ), 'Either num_heads or num_head_channels has to be set'
 
         self.image_size = image_size
         self.in_channels = in_channels
@@ -545,8 +583,12 @@ class UNetModel(nn.Module):
                         num_heads = ch // num_head_channels
                         dim_head = num_head_channels
                     if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                        # num_heads = 1
+                        dim_head = (
+                            ch // num_heads
+                            if use_spatial_transformer
+                            else num_head_channels
+                        )
                     layers.append(
                         AttentionBlock(
                             ch,
@@ -554,8 +596,14 @@ class UNetModel(nn.Module):
                             num_heads=num_heads,
                             num_head_channels=dim_head,
                             use_new_attention_order=use_new_attention_order,
-                        ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+                        )
+                        if not use_spatial_transformer
+                        else SpatialTransformer(
+                            ch,
+                            num_heads,
+                            dim_head,
+                            depth=transformer_depth,
+                            context_dim=context_dim,
                         )
                     )
                 self.input_blocks.append(TimestepEmbedSequential(*layers))
@@ -592,8 +640,12 @@ class UNetModel(nn.Module):
             num_heads = ch // num_head_channels
             dim_head = num_head_channels
         if legacy:
-            #num_heads = 1
-            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+            # num_heads = 1
+            dim_head = (
+                ch // num_heads
+                if use_spatial_transformer
+                else num_head_channels
+            )
         self.middle_block = TimestepEmbedSequential(
             ResBlock(
                 ch,
@@ -609,9 +661,15 @@ class UNetModel(nn.Module):
                 num_heads=num_heads,
                 num_head_channels=dim_head,
                 use_new_attention_order=use_new_attention_order,
-            ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
-                        ),
+            )
+            if not use_spatial_transformer
+            else SpatialTransformer(
+                ch,
+                num_heads,
+                dim_head,
+                depth=transformer_depth,
+                context_dim=context_dim,
+            ),
             ResBlock(
                 ch,
                 time_embed_dim,
@@ -646,8 +704,12 @@ class UNetModel(nn.Module):
                         num_heads = ch // num_head_channels
                         dim_head = num_head_channels
                     if legacy:
-                        #num_heads = 1
-                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                        # num_heads = 1
+                        dim_head = (
+                            ch // num_heads
+                            if use_spatial_transformer
+                            else num_head_channels
+                        )
                     layers.append(
                         AttentionBlock(
                             ch,
@@ -655,8 +717,14 @@ class UNetModel(nn.Module):
                             num_heads=num_heads_upsample,
                             num_head_channels=dim_head,
                             use_new_attention_order=use_new_attention_order,
-                        ) if not use_spatial_transformer else SpatialTransformer(
-                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
+                        )
+                        if not use_spatial_transformer
+                        else SpatialTransformer(
+                            ch,
+                            num_heads,
+                            dim_head,
+                            depth=transformer_depth,
+                            context_dim=context_dim,
                         )
                     )
                 if level and i == num_res_blocks:
@@ -673,7 +741,9 @@ class UNetModel(nn.Module):
                             up=True,
                         )
                         if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                        else Upsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
                     )
                     ds //= 2
                 self.output_blocks.append(TimestepEmbedSequential(*layers))
@@ -682,14 +752,16 @@ class UNetModel(nn.Module):
         self.out = nn.Sequential(
             normalization(ch),
             nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+            zero_module(
+                conv_nd(dims, model_channels, out_channels, 3, padding=1)
+            ),
         )
         if self.predict_codebook_ids:
             self.id_predictor = nn.Sequential(
-            normalization(ch),
-            conv_nd(dims, model_channels, n_embed, 1),
-            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
-        )
+                normalization(ch),
+                conv_nd(dims, model_channels, n_embed, 1),
+                # nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
+            )
 
     def convert_to_fp16(self):
         """
@@ -707,7 +779,7 @@ class UNetModel(nn.Module):
         self.middle_block.apply(convert_module_to_f32)
         self.output_blocks.apply(convert_module_to_f32)
 
-    def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
         """
         Apply the model to an input batch.
         :param x: an [N x C x ...] Tensor of inputs.
@@ -718,9 +790,11 @@ class UNetModel(nn.Module):
         """
         assert (y is not None) == (
             self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
+        ), 'must specify y if and only if the model is class-conditional'
         hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        t_emb = timestep_embedding(
+            timesteps, self.model_channels, repeat_only=False
+        )
         emb = self.time_embed(t_emb)
 
         if self.num_classes is not None:
@@ -768,9 +842,9 @@ class EncoderUNetModel(nn.Module):
         use_scale_shift_norm=False,
         resblock_updown=False,
         use_new_attention_order=False,
-        pool="adaptive",
+        pool='adaptive',
         *args,
-        **kwargs
+        **kwargs,
     ):
         super().__init__()
 
@@ -888,7 +962,7 @@ class EncoderUNetModel(nn.Module):
         )
         self._feature_size += ch
         self.pool = pool
-        if pool == "adaptive":
+        if pool == 'adaptive':
             self.out = nn.Sequential(
                 normalization(ch),
                 nn.SiLU(),
@@ -896,7 +970,7 @@ class EncoderUNetModel(nn.Module):
                 zero_module(conv_nd(dims, ch, out_channels, 1)),
                 nn.Flatten(),
             )
-        elif pool == "attention":
+        elif pool == 'attention':
             assert num_head_channels != -1
             self.out = nn.Sequential(
                 normalization(ch),
@@ -905,13 +979,13 @@ class EncoderUNetModel(nn.Module):
                     (image_size // ds), ch, num_head_channels, out_channels
                 ),
             )
-        elif pool == "spatial":
+        elif pool == 'spatial':
             self.out = nn.Sequential(
                 nn.Linear(self._feature_size, 2048),
                 nn.ReLU(),
                 nn.Linear(2048, self.out_channels),
             )
-        elif pool == "spatial_v2":
+        elif pool == 'spatial_v2':
             self.out = nn.Sequential(
                 nn.Linear(self._feature_size, 2048),
                 normalization(2048),
@@ -919,7 +993,7 @@ class EncoderUNetModel(nn.Module):
                 nn.Linear(2048, self.out_channels),
             )
         else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
+            raise NotImplementedError(f'Unexpected {pool} pooling')
 
     def convert_to_fp16(self):
         """
@@ -942,20 +1016,21 @@ class EncoderUNetModel(nn.Module):
         :param timesteps: a 1-D batch of timesteps.
         :return: an [N x K] Tensor of outputs.
         """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        emb = self.time_embed(
+            timestep_embedding(timesteps, self.model_channels)
+        )
 
         results = []
         h = x.type(self.dtype)
         for module in self.input_blocks:
             h = module(h, emb)
-            if self.pool.startswith("spatial"):
+            if self.pool.startswith('spatial'):
                 results.append(h.type(x.dtype).mean(dim=(2, 3)))
         h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
+        if self.pool.startswith('spatial'):
             results.append(h.type(x.dtype).mean(dim=(2, 3)))
             h = th.cat(results, axis=-1)
             return self.out(h)
         else:
             h = h.type(x.dtype)
             return self.out(h)
-

ldm/modules/diffusionmodules/util.py

@@ -18,15 +18,24 @@ from einops import repeat
 from ldm.util import instantiate_from_config
 
 
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
+def make_beta_schedule(
+    schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
+):
+    if schedule == 'linear':
         betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+            torch.linspace(
+                linear_start**0.5,
+                linear_end**0.5,
+                n_timestep,
+                dtype=torch.float64,
+            )
+            ** 2
         )
 
-    elif schedule == "cosine":
+    elif schedule == 'cosine':
         timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+            torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep
+            + cosine_s
         )
         alphas = timesteps / (1 + cosine_s) * np.pi / 2
         alphas = torch.cos(alphas).pow(2)
@@ -34,23 +43,41 @@ def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2,
         betas = 1 - alphas[1:] / alphas[:-1]
         betas = np.clip(betas, a_min=0, a_max=0.999)
 
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+    elif schedule == 'sqrt_linear':
+        betas = torch.linspace(
+            linear_start, linear_end, n_timestep, dtype=torch.float64
+        )
+    elif schedule == 'sqrt':
+        betas = (
+            torch.linspace(
+                linear_start, linear_end, n_timestep, dtype=torch.float64
+            )
+            ** 0.5
+        )
     else:
         raise ValueError(f"schedule '{schedule}' unknown.")
     return betas.numpy()
 
 
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+def make_ddim_timesteps(
+    ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
+):
     if ddim_discr_method == 'uniform':
         c = num_ddpm_timesteps // num_ddim_timesteps
         ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
     elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+        ddim_timesteps = (
+            (
+                np.linspace(
+                    0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps
+                )
+            )
+            ** 2
+        ).astype(int)
     else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+        raise NotImplementedError(
+            f'There is no ddim discretization method called "{ddim_discr_method}"'
+        )
 
     # assert ddim_timesteps.shape[0] == num_ddim_timesteps
     # add one to get the final alpha values right (the ones from first scale to data during sampling)
@@ -60,17 +87,27 @@ def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timestep
     return steps_out
 
 
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+def make_ddim_sampling_parameters(
+    alphacums, ddim_timesteps, eta, verbose=True
+):
     # select alphas for computing the variance schedule
     alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+    alphas_prev = np.asarray(
+        [alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()
+    )
 
     # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+    sigmas = eta * np.sqrt(
+        (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)
+    )
     if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+        print(
+            f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}'
+        )
+        print(
+            f'For the chosen value of eta, which is {eta}, '
+            f'this results in the following sigma_t schedule for ddim sampler {sigmas}'
+        )
     return sigmas, alphas, alphas_prev
 
 
@@ -109,7 +146,9 @@ def checkpoint(func, inputs, params, flag):
                    explicitly take as arguments.
     :param flag: if False, disable gradient checkpointing.
     """
-    if False: # disabled checkpointing to allow requires_grad = False for main model
+    if (
+        False
+    ):   # disabled checkpointing to allow requires_grad = False for main model
         args = tuple(inputs) + tuple(params)
         return CheckpointFunction.apply(func, len(inputs), *args)
     else:
@@ -129,7 +168,9 @@ class CheckpointFunction(torch.autograd.Function):
 
     @staticmethod
     def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+        ctx.input_tensors = [
+            x.detach().requires_grad_(True) for x in ctx.input_tensors
+        ]
         with torch.enable_grad():
             # Fixes a bug where the first op in run_function modifies the
             # Tensor storage in place, which is not allowed for detach()'d
@@ -160,12 +201,16 @@ def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
     if not repeat_only:
         half = dim // 2
         freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32)
+            / half
         ).to(device=timesteps.device)
         args = timesteps[:, None].float() * freqs[None]
         embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
         if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
     else:
         embedding = repeat(timesteps, 'b -> b d', d=dim)
     return embedding
@@ -215,6 +260,7 @@ class GroupNorm32(nn.GroupNorm):
     def forward(self, x):
         return super().forward(x.float()).type(x.dtype)
 
+
 def conv_nd(dims, *args, **kwargs):
     """
     Create a 1D, 2D, or 3D convolution module.
@@ -225,7 +271,7 @@ def conv_nd(dims, *args, **kwargs):
         return nn.Conv2d(*args, **kwargs)
     elif dims == 3:
         return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
+    raise ValueError(f'unsupported dimensions: {dims}')
 
 
 def linear(*args, **kwargs):
@@ -245,15 +291,16 @@ def avg_pool_nd(dims, *args, **kwargs):
         return nn.AvgPool2d(*args, **kwargs)
     elif dims == 3:
         return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
+    raise ValueError(f'unsupported dimensions: {dims}')
 
 
 class HybridConditioner(nn.Module):
-
     def __init__(self, c_concat_config, c_crossattn_config):
         super().__init__()
         self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+        self.crossattn_conditioner = instantiate_from_config(
+            c_crossattn_config
+        )
 
     def forward(self, c_concat, c_crossattn):
         c_concat = self.concat_conditioner(c_concat)
@@ -262,6 +309,8 @@ class HybridConditioner(nn.Module):
 
 
 def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
+        shape[0], *((1,) * (len(shape) - 1))
+    )
     noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
+    return repeat_noise() if repeat else noise()

ldm/modules/distributions/distributions.py

@@ -30,33 +30,45 @@ class DiagonalGaussianDistribution(object):
         self.std = torch.exp(0.5 * self.logvar)
         self.var = torch.exp(self.logvar)
         if self.deterministic:
-            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+            self.var = self.std = torch.zeros_like(self.mean).to(
+                device=self.parameters.device
+            )
 
     def sample(self):
-        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+        x = self.mean + self.std * torch.randn(self.mean.shape).to(
+            device=self.parameters.device
+        )
         return x
 
     def kl(self, other=None):
         if self.deterministic:
-            return torch.Tensor([0.])
+            return torch.Tensor([0.0])
         else:
             if other is None:
-                return 0.5 * torch.sum(torch.pow(self.mean, 2)
-                                       + self.var - 1.0 - self.logvar,
-                                       dim=[1, 2, 3])
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                    dim=[1, 2, 3],
+                )
             else:
                 return 0.5 * torch.sum(
                     torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
-                    dim=[1, 2, 3])
+                    + self.var / other.var
+                    - 1.0
+                    - self.logvar
+                    + other.logvar,
+                    dim=[1, 2, 3],
+                )
 
-    def nll(self, sample, dims=[1,2,3]):
+    def nll(self, sample, dims=[1, 2, 3]):
         if self.deterministic:
-            return torch.Tensor([0.])
+            return torch.Tensor([0.0])
         logtwopi = np.log(2.0 * np.pi)
         return 0.5 * torch.sum(
-            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
-            dim=dims)
+            logtwopi
+            + self.logvar
+            + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims,
+        )
 
     def mode(self):
         return self.mean
@@ -74,7 +86,7 @@ def normal_kl(mean1, logvar1, mean2, logvar2):
         if isinstance(obj, torch.Tensor):
             tensor = obj
             break
-    assert tensor is not None, "at least one argument must be a Tensor"
+    assert tensor is not None, 'at least one argument must be a Tensor'
 
     # Force variances to be Tensors. Broadcasting helps convert scalars to
     # Tensors, but it does not work for torch.exp().

ldm/modules/ema.py

@@ -10,24 +10,30 @@ class LitEma(nn.Module):
 
         self.m_name2s_name = {}
         self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
-        self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
-                             else torch.tensor(-1,dtype=torch.int))
+        self.register_buffer(
+            'num_updates',
+            torch.tensor(0, dtype=torch.int)
+            if use_num_upates
+            else torch.tensor(-1, dtype=torch.int),
+        )
 
         for name, p in model.named_parameters():
             if p.requires_grad:
-                #remove as '.'-character is not allowed in buffers
-                s_name = name.replace('.','')
-                self.m_name2s_name.update({name:s_name})
-                self.register_buffer(s_name,p.clone().detach().data)
+                # remove as '.'-character is not allowed in buffers
+                s_name = name.replace('.', '')
+                self.m_name2s_name.update({name: s_name})
+                self.register_buffer(s_name, p.clone().detach().data)
 
         self.collected_params = []
 
-    def forward(self,model):
+    def forward(self, model):
         decay = self.decay
 
         if self.num_updates >= 0:
             self.num_updates += 1
-            decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
+            decay = min(
+                self.decay, (1 + self.num_updates) / (10 + self.num_updates)
+            )
 
         one_minus_decay = 1.0 - decay
 
@@ -38,8 +44,12 @@ class LitEma(nn.Module):
             for key in m_param:
                 if m_param[key].requires_grad:
                     sname = self.m_name2s_name[key]
-                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
-                    shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+                    shadow_params[sname] = shadow_params[sname].type_as(
+                        m_param[key]
+                    )
+                    shadow_params[sname].sub_(
+                        one_minus_decay * (shadow_params[sname] - m_param[key])
+                    )
                 else:
                     assert not key in self.m_name2s_name
 
@@ -48,7 +58,9 @@ class LitEma(nn.Module):
         shadow_params = dict(self.named_buffers())
         for key in m_param:
             if m_param[key].requires_grad:
-                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+                m_param[key].data.copy_(
+                    shadow_params[self.m_name2s_name[key]].data
+                )
             else:
                 assert not key in self.m_name2s_name
 

ldm/modules/embedding_manager.py

@@ -8,18 +8,29 @@ from ldm.data.personalized import per_img_token_list
 from transformers import CLIPTokenizer
 from functools import partial
 
-DEFAULT_PLACEHOLDER_TOKEN = ["*"]
+DEFAULT_PLACEHOLDER_TOKEN = ['*']
 
 PROGRESSIVE_SCALE = 2000
 
+
 def get_clip_token_for_string(tokenizer, string):
-    batch_encoding = tokenizer(string, truncation=True, max_length=77, return_length=True,
-                               return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-    tokens = batch_encoding["input_ids"]
-    assert torch.count_nonzero(tokens - 49407) == 2, f"String '{string}' maps to more than a single token. Please use another string"
+    batch_encoding = tokenizer(
+        string,
+        truncation=True,
+        max_length=77,
+        return_length=True,
+        return_overflowing_tokens=False,
+        padding='max_length',
+        return_tensors='pt',
+    )
+    tokens = batch_encoding['input_ids']
+    assert (
+        torch.count_nonzero(tokens - 49407) == 2
+    ), f"String '{string}' maps to more than a single token. Please use another string"
 
     return tokens[0, 1]
 
+
 def get_bert_token_for_string(tokenizer, string):
     token = tokenizer(string)
     # assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
@@ -28,42 +39,54 @@ def get_bert_token_for_string(tokenizer, string):
 
     return token
 
+
 def get_embedding_for_clip_token(embedder, token):
     return embedder(token.unsqueeze(0))[0, 0]
 
 
 class EmbeddingManager(nn.Module):
     def __init__(
-            self,
-            embedder,
-            placeholder_strings=None,
-            initializer_words=None,
-            per_image_tokens=False,
-            num_vectors_per_token=1,
-            progressive_words=False,
-            **kwargs
+        self,
+        embedder,
+        placeholder_strings=None,
+        initializer_words=None,
+        per_image_tokens=False,
+        num_vectors_per_token=1,
+        progressive_words=False,
+        **kwargs,
     ):
         super().__init__()
 
         self.string_to_token_dict = {}
-        
+
         self.string_to_param_dict = nn.ParameterDict()
 
-        self.initial_embeddings = nn.ParameterDict() # These should not be optimized
+        self.initial_embeddings = (
+            nn.ParameterDict()
+        )   # These should not be optimized
 
         self.progressive_words = progressive_words
         self.progressive_counter = 0
 
         self.max_vectors_per_token = num_vectors_per_token
 
-        if hasattr(embedder, 'tokenizer'): # using Stable Diffusion's CLIP encoder
+        if hasattr(
+            embedder, 'tokenizer'
+        ):   # using Stable Diffusion's CLIP encoder
             self.is_clip = True
-            get_token_for_string = partial(get_clip_token_for_string, embedder.tokenizer)
-            get_embedding_for_tkn = partial(get_embedding_for_clip_token, embedder.transformer.text_model.embeddings)
+            get_token_for_string = partial(
+                get_clip_token_for_string, embedder.tokenizer
+            )
+            get_embedding_for_tkn = partial(
+                get_embedding_for_clip_token,
+                embedder.transformer.text_model.embeddings,
+            )
             token_dim = 1280
-        else: # using LDM's BERT encoder
+        else:   # using LDM's BERT encoder
             self.is_clip = False
-            get_token_for_string = partial(get_bert_token_for_string, embedder.tknz_fn)
+            get_token_for_string = partial(
+                get_bert_token_for_string, embedder.tknz_fn
+            )
             get_embedding_for_tkn = embedder.transformer.token_emb
             token_dim = 1280
 
@@ -71,79 +94,142 @@ class EmbeddingManager(nn.Module):
             placeholder_strings.extend(per_img_token_list)
 
         for idx, placeholder_string in enumerate(placeholder_strings):
-            
+
             token = get_token_for_string(placeholder_string)
 
             if initializer_words and idx < len(initializer_words):
                 init_word_token = get_token_for_string(initializer_words[idx])
 
                 with torch.no_grad():
-                    init_word_embedding = get_embedding_for_tkn(init_word_token.cpu())
+                    init_word_embedding = get_embedding_for_tkn(
+                        init_word_token.cpu()
+                    )
 
-                token_params = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=True)
-                self.initial_embeddings[placeholder_string] = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=False)
+                token_params = torch.nn.Parameter(
+                    init_word_embedding.unsqueeze(0).repeat(
+                        num_vectors_per_token, 1
+                    ),
+                    requires_grad=True,
+                )
+                self.initial_embeddings[
+                    placeholder_string
+                ] = torch.nn.Parameter(
+                    init_word_embedding.unsqueeze(0).repeat(
+                        num_vectors_per_token, 1
+                    ),
+                    requires_grad=False,
+                )
             else:
-                token_params = torch.nn.Parameter(torch.rand(size=(num_vectors_per_token, token_dim), requires_grad=True))
-            
+                token_params = torch.nn.Parameter(
+                    torch.rand(
+                        size=(num_vectors_per_token, token_dim),
+                        requires_grad=True,
+                    )
+                )
+
             self.string_to_token_dict[placeholder_string] = token
             self.string_to_param_dict[placeholder_string] = token_params
 
     def forward(
-            self,
-            tokenized_text,
-            embedded_text,
+        self,
+        tokenized_text,
+        embedded_text,
     ):
         b, n, device = *tokenized_text.shape, tokenized_text.device
 
-        for placeholder_string, placeholder_token in self.string_to_token_dict.items():
+        for (
+            placeholder_string,
+            placeholder_token,
+        ) in self.string_to_token_dict.items():
 
-            placeholder_embedding = self.string_to_param_dict[placeholder_string].to(device)
+            placeholder_embedding = self.string_to_param_dict[
+                placeholder_string
+            ].to(device)
 
-            if self.max_vectors_per_token == 1: # If there's only one vector per token, we can do a simple replacement
-                placeholder_idx = torch.where(tokenized_text == placeholder_token.to(device))
+            if (
+                self.max_vectors_per_token == 1
+            ):   # If there's only one vector per token, we can do a simple replacement
+                placeholder_idx = torch.where(
+                    tokenized_text == placeholder_token.to(device)
+                )
                 embedded_text[placeholder_idx] = placeholder_embedding
-            else: # otherwise, need to insert and keep track of changing indices
+            else:   # otherwise, need to insert and keep track of changing indices
                 if self.progressive_words:
                     self.progressive_counter += 1
-                    max_step_tokens = 1 + self.progressive_counter // PROGRESSIVE_SCALE
+                    max_step_tokens = (
+                        1 + self.progressive_counter // PROGRESSIVE_SCALE
+                    )
                 else:
                     max_step_tokens = self.max_vectors_per_token
 
-                num_vectors_for_token = min(placeholder_embedding.shape[0], max_step_tokens)
+                num_vectors_for_token = min(
+                    placeholder_embedding.shape[0], max_step_tokens
+                )
 
-                placeholder_rows, placeholder_cols = torch.where(tokenized_text == placeholder_token.to(device))
+                placeholder_rows, placeholder_cols = torch.where(
+                    tokenized_text == placeholder_token.to(device)
+                )
 
                 if placeholder_rows.nelement() == 0:
                     continue
 
-                sorted_cols, sort_idx = torch.sort(placeholder_cols, descending=True)
+                sorted_cols, sort_idx = torch.sort(
+                    placeholder_cols, descending=True
+                )
                 sorted_rows = placeholder_rows[sort_idx]
 
                 for idx in range(len(sorted_rows)):
                     row = sorted_rows[idx]
                     col = sorted_cols[idx]
 
-                    new_token_row = torch.cat([tokenized_text[row][:col], placeholder_token.repeat(num_vectors_for_token).to(device), tokenized_text[row][col + 1:]], axis=0)[:n]
-                    new_embed_row = torch.cat([embedded_text[row][:col], placeholder_embedding[:num_vectors_for_token], embedded_text[row][col + 1:]], axis=0)[:n]
+                    new_token_row = torch.cat(
+                        [
+                            tokenized_text[row][:col],
+                            placeholder_token.repeat(num_vectors_for_token).to(
+                                device
+                            ),
+                            tokenized_text[row][col + 1 :],
+                        ],
+                        axis=0,
+                    )[:n]
+                    new_embed_row = torch.cat(
+                        [
+                            embedded_text[row][:col],
+                            placeholder_embedding[:num_vectors_for_token],
+                            embedded_text[row][col + 1 :],
+                        ],
+                        axis=0,
+                    )[:n]
 
-                    embedded_text[row]  = new_embed_row
+                    embedded_text[row] = new_embed_row
                     tokenized_text[row] = new_token_row
 
         return embedded_text
 
     def save(self, ckpt_path):
-        torch.save({"string_to_token": self.string_to_token_dict,
-                    "string_to_param": self.string_to_param_dict}, ckpt_path)
+        torch.save(
+            {
+                'string_to_token': self.string_to_token_dict,
+                'string_to_param': self.string_to_param_dict,
+            },
+            ckpt_path,
+        )
 
-    def load(self, ckpt_path):
+    def load(self, ckpt_path, full=True):
         ckpt = torch.load(ckpt_path, map_location='cpu')
-
         self.string_to_token_dict = ckpt["string_to_token"]
         self.string_to_param_dict = ckpt["string_to_param"]
+        if not full:
+            for key, value in self.string_to_param_dict.items():
+                self.string_to_param_dict[key] = torch.nn.Parameter(value.half())
 
     def get_embedding_norms_squared(self):
-        all_params = torch.cat(list(self.string_to_param_dict.values()), axis=0) # num_placeholders x embedding_dim
-        param_norm_squared = (all_params * all_params).sum(axis=-1)              # num_placeholders
+        all_params = torch.cat(
+            list(self.string_to_param_dict.values()), axis=0
+        )   # num_placeholders x embedding_dim
+        param_norm_squared = (all_params * all_params).sum(
+            axis=-1
+        )              # num_placeholders
 
         return param_norm_squared
 
@@ -151,14 +237,19 @@ class EmbeddingManager(nn.Module):
         return self.string_to_param_dict.parameters()
 
     def embedding_to_coarse_loss(self):
-        
-        loss = 0.
+
+        loss = 0.0
         num_embeddings = len(self.initial_embeddings)
 
         for key in self.initial_embeddings:
             optimized = self.string_to_param_dict[key]
             coarse = self.initial_embeddings[key].clone().to(optimized.device)
 
-            loss = loss + (optimized - coarse) @ (optimized - coarse).T / num_embeddings
+            loss = (
+                loss
+                + (optimized - coarse)
+                @ (optimized - coarse).T
+                / num_embeddings
+            )
 
-        return loss
+        return loss

ldm/modules/encoders/modules.py

@@ -5,30 +5,41 @@ import clip
 from einops import rearrange, repeat
 from transformers import CLIPTokenizer, CLIPTextModel
 import kornia
+from ldm.dream.devices import choose_torch_device
 
-from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
+from ldm.modules.x_transformer import (
+    Encoder,
+    TransformerWrapper,
+)  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
 
-def _expand_mask(mask, dtype, tgt_len = None):
+
+def _expand_mask(mask, dtype, tgt_len=None):
     """
     Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
     """
     bsz, src_len = mask.size()
     tgt_len = tgt_len if tgt_len is not None else src_len
 
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+    expanded_mask = (
+        mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+    )
 
     inverted_mask = 1.0 - expanded_mask
 
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
 
 def _build_causal_attention_mask(bsz, seq_len, dtype):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
-        mask.fill_(torch.tensor(torch.finfo(dtype).min))
-        mask.triu_(1)  # zero out the lower diagonal
-        mask = mask.unsqueeze(1)  # expand mask
-        return mask
+    # lazily create causal attention mask, with full attention between the vision tokens
+    # pytorch uses additive attention mask; fill with -inf
+    mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
+    mask.fill_(torch.tensor(torch.finfo(dtype).min))
+    mask.triu_(1)  # zero out the lower diagonal
+    mask = mask.unsqueeze(1)  # expand mask
+    return mask
+
 
 class AbstractEncoder(nn.Module):
     def __init__(self):
@@ -38,7 +49,6 @@ class AbstractEncoder(nn.Module):
         raise NotImplementedError
 
 
-
 class ClassEmbedder(nn.Module):
     def __init__(self, embed_dim, n_classes=1000, key='class'):
         super().__init__()
@@ -56,11 +66,22 @@ class ClassEmbedder(nn.Module):
 
 class TransformerEmbedder(AbstractEncoder):
     """Some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
+
+    def __init__(
+        self,
+        n_embed,
+        n_layer,
+        vocab_size,
+        max_seq_len=77,
+        device=choose_torch_device(),
+    ):
         super().__init__()
         self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer))
+        self.transformer = TransformerWrapper(
+            num_tokens=vocab_size,
+            max_seq_len=max_seq_len,
+            attn_layers=Encoder(dim=n_embed, depth=n_layer),
+        )
 
     def forward(self, tokens):
         tokens = tokens.to(self.device)  # meh
@@ -72,27 +93,44 @@ class TransformerEmbedder(AbstractEncoder):
 
 
 class BERTTokenizer(AbstractEncoder):
-    """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
-    def __init__(self, device="cuda", vq_interface=True, max_length=77):
+    """Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
+
+    def __init__(
+        self, device=choose_torch_device(), vq_interface=True, max_length=77
+    ):
         super().__init__()
-        from transformers import BertTokenizerFast  # TODO: add to reuquirements
+        from transformers import (
+            BertTokenizerFast,
+        )  # TODO: add to reuquirements
+
         # Modified to allow to run on non-internet connected compute nodes.
         # Model needs to be loaded into cache from an internet-connected machine
         # by running:
         #   from transformers import BertTokenizerFast
         #   BertTokenizerFast.from_pretrained("bert-base-uncased")
         try:
-            self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased",local_files_only=True)
+            self.tokenizer = BertTokenizerFast.from_pretrained(
+                'bert-base-uncased', local_files_only=True
+            )
         except OSError:
-            raise SystemExit("* Couldn't load Bert tokenizer files. Try running scripts/preload_models.py from an internet-conected machine.")
+            raise SystemExit(
+                "* Couldn't load Bert tokenizer files. Try running scripts/preload_models.py from an internet-conected machine."
+            )
         self.device = device
         self.vq_interface = vq_interface
         self.max_length = max_length
 
     def forward(self, text):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding='max_length',
+            return_tensors='pt',
+        )
+        tokens = batch_encoding['input_ids'].to(self.device)
         return tokens
 
     @torch.no_grad()
@@ -108,53 +146,84 @@ class BERTTokenizer(AbstractEncoder):
 
 class BERTEmbedder(AbstractEncoder):
     """Uses the BERT tokenizr model and add some transformer encoder layers"""
-    def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
-                 device="cuda",use_tokenizer=True, embedding_dropout=0.0):
+
+    def __init__(
+        self,
+        n_embed,
+        n_layer,
+        vocab_size=30522,
+        max_seq_len=77,
+        device=choose_torch_device(),
+        use_tokenizer=True,
+        embedding_dropout=0.0,
+    ):
         super().__init__()
         self.use_tknz_fn = use_tokenizer
         if self.use_tknz_fn:
-            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
+            self.tknz_fn = BERTTokenizer(
+                vq_interface=False, max_length=max_seq_len
+            )
         self.device = device
-        self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
-                                              attn_layers=Encoder(dim=n_embed, depth=n_layer),
-                                              emb_dropout=embedding_dropout)
+        self.transformer = TransformerWrapper(
+            num_tokens=vocab_size,
+            max_seq_len=max_seq_len,
+            attn_layers=Encoder(dim=n_embed, depth=n_layer),
+            emb_dropout=embedding_dropout,
+        )
 
     def forward(self, text, embedding_manager=None):
         if self.use_tknz_fn:
-            tokens = self.tknz_fn(text)#.to(self.device)
+            tokens = self.tknz_fn(text)  # .to(self.device)
         else:
             tokens = text
-        z = self.transformer(tokens, return_embeddings=True, embedding_manager=embedding_manager)
+        z = self.transformer(
+            tokens, return_embeddings=True, embedding_manager=embedding_manager
+        )
         return z
 
     def encode(self, text, **kwargs):
         # output of length 77
         return self(text, **kwargs)
 
+
 class SpatialRescaler(nn.Module):
-    def __init__(self,
-                 n_stages=1,
-                 method='bilinear',
-                 multiplier=0.5,
-                 in_channels=3,
-                 out_channels=None,
-                 bias=False):
+    def __init__(
+        self,
+        n_stages=1,
+        method='bilinear',
+        multiplier=0.5,
+        in_channels=3,
+        out_channels=None,
+        bias=False,
+    ):
         super().__init__()
         self.n_stages = n_stages
         assert self.n_stages >= 0
-        assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
+        assert method in [
+            'nearest',
+            'linear',
+            'bilinear',
+            'trilinear',
+            'bicubic',
+            'area',
+        ]
         self.multiplier = multiplier
-        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
+        self.interpolator = partial(
+            torch.nn.functional.interpolate, mode=method
+        )
         self.remap_output = out_channels is not None
         if self.remap_output:
-            print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
-            self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
+            print(
+                f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.'
+            )
+            self.channel_mapper = nn.Conv2d(
+                in_channels, out_channels, 1, bias=bias
+            )
 
-    def forward(self,x):
+    def forward(self, x):
         for stage in range(self.n_stages):
             x = self.interpolator(x, scale_factor=self.multiplier)
 
-
         if self.remap_output:
             x = self.channel_mapper(x)
         return x
@@ -162,57 +231,83 @@ class SpatialRescaler(nn.Module):
     def encode(self, x):
         return self(x)
 
+
 class FrozenCLIPEmbedder(AbstractEncoder):
     """Uses the CLIP transformer encoder for text (from Hugging Face)"""
-    def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
+
+    def __init__(
+        self,
+        version='openai/clip-vit-large-patch14',
+        device=choose_torch_device(),
+        max_length=77,
+    ):
         super().__init__()
-        self.tokenizer = CLIPTokenizer.from_pretrained(version,local_files_only=True)
-        self.transformer = CLIPTextModel.from_pretrained(version,local_files_only=True)
+        self.tokenizer = CLIPTokenizer.from_pretrained(
+            version, local_files_only=True
+        )
+        self.transformer = CLIPTextModel.from_pretrained(
+            version, local_files_only=True
+        )
         self.device = device
         self.max_length = max_length
         self.freeze()
 
         def embedding_forward(
-                self,
-                input_ids = None,
-                position_ids = None,
-                inputs_embeds = None,
-                embedding_manager = None,
-            ) -> torch.Tensor:
+            self,
+            input_ids=None,
+            position_ids=None,
+            inputs_embeds=None,
+            embedding_manager=None,
+        ) -> torch.Tensor:
 
-                seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+            seq_length = (
+                input_ids.shape[-1]
+                if input_ids is not None
+                else inputs_embeds.shape[-2]
+            )
 
-                if position_ids is None:
-                    position_ids = self.position_ids[:, :seq_length]
+            if position_ids is None:
+                position_ids = self.position_ids[:, :seq_length]
 
-                if inputs_embeds is None:
-                    inputs_embeds = self.token_embedding(input_ids)
+            if inputs_embeds is None:
+                inputs_embeds = self.token_embedding(input_ids)
 
-                if embedding_manager is not None:
-                    inputs_embeds = embedding_manager(input_ids, inputs_embeds)
+            if embedding_manager is not None:
+                inputs_embeds = embedding_manager(input_ids, inputs_embeds)
 
+            position_embeddings = self.position_embedding(position_ids)
+            embeddings = inputs_embeds + position_embeddings
 
-                position_embeddings = self.position_embedding(position_ids)
-                embeddings = inputs_embeds + position_embeddings
-                
-                return embeddings      
+            return embeddings
 
-        self.transformer.text_model.embeddings.forward = embedding_forward.__get__(self.transformer.text_model.embeddings)
+        self.transformer.text_model.embeddings.forward = (
+            embedding_forward.__get__(self.transformer.text_model.embeddings)
+        )
 
         def encoder_forward(
             self,
             inputs_embeds,
-            attention_mask = None,
-            causal_attention_mask = None,
-            output_attentions = None,
-            output_hidden_states = None,
-            return_dict = None,
+            attention_mask=None,
+            causal_attention_mask=None,
+            output_attentions=None,
+            output_hidden_states=None,
+            return_dict=None,
         ):
-            output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-            output_hidden_states = (
-                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+            output_attentions = (
+                output_attentions
+                if output_attentions is not None
+                else self.config.output_attentions
+            )
+            output_hidden_states = (
+                output_hidden_states
+                if output_hidden_states is not None
+                else self.config.output_hidden_states
+            )
+            return_dict = (
+                return_dict
+                if return_dict is not None
+                else self.config.use_return_dict
             )
-            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
             encoder_states = () if output_hidden_states else None
             all_attentions = () if output_attentions else None
@@ -239,44 +334,61 @@ class FrozenCLIPEmbedder(AbstractEncoder):
 
             return hidden_states
 
-        self.transformer.text_model.encoder.forward = encoder_forward.__get__(self.transformer.text_model.encoder)
-
+        self.transformer.text_model.encoder.forward = encoder_forward.__get__(
+            self.transformer.text_model.encoder
+        )
 
         def text_encoder_forward(
             self,
-            input_ids = None,
-            attention_mask = None,
-            position_ids = None,
-            output_attentions = None,
-            output_hidden_states = None,
-            return_dict = None,
-            embedding_manager = None,
+            input_ids=None,
+            attention_mask=None,
+            position_ids=None,
+            output_attentions=None,
+            output_hidden_states=None,
+            return_dict=None,
+            embedding_manager=None,
         ):
-            output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-            output_hidden_states = (
-                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+            output_attentions = (
+                output_attentions
+                if output_attentions is not None
+                else self.config.output_attentions
+            )
+            output_hidden_states = (
+                output_hidden_states
+                if output_hidden_states is not None
+                else self.config.output_hidden_states
+            )
+            return_dict = (
+                return_dict
+                if return_dict is not None
+                else self.config.use_return_dict
             )
-            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
             if input_ids is None:
-                raise ValueError("You have to specify either input_ids")
+                raise ValueError('You have to specify either input_ids')
 
             input_shape = input_ids.size()
             input_ids = input_ids.view(-1, input_shape[-1])
 
-            hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, embedding_manager=embedding_manager)
+            hidden_states = self.embeddings(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                embedding_manager=embedding_manager,
+            )
 
             bsz, seq_len = input_shape
             # CLIP's text model uses causal mask, prepare it here.
             # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
-            causal_attention_mask = _build_causal_attention_mask(bsz, seq_len, hidden_states.dtype).to(
-                hidden_states.device
-            )
+            causal_attention_mask = _build_causal_attention_mask(
+                bsz, seq_len, hidden_states.dtype
+            ).to(hidden_states.device)
 
             # expand attention_mask
             if attention_mask is not None:
                 # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-                attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
+                attention_mask = _expand_mask(
+                    attention_mask, hidden_states.dtype
+                )
 
             last_hidden_state = self.encoder(
                 inputs_embeds=hidden_states,
@@ -291,17 +403,19 @@ class FrozenCLIPEmbedder(AbstractEncoder):
 
             return last_hidden_state
 
-        self.transformer.text_model.forward = text_encoder_forward.__get__(self.transformer.text_model)
+        self.transformer.text_model.forward = text_encoder_forward.__get__(
+            self.transformer.text_model
+        )
 
         def transformer_forward(
             self,
-            input_ids = None,
-            attention_mask = None,
-            position_ids = None,
-            output_attentions = None,
-            output_hidden_states = None,
-            return_dict = None,
-            embedding_manager = None,
+            input_ids=None,
+            attention_mask=None,
+            position_ids=None,
+            output_attentions=None,
+            output_hidden_states=None,
+            return_dict=None,
+            embedding_manager=None,
         ):
             return self.text_model(
                 input_ids=input_ids,
@@ -310,11 +424,12 @@ class FrozenCLIPEmbedder(AbstractEncoder):
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
-                embedding_manager = embedding_manager
+                embedding_manager=embedding_manager,
             )
 
-        self.transformer.forward = transformer_forward.__get__(self.transformer)
-
+        self.transformer.forward = transformer_forward.__get__(
+            self.transformer
+        )
 
     def freeze(self):
         self.transformer = self.transformer.eval()
@@ -322,9 +437,16 @@ class FrozenCLIPEmbedder(AbstractEncoder):
             param.requires_grad = False
 
     def forward(self, text, **kwargs):
-        batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
-                                        return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-        tokens = batch_encoding["input_ids"].to(self.device)
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding='max_length',
+            return_tensors='pt',
+        )
+        tokens = batch_encoding['input_ids'].to(self.device)
         z = self.transformer(input_ids=tokens, **kwargs)
 
         return z
@@ -337,9 +459,17 @@ class FrozenCLIPTextEmbedder(nn.Module):
     """
     Uses the CLIP transformer encoder for text.
     """
-    def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
+
+    def __init__(
+        self,
+        version='ViT-L/14',
+        device=choose_torch_device(),
+        max_length=77,
+        n_repeat=1,
+        normalize=True,
+    ):
         super().__init__()
-        self.model, _ = clip.load(version, jit=False, device="cpu")
+        self.model, _ = clip.load(version, jit=False, device=device)
         self.device = device
         self.max_length = max_length
         self.n_repeat = n_repeat
@@ -359,7 +489,7 @@ class FrozenCLIPTextEmbedder(nn.Module):
 
     def encode(self, text):
         z = self(text)
-        if z.ndim==2:
+        if z.ndim == 2:
             z = z[:, None, :]
         z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
         return z
@@ -367,29 +497,42 @@ class FrozenCLIPTextEmbedder(nn.Module):
 
 class FrozenClipImageEmbedder(nn.Module):
     """
-        Uses the CLIP image encoder.
-        """
+    Uses the CLIP image encoder.
+    """
+
     def __init__(
-            self,
-            model,
-            jit=False,
-            device='cuda' if torch.cuda.is_available() else 'cpu',
-            antialias=False,
-        ):
+        self,
+        model,
+        jit=False,
+        device=choose_torch_device(),
+        antialias=False,
+    ):
         super().__init__()
         self.model, _ = clip.load(name=model, device=device, jit=jit)
 
         self.antialias = antialias
 
-        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
-        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
+        self.register_buffer(
+            'mean',
+            torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
+            persistent=False,
+        )
+        self.register_buffer(
+            'std',
+            torch.Tensor([0.26862954, 0.26130258, 0.27577711]),
+            persistent=False,
+        )
 
     def preprocess(self, x):
         # normalize to [0,1]
-        x = kornia.geometry.resize(x, (224, 224),
-                                   interpolation='bicubic',align_corners=True,
-                                   antialias=self.antialias)
-        x = (x + 1.) / 2.
+        x = kornia.geometry.resize(
+            x,
+            (224, 224),
+            interpolation='bicubic',
+            align_corners=True,
+            antialias=self.antialias,
+        )
+        x = (x + 1.0) / 2.0
         # renormalize according to clip
         x = kornia.enhance.normalize(x, self.mean, self.std)
         return x
@@ -399,7 +542,8 @@ class FrozenClipImageEmbedder(nn.Module):
         return self.model.encode_image(self.preprocess(x))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     from ldm.util import count_params
+
     model = FrozenCLIPEmbedder()
     count_params(model, verbose=True)

ldm/modules/image_degradation/__init__.py

@@ -1,2 +1,6 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
+from ldm.modules.image_degradation.bsrgan import (
+    degradation_bsrgan_variant as degradation_fn_bsr,
+)
+from ldm.modules.image_degradation.bsrgan_light import (
+    degradation_bsrgan_variant as degradation_fn_bsr_light,
+)

ldm/modules/image_degradation/bsrgan.py

@@ -27,16 +27,16 @@ import ldm.modules.image_degradation.utils_image as util
 
 
 def modcrop_np(img, sf):
-    '''
+    """
     Args:
         img: numpy image, WxH or WxHxC
         sf: scale factor
     Return:
         cropped image
-    '''
+    """
     w, h = img.shape[:2]
     im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
+    return im[: w - w % sf, : h - h % sf, ...]
 
 
 """
@@ -54,7 +54,9 @@ def analytic_kernel(k):
     # Loop over the small kernel to fill the big one
     for r in range(k_size):
         for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+            big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += (
+                k[r, c] * k
+            )
     # Crop the edges of the big kernel to ignore very small values and increase run time of SR
     crop = k_size // 2
     cropped_big_k = big_k[crop:-crop, crop:-crop]
@@ -63,7 +65,7 @@ def analytic_kernel(k):
 
 
 def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
+    """generate an anisotropic Gaussian kernel
     Args:
         ksize : e.g., 15, kernel size
         theta : [0,  pi], rotation angle range
@@ -74,7 +76,12 @@ def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
         k     : kernel
     """
 
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+    v = np.dot(
+        np.array(
+            [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+        ),
+        np.array([1.0, 0.0]),
+    )
     V = np.array([[v[0], v[1]], [v[1], -v[0]]])
     D = np.array([[l1, 0], [0, l2]])
     Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
@@ -126,24 +133,32 @@ def shift_pixel(x, sf, upper_left=True):
 
 
 def blur(x, k):
-    '''
+    """
     x: image, NxcxHxW
     k: kernel, Nx1xhxw
-    '''
+    """
     n, c = x.shape[:2]
     p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
     x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
     k = k.repeat(1, c, 1, 1)
     k = k.view(-1, 1, k.shape[2], k.shape[3])
     x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+    x = torch.nn.functional.conv2d(
+        x, k, bias=None, stride=1, padding=0, groups=n * c
+    )
     x = x.view(n, c, x.shape[2], x.shape[3])
 
     return x
 
 
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
+def gen_kernel(
+    k_size=np.array([15, 15]),
+    scale_factor=np.array([4, 4]),
+    min_var=0.6,
+    max_var=10.0,
+    noise_level=0,
+):
+    """ "
     # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
     # Kai Zhang
     # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
@@ -157,13 +172,16 @@ def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var
 
     # Set COV matrix using Lambdas and Theta
     LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
+    Q = np.array(
+        [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+    )
     SIGMA = Q @ LAMBDA @ Q.T
     INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
 
     # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
+    MU = k_size // 2 - 0.5 * (
+        scale_factor - 1
+    )  # - 0.5 * (scale_factor - k_size % 2)
     MU = MU[None, None, :, None]
 
     # Create meshgrid for Gaussian
@@ -188,7 +206,9 @@ def fspecial_gaussian(hsize, sigma):
     hsize = [hsize, hsize]
     siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
     std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+    [x, y] = np.meshgrid(
+        np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)
+    )
     arg = -(x * x + y * y) / (2 * std * std)
     h = np.exp(arg)
     h[h < scipy.finfo(float).eps * h.max()] = 0
@@ -208,10 +228,10 @@ def fspecial_laplacian(alpha):
 
 
 def fspecial(filter_type, *args, **kwargs):
-    '''
+    """
     python code from:
     https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
+    """
     if filter_type == 'gaussian':
         return fspecial_gaussian(*args, **kwargs)
     if filter_type == 'laplacian':
@@ -226,19 +246,19 @@ def fspecial(filter_type, *args, **kwargs):
 
 
 def bicubic_degradation(x, sf=3):
-    '''
+    """
     Args:
         x: HxWxC image, [0, 1]
         sf: down-scale factor
     Return:
         bicubicly downsampled LR image
-    '''
+    """
     x = util.imresize_np(x, scale=1 / sf)
     return x
 
 
 def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
+    """blur + bicubic downsampling
     Args:
         x: HxWxC image, [0, 1]
         k: hxw, double
@@ -253,14 +273,16 @@ def srmd_degradation(x, k, sf=3):
           pages={3262--3271},
           year={2018}
         }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
+    """
+    x = ndimage.filters.convolve(
+        x, np.expand_dims(k, axis=2), mode='wrap'
+    )  # 'nearest' | 'mirror'
     x = bicubic_degradation(x, sf=sf)
     return x
 
 
 def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
+    """bicubic downsampling + blur
     Args:
         x: HxWxC image, [0, 1]
         k: hxw, double
@@ -275,21 +297,21 @@ def dpsr_degradation(x, k, sf=3):
           pages={1671--1681},
           year={2019}
         }
-    '''
+    """
     x = bicubic_degradation(x, sf=sf)
     x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
     return x
 
 
 def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
+    """blur + downsampling
     Args:
         x: HxWxC image, [0, 1]/[0, 255]
         k: hxw, double
         sf: down-scale factor
     Return:
         downsampled LR image
-    '''
+    """
     x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
     # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
     st = 0
@@ -328,10 +350,19 @@ def add_blur(img, sf=4):
     if random.random() < 0.5:
         l1 = wd2 * random.random()
         l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+        k = anisotropic_Gaussian(
+            ksize=2 * random.randint(2, 11) + 3,
+            theta=random.random() * np.pi,
+            l1=l1,
+            l2=l2,
+        )
     else:
-        k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+        k = fspecial(
+            'gaussian', 2 * random.randint(2, 11) + 3, wd * random.random()
+        )
+    img = ndimage.filters.convolve(
+        img, np.expand_dims(k, axis=2), mode='mirror'
+    )
 
     return img
 
@@ -344,7 +375,11 @@ def add_resize(img, sf=4):
         sf1 = random.uniform(0.5 / sf, 1)
     else:
         sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+    img = cv2.resize(
+        img,
+        (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
+        interpolation=random.choice([1, 2, 3]),
+    )
     img = np.clip(img, 0.0, 1.0)
 
     return img
@@ -366,19 +401,26 @@ def add_resize(img, sf=4):
 #     img = np.clip(img, 0.0, 1.0)
 #     return img
 
+
 def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
     noise_level = random.randint(noise_level1, noise_level2)
     rnum = np.random.rand()
     if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
+            np.float32
+        )
     elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+        img = img + np.random.normal(
+            0, noise_level / 255.0, (*img.shape[:2], 1)
+        ).astype(np.float32)
     else:  # add  noise
-        L = noise_level2 / 255.
+        L = noise_level2 / 255.0
         D = np.diag(np.random.rand(3))
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+        img = img + np.random.multivariate_normal(
+            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
+        ).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
 
@@ -388,28 +430,37 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
     img = np.clip(img, 0.0, 1.0)
     rnum = random.random()
     if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+        img += img * np.random.normal(
+            0, noise_level / 255.0, img.shape
+        ).astype(np.float32)
     elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+        img += img * np.random.normal(
+            0, noise_level / 255.0, (*img.shape[:2], 1)
+        ).astype(np.float32)
     else:
-        L = noise_level2 / 255.
+        L = noise_level2 / 255.0
         D = np.diag(np.random.rand(3))
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+        img += img * np.random.multivariate_normal(
+            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
+        ).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
 
 
 def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
+    img = np.clip((img * 255.0).round(), 0, 255) / 255.0
     vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
     if random.random() < 0.5:
         img = np.random.poisson(img * vals).astype(np.float32) / vals
     else:
         img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
+        noise_gray = (
+            np.random.poisson(img_gray * vals).astype(np.float32) / vals
+            - img_gray
+        )
         img += noise_gray[:, :, np.newaxis]
     img = np.clip(img, 0.0, 1.0)
     return img
@@ -418,7 +469,9 @@ def add_Poisson_noise(img):
 def add_JPEG_noise(img):
     quality_factor = random.randint(30, 95)
     img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+    result, encimg = cv2.imencode(
+        '.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
+    )
     img = cv2.imdecode(encimg, 1)
     img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
     return img
@@ -428,10 +481,14 @@ def random_crop(lq, hq, sf=4, lq_patchsize=64):
     h, w = lq.shape[:2]
     rnd_h = random.randint(0, h - lq_patchsize)
     rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+    lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]
 
     rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+    hq = hq[
+        rnd_h_H : rnd_h_H + lq_patchsize * sf,
+        rnd_w_H : rnd_w_H + lq_patchsize * sf,
+        :,
+    ]
     return lq, hq
 
 
@@ -452,7 +509,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
     sf_ori = sf
 
     h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
     h, w = img.shape[:2]
 
     if h < lq_patchsize * sf or w < lq_patchsize * sf:
@@ -462,8 +519,11 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
 
     if sf == 4 and random.random() < scale2_prob:  # downsample1
         if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
+            img = cv2.resize(
+                img,
+                (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+                interpolation=random.choice([1, 2, 3]),
+            )
         else:
             img = util.imresize_np(img, 1 / 2, True)
         img = np.clip(img, 0.0, 1.0)
@@ -472,7 +532,10 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
     shuffle_order = random.sample(range(7), 7)
     idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
     if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+        shuffle_order[idx1], shuffle_order[idx2] = (
+            shuffle_order[idx2],
+            shuffle_order[idx1],
+        )
 
     for i in shuffle_order:
 
@@ -487,19 +550,30 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
             # downsample2
             if random.random() < 0.75:
                 sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
+                img = cv2.resize(
+                    img,
+                    (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+                    interpolation=random.choice([1, 2, 3]),
+                )
             else:
                 k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
                 k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                k_shifted = (
+                    k_shifted / k_shifted.sum()
+                )  # blur with shifted kernel
+                img = ndimage.filters.convolve(
+                    img, np.expand_dims(k_shifted, axis=2), mode='mirror'
+                )
                 img = img[0::sf, 0::sf, ...]  # nearest downsampling
             img = np.clip(img, 0.0, 1.0)
 
         elif i == 3:
             # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            img = cv2.resize(
+                img,
+                (int(1 / sf * a), int(1 / sf * b)),
+                interpolation=random.choice([1, 2, 3]),
+            )
             img = np.clip(img, 0.0, 1.0)
 
         elif i == 4:
@@ -544,15 +618,18 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
     sf_ori = sf
 
     h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
     h, w = image.shape[:2]
 
     hq = image.copy()
 
     if sf == 4 and random.random() < scale2_prob:  # downsample1
         if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
+            image = cv2.resize(
+                image,
+                (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+                interpolation=random.choice([1, 2, 3]),
+            )
         else:
             image = util.imresize_np(image, 1 / 2, True)
         image = np.clip(image, 0.0, 1.0)
@@ -561,7 +638,10 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
     shuffle_order = random.sample(range(7), 7)
     idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
     if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+        shuffle_order[idx1], shuffle_order[idx2] = (
+            shuffle_order[idx2],
+            shuffle_order[idx1],
+        )
 
     for i in shuffle_order:
 
@@ -576,19 +656,33 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
             # downsample2
             if random.random() < 0.75:
                 sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
+                image = cv2.resize(
+                    image,
+                    (
+                        int(1 / sf1 * image.shape[1]),
+                        int(1 / sf1 * image.shape[0]),
+                    ),
+                    interpolation=random.choice([1, 2, 3]),
+                )
             else:
                 k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
                 k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                k_shifted = (
+                    k_shifted / k_shifted.sum()
+                )  # blur with shifted kernel
+                image = ndimage.filters.convolve(
+                    image, np.expand_dims(k_shifted, axis=2), mode='mirror'
+                )
                 image = image[0::sf, 0::sf, ...]  # nearest downsampling
             image = np.clip(image, 0.0, 1.0)
 
         elif i == 3:
             # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            image = cv2.resize(
+                image,
+                (int(1 / sf * a), int(1 / sf * b)),
+                interpolation=random.choice([1, 2, 3]),
+            )
             image = np.clip(image, 0.0, 1.0)
 
         elif i == 4:
@@ -609,12 +703,19 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
     # add final JPEG compression noise
     image = add_JPEG_noise(image)
     image = util.single2uint(image)
-    example = {"image":image}
+    example = {'image': image}
     return example
 
 
 # TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
-def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
+def degradation_bsrgan_plus(
+    img,
+    sf=4,
+    shuffle_prob=0.5,
+    use_sharp=True,
+    lq_patchsize=64,
+    isp_model=None,
+):
     """
     This is an extended degradation model by combining
     the degradation models of BSRGAN and Real-ESRGAN
@@ -630,7 +731,7 @@ def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patc
     """
 
     h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
     h, w = img.shape[:2]
 
     if h < lq_patchsize * sf or w < lq_patchsize * sf:
@@ -645,8 +746,12 @@ def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patc
     else:
         shuffle_order = list(range(13))
         # local shuffle for noise, JPEG is always the last one
-        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
-        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
+        shuffle_order[2:6] = random.sample(
+            shuffle_order[2:6], len(range(2, 6))
+        )
+        shuffle_order[9:13] = random.sample(
+            shuffle_order[9:13], len(range(9, 13))
+        )
 
     poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
 
@@ -689,8 +794,11 @@ def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patc
             print('check the shuffle!')
 
     # resize to desired size
-    img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
-                     interpolation=random.choice([1, 2, 3]))
+    img = cv2.resize(
+        img,
+        (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
+        interpolation=random.choice([1, 2, 3]),
+    )
 
     # add final JPEG compression noise
     img = add_JPEG_noise(img)
@@ -702,29 +810,37 @@ def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patc
 
 
 if __name__ == '__main__':
-	print("hey")
-	img = util.imread_uint('utils/test.png', 3)
-	print(img)
-	img = util.uint2single(img)
-	print(img)
-	img = img[:448, :448]
-	h = img.shape[0] // 4
-	print("resizing to", h)
-	sf = 4
-	deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-	for i in range(20):
-		print(i)
-		img_lq = deg_fn(img)
-		print(img_lq)
-		img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
-		print(img_lq.shape)
-		print("bicubic", img_lq_bicubic.shape)
-		print(img_hq.shape)
-		lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-		                        interpolation=0)
-		img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-		util.imsave(img_concat, str(i) + '.png')
-
-
+    print('hey')
+    img = util.imread_uint('utils/test.png', 3)
+    print(img)
+    img = util.uint2single(img)
+    print(img)
+    img = img[:448, :448]
+    h = img.shape[0] // 4
+    print('resizing to', h)
+    sf = 4
+    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+    for i in range(20):
+        print(i)
+        img_lq = deg_fn(img)
+        print(img_lq)
+        img_lq_bicubic = albumentations.SmallestMaxSize(
+            max_size=h, interpolation=cv2.INTER_CUBIC
+        )(image=img)['image']
+        print(img_lq.shape)
+        print('bicubic', img_lq_bicubic.shape)
+        print(img_hq.shape)
+        lq_nearest = cv2.resize(
+            util.single2uint(img_lq),
+            (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+            interpolation=0,
+        )
+        lq_bicubic_nearest = cv2.resize(
+            util.single2uint(img_lq_bicubic),
+            (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+            interpolation=0,
+        )
+        img_concat = np.concatenate(
+            [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
+        )
+        util.imsave(img_concat, str(i) + '.png')

ldm/modules/image_degradation/bsrgan_light.py

@@ -27,16 +27,16 @@ import ldm.modules.image_degradation.utils_image as util
 
 
 def modcrop_np(img, sf):
-    '''
+    """
     Args:
         img: numpy image, WxH or WxHxC
         sf: scale factor
     Return:
         cropped image
-    '''
+    """
     w, h = img.shape[:2]
     im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
+    return im[: w - w % sf, : h - h % sf, ...]
 
 
 """
@@ -54,7 +54,9 @@ def analytic_kernel(k):
     # Loop over the small kernel to fill the big one
     for r in range(k_size):
         for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+            big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += (
+                k[r, c] * k
+            )
     # Crop the edges of the big kernel to ignore very small values and increase run time of SR
     crop = k_size // 2
     cropped_big_k = big_k[crop:-crop, crop:-crop]
@@ -63,7 +65,7 @@ def analytic_kernel(k):
 
 
 def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
+    """generate an anisotropic Gaussian kernel
     Args:
         ksize : e.g., 15, kernel size
         theta : [0,  pi], rotation angle range
@@ -74,7 +76,12 @@ def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
         k     : kernel
     """
 
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+    v = np.dot(
+        np.array(
+            [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+        ),
+        np.array([1.0, 0.0]),
+    )
     V = np.array([[v[0], v[1]], [v[1], -v[0]]])
     D = np.array([[l1, 0], [0, l2]])
     Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
@@ -126,24 +133,32 @@ def shift_pixel(x, sf, upper_left=True):
 
 
 def blur(x, k):
-    '''
+    """
     x: image, NxcxHxW
     k: kernel, Nx1xhxw
-    '''
+    """
     n, c = x.shape[:2]
     p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
     x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
     k = k.repeat(1, c, 1, 1)
     k = k.view(-1, 1, k.shape[2], k.shape[3])
     x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+    x = torch.nn.functional.conv2d(
+        x, k, bias=None, stride=1, padding=0, groups=n * c
+    )
     x = x.view(n, c, x.shape[2], x.shape[3])
 
     return x
 
 
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
+def gen_kernel(
+    k_size=np.array([15, 15]),
+    scale_factor=np.array([4, 4]),
+    min_var=0.6,
+    max_var=10.0,
+    noise_level=0,
+):
+    """ "
     # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
     # Kai Zhang
     # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
@@ -157,13 +172,16 @@ def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var
 
     # Set COV matrix using Lambdas and Theta
     LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
+    Q = np.array(
+        [[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
+    )
     SIGMA = Q @ LAMBDA @ Q.T
     INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
 
     # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
+    MU = k_size // 2 - 0.5 * (
+        scale_factor - 1
+    )  # - 0.5 * (scale_factor - k_size % 2)
     MU = MU[None, None, :, None]
 
     # Create meshgrid for Gaussian
@@ -188,7 +206,9 @@ def fspecial_gaussian(hsize, sigma):
     hsize = [hsize, hsize]
     siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
     std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+    [x, y] = np.meshgrid(
+        np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)
+    )
     arg = -(x * x + y * y) / (2 * std * std)
     h = np.exp(arg)
     h[h < scipy.finfo(float).eps * h.max()] = 0
@@ -208,10 +228,10 @@ def fspecial_laplacian(alpha):
 
 
 def fspecial(filter_type, *args, **kwargs):
-    '''
+    """
     python code from:
     https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
+    """
     if filter_type == 'gaussian':
         return fspecial_gaussian(*args, **kwargs)
     if filter_type == 'laplacian':
@@ -226,19 +246,19 @@ def fspecial(filter_type, *args, **kwargs):
 
 
 def bicubic_degradation(x, sf=3):
-    '''
+    """
     Args:
         x: HxWxC image, [0, 1]
         sf: down-scale factor
     Return:
         bicubicly downsampled LR image
-    '''
+    """
     x = util.imresize_np(x, scale=1 / sf)
     return x
 
 
 def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
+    """blur + bicubic downsampling
     Args:
         x: HxWxC image, [0, 1]
         k: hxw, double
@@ -253,14 +273,16 @@ def srmd_degradation(x, k, sf=3):
           pages={3262--3271},
           year={2018}
         }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
+    """
+    x = ndimage.filters.convolve(
+        x, np.expand_dims(k, axis=2), mode='wrap'
+    )  # 'nearest' | 'mirror'
     x = bicubic_degradation(x, sf=sf)
     return x
 
 
 def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
+    """bicubic downsampling + blur
     Args:
         x: HxWxC image, [0, 1]
         k: hxw, double
@@ -275,21 +297,21 @@ def dpsr_degradation(x, k, sf=3):
           pages={1671--1681},
           year={2019}
         }
-    '''
+    """
     x = bicubic_degradation(x, sf=sf)
     x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
     return x
 
 
 def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
+    """blur + downsampling
     Args:
         x: HxWxC image, [0, 1]/[0, 255]
         k: hxw, double
         sf: down-scale factor
     Return:
         downsampled LR image
-    '''
+    """
     x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
     # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
     st = 0
@@ -326,16 +348,25 @@ def add_blur(img, sf=4):
     wd2 = 4.0 + sf
     wd = 2.0 + 0.2 * sf
 
-    wd2 = wd2/4
-    wd = wd/4
+    wd2 = wd2 / 4
+    wd = wd / 4
 
     if random.random() < 0.5:
         l1 = wd2 * random.random()
         l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+        k = anisotropic_Gaussian(
+            ksize=random.randint(2, 11) + 3,
+            theta=random.random() * np.pi,
+            l1=l1,
+            l2=l2,
+        )
     else:
-        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+        k = fspecial(
+            'gaussian', random.randint(2, 4) + 3, wd * random.random()
+        )
+    img = ndimage.filters.convolve(
+        img, np.expand_dims(k, axis=2), mode='mirror'
+    )
 
     return img
 
@@ -348,7 +379,11 @@ def add_resize(img, sf=4):
         sf1 = random.uniform(0.5 / sf, 1)
     else:
         sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+    img = cv2.resize(
+        img,
+        (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
+        interpolation=random.choice([1, 2, 3]),
+    )
     img = np.clip(img, 0.0, 1.0)
 
     return img
@@ -370,19 +405,26 @@ def add_resize(img, sf=4):
 #     img = np.clip(img, 0.0, 1.0)
 #     return img
 
+
 def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
     noise_level = random.randint(noise_level1, noise_level2)
     rnum = np.random.rand()
     if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
+            np.float32
+        )
     elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+        img = img + np.random.normal(
+            0, noise_level / 255.0, (*img.shape[:2], 1)
+        ).astype(np.float32)
     else:  # add  noise
-        L = noise_level2 / 255.
+        L = noise_level2 / 255.0
         D = np.diag(np.random.rand(3))
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+        img = img + np.random.multivariate_normal(
+            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
+        ).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
 
@@ -392,28 +434,37 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
     img = np.clip(img, 0.0, 1.0)
     rnum = random.random()
     if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+        img += img * np.random.normal(
+            0, noise_level / 255.0, img.shape
+        ).astype(np.float32)
     elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+        img += img * np.random.normal(
+            0, noise_level / 255.0, (*img.shape[:2], 1)
+        ).astype(np.float32)
     else:
-        L = noise_level2 / 255.
+        L = noise_level2 / 255.0
         D = np.diag(np.random.rand(3))
         U = orth(np.random.rand(3, 3))
         conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+        img += img * np.random.multivariate_normal(
+            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
+        ).astype(np.float32)
     img = np.clip(img, 0.0, 1.0)
     return img
 
 
 def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
+    img = np.clip((img * 255.0).round(), 0, 255) / 255.0
     vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
     if random.random() < 0.5:
         img = np.random.poisson(img * vals).astype(np.float32) / vals
     else:
         img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
+        noise_gray = (
+            np.random.poisson(img_gray * vals).astype(np.float32) / vals
+            - img_gray
+        )
         img += noise_gray[:, :, np.newaxis]
     img = np.clip(img, 0.0, 1.0)
     return img
@@ -422,7 +473,9 @@ def add_Poisson_noise(img):
 def add_JPEG_noise(img):
     quality_factor = random.randint(80, 95)
     img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+    result, encimg = cv2.imencode(
+        '.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
+    )
     img = cv2.imdecode(encimg, 1)
     img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
     return img
@@ -432,10 +485,14 @@ def random_crop(lq, hq, sf=4, lq_patchsize=64):
     h, w = lq.shape[:2]
     rnd_h = random.randint(0, h - lq_patchsize)
     rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+    lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]
 
     rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+    hq = hq[
+        rnd_h_H : rnd_h_H + lq_patchsize * sf,
+        rnd_w_H : rnd_w_H + lq_patchsize * sf,
+        :,
+    ]
     return lq, hq
 
 
@@ -456,7 +513,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
     sf_ori = sf
 
     h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
     h, w = img.shape[:2]
 
     if h < lq_patchsize * sf or w < lq_patchsize * sf:
@@ -466,8 +523,11 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
 
     if sf == 4 and random.random() < scale2_prob:  # downsample1
         if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
+            img = cv2.resize(
+                img,
+                (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+                interpolation=random.choice([1, 2, 3]),
+            )
         else:
             img = util.imresize_np(img, 1 / 2, True)
         img = np.clip(img, 0.0, 1.0)
@@ -476,7 +536,10 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
     shuffle_order = random.sample(range(7), 7)
     idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
     if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+        shuffle_order[idx1], shuffle_order[idx2] = (
+            shuffle_order[idx2],
+            shuffle_order[idx1],
+        )
 
     for i in shuffle_order:
 
@@ -491,19 +554,30 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
             # downsample2
             if random.random() < 0.75:
                 sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
+                img = cv2.resize(
+                    img,
+                    (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+                    interpolation=random.choice([1, 2, 3]),
+                )
             else:
                 k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
                 k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                k_shifted = (
+                    k_shifted / k_shifted.sum()
+                )  # blur with shifted kernel
+                img = ndimage.filters.convolve(
+                    img, np.expand_dims(k_shifted, axis=2), mode='mirror'
+                )
                 img = img[0::sf, 0::sf, ...]  # nearest downsampling
             img = np.clip(img, 0.0, 1.0)
 
         elif i == 3:
             # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            img = cv2.resize(
+                img,
+                (int(1 / sf * a), int(1 / sf * b)),
+                interpolation=random.choice([1, 2, 3]),
+            )
             img = np.clip(img, 0.0, 1.0)
 
         elif i == 4:
@@ -548,15 +622,18 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
     sf_ori = sf
 
     h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
+    image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
     h, w = image.shape[:2]
 
     hq = image.copy()
 
     if sf == 4 and random.random() < scale2_prob:  # downsample1
         if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
+            image = cv2.resize(
+                image,
+                (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+                interpolation=random.choice([1, 2, 3]),
+            )
         else:
             image = util.imresize_np(image, 1 / 2, True)
         image = np.clip(image, 0.0, 1.0)
@@ -565,7 +642,10 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
     shuffle_order = random.sample(range(7), 7)
     idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
     if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+        shuffle_order[idx1], shuffle_order[idx2] = (
+            shuffle_order[idx2],
+            shuffle_order[idx1],
+        )
 
     for i in shuffle_order:
 
@@ -583,20 +663,34 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
             # downsample2
             if random.random() < 0.8:
                 sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
+                image = cv2.resize(
+                    image,
+                    (
+                        int(1 / sf1 * image.shape[1]),
+                        int(1 / sf1 * image.shape[0]),
+                    ),
+                    interpolation=random.choice([1, 2, 3]),
+                )
             else:
                 k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
                 k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+                k_shifted = (
+                    k_shifted / k_shifted.sum()
+                )  # blur with shifted kernel
+                image = ndimage.filters.convolve(
+                    image, np.expand_dims(k_shifted, axis=2), mode='mirror'
+                )
                 image = image[0::sf, 0::sf, ...]  # nearest downsampling
 
             image = np.clip(image, 0.0, 1.0)
 
         elif i == 3:
             # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+            image = cv2.resize(
+                image,
+                (int(1 / sf * a), int(1 / sf * b)),
+                interpolation=random.choice([1, 2, 3]),
+            )
             image = np.clip(image, 0.0, 1.0)
 
         elif i == 4:
@@ -617,34 +711,41 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
     # add final JPEG compression noise
     image = add_JPEG_noise(image)
     image = util.single2uint(image)
-    example = {"image": image}
+    example = {'image': image}
     return example
 
 
-
-
 if __name__ == '__main__':
-    print("hey")
+    print('hey')
     img = util.imread_uint('utils/test.png', 3)
     img = img[:448, :448]
     h = img.shape[0] // 4
-    print("resizing to", h)
+    print('resizing to', h)
     sf = 4
     deg_fn = partial(degradation_bsrgan_variant, sf=sf)
     for i in range(20):
         print(i)
         img_hq = img
-        img_lq = deg_fn(img)["image"]
+        img_lq = deg_fn(img)['image']
         img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
         print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
+        img_lq_bicubic = albumentations.SmallestMaxSize(
+            max_size=h, interpolation=cv2.INTER_CUBIC
+        )(image=img_hq)['image']
         print(img_lq.shape)
-        print("bicubic", img_lq_bicubic.shape)
+        print('bicubic', img_lq_bicubic.shape)
         print(img_hq.shape)
-        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                interpolation=0)
-        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
-                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                        interpolation=0)
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+        lq_nearest = cv2.resize(
+            util.single2uint(img_lq),
+            (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+            interpolation=0,
+        )
+        lq_bicubic_nearest = cv2.resize(
+            util.single2uint(img_lq_bicubic),
+            (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+            interpolation=0,
+        )
+        img_concat = np.concatenate(
+            [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
+        )
         util.imsave(img_concat, str(i) + '.png')

ldm/modules/image_degradation/utils_image.py

@@ -6,13 +6,14 @@ import torch
 import cv2
 from torchvision.utils import make_grid
 from datetime import datetime
-#import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
+
+# import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
 
 
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
 
 
-'''
+"""
 # --------------------------------------------
 # Kai Zhang (github: https://github.com/cszn)
 # 03/Mar/2019
@@ -20,10 +21,22 @@ os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
 # https://github.com/twhui/SRGAN-pyTorch
 # https://github.com/xinntao/BasicSR
 # --------------------------------------------
-'''
+"""
 
 
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
+IMG_EXTENSIONS = [
+    '.jpg',
+    '.JPG',
+    '.jpeg',
+    '.JPEG',
+    '.png',
+    '.PNG',
+    '.ppm',
+    '.PPM',
+    '.bmp',
+    '.BMP',
+    '.tif',
+]
 
 
 def is_image_file(filename):
@@ -49,19 +62,19 @@ def surf(Z, cmap='rainbow', figsize=None):
     ax3 = plt.axes(projection='3d')
 
     w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
+    xx = np.arange(0, w, 1)
+    yy = np.arange(0, h, 1)
     X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
+    ax3.plot_surface(X, Y, Z, cmap=cmap)
+    # ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
     plt.show()
 
 
-'''
+"""
 # --------------------------------------------
 # get image pathes
 # --------------------------------------------
-'''
+"""
 
 
 def get_image_paths(dataroot):
@@ -83,26 +96,26 @@ def _get_paths_from_images(path):
     return images
 
 
-'''
+"""
 # --------------------------------------------
 # split large images into small images 
 # --------------------------------------------
-'''
+"""
 
 
 def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
     w, h = img.shape[:2]
     patches = []
     if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-#        print(w1)
-#        print(h1)
+        w1 = list(np.arange(0, w - p_size, p_size - p_overlap, dtype=np.int))
+        h1 = list(np.arange(0, h - p_size, p_size - p_overlap, dtype=np.int))
+        w1.append(w - p_size)
+        h1.append(h - p_size)
+        #        print(w1)
+        #        print(h1)
         for i in w1:
             for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
+                patches.append(img[i : i + p_size, j : j + p_size, :])
     else:
         patches.append(img)
 
@@ -118,11 +131,21 @@ def imssave(imgs, img_path):
     for i, img in enumerate(imgs):
         if img.ndim == 3:
             img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
+        new_path = os.path.join(
+            os.path.dirname(img_path),
+            img_name + str('_s{:04d}'.format(i)) + '.png',
+        )
         cv2.imwrite(new_path, img)
 
 
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
+def split_imageset(
+    original_dataroot,
+    taget_dataroot,
+    n_channels=3,
+    p_size=800,
+    p_overlap=96,
+    p_max=1000,
+):
     """
     split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
     and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
@@ -139,15 +162,18 @@ def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800,
         # img_name, ext = os.path.splitext(os.path.basename(img_path))
         img = imread_uint(img_path, n_channels=n_channels)
         patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
+        imssave(
+            patches, os.path.join(taget_dataroot, os.path.basename(img_path))
+        )
+        # if original_dataroot == taget_dataroot:
+        # del img_path
 
-'''
+
+"""
 # --------------------------------------------
 # makedir
 # --------------------------------------------
-'''
+"""
 
 
 def mkdir(path):
@@ -171,12 +197,12 @@ def mkdir_and_rename(path):
     os.makedirs(path)
 
 
-'''
+"""
 # --------------------------------------------
 # read image from path
 # opencv is fast, but read BGR numpy image
 # --------------------------------------------
-'''
+"""
 
 
 # --------------------------------------------
@@ -206,6 +232,7 @@ def imsave(img, img_path):
         img = img[:, :, [2, 1, 0]]
     cv2.imwrite(img_path, img)
 
+
 def imwrite(img, img_path):
     img = np.squeeze(img)
     if img.ndim == 3:
@@ -213,7 +240,6 @@ def imwrite(img, img_path):
     cv2.imwrite(img_path, img)
 
 
-
 # --------------------------------------------
 # get single image of size HxWxn_channles (BGR)
 # --------------------------------------------
@@ -221,7 +247,7 @@ def read_img(path):
     # read image by cv2
     # return: Numpy float32, HWC, BGR, [0,1]
     img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
+    img = img.astype(np.float32) / 255.0
     if img.ndim == 2:
         img = np.expand_dims(img, axis=2)
     # some images have 4 channels
@@ -230,7 +256,7 @@ def read_img(path):
     return img
 
 
-'''
+"""
 # --------------------------------------------
 # image format conversion
 # --------------------------------------------
@@ -238,7 +264,7 @@ def read_img(path):
 # numpy(single) <--->  tensor
 # numpy(unit)   <--->  tensor
 # --------------------------------------------
-'''
+"""
 
 
 # --------------------------------------------
@@ -248,22 +274,22 @@ def read_img(path):
 
 def uint2single(img):
 
-    return np.float32(img/255.)
+    return np.float32(img / 255.0)
 
 
 def single2uint(img):
 
-    return np.uint8((img.clip(0, 1)*255.).round())
+    return np.uint8((img.clip(0, 1) * 255.0).round())
 
 
 def uint162single(img):
 
-    return np.float32(img/65535.)
+    return np.float32(img / 65535.0)
 
 
 def single2uint16(img):
 
-    return np.uint16((img.clip(0, 1)*65535.).round())
+    return np.uint16((img.clip(0, 1) * 65535.0).round())
 
 
 # --------------------------------------------
@@ -275,14 +301,25 @@ def single2uint16(img):
 def uint2tensor4(img):
     if img.ndim == 2:
         img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
+    return (
+        torch.from_numpy(np.ascontiguousarray(img))
+        .permute(2, 0, 1)
+        .float()
+        .div(255.0)
+        .unsqueeze(0)
+    )
 
 
 # convert uint to 3-dimensional torch tensor
 def uint2tensor3(img):
     if img.ndim == 2:
         img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
+    return (
+        torch.from_numpy(np.ascontiguousarray(img))
+        .permute(2, 0, 1)
+        .float()
+        .div(255.0)
+    )
 
 
 # convert 2/3/4-dimensional torch tensor to uint
@@ -290,7 +327,7 @@ def tensor2uint(img):
     img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
     if img.ndim == 3:
         img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
+    return np.uint8((img * 255.0).round())
 
 
 # --------------------------------------------
@@ -305,7 +342,12 @@ def single2tensor3(img):
 
 # convert single (HxWxC) to 4-dimensional torch tensor
 def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
+    return (
+        torch.from_numpy(np.ascontiguousarray(img))
+        .permute(2, 0, 1)
+        .float()
+        .unsqueeze(0)
+    )
 
 
 # convert torch tensor to single
@@ -316,6 +358,7 @@ def tensor2single(img):
 
     return img
 
+
 # convert torch tensor to single
 def tensor2single3(img):
     img = img.data.squeeze().float().cpu().numpy()
@@ -327,30 +370,48 @@ def tensor2single3(img):
 
 
 def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
+    return (
+        torch.from_numpy(np.ascontiguousarray(img))
+        .permute(2, 0, 1, 3)
+        .float()
+        .unsqueeze(0)
+    )
 
 
 def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
+    return (
+        torch.from_numpy(np.ascontiguousarray(img))
+        .float()
+        .unsqueeze(0)
+        .unsqueeze(0)
+    )
 
 
 def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
+    return (
+        torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
+    )
 
 
 # from skimage.io import imread, imsave
 def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
+    """
     Converts a torch Tensor into an image Numpy array of BGR channel order
     Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
     Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
+    """
+    tensor = (
+        tensor.squeeze().float().cpu().clamp_(*min_max)
+    )  # squeeze first, then clamp
+    tensor = (tensor - min_max[0]) / (
+        min_max[1] - min_max[0]
+    )  # to range [0,1]
     n_dim = tensor.dim()
     if n_dim == 4:
         n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
+        img_np = make_grid(
+            tensor, nrow=int(math.sqrt(n_img)), normalize=False
+        ).numpy()
         img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
     elif n_dim == 3:
         img_np = tensor.numpy()
@@ -359,14 +420,17 @@ def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
         img_np = tensor.numpy()
     else:
         raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
+            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(
+                n_dim
+            )
+        )
     if out_type == np.uint8:
         img_np = (img_np * 255.0).round()
         # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
     return img_np.astype(out_type)
 
 
-'''
+"""
 # --------------------------------------------
 # Augmentation, flipe and/or rotate
 # --------------------------------------------
@@ -374,12 +438,11 @@ def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
 # (1) augmet_img: numpy image of WxHxC or WxH
 # (2) augment_img_tensor4: tensor image 1xCxWxH
 # --------------------------------------------
-'''
+"""
 
 
 def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
+    """Kai Zhang (github: https://github.com/cszn)"""
     if mode == 0:
         return img
     elif mode == 1:
@@ -399,8 +462,7 @@ def augment_img(img, mode=0):
 
 
 def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
+    """Kai Zhang (github: https://github.com/cszn)"""
     if mode == 0:
         return img
     elif mode == 1:
@@ -420,8 +482,7 @@ def augment_img_tensor4(img, mode=0):
 
 
 def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
+    """Kai Zhang (github: https://github.com/cszn)"""
     img_size = img.size()
     img_np = img.data.cpu().numpy()
     if len(img_size) == 3:
@@ -484,11 +545,11 @@ def augment_imgs(img_list, hflip=True, rot=True):
     return [_augment(img) for img in img_list]
 
 
-'''
+"""
 # --------------------------------------------
 # modcrop and shave
 # --------------------------------------------
-'''
+"""
 
 
 def modcrop(img_in, scale):
@@ -497,11 +558,11 @@ def modcrop(img_in, scale):
     if img.ndim == 2:
         H, W = img.shape
         H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
+        img = img[: H - H_r, : W - W_r]
     elif img.ndim == 3:
         H, W, C = img.shape
         H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
+        img = img[: H - H_r, : W - W_r, :]
     else:
         raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
     return img
@@ -511,11 +572,11 @@ def shave(img_in, border=0):
     # img_in: Numpy, HWC or HW
     img = np.copy(img_in)
     h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
+    img = img[border : h - border, border : w - border]
     return img
 
 
-'''
+"""
 # --------------------------------------------
 # image processing process on numpy image
 # channel_convert(in_c, tar_type, img_list):
@@ -523,74 +584,92 @@ def shave(img_in, border=0):
 # bgr2ycbcr(img, only_y=True):
 # ycbcr2rgb(img):
 # --------------------------------------------
-'''
+"""
 
 
 def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
+    """same as matlab rgb2ycbcr
     only_y: only return Y channel
     Input:
         uint8, [0, 255]
         float, [0, 1]
-    '''
+    """
     in_img_type = img.dtype
     img.astype(np.float32)
     if in_img_type != np.uint8:
-        img *= 255.
+        img *= 255.0
     # convert
     if only_y:
         rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
     else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
+        rlt = np.matmul(
+            img,
+            [
+                [65.481, -37.797, 112.0],
+                [128.553, -74.203, -93.786],
+                [24.966, 112.0, -18.214],
+            ],
+        ) / 255.0 + [16, 128, 128]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
-        rlt /= 255.
+        rlt /= 255.0
     return rlt.astype(in_img_type)
 
 
 def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
+    """same as matlab ycbcr2rgb
     Input:
         uint8, [0, 255]
         float, [0, 1]
-    '''
+    """
     in_img_type = img.dtype
     img.astype(np.float32)
     if in_img_type != np.uint8:
-        img *= 255.
+        img *= 255.0
     # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
+    rlt = np.matmul(
+        img,
+        [
+            [0.00456621, 0.00456621, 0.00456621],
+            [0, -0.00153632, 0.00791071],
+            [0.00625893, -0.00318811, 0],
+        ],
+    ) * 255.0 + [-222.921, 135.576, -276.836]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
-        rlt /= 255.
+        rlt /= 255.0
     return rlt.astype(in_img_type)
 
 
 def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
+    """bgr version of rgb2ycbcr
     only_y: only return Y channel
     Input:
         uint8, [0, 255]
         float, [0, 1]
-    '''
+    """
     in_img_type = img.dtype
     img.astype(np.float32)
     if in_img_type != np.uint8:
-        img *= 255.
+        img *= 255.0
     # convert
     if only_y:
         rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
     else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
+        rlt = np.matmul(
+            img,
+            [
+                [24.966, 112.0, -18.214],
+                [128.553, -74.203, -93.786],
+                [65.481, -37.797, 112.0],
+            ],
+        ) / 255.0 + [16, 128, 128]
     if in_img_type == np.uint8:
         rlt = rlt.round()
     else:
-        rlt /= 255.
+        rlt /= 255.0
     return rlt.astype(in_img_type)
 
 
@@ -608,11 +687,11 @@ def channel_convert(in_c, tar_type, img_list):
         return img_list
 
 
-'''
+"""
 # --------------------------------------------
 # metric, PSNR and SSIM
 # --------------------------------------------
-'''
+"""
 
 
 # --------------------------------------------
@@ -620,17 +699,17 @@ def channel_convert(in_c, tar_type, img_list):
 # --------------------------------------------
 def calculate_psnr(img1, img2, border=0):
     # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
+    # img1 = img1.squeeze()
+    # img2 = img2.squeeze()
     if not img1.shape == img2.shape:
         raise ValueError('Input images must have the same dimensions.')
     h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
+    img1 = img1[border : h - border, border : w - border]
+    img2 = img2[border : h - border, border : w - border]
 
     img1 = img1.astype(np.float64)
     img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
+    mse = np.mean((img1 - img2) ** 2)
     if mse == 0:
         return float('inf')
     return 20 * math.log10(255.0 / math.sqrt(mse))
@@ -640,17 +719,17 @@ def calculate_psnr(img1, img2, border=0):
 # SSIM
 # --------------------------------------------
 def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
+    """calculate SSIM
     the same outputs as MATLAB's
     img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
+    """
+    # img1 = img1.squeeze()
+    # img2 = img2.squeeze()
     if not img1.shape == img2.shape:
         raise ValueError('Input images must have the same dimensions.')
     h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
+    img1 = img1[border : h - border, border : w - border]
+    img2 = img2[border : h - border, border : w - border]
 
     if img1.ndim == 2:
         return ssim(img1, img2)
@@ -658,7 +737,7 @@ def calculate_ssim(img1, img2, border=0):
         if img1.shape[2] == 3:
             ssims = []
             for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
+                ssims.append(ssim(img1[:, :, i], img2[:, :, i]))
             return np.array(ssims).mean()
         elif img1.shape[2] == 1:
             return ssim(np.squeeze(img1), np.squeeze(img2))
@@ -667,8 +746,8 @@ def calculate_ssim(img1, img2, border=0):
 
 
 def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
+    C1 = (0.01 * 255) ** 2
+    C2 = (0.03 * 255) ** 2
 
     img1 = img1.astype(np.float64)
     img2 = img2.astype(np.float64)
@@ -684,16 +763,17 @@ def ssim(img1, img2):
     sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
     sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
 
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
+        (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
+    )
     return ssim_map.mean()
 
 
-'''
+"""
 # --------------------------------------------
 # matlab's bicubic imresize (numpy and torch) [0, 1]
 # --------------------------------------------
-'''
+"""
 
 
 # matlab 'imresize' function, now only support 'bicubic'
@@ -701,11 +781,14 @@ def cubic(x):
     absx = torch.abs(x)
     absx2 = absx**2
     absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
+    return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + (
+        -0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2
+    ) * (((absx > 1) * (absx <= 2)).type_as(absx))
 
 
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
+def calculate_weights_indices(
+    in_length, out_length, scale, kernel, kernel_width, antialiasing
+):
     if (scale < 1) and (antialiasing):
         # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
         kernel_width = kernel_width / scale
@@ -729,8 +812,9 @@ def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width
 
     # The indices of the input pixels involved in computing the k-th output
     # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
+    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(
+        0, P - 1, P
+    ).view(1, P).expand(out_length, P)
 
     # The weights used to compute the k-th output pixel are in row k of the
     # weights matrix.
@@ -771,7 +855,11 @@ def imresize(img, scale, antialiasing=True):
     if need_squeeze:
         img.unsqueeze_(0)
     in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+    out_C, out_H, out_W = (
+        in_C,
+        math.ceil(in_H * scale),
+        math.ceil(in_W * scale),
+    )
     kernel_width = 4
     kernel = 'cubic'
 
@@ -782,9 +870,11 @@ def imresize(img, scale, antialiasing=True):
 
     # get weights and indices
     weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
+        in_H, out_H, scale, kernel, kernel_width, antialiasing
+    )
     weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
+        in_W, out_W, scale, kernel, kernel_width, antialiasing
+    )
     # process H dimension
     # symmetric copying
     img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
@@ -805,7 +895,11 @@ def imresize(img, scale, antialiasing=True):
     for i in range(out_H):
         idx = int(indices_H[i][0])
         for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
+            out_1[j, i, :] = (
+                img_aug[j, idx : idx + kernel_width, :]
+                .transpose(0, 1)
+                .mv(weights_H[i])
+            )
 
     # process W dimension
     # symmetric copying
@@ -827,7 +921,9 @@ def imresize(img, scale, antialiasing=True):
     for i in range(out_W):
         idx = int(indices_W[i][0])
         for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
+            out_2[j, :, i] = out_1_aug[j, :, idx : idx + kernel_width].mv(
+                weights_W[i]
+            )
     if need_squeeze:
         out_2.squeeze_()
     return out_2
@@ -846,7 +942,11 @@ def imresize_np(img, scale, antialiasing=True):
         img.unsqueeze_(2)
 
     in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+    out_C, out_H, out_W = (
+        in_C,
+        math.ceil(in_H * scale),
+        math.ceil(in_W * scale),
+    )
     kernel_width = 4
     kernel = 'cubic'
 
@@ -857,9 +957,11 @@ def imresize_np(img, scale, antialiasing=True):
 
     # get weights and indices
     weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
+        in_H, out_H, scale, kernel, kernel_width, antialiasing
+    )
     weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
+        in_W, out_W, scale, kernel, kernel_width, antialiasing
+    )
     # process H dimension
     # symmetric copying
     img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
@@ -880,7 +982,11 @@ def imresize_np(img, scale, antialiasing=True):
     for i in range(out_H):
         idx = int(indices_H[i][0])
         for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
+            out_1[i, :, j] = (
+                img_aug[idx : idx + kernel_width, :, j]
+                .transpose(0, 1)
+                .mv(weights_H[i])
+            )
 
     # process W dimension
     # symmetric copying
@@ -902,7 +1008,9 @@ def imresize_np(img, scale, antialiasing=True):
     for i in range(out_W):
         idx = int(indices_W[i][0])
         for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
+            out_2[:, i, j] = out_1_aug[:, idx : idx + kernel_width, j].mv(
+                weights_W[i]
+            )
     if need_squeeze:
         out_2.squeeze_()
 
@@ -913,4 +1021,4 @@ if __name__ == '__main__':
     print('---')
 #    img = imread_uint('test.bmp', 3)
 #    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)
+#    img_bicubic = imresize_np(img, 1/4)

ldm/modules/losses/__init__.py

@@ -1 +1 @@
-from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator
+from ldm.modules.losses.contperceptual import LPIPSWithDiscriminator

ldm/modules/losses/contperceptual.py

@@ -5,13 +5,24 @@ from taming.modules.losses.vqperceptual import *  # TODO: taming dependency yes/
 
 
 class LPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_loss="hinge"):
+    def __init__(
+        self,
+        disc_start,
+        logvar_init=0.0,
+        kl_weight=1.0,
+        pixelloss_weight=1.0,
+        disc_num_layers=3,
+        disc_in_channels=3,
+        disc_factor=1.0,
+        disc_weight=1.0,
+        perceptual_weight=1.0,
+        use_actnorm=False,
+        disc_conditional=False,
+        disc_loss='hinge',
+    ):
 
         super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
+        assert disc_loss in ['hinge', 'vanilla']
         self.kl_weight = kl_weight
         self.pixel_weight = pixelloss_weight
         self.perceptual_loss = LPIPS().eval()
@@ -19,42 +30,68 @@ class LPIPSWithDiscriminator(nn.Module):
         # output log variance
         self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
 
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm
-                                                 ).apply(weights_init)
+        self.discriminator = NLayerDiscriminator(
+            input_nc=disc_in_channels,
+            n_layers=disc_num_layers,
+            use_actnorm=use_actnorm,
+        ).apply(weights_init)
         self.discriminator_iter_start = disc_start
-        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_loss = (
+            hinge_d_loss if disc_loss == 'hinge' else vanilla_d_loss
+        )
         self.disc_factor = disc_factor
         self.discriminator_weight = disc_weight
         self.disc_conditional = disc_conditional
 
     def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
         if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+            nll_grads = torch.autograd.grad(
+                nll_loss, last_layer, retain_graph=True
+            )[0]
+            g_grads = torch.autograd.grad(
+                g_loss, last_layer, retain_graph=True
+            )[0]
         else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
+            nll_grads = torch.autograd.grad(
+                nll_loss, self.last_layer[0], retain_graph=True
+            )[0]
+            g_grads = torch.autograd.grad(
+                g_loss, self.last_layer[0], retain_graph=True
+            )[0]
 
         d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
         d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
         d_weight = d_weight * self.discriminator_weight
         return d_weight
 
-    def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train",
-                weights=None):
-        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+    def forward(
+        self,
+        inputs,
+        reconstructions,
+        posteriors,
+        optimizer_idx,
+        global_step,
+        last_layer=None,
+        cond=None,
+        split='train',
+        weights=None,
+    ):
+        rec_loss = torch.abs(
+            inputs.contiguous() - reconstructions.contiguous()
+        )
         if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
+            p_loss = self.perceptual_loss(
+                inputs.contiguous(), reconstructions.contiguous()
+            )
             rec_loss = rec_loss + self.perceptual_weight * p_loss
 
         nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
         weighted_nll_loss = nll_loss
         if weights is not None:
-            weighted_nll_loss = weights*nll_loss
-        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+            weighted_nll_loss = weights * nll_loss
+        weighted_nll_loss = (
+            torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+        )
         nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
         kl_loss = posteriors.kl()
         kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
@@ -67,45 +104,72 @@ class LPIPSWithDiscriminator(nn.Module):
                 logits_fake = self.discriminator(reconstructions.contiguous())
             else:
                 assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
+                logits_fake = self.discriminator(
+                    torch.cat((reconstructions.contiguous(), cond), dim=1)
+                )
             g_loss = -torch.mean(logits_fake)
 
             if self.disc_factor > 0.0:
                 try:
-                    d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
+                    d_weight = self.calculate_adaptive_weight(
+                        nll_loss, g_loss, last_layer=last_layer
+                    )
                 except RuntimeError:
                     assert not self.training
                     d_weight = torch.tensor(0.0)
             else:
                 d_weight = torch.tensor(0.0)
 
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss
+            disc_factor = adopt_weight(
+                self.disc_factor,
+                global_step,
+                threshold=self.discriminator_iter_start,
+            )
+            loss = (
+                weighted_nll_loss
+                + self.kl_weight * kl_loss
+                + d_weight * disc_factor * g_loss
+            )
 
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(), "{}/logvar".format(split): self.logvar.detach(),
-                   "{}/kl_loss".format(split): kl_loss.detach().mean(), "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
+            log = {
+                '{}/total_loss'.format(split): loss.clone().detach().mean(),
+                '{}/logvar'.format(split): self.logvar.detach(),
+                '{}/kl_loss'.format(split): kl_loss.detach().mean(),
+                '{}/nll_loss'.format(split): nll_loss.detach().mean(),
+                '{}/rec_loss'.format(split): rec_loss.detach().mean(),
+                '{}/d_weight'.format(split): d_weight.detach(),
+                '{}/disc_factor'.format(split): torch.tensor(disc_factor),
+                '{}/g_loss'.format(split): g_loss.detach().mean(),
+            }
             return loss, log
 
         if optimizer_idx == 1:
             # second pass for discriminator update
             if cond is None:
                 logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
+                logits_fake = self.discriminator(
+                    reconstructions.contiguous().detach()
+                )
             else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
+                logits_real = self.discriminator(
+                    torch.cat((inputs.contiguous().detach(), cond), dim=1)
+                )
+                logits_fake = self.discriminator(
+                    torch.cat(
+                        (reconstructions.contiguous().detach(), cond), dim=1
+                    )
+                )
 
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            disc_factor = adopt_weight(
+                self.disc_factor,
+                global_step,
+                threshold=self.discriminator_iter_start,
+            )
             d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
 
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
+            log = {
+                '{}/disc_loss'.format(split): d_loss.clone().detach().mean(),
+                '{}/logits_real'.format(split): logits_real.detach().mean(),
+                '{}/logits_fake'.format(split): logits_fake.detach().mean(),
+            }
             return d_loss, log
-

ldm/modules/losses/vqperceptual.py

@@ -3,21 +3,25 @@ from torch import nn
 import torch.nn.functional as F
 from einops import repeat
 
-from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
+from taming.modules.discriminator.model import (
+    NLayerDiscriminator,
+    weights_init,
+)
 from taming.modules.losses.lpips import LPIPS
 from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
 
 
 def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
     assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
-    loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
-    loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
+    loss_real = torch.mean(F.relu(1.0 - logits_real), dim=[1, 2, 3])
+    loss_fake = torch.mean(F.relu(1.0 + logits_fake), dim=[1, 2, 3])
     loss_real = (weights * loss_real).sum() / weights.sum()
     loss_fake = (weights * loss_fake).sum() / weights.sum()
     d_loss = 0.5 * (loss_real + loss_fake)
     return d_loss
 
-def adopt_weight(weight, global_step, threshold=0, value=0.):
+
+def adopt_weight(weight, global_step, threshold=0, value=0.0):
     if global_step < threshold:
         weight = value
     return weight
@@ -26,57 +30,76 @@ def adopt_weight(weight, global_step, threshold=0, value=0.):
 def measure_perplexity(predicted_indices, n_embed):
     # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
     # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
-    encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
+    encodings = (
+        F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
+    )
     avg_probs = encodings.mean(0)
     perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
     cluster_use = torch.sum(avg_probs > 0)
     return perplexity, cluster_use
 
+
 def l1(x, y):
-    return torch.abs(x-y)
+    return torch.abs(x - y)
 
 
 def l2(x, y):
-    return torch.pow((x-y), 2)
+    return torch.pow((x - y), 2)
 
 
 class VQLPIPSWithDiscriminator(nn.Module):
-    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips",
-                 pixel_loss="l1"):
+    def __init__(
+        self,
+        disc_start,
+        codebook_weight=1.0,
+        pixelloss_weight=1.0,
+        disc_num_layers=3,
+        disc_in_channels=3,
+        disc_factor=1.0,
+        disc_weight=1.0,
+        perceptual_weight=1.0,
+        use_actnorm=False,
+        disc_conditional=False,
+        disc_ndf=64,
+        disc_loss='hinge',
+        n_classes=None,
+        perceptual_loss='lpips',
+        pixel_loss='l1',
+    ):
         super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        assert perceptual_loss in ["lpips", "clips", "dists"]
-        assert pixel_loss in ["l1", "l2"]
+        assert disc_loss in ['hinge', 'vanilla']
+        assert perceptual_loss in ['lpips', 'clips', 'dists']
+        assert pixel_loss in ['l1', 'l2']
         self.codebook_weight = codebook_weight
         self.pixel_weight = pixelloss_weight
-        if perceptual_loss == "lpips":
-            print(f"{self.__class__.__name__}: Running with LPIPS.")
+        if perceptual_loss == 'lpips':
+            print(f'{self.__class__.__name__}: Running with LPIPS.')
             self.perceptual_loss = LPIPS().eval()
         else:
-            raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
+            raise ValueError(
+                f'Unknown perceptual loss: >> {perceptual_loss} <<'
+            )
         self.perceptual_weight = perceptual_weight
 
-        if pixel_loss == "l1":
+        if pixel_loss == 'l1':
             self.pixel_loss = l1
         else:
             self.pixel_loss = l2
 
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ndf=disc_ndf
-                                                 ).apply(weights_init)
+        self.discriminator = NLayerDiscriminator(
+            input_nc=disc_in_channels,
+            n_layers=disc_num_layers,
+            use_actnorm=use_actnorm,
+            ndf=disc_ndf,
+        ).apply(weights_init)
         self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
+        if disc_loss == 'hinge':
             self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
+        elif disc_loss == 'vanilla':
             self.disc_loss = vanilla_d_loss
         else:
             raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
+        print(f'VQLPIPSWithDiscriminator running with {disc_loss} loss.')
         self.disc_factor = disc_factor
         self.discriminator_weight = disc_weight
         self.disc_conditional = disc_conditional
@@ -84,31 +107,53 @@ class VQLPIPSWithDiscriminator(nn.Module):
 
     def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
         if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+            nll_grads = torch.autograd.grad(
+                nll_loss, last_layer, retain_graph=True
+            )[0]
+            g_grads = torch.autograd.grad(
+                g_loss, last_layer, retain_graph=True
+            )[0]
         else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
+            nll_grads = torch.autograd.grad(
+                nll_loss, self.last_layer[0], retain_graph=True
+            )[0]
+            g_grads = torch.autograd.grad(
+                g_loss, self.last_layer[0], retain_graph=True
+            )[0]
 
         d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
         d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
         d_weight = d_weight * self.discriminator_weight
         return d_weight
 
-    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
+    def forward(
+        self,
+        codebook_loss,
+        inputs,
+        reconstructions,
+        optimizer_idx,
+        global_step,
+        last_layer=None,
+        cond=None,
+        split='train',
+        predicted_indices=None,
+    ):
         if not exists(codebook_loss):
-            codebook_loss = torch.tensor([0.]).to(inputs.device)
-        #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
+            codebook_loss = torch.tensor([0.0]).to(inputs.device)
+        # rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        rec_loss = self.pixel_loss(
+            inputs.contiguous(), reconstructions.contiguous()
+        )
         if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
+            p_loss = self.perceptual_loss(
+                inputs.contiguous(), reconstructions.contiguous()
+            )
             rec_loss = rec_loss + self.perceptual_weight * p_loss
         else:
             p_loss = torch.tensor([0.0])
 
         nll_loss = rec_loss
-        #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+        # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
         nll_loss = torch.mean(nll_loss)
 
         # now the GAN part
@@ -119,49 +164,77 @@ class VQLPIPSWithDiscriminator(nn.Module):
                 logits_fake = self.discriminator(reconstructions.contiguous())
             else:
                 assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
+                logits_fake = self.discriminator(
+                    torch.cat((reconstructions.contiguous(), cond), dim=1)
+                )
             g_loss = -torch.mean(logits_fake)
 
             try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
+                d_weight = self.calculate_adaptive_weight(
+                    nll_loss, g_loss, last_layer=last_layer
+                )
             except RuntimeError:
                 assert not self.training
                 d_weight = torch.tensor(0.0)
 
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()
+            disc_factor = adopt_weight(
+                self.disc_factor,
+                global_step,
+                threshold=self.discriminator_iter_start,
+            )
+            loss = (
+                nll_loss
+                + d_weight * disc_factor * g_loss
+                + self.codebook_weight * codebook_loss.mean()
+            )
 
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/p_loss".format(split): p_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   }
+            log = {
+                '{}/total_loss'.format(split): loss.clone().detach().mean(),
+                '{}/quant_loss'.format(split): codebook_loss.detach().mean(),
+                '{}/nll_loss'.format(split): nll_loss.detach().mean(),
+                '{}/rec_loss'.format(split): rec_loss.detach().mean(),
+                '{}/p_loss'.format(split): p_loss.detach().mean(),
+                '{}/d_weight'.format(split): d_weight.detach(),
+                '{}/disc_factor'.format(split): torch.tensor(disc_factor),
+                '{}/g_loss'.format(split): g_loss.detach().mean(),
+            }
             if predicted_indices is not None:
                 assert self.n_classes is not None
                 with torch.no_grad():
-                    perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
-                log[f"{split}/perplexity"] = perplexity
-                log[f"{split}/cluster_usage"] = cluster_usage
+                    perplexity, cluster_usage = measure_perplexity(
+                        predicted_indices, self.n_classes
+                    )
+                log[f'{split}/perplexity'] = perplexity
+                log[f'{split}/cluster_usage'] = cluster_usage
             return loss, log
 
         if optimizer_idx == 1:
             # second pass for discriminator update
             if cond is None:
                 logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
+                logits_fake = self.discriminator(
+                    reconstructions.contiguous().detach()
+                )
             else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
+                logits_real = self.discriminator(
+                    torch.cat((inputs.contiguous().detach(), cond), dim=1)
+                )
+                logits_fake = self.discriminator(
+                    torch.cat(
+                        (reconstructions.contiguous().detach(), cond), dim=1
+                    )
+                )
 
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
+            disc_factor = adopt_weight(
+                self.disc_factor,
+                global_step,
+                threshold=self.discriminator_iter_start,
+            )
             d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
 
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
+            log = {
+                '{}/disc_loss'.format(split): d_loss.clone().detach().mean(),
+                '{}/logits_real'.format(split): logits_real.detach().mean(),
+                '{}/logits_fake'.format(split): logits_fake.detach().mean(),
+            }
             return d_loss, log

ldm/modules/x_transformer.py

@@ -11,15 +11,13 @@ from einops import rearrange, repeat, reduce
 
 DEFAULT_DIM_HEAD = 64
 
-Intermediates = namedtuple('Intermediates', [
-    'pre_softmax_attn',
-    'post_softmax_attn'
-])
+Intermediates = namedtuple(
+    'Intermediates', ['pre_softmax_attn', 'post_softmax_attn']
+)
 
-LayerIntermediates = namedtuple('Intermediates', [
-    'hiddens',
-    'attn_intermediates'
-])
+LayerIntermediates = namedtuple(
+    'Intermediates', ['hiddens', 'attn_intermediates']
+)
 
 
 class AbsolutePositionalEmbedding(nn.Module):
@@ -39,11 +37,16 @@ class AbsolutePositionalEmbedding(nn.Module):
 class FixedPositionalEmbedding(nn.Module):
     def __init__(self, dim):
         super().__init__()
-        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
         self.register_buffer('inv_freq', inv_freq)
 
     def forward(self, x, seq_dim=1, offset=0):
-        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
+        t = (
+            torch.arange(x.shape[seq_dim], device=x.device).type_as(
+                self.inv_freq
+            )
+            + offset
+        )
         sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
         emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
         return emb[None, :, :]
@@ -51,6 +54,7 @@ class FixedPositionalEmbedding(nn.Module):
 
 # helpers
 
+
 def exists(val):
     return val is not None
 
@@ -64,18 +68,21 @@ def default(val, d):
 def always(val):
     def inner(*args, **kwargs):
         return val
+
     return inner
 
 
 def not_equals(val):
     def inner(x):
         return x != val
+
     return inner
 
 
 def equals(val):
     def inner(x):
         return x == val
+
     return inner
 
 
@@ -85,6 +92,7 @@ def max_neg_value(tensor):
 
 # keyword argument helpers
 
+
 def pick_and_pop(keys, d):
     values = list(map(lambda key: d.pop(key), keys))
     return dict(zip(keys, values))
@@ -108,8 +116,15 @@ def group_by_key_prefix(prefix, d):
 
 
 def groupby_prefix_and_trim(prefix, d):
-    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
-    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+    kwargs_with_prefix, kwargs = group_dict_by_key(
+        partial(string_begins_with, prefix), d
+    )
+    kwargs_without_prefix = dict(
+        map(
+            lambda x: (x[0][len(prefix) :], x[1]),
+            tuple(kwargs_with_prefix.items()),
+        )
+    )
     return kwargs_without_prefix, kwargs
 
 
@@ -139,7 +154,7 @@ class Rezero(nn.Module):
 class ScaleNorm(nn.Module):
     def __init__(self, dim, eps=1e-5):
         super().__init__()
-        self.scale = dim ** -0.5
+        self.scale = dim**-0.5
         self.eps = eps
         self.g = nn.Parameter(torch.ones(1))
 
@@ -151,7 +166,7 @@ class ScaleNorm(nn.Module):
 class RMSNorm(nn.Module):
     def __init__(self, dim, eps=1e-8):
         super().__init__()
-        self.scale = dim ** -0.5
+        self.scale = dim**-0.5
         self.eps = eps
         self.g = nn.Parameter(torch.ones(dim))
 
@@ -173,7 +188,7 @@ class GRUGating(nn.Module):
     def forward(self, x, residual):
         gated_output = self.gru(
             rearrange(x, 'b n d -> (b n) d'),
-            rearrange(residual, 'b n d -> (b n) d')
+            rearrange(residual, 'b n d -> (b n) d'),
         )
 
         return gated_output.reshape_as(x)
@@ -181,6 +196,7 @@ class GRUGating(nn.Module):
 
 # feedforward
 
+
 class GEGLU(nn.Module):
     def __init__(self, dim_in, dim_out):
         super().__init__()
@@ -192,19 +208,18 @@ class GEGLU(nn.Module):
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
+        project_in = (
+            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+            if not glu
+            else GEGLU(dim, inner_dim)
+        )
 
         self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
+            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
         )
 
     def forward(self, x):
@@ -214,23 +229,25 @@ class FeedForward(nn.Module):
 # attention.
 class Attention(nn.Module):
     def __init__(
-            self,
-            dim,
-            dim_head=DEFAULT_DIM_HEAD,
-            heads=8,
-            causal=False,
-            mask=None,
-            talking_heads=False,
-            sparse_topk=None,
-            use_entmax15=False,
-            num_mem_kv=0,
-            dropout=0.,
-            on_attn=False
+        self,
+        dim,
+        dim_head=DEFAULT_DIM_HEAD,
+        heads=8,
+        causal=False,
+        mask=None,
+        talking_heads=False,
+        sparse_topk=None,
+        use_entmax15=False,
+        num_mem_kv=0,
+        dropout=0.0,
+        on_attn=False,
     ):
         super().__init__()
         if use_entmax15:
-            raise NotImplementedError("Check out entmax activation instead of softmax activation!")
-        self.scale = dim_head ** -0.5
+            raise NotImplementedError(
+                'Check out entmax activation instead of softmax activation!'
+            )
+        self.scale = dim_head**-0.5
         self.heads = heads
         self.causal = causal
         self.mask = mask
@@ -252,7 +269,7 @@ class Attention(nn.Module):
         self.sparse_topk = sparse_topk
 
         # entmax
-        #self.attn_fn = entmax15 if use_entmax15 else F.softmax
+        # self.attn_fn = entmax15 if use_entmax15 else F.softmax
         self.attn_fn = F.softmax
 
         # add memory key / values
@@ -263,20 +280,29 @@ class Attention(nn.Module):
 
         # attention on attention
         self.attn_on_attn = on_attn
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
+        self.to_out = (
+            nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU())
+            if on_attn
+            else nn.Linear(inner_dim, dim)
+        )
 
     def forward(
-            self,
-            x,
-            context=None,
-            mask=None,
-            context_mask=None,
-            rel_pos=None,
-            sinusoidal_emb=None,
-            prev_attn=None,
-            mem=None
+        self,
+        x,
+        context=None,
+        mask=None,
+        context_mask=None,
+        rel_pos=None,
+        sinusoidal_emb=None,
+        prev_attn=None,
+        mem=None,
     ):
-        b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
+        b, n, _, h, talking_heads, device = (
+            *x.shape,
+            self.heads,
+            self.talking_heads,
+            x.device,
+        )
         kv_input = default(context, x)
 
         q_input = x
@@ -297,23 +323,35 @@ class Attention(nn.Module):
         k = self.to_k(k_input)
         v = self.to_v(v_input)
 
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
+        q, k, v = map(
+            lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)
+        )
 
         input_mask = None
         if any(map(exists, (mask, context_mask))):
-            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
+            q_mask = default(
+                mask, lambda: torch.ones((b, n), device=device).bool()
+            )
             k_mask = q_mask if not exists(context) else context_mask
-            k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
+            k_mask = default(
+                k_mask,
+                lambda: torch.ones((b, k.shape[-2]), device=device).bool(),
+            )
             q_mask = rearrange(q_mask, 'b i -> b () i ()')
             k_mask = rearrange(k_mask, 'b j -> b () () j')
             input_mask = q_mask * k_mask
 
         if self.num_mem_kv > 0:
-            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
+            mem_k, mem_v = map(
+                lambda t: repeat(t, 'h n d -> b h n d', b=b),
+                (self.mem_k, self.mem_v),
+            )
             k = torch.cat((mem_k, k), dim=-2)
             v = torch.cat((mem_v, v), dim=-2)
             if exists(input_mask):
-                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
+                input_mask = F.pad(
+                    input_mask, (self.num_mem_kv, 0), value=True
+                )
 
         dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
         mask_value = max_neg_value(dots)
@@ -324,7 +362,9 @@ class Attention(nn.Module):
         pre_softmax_attn = dots
 
         if talking_heads:
-            dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
+            dots = einsum(
+                'b h i j, h k -> b k i j', dots, self.pre_softmax_proj
+            ).contiguous()
 
         if exists(rel_pos):
             dots = rel_pos(dots)
@@ -336,7 +376,9 @@ class Attention(nn.Module):
         if self.causal:
             i, j = dots.shape[-2:]
             r = torch.arange(i, device=device)
-            mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
+            mask = rearrange(r, 'i -> () () i ()') < rearrange(
+                r, 'j -> () () () j'
+            )
             mask = F.pad(mask, (j - i, 0), value=False)
             dots.masked_fill_(mask, mask_value)
             del mask
@@ -354,14 +396,16 @@ class Attention(nn.Module):
         attn = self.dropout(attn)
 
         if talking_heads:
-            attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
+            attn = einsum(
+                'b h i j, h k -> b k i j', attn, self.post_softmax_proj
+            ).contiguous()
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')
 
         intermediates = Intermediates(
             pre_softmax_attn=pre_softmax_attn,
-            post_softmax_attn=post_softmax_attn
+            post_softmax_attn=post_softmax_attn,
         )
 
         return self.to_out(out), intermediates
@@ -369,28 +413,28 @@ class Attention(nn.Module):
 
 class AttentionLayers(nn.Module):
     def __init__(
-            self,
-            dim,
-            depth,
-            heads=8,
-            causal=False,
-            cross_attend=False,
-            only_cross=False,
-            use_scalenorm=False,
-            use_rmsnorm=False,
-            use_rezero=False,
-            rel_pos_num_buckets=32,
-            rel_pos_max_distance=128,
-            position_infused_attn=False,
-            custom_layers=None,
-            sandwich_coef=None,
-            par_ratio=None,
-            residual_attn=False,
-            cross_residual_attn=False,
-            macaron=False,
-            pre_norm=True,
-            gate_residual=False,
-            **kwargs
+        self,
+        dim,
+        depth,
+        heads=8,
+        causal=False,
+        cross_attend=False,
+        only_cross=False,
+        use_scalenorm=False,
+        use_rmsnorm=False,
+        use_rezero=False,
+        rel_pos_num_buckets=32,
+        rel_pos_max_distance=128,
+        position_infused_attn=False,
+        custom_layers=None,
+        sandwich_coef=None,
+        par_ratio=None,
+        residual_attn=False,
+        cross_residual_attn=False,
+        macaron=False,
+        pre_norm=True,
+        gate_residual=False,
+        **kwargs,
     ):
         super().__init__()
         ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
@@ -403,10 +447,14 @@ class AttentionLayers(nn.Module):
         self.layers = nn.ModuleList([])
 
         self.has_pos_emb = position_infused_attn
-        self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
+        self.pia_pos_emb = (
+            FixedPositionalEmbedding(dim) if position_infused_attn else None
+        )
         self.rotary_pos_emb = always(None)
 
-        assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
+        assert (
+            rel_pos_num_buckets <= rel_pos_max_distance
+        ), 'number of relative position buckets must be less than the relative position max distance'
         self.rel_pos = None
 
         self.pre_norm = pre_norm
@@ -438,15 +486,27 @@ class AttentionLayers(nn.Module):
             assert 1 < par_ratio <= par_depth, 'par ratio out of range'
             default_block = tuple(filter(not_equals('f'), default_block))
             par_attn = par_depth // par_ratio
-            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
+            depth_cut = (
+                par_depth * 2 // 3
+            )  # 2 / 3 attention layer cutoff suggested by PAR paper
             par_width = (depth_cut + depth_cut // par_attn) // par_attn
-            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
-            par_block = default_block + ('f',) * (par_width - len(default_block))
+            assert (
+                len(default_block) <= par_width
+            ), 'default block is too large for par_ratio'
+            par_block = default_block + ('f',) * (
+                par_width - len(default_block)
+            )
             par_head = par_block * par_attn
             layer_types = par_head + ('f',) * (par_depth - len(par_head))
         elif exists(sandwich_coef):
-            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
-            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
+            assert (
+                sandwich_coef > 0 and sandwich_coef <= depth
+            ), 'sandwich coefficient should be less than the depth'
+            layer_types = (
+                ('a',) * sandwich_coef
+                + default_block * (depth - sandwich_coef)
+                + ('f',) * sandwich_coef
+            )
         else:
             layer_types = default_block * depth
 
@@ -455,7 +515,9 @@ class AttentionLayers(nn.Module):
 
         for layer_type in self.layer_types:
             if layer_type == 'a':
-                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
+                layer = Attention(
+                    dim, heads=heads, causal=causal, **attn_kwargs
+                )
             elif layer_type == 'c':
                 layer = Attention(dim, heads=heads, **attn_kwargs)
             elif layer_type == 'f':
@@ -472,21 +534,17 @@ class AttentionLayers(nn.Module):
             else:
                 residual_fn = Residual()
 
-            self.layers.append(nn.ModuleList([
-                norm_fn(),
-                layer,
-                residual_fn
-            ]))
+            self.layers.append(nn.ModuleList([norm_fn(), layer, residual_fn]))
 
     def forward(
-            self,
-            x,
-            context=None,
-            mask=None,
-            context_mask=None,
-            mems=None,
-            return_hiddens=False,
-            **kwargs
+        self,
+        x,
+        context=None,
+        mask=None,
+        context_mask=None,
+        mems=None,
+        return_hiddens=False,
+        **kwargs,
     ):
         hiddens = []
         intermediates = []
@@ -495,7 +553,9 @@ class AttentionLayers(nn.Module):
 
         mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
 
-        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(
+            zip(self.layer_types, self.layers)
+        ):
             is_last = ind == (len(self.layers) - 1)
 
             if layer_type == 'a':
@@ -508,10 +568,22 @@ class AttentionLayers(nn.Module):
                 x = norm(x)
 
             if layer_type == 'a':
-                out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
-                                   prev_attn=prev_attn, mem=layer_mem)
+                out, inter = block(
+                    x,
+                    mask=mask,
+                    sinusoidal_emb=self.pia_pos_emb,
+                    rel_pos=self.rel_pos,
+                    prev_attn=prev_attn,
+                    mem=layer_mem,
+                )
             elif layer_type == 'c':
-                out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
+                out, inter = block(
+                    x,
+                    context=context,
+                    mask=mask,
+                    context_mask=context_mask,
+                    prev_attn=prev_cross_attn,
+                )
             elif layer_type == 'f':
                 out = block(x)
 
@@ -530,8 +602,7 @@ class AttentionLayers(nn.Module):
 
         if return_hiddens:
             intermediates = LayerIntermediates(
-                hiddens=hiddens,
-                attn_intermediates=intermediates
+                hiddens=hiddens, attn_intermediates=intermediates
             )
 
             return x, intermediates
@@ -545,23 +616,24 @@ class Encoder(AttentionLayers):
         super().__init__(causal=False, **kwargs)
 
 
-
 class TransformerWrapper(nn.Module):
     def __init__(
-            self,
-            *,
-            num_tokens,
-            max_seq_len,
-            attn_layers,
-            emb_dim=None,
-            max_mem_len=0.,
-            emb_dropout=0.,
-            num_memory_tokens=None,
-            tie_embedding=False,
-            use_pos_emb=True
+        self,
+        *,
+        num_tokens,
+        max_seq_len,
+        attn_layers,
+        emb_dim=None,
+        max_mem_len=0.0,
+        emb_dropout=0.0,
+        num_memory_tokens=None,
+        tie_embedding=False,
+        use_pos_emb=True,
     ):
         super().__init__()
-        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+        assert isinstance(
+            attn_layers, AttentionLayers
+        ), 'attention layers must be one of Encoder or Decoder'
 
         dim = attn_layers.dim
         emb_dim = default(emb_dim, dim)
@@ -571,23 +643,34 @@ class TransformerWrapper(nn.Module):
         self.num_tokens = num_tokens
 
         self.token_emb = nn.Embedding(num_tokens, emb_dim)
-        self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
-                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+        self.pos_emb = (
+            AbsolutePositionalEmbedding(emb_dim, max_seq_len)
+            if (use_pos_emb and not attn_layers.has_pos_emb)
+            else always(0)
+        )
         self.emb_dropout = nn.Dropout(emb_dropout)
 
-        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+        self.project_emb = (
+            nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+        )
         self.attn_layers = attn_layers
         self.norm = nn.LayerNorm(dim)
 
         self.init_()
 
-        self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
+        self.to_logits = (
+            nn.Linear(dim, num_tokens)
+            if not tie_embedding
+            else lambda t: t @ self.token_emb.weight.t()
+        )
 
         # memory tokens (like [cls]) from Memory Transformers paper
         num_memory_tokens = default(num_memory_tokens, 0)
         self.num_memory_tokens = num_memory_tokens
         if num_memory_tokens > 0:
-            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+            self.memory_tokens = nn.Parameter(
+                torch.randn(num_memory_tokens, dim)
+            )
 
             # let funnel encoder know number of memory tokens, if specified
             if hasattr(attn_layers, 'num_memory_tokens'):
@@ -597,20 +680,20 @@ class TransformerWrapper(nn.Module):
         nn.init.normal_(self.token_emb.weight, std=0.02)
 
     def forward(
-            self,
-            x,
-            return_embeddings=False,
-            mask=None,
-            return_mems=False,
-            return_attn=False,
-            mems=None,
-            embedding_manager=None,
-            **kwargs
+        self,
+        x,
+        return_embeddings=False,
+        mask=None,
+        return_mems=False,
+        return_attn=False,
+        mems=None,
+        embedding_manager=None,
+        **kwargs,
     ):
         b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
 
         embedded_x = self.token_emb(x)
-        
+
         if embedding_manager:
             x = embedding_manager(x, embedded_x)
         else:
@@ -629,7 +712,9 @@ class TransformerWrapper(nn.Module):
             if exists(mask):
                 mask = F.pad(mask, (num_mem, 0), value=True)
 
-        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+        x, intermediates = self.attn_layers(
+            x, mask=mask, mems=mems, return_hiddens=True, **kwargs
+        )
         x = self.norm(x)
 
         mem, x = x[:, :num_mem], x[:, num_mem:]
@@ -638,13 +723,30 @@ class TransformerWrapper(nn.Module):
 
         if return_mems:
             hiddens = intermediates.hiddens
-            new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
-            new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
+            new_mems = (
+                list(
+                    map(
+                        lambda pair: torch.cat(pair, dim=-2),
+                        zip(mems, hiddens),
+                    )
+                )
+                if exists(mems)
+                else hiddens
+            )
+            new_mems = list(
+                map(
+                    lambda t: t[..., -self.max_mem_len :, :].detach(), new_mems
+                )
+            )
             return out, new_mems
 
         if return_attn:
-            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            attn_maps = list(
+                map(
+                    lambda t: t.post_softmax_attn,
+                    intermediates.attn_intermediates,
+                )
+            )
             return out, attn_maps
 
         return out
-

ldm/util.py

@@ -13,22 +13,25 @@ from queue import Queue
 from inspect import isfunction
 from PIL import Image, ImageDraw, ImageFont
 
+
 def log_txt_as_img(wh, xc, size=10):
     # wh a tuple of (width, height)
     # xc a list of captions to plot
     b = len(xc)
     txts = list()
     for bi in range(b):
-        txt = Image.new("RGB", wh, color="white")
+        txt = Image.new('RGB', wh, color='white')
         draw = ImageDraw.Draw(txt)
         font = ImageFont.load_default()
         nc = int(40 * (wh[0] / 256))
-        lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
+        lines = '\n'.join(
+            xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
+        )
 
         try:
-            draw.text((0, 0), lines, fill="black", font=font)
+            draw.text((0, 0), lines, fill='black', font=font)
         except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
+            print('Cant encode string for logging. Skipping.')
 
         txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
         txts.append(txt)
@@ -70,22 +73,26 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
+        print(
+            f'{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'
+        )
     return total_params
 
 
 def instantiate_from_config(config, **kwargs):
-    if not "target" in config:
+    if not 'target' in config:
         if config == '__is_first_stage__':
             return None
-        elif config == "__is_unconditional__":
+        elif config == '__is_unconditional__':
             return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()), **kwargs)
+        raise KeyError('Expected key `target` to instantiate.')
+    return get_obj_from_str(config['target'])(
+        **config.get('params', dict()), **kwargs
+    )
 
 
 def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
+    module, cls = string.rsplit('.', 1)
     if reload:
         module_imp = importlib.import_module(module)
         importlib.reload(module_imp)
@@ -101,31 +108,36 @@ def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
     else:
         res = func(data)
     Q.put([idx, res])
-    Q.put("Done")
+    Q.put('Done')
 
 
 def parallel_data_prefetch(
-        func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
+    func: callable,
+    data,
+    n_proc,
+    target_data_type='ndarray',
+    cpu_intensive=True,
+    use_worker_id=False,
 ):
     # if target_data_type not in ["ndarray", "list"]:
     #     raise ValueError(
     #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
     #     )
-    if isinstance(data, np.ndarray) and target_data_type == "list":
-        raise ValueError("list expected but function got ndarray.")
+    if isinstance(data, np.ndarray) and target_data_type == 'list':
+        raise ValueError('list expected but function got ndarray.')
     elif isinstance(data, abc.Iterable):
         if isinstance(data, dict):
             print(
                 f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
             )
             data = list(data.values())
-        if target_data_type == "ndarray":
+        if target_data_type == 'ndarray':
             data = np.asarray(data)
         else:
             data = list(data)
     else:
         raise TypeError(
-            f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
+            f'The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}.'
         )
 
     if cpu_intensive:
@@ -135,7 +147,7 @@ def parallel_data_prefetch(
         Q = Queue(1000)
         proc = Thread
     # spawn processes
-    if target_data_type == "ndarray":
+    if target_data_type == 'ndarray':
         arguments = [
             [func, Q, part, i, use_worker_id]
             for i, part in enumerate(np.array_split(data, n_proc))
@@ -149,7 +161,7 @@ def parallel_data_prefetch(
         arguments = [
             [func, Q, part, i, use_worker_id]
             for i, part in enumerate(
-                [data[i: i + step] for i in range(0, len(data), step)]
+                [data[i : i + step] for i in range(0, len(data), step)]
             )
         ]
     processes = []
@@ -158,7 +170,7 @@ def parallel_data_prefetch(
         processes += [p]
 
     # start processes
-    print(f"Start prefetching...")
+    print(f'Start prefetching...')
     import time
 
     start = time.time()
@@ -171,13 +183,13 @@ def parallel_data_prefetch(
         while k < n_proc:
             # get result
             res = Q.get()
-            if res == "Done":
+            if res == 'Done':
                 k += 1
             else:
                 gather_res[res[0]] = res[1]
 
     except Exception as e:
-        print("Exception: ", e)
+        print('Exception: ', e)
         for p in processes:
             p.terminate()
 
@@ -185,7 +197,7 @@ def parallel_data_prefetch(
     finally:
         for p in processes:
             p.join()
-        print(f"Prefetching complete. [{time.time() - start} sec.]")
+        print(f'Prefetching complete. [{time.time() - start} sec.]')
 
     if target_data_type == 'ndarray':
         if not isinstance(gather_res[0], np.ndarray):

notebook_helpers.py

@@ -14,7 +14,7 @@ from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import ismap
 import time
 from omegaconf import OmegaConf
-
+from ldm.dream.devices import choose_torch_device
 
 def download_models(mode):
 
@@ -117,7 +117,8 @@ def get_cond(mode, selected_path):
         c = rearrange(c, '1 c h w -> 1 h w c')
         c = 2. * c - 1.
 
-        c = c.to(torch.device("cuda"))
+        device = choose_torch_device()
+        c = c.to(device)
         example["LR_image"] = c
         example["image"] = c_up
 
@@ -267,4 +268,4 @@ def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, e
     log["sample"] = x_sample
     log["time"] = t1 - t0
 
-    return log
+    return log

requirements.txt

@@ -1,23 +1,23 @@
-accelerate==0.12.0
 albumentations==0.4.3
 einops==0.3.0
 huggingface-hub==0.8.1
 imageio==2.9.0
 imageio-ffmpeg==0.4.2
 kornia==0.6.0
-numpy==1.19.2
+numpy==1.23.1
+--pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
 omegaconf==2.1.1
-opencv-python==4.1.2.30
-pillow==9.0.1
+opencv-python==4.6.0.66
+pillow==9.2.0
 pudb==2019.2
-pytorch
+torch==1.12.1
+torchvision==0.12.0
 pytorch-lightning==1.4.2
 streamlit==1.12.0
 test-tube>=0.7.5
 torch-fidelity==0.3.0
 torchmetrics==0.6.0
-torchvision
 transformers==4.19.2
 -e git+https://github.com/openai/CLIP.git@main#egg=clip
 -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
--e git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
+-e git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion

scripts/dream.py

@@ -3,426 +3,631 @@
 
 import argparse
 import shlex
-import atexit
 import os
+import re
 import sys
 import copy
-from PIL import Image,PngImagePlugin
-
-# readline unavailable on windows systems
-try:
-    import readline
-    readline_available = True
-except:
-    readline_available = False
-
-debugging = False
+import warnings
+import time
+import ldm.dream.readline
+from ldm.dream.pngwriter import PngWriter, PromptFormatter
+from ldm.dream.server import DreamServer, ThreadingDreamServer
+from ldm.dream.image_util import make_grid
+from omegaconf import OmegaConf
 
 def main():
-    ''' Initialize command-line parsers and the diffusion model '''
+    """Initialize command-line parsers and the diffusion model"""
     arg_parser = create_argv_parser()
-    opt        = arg_parser.parse_args()
+    opt = arg_parser.parse_args()
+    
     if opt.laion400m:
-        # defaults suitable to the older latent diffusion weights
-        width   = 256
-        height  = 256
-        config  = "configs/latent-diffusion/txt2img-1p4B-eval.yaml"
-        weights = "models/ldm/text2img-large/model.ckpt"
-    else:
-        # some defaults suitable for stable diffusion weights
-        width   = 512
-        height  = 512
-        config  = "configs/stable-diffusion/v1-inference.yaml"
-        weights = "models/ldm/stable-diffusion-v1/model.ckpt"
+        print('--laion400m flag has been deprecated. Please use --model laion400m instead.')
+        sys.exit(-1)
+    if opt.weights != 'model':
+        print('--weights argument has been deprecated. Please configure ./configs/models.yaml, and call it using --model instead.')
+        sys.exit(-1)
+        
+    try:
+        models  = OmegaConf.load(opt.config)
+        width   = models[opt.model].width
+        height  = models[opt.model].height
+        config  = models[opt.model].config
+        weights = models[opt.model].weights
+    except (FileNotFoundError, IOError, KeyError) as e:
+        print(f'{e}. Aborting.')
+        sys.exit(-1)
 
-    # command line history will be stored in a file called "~/.dream_history"
-    if readline_available:
-        setup_readline()
-
-    print("* Initializing, be patient...\n")
+    print('* Initializing, be patient...\n')
     sys.path.append('.')
     from pytorch_lightning import logging
     from ldm.simplet2i import T2I
+
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
     import transformers
+
     transformers.logging.set_verbosity_error()
-    
+
     # creating a simple text2image object with a handful of
     # defaults passed on the command line.
     # additional parameters will be added (or overriden) during
     # the user input loop
-    t2i = T2I(width=width,
-              height=height,
-              batch_size=opt.batch_size,
-              outdir=opt.outdir,
-              sampler_name=opt.sampler_name,
-              weights=weights,
-              full_precision=opt.full_precision,
-              config=config,
-              latent_diffusion_weights=opt.laion400m, # this is solely for recreating the prompt
-              embedding_path=opt.embedding_path,
-              device=opt.device
+    t2i = T2I(
+        width=width,
+        height=height,
+        sampler_name=opt.sampler_name,
+        weights=weights,
+        full_precision=opt.full_precision,
+        config=config,
+        grid  = opt.grid,
+        # this is solely for recreating the prompt
+        latent_diffusion_weights=opt.laion400m,
+        embedding_path=opt.embedding_path,
+        device_type=opt.device
     )
 
     # make sure the output directory exists
     if not os.path.exists(opt.outdir):
         os.makedirs(opt.outdir)
-        
-    # gets rid of annoying messages about random seed
-    logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
 
+    # gets rid of annoying messages about random seed
+    logging.getLogger('pytorch_lightning').setLevel(logging.ERROR)
+
+    # load the infile as a list of lines
     infile = None
-    try:
-        if opt.infile is not None:
-            infile = open(opt.infile,'r')
-    except FileNotFoundError as e:
-        print(e)
-        exit(-1)
+    if opt.infile:
+        try:
+            if os.path.isfile(opt.infile):
+                infile = open(opt.infile, 'r', encoding='utf-8')
+            elif opt.infile == '-':  # stdin
+                infile = sys.stdin
+            else:
+                raise FileNotFoundError(f'{opt.infile} not found.')
+        except (FileNotFoundError, IOError) as e:
+            print(f'{e}. Aborting.')
+            sys.exit(-1)
 
     # preload the model
+    tic = time.time()
     t2i.load_model()
-    print("\n* Initialization done! Awaiting your command (-h for help, 'q' to quit, 'cd' to change output dir, 'pwd' to print output dir)...")
+    print(
+        f'>> model loaded in', '%4.2fs' % (time.time() - tic)
+    )
 
-    log_path   = os.path.join(opt.outdir,'dream_log.txt')
-    with open(log_path,'a') as log:
-        cmd_parser = create_cmd_parser()
-        main_loop(t2i,cmd_parser,log,infile)
-        log.close()
-    if infile:
-        infile.close()
+    if not infile:
+        print(
+            "\n* Initialization done! Awaiting your command (-h for help, 'q' to quit)"
+        )
+
+    cmd_parser = create_cmd_parser()
+    if opt.web:
+        dream_server_loop(t2i, opt.host, opt.port)
+    else:
+        main_loop(t2i, opt.outdir, opt.prompt_as_dir, cmd_parser, infile)
 
 
-def main_loop(t2i,parser,log,infile):
-    ''' prompt/read/execute loop '''
+def main_loop(t2i, outdir, prompt_as_dir, parser, infile):
+    """prompt/read/execute loop"""
     done = False
-    
+    last_seeds = []
+    path_filter = re.compile(r'[<>:"/\\|?*]')
+
+    # os.pathconf is not available on Windows
+    if hasattr(os, 'pathconf'):
+        path_max = os.pathconf(outdir, 'PC_PATH_MAX')
+        name_max = os.pathconf(outdir, 'PC_NAME_MAX')
+    else:
+        path_max = 260
+        name_max = 255
+
     while not done:
         try:
-            command = infile.readline() if infile else input("dream> ") 
+            command = get_next_command(infile)
         except EOFError:
             done = True
             break
 
-        if infile and len(command)==0:
-            done = True
-            break
+        # skip empty lines
+        if not command.strip():
+            continue
 
-        if command.startswith(('#','//')):
+        if command.startswith(('#', '//')):
             continue
 
         # before splitting, escape single quotes so as not to mess
         # up the parser
-        command = command.replace("'","\\'")
+        command = command.replace("'", "\\'")
 
         try:
             elements = shlex.split(command)
         except ValueError as e:
             print(str(e))
             continue
-        
-        if len(elements)==0:
-            continue
 
-        if elements[0]=='q':
+        if elements[0] == 'q':
             done = True
             break
 
-        if elements[0]=='cd' and len(elements)>1:
-            if os.path.exists(elements[1]):
-                print(f"setting image output directory to {elements[1]}")
-                t2i.outdir=elements[1]
-            else:
-                print(f"directory {elements[1]} does not exist")
-            continue
-
-        if elements[0]=='pwd':
-            print(f"current output directory is {t2i.outdir}")
-            continue
-        
-        if elements[0].startswith('!dream'): # in case a stored prompt still contains the !dream command
+        if elements[0].startswith(
+            '!dream'
+        ):   # in case a stored prompt still contains the !dream command
             elements.pop(0)
-            
+
         # rearrange the arguments to mimic how it works in the Dream bot.
         switches = ['']
         switches_started = False
 
         for el in elements:
-            if el[0]=='-' and not switches_started:
+            if el[0] == '-' and not switches_started:
                 switches_started = True
             if switches_started:
                 switches.append(el)
             else:
                 switches[0] += el
                 switches[0] += ' '
-        switches[0] = switches[0][:len(switches[0])-1]
+        switches[0] = switches[0][: len(switches[0]) - 1]
 
         try:
-            opt      = parser.parse_args(switches)
+            opt = parser.parse_args(switches)
         except SystemExit:
             parser.print_help()
             continue
-        if len(opt.prompt)==0:
-            print("Try again with a prompt!")
+        if len(opt.prompt) == 0:
+            print('Try again with a prompt!')
             continue
+        if opt.seed is not None and opt.seed < 0:   # retrieve previous value!
+            try:
+                opt.seed = last_seeds[opt.seed]
+                print(f'reusing previous seed {opt.seed}')
+            except IndexError:
+                print(f'No previous seed at position {opt.seed} found')
+                opt.seed = None
 
-        try:
-            if opt.init_img is None:
-                results = t2i.txt2img(**vars(opt))
+        do_grid           = opt.grid or t2i.grid
+
+        if opt.with_variations is not None:
+            # shotgun parsing, woo
+            parts = []
+            broken = False # python doesn't have labeled loops...
+            for part in opt.with_variations.split(','):
+                seed_and_weight = part.split(':')
+                if len(seed_and_weight) != 2:
+                    print(f'could not parse with_variation part "{part}"')
+                    broken = True
+                    break
+                try:
+                    seed = int(seed_and_weight[0])
+                    weight = float(seed_and_weight[1])
+                except ValueError:
+                    print(f'could not parse with_variation part "{part}"')
+                    broken = True
+                    break
+                parts.append([seed, weight])
+            if broken:
+                continue
+            if len(parts) > 0:
+                opt.with_variations = parts
             else:
-                assert os.path.exists(opt.init_img),f"No file found at {opt.init_img}. On Linux systems, pressing <tab> after -I will autocomplete a list of possible image files."
-                if None not in (opt.width,opt.height):
-                    print('Warning: width and height options are ignored when modifying an init image')
-                results = t2i.img2img(**vars(opt))
+                opt.with_variations = None
+
+        if opt.outdir:
+            if not os.path.exists(opt.outdir):
+                os.makedirs(opt.outdir)
+            current_outdir = opt.outdir
+        elif prompt_as_dir:
+            # sanitize the prompt to a valid folder name
+            subdir = path_filter.sub('_', opt.prompt)[:name_max].rstrip(' .')
+
+            # truncate path to maximum allowed length
+            # 27 is the length of '######.##########.##.png', plus two separators and a NUL
+            subdir = subdir[:(path_max - 27 - len(os.path.abspath(outdir)))]
+            current_outdir = os.path.join(outdir, subdir)
+
+            print ('Writing files to directory: "' + current_outdir + '"')
+
+            # make sure the output directory exists
+            if not os.path.exists(current_outdir):
+                os.makedirs(current_outdir)
+        else:
+            current_outdir = outdir
+
+        # Here is where the images are actually generated!
+        try:
+            file_writer = PngWriter(current_outdir)
+            prefix = file_writer.unique_prefix()
+            seeds = set()
+            results = [] # list of filename, prompt pairs
+            grid_images = dict() # seed -> Image, only used if `do_grid`
+            def image_writer(image, seed, upscaled=False):
+                if do_grid:
+                    grid_images[seed] = image
+                else:
+                    if upscaled and opt.save_original:
+                        filename = f'{prefix}.{seed}.postprocessed.png'
+                    else:
+                        filename = f'{prefix}.{seed}.png'
+                    if opt.variation_amount > 0:
+                        iter_opt = argparse.Namespace(**vars(opt)) # copy
+                        this_variation = [[seed, opt.variation_amount]]
+                        if opt.with_variations is None:
+                            iter_opt.with_variations = this_variation
+                        else:
+                            iter_opt.with_variations = opt.with_variations + this_variation
+                        iter_opt.variation_amount = 0
+                        normalized_prompt = PromptFormatter(t2i, iter_opt).normalize_prompt()
+                        metadata_prompt = f'{normalized_prompt} -S{iter_opt.seed}'
+                    elif opt.with_variations is not None:
+                        normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
+                        metadata_prompt = f'{normalized_prompt} -S{opt.seed}' # use the original seed - the per-iteration value is the last variation-seed
+                    else:
+                        normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
+                        metadata_prompt = f'{normalized_prompt} -S{seed}'
+                    path = file_writer.save_image_and_prompt_to_png(image, metadata_prompt, filename)
+                    if (not upscaled) or opt.save_original:
+                        # only append to results if we didn't overwrite an earlier output
+                        results.append([path, metadata_prompt])
+
+                seeds.add(seed)
+
+            t2i.prompt2image(image_callback=image_writer, **vars(opt))
+
+            if do_grid and len(grid_images) > 0:
+                grid_img = make_grid(list(grid_images.values()))
+                first_seed = next(iter(seeds))
+                filename = f'{prefix}.{first_seed}.png'
+                # TODO better metadata for grid images
+                normalized_prompt = PromptFormatter(t2i, opt).normalize_prompt()
+                metadata_prompt = f'{normalized_prompt} -S{first_seed} --grid -N{len(grid_images)}'
+                path = file_writer.save_image_and_prompt_to_png(
+                    grid_img, metadata_prompt, filename
+                )
+                results = [[path, metadata_prompt]]
+
+            last_seeds = list(seeds)
+
         except AssertionError as e:
             print(e)
             continue
 
+        except OSError as e:
+            print(e)
+            continue
 
-        allVariantResults = []
-        if opt.variants is not None:
-            print(f"Generating {opt.variants} variant(s)...")
-            newopt = copy.deepcopy(opt)
-            newopt.variants = None
-            for r in results:
-                newopt.init_img = r[0]
-                print(f"\t generating variant for {newopt.init_img}")
-                for j in range(0, opt.variants):
-                    try:
-                        variantResults = t2i.img2img(**vars(newopt))
-                        allVariantResults.append([newopt,variantResults])
-                    except AssertionError as e:
-                        print(e)
-                        continue
-            print(f"{opt.variants} Variants generated!")
+        print('Outputs:')
+        log_path = os.path.join(current_outdir, 'dream_log.txt')
+        write_log_message(results, log_path)
 
-        print("Outputs:")
-        write_log_message(t2i,opt,results,log)
-            
-        if allVariantResults:
-            print("Variant outputs:")
-            for vr in allVariantResults:
-                write_log_message(t2i,vr[0],vr[1],log)
-            
-
-    print("goodbye!")
+    print('goodbye!')
 
 
-def write_log_message(t2i,opt,results,logfile):
-    ''' logs the name of the output image, its prompt and seed to the terminal, log file, and a Dream text chunk in the PNG metadata '''
-    switches = _reconstruct_switches(t2i,opt)
-    prompt_str = ' '.join(switches)
-
-    # when multiple images are produced in batch, then we keep track of where each starts
-    last_seed  = None
-    img_num    = 1
-    batch_size = opt.batch_size or t2i.batch_size
-    seenit     = {}
-
-    seeds = [a[1] for a in results]
-    if batch_size > 1:
-        seeds = f"(seeds for each batch row: {seeds})"
+def get_next_command(infile=None) -> str: #command string
+    if infile is None:
+        command = input('dream> ')
     else:
-        seeds = f"(seeds for individual images: {seeds})"
+        command = infile.readline()
+        if not command:
+            raise EOFError
+        else:
+            command = command.strip()
+        print(f'#{command}')
+    return command
 
-    for r in results:
-        seed = r[1]
-        log_message = (f'{r[0]}: {prompt_str} -S{seed}')
+def dream_server_loop(t2i, host, port):
+    print('\n* --web was specified, starting web server...')
+    # Change working directory to the stable-diffusion directory
+    os.chdir(
+        os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+    )
 
-        if batch_size > 1:
-            if seed != last_seed:
-                img_num = 1
-                log_message += f' # (batch image {img_num} of {batch_size})'
-            else:
-                img_num += 1
-                log_message += f' # (batch image {img_num} of {batch_size})'
-            last_seed = seed
-        print(log_message)
-        logfile.write(log_message+"\n")
-        logfile.flush()
-        if r[0] not in seenit:
-            seenit[r[0]] = True
-            try:
-                if opt.grid:
-                    _write_prompt_to_png(r[0],f'{prompt_str} -g -S{seed} {seeds}')
-                else:
-                    _write_prompt_to_png(r[0],f'{prompt_str} -S{seed}')
-            except FileNotFoundError:
-                print(f"Could not open file '{r[0]}' for reading")
+    # Start server
+    DreamServer.model = t2i
+    dream_server = ThreadingDreamServer((host, port))
+    print(">> Started Stable Diffusion dream server!")
+    if host == '0.0.0.0':
+        print(f"Point your browser at http://localhost:{port} or use the host's DNS name or IP address.")
+    else:
+        print(">> Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address.")
+        print(f">> Point your browser at http://{host}:{port}.")
 
-def _reconstruct_switches(t2i,opt):
-    '''Normalize the prompt and switches'''
-    switches = list()
-    switches.append(f'"{opt.prompt}"')
-    switches.append(f'-s{opt.steps        or t2i.steps}')
-    switches.append(f'-b{opt.batch_size   or t2i.batch_size}')
-    switches.append(f'-W{opt.width        or t2i.width}')
-    switches.append(f'-H{opt.height       or t2i.height}')
-    switches.append(f'-C{opt.cfg_scale    or t2i.cfg_scale}')
-    switches.append(f'-m{t2i.sampler_name}')
-    if opt.variants:
-        switches.append(f'-v{opt.variants}')
-    if opt.init_img:
-        switches.append(f'-I{opt.init_img}')
-    if opt.strength and opt.init_img is not None:
-        switches.append(f'-f{opt.strength or t2i.strength}')
-    if t2i.full_precision:
-        switches.append('-F')
-    return switches
+    try:
+        dream_server.serve_forever()
+    except KeyboardInterrupt:
+        pass
+
+    dream_server.server_close()
+
+
+def write_log_message(results, log_path):
+    """logs the name of the output image, prompt, and prompt args to the terminal and log file"""
+    log_lines = [f'{path}: {prompt}\n' for path, prompt in results]
+    print(*log_lines, sep='')
+
+    with open(log_path, 'a', encoding='utf-8') as file:
+        file.writelines(log_lines)
+
+
+SAMPLER_CHOICES=[
+    'ddim',
+    'k_dpm_2_a',
+    'k_dpm_2',
+    'k_euler_a',
+    'k_euler',
+    'k_heun',
+    'k_lms',
+    'plms',
+]
 
-def _write_prompt_to_png(path,prompt):
-    info = PngImagePlugin.PngInfo()
-    info.add_text("Dream",prompt)
-    im = Image.open(path)
-    im.save(path,"PNG",pnginfo=info)
-    
 def create_argv_parser():
-    parser = argparse.ArgumentParser(description="Parse script's command line args")
-    parser.add_argument("--laion400m",
-                        "--latent_diffusion",
-                        "-l",
-                        dest='laion400m',
-                        action='store_true',
-                        help="fallback to the latent diffusion (laion400m) weights and config")
-    parser.add_argument("--from_file",
-                        dest='infile',
-                        type=str,
-                        help="if specified, load prompts from this file")
-    parser.add_argument('-n','--iterations',
-                        type=int,
-                        default=1,
-                        help="number of images to generate")
-    parser.add_argument('-F','--full_precision',
-                        dest='full_precision',
-                        action='store_true',
-                        help="use slower full precision math for calculations")
-    parser.add_argument('-b','--batch_size',
-                        type=int,
-                        default=1,
-                        help="number of images to produce per iteration (faster, but doesn't generate individual seeds")
-    parser.add_argument('--sampler','-m',
-                        dest="sampler_name",
-                        choices=['ddim', 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms', 'plms'],
-                        default='k_lms',
-                        help="which sampler to use (k_lms) - can only be set on command line")
-    parser.add_argument('--outdir',
-                        '-o',
-                        type=str,
-                        default="outputs/img-samples",
-                        help="directory in which to place generated images and a log of prompts and seeds")
-    parser.add_argument('--embedding_path',
-                        type=str,
-                        help="Path to a pre-trained embedding manager checkpoint - can only be set on command line")
-    parser.add_argument('--device',
-                        '-d',
-                        type=str,
-                        default="cuda",
-                        help="device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if avalible")
+    parser = argparse.ArgumentParser(
+        description="""Generate images using Stable Diffusion.
+        Use --web to launch the web interface. 
+        Use --from_file to load prompts from a file path or standard input ("-").
+        Otherwise you will be dropped into an interactive command prompt (type -h for help.)
+        Other command-line arguments are defaults that can usually be overridden
+        prompt the command prompt.
+"""
+    )
+    parser.add_argument(
+        '--laion400m',
+        '--latent_diffusion',
+        '-l',
+        dest='laion400m',
+        action='store_true',
+        help='Fallback to the latent diffusion (laion400m) weights and config',
+    )
+    parser.add_argument(
+        '--from_file',
+        dest='infile',
+        type=str,
+        help='If specified, load prompts from this file',
+    )
+    parser.add_argument(
+        '-n',
+        '--iterations',
+        type=int,
+        default=1,
+        help='Number of images to generate',
+    )
+    parser.add_argument(
+        '-F',
+        '--full_precision',
+        dest='full_precision',
+        action='store_true',
+        help='Use more memory-intensive full precision math for calculations',
+    )
+    parser.add_argument(
+        '-g',
+        '--grid',
+        action='store_true',
+        help='Generate a grid instead of individual images',
+    )
+    parser.add_argument(
+        '-A',
+        '-m',
+        '--sampler',
+        dest='sampler_name',
+        choices=SAMPLER_CHOICES,
+        metavar='SAMPLER_NAME',
+        default='k_lms',
+        help=f'Set the initial sampler. Default: k_lms. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
+    )
+    parser.add_argument(
+        '--outdir',
+        '-o',
+        type=str,
+        default='outputs/img-samples',
+        help='Directory to save generated images and a log of prompts and seeds. Default: outputs/img-samples',
+    )
+    parser.add_argument(
+        '--embedding_path',
+        type=str,
+        help='Path to a pre-trained embedding manager checkpoint - can only be set on command line',
+    )
+    parser.add_argument(
+        '--prompt_as_dir',
+        '-p',
+        action='store_true',
+        help='Place images in subdirectories named after the prompt.',
+    )
+    # GFPGAN related args
+    parser.add_argument(
+        '--gfpgan_bg_upsampler',
+        type=str,
+        default='realesrgan',
+        help='Background upsampler. Default: realesrgan. Options: realesrgan, none. Only used if --gfpgan is specified',
+
+    )
+    parser.add_argument(
+        '--gfpgan_bg_tile',
+        type=int,
+        default=400,
+        help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
+    )
+    parser.add_argument(
+        '--gfpgan_model_path',
+        type=str,
+        default='experiments/pretrained_models/GFPGANv1.3.pth',
+        help='Indicates the path to the GFPGAN model, relative to --gfpgan_dir.',
+    )
+    parser.add_argument(
+        '--gfpgan_dir',
+        type=str,
+        default='../GFPGAN',
+        help='Indicates the directory containing the GFPGAN code.',
+    )
+    parser.add_argument(
+        '--web',
+        dest='web',
+        action='store_true',
+        help='Start in web server mode.',
+    )
+    parser.add_argument(
+        '--host',
+        type=str,
+        default='127.0.0.1',
+        help='Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.'
+    )
+    parser.add_argument(
+        '--port',
+        type=int,
+        default='9090',
+        help='Web server: Port to listen on'
+    )
+    parser.add_argument(
+        '--weights',
+        default='model',
+        help='Indicates the Stable Diffusion model to use.',
+    )
+    parser.add_argument(
+        '--device',
+        '-d',
+        type=str,
+        default='cuda',
+        help="device to run stable diffusion on. defaults to cuda `torch.cuda.current_device()` if available"
+    )
+    parser.add_argument(
+        '--model',
+        default='stable-diffusion-1.4',
+        help='Indicates which diffusion model to load. (currently "stable-diffusion-1.4" (default) or "laion400m")',
+    )
+    parser.add_argument(
+        '--config',
+        default ='configs/models.yaml',
+        help    ='Path to configuration file for alternate models.',
+    )
     return parser
-                        
-    
+
+
 def create_cmd_parser():
-    parser = argparse.ArgumentParser(description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12')
+    parser = argparse.ArgumentParser(
+        description='Example: dream> a fantastic alien landscape -W1024 -H960 -s100 -n12'
+    )
     parser.add_argument('prompt')
-    parser.add_argument('-s','--steps',type=int,help="number of steps")
-    parser.add_argument('-S','--seed',type=int,help="image seed")
-    parser.add_argument('-n','--iterations',type=int,default=1,help="number of samplings to perform (slower, but will provide seeds for individual images)")
-    parser.add_argument('-b','--batch_size',type=int,default=1,help="number of images to produce per sampling (will not provide seeds for individual images!)")
-    parser.add_argument('-W','--width',type=int,help="image width, multiple of 64")
-    parser.add_argument('-H','--height',type=int,help="image height, multiple of 64")
-    parser.add_argument('-C','--cfg_scale',default=7.5,type=float,help="prompt configuration scale")
-    parser.add_argument('-g','--grid',action='store_true',help="generate a grid")
-    parser.add_argument('-i','--individual',action='store_true',help="generate individual files (default)")
-    parser.add_argument('-I','--init_img',type=str,help="path to input image for img2img mode (supersedes width and height)")
-    parser.add_argument('-f','--strength',default=0.75,type=float,help="strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely")
-    parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
-    parser.add_argument('-x','--skip_normalize',action='store_true',help="skip subprompt weight normalization")
+    parser.add_argument('-s', '--steps', type=int, help='Number of steps')
+    parser.add_argument(
+        '-S',
+        '--seed',
+        type=int,
+        help='Image seed; a +ve integer, or use -1 for the previous seed, -2 for the one before that, etc',
+    )
+    parser.add_argument(
+        '-n',
+        '--iterations',
+        type=int,
+        default=1,
+        help='Number of samplings to perform (slower, but will provide seeds for individual images)',
+    )
+    parser.add_argument(
+        '-W', '--width', type=int, help='Image width, multiple of 64'
+    )
+    parser.add_argument(
+        '-H', '--height', type=int, help='Image height, multiple of 64'
+    )
+    parser.add_argument(
+        '-C',
+        '--cfg_scale',
+        default=7.5,
+        type=float,
+        help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
+    )
+    parser.add_argument(
+        '-g', '--grid', action='store_true', help='generate a grid'
+    )
+    parser.add_argument(
+        '--outdir',
+        '-o',
+        type=str,
+        default=None,
+        help='Directory to save generated images and a log of prompts and seeds',
+    )
+    parser.add_argument(
+        '-i',
+        '--individual',
+        action='store_true',
+        help='Generate individual files (default)',
+    )
+    parser.add_argument(
+        '-I',
+        '--init_img',
+        type=str,
+        help='Path to input image for img2img mode (supersedes width and height)',
+    )
+    parser.add_argument(
+        '-T',
+        '-fit',
+        '--fit',
+        action='store_true',
+        help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
+    )
+    parser.add_argument(
+        '-f',
+        '--strength',
+        default=0.75,
+        type=float,
+        help='Strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
+    )
+    parser.add_argument(
+        '-G',
+        '--gfpgan_strength',
+        default=0,
+        type=float,
+        help='The strength at which to apply the GFPGAN model to the result, in order to improve faces.',
+    )
+    parser.add_argument(
+        '-U',
+        '--upscale',
+        nargs='+',
+        default=None,
+        type=float,
+        help='Scale factor (2, 4) for upscaling followed by upscaling strength (0-1.0). If strength not specified, defaults to 0.75'
+    )
+    parser.add_argument(
+        '-save_orig',
+        '--save_original',
+        action='store_true',
+        help='Save original. Use it when upscaling to save both versions.',
+    )
+    # variants is going to be superseded by a generalized "prompt-morph" function
+    #    parser.add_argument('-v','--variants',type=int,help="in img2img mode, the first generated image will get passed back to img2img to generate the requested number of variants")
+    parser.add_argument(
+        '-x',
+        '--skip_normalize',
+        action='store_true',
+        help='Skip subprompt weight normalization',
+    )
+    parser.add_argument(
+        '-A',
+        '-m',
+        '--sampler',
+        dest='sampler_name',
+        default=None,
+        type=str,
+        choices=SAMPLER_CHOICES,
+        metavar='SAMPLER_NAME',
+        help=f'Switch to a different sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
+    )
+    parser.add_argument(
+        '-t',
+        '--log_tokenization',
+        action='store_true',
+        help='shows how the prompt is split into tokens'
+    )
+    parser.add_argument(
+        '-v',
+        '--variation_amount',
+        default=0.0,
+        type=float,
+        help='If > 0, generates variations on the initial seed instead of random seeds per iteration. Must be between 0 and 1. Higher values will be more different.'
+    )
+    parser.add_argument(
+        '-V',
+        '--with_variations',
+        default=None,
+        type=str,
+        help='list of variations to apply, in the format `seed:weight,seed:weight,...'
+    )
     return parser
 
-if readline_available:
-    def setup_readline():
-        readline.set_completer(Completer(['cd','pwd',
-                                          '--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
-                                          '--width','-W','--height','-H','--cfg_scale','-C','--grid','-g',
-                                          '--individual','-i','--init_img','-I','--strength','-f','-v','--variants']).complete)
-        readline.set_completer_delims(" ")
-        readline.parse_and_bind('tab: complete')
-        load_history()
 
-    def load_history():
-        histfile = os.path.join(os.path.expanduser('~'),".dream_history")
-        try:
-            readline.read_history_file(histfile)
-            readline.set_history_length(1000)
-        except FileNotFoundError:
-            pass
-        atexit.register(readline.write_history_file,histfile)
-
-    class Completer():
-        def __init__(self,options):
-            self.options = sorted(options)
-            return
-
-        def complete(self,text,state):
-            buffer = readline.get_line_buffer()
-            
-            if text.startswith(('-I','--init_img')):
-                return self._path_completions(text,state,('.png'))
-
-            if buffer.strip().endswith('cd') or text.startswith(('.','/')):
-                return self._path_completions(text,state,())
-
-            response = None
-            if state == 0:
-                # This is the first time for this text, so build a match list.
-                if text:
-                    self.matches = [s 
-                                    for s in self.options
-                                    if s and s.startswith(text)]
-                else:
-                    self.matches = self.options[:]
-
-            # Return the state'th item from the match list,
-            # if we have that many.
-            try:
-                response = self.matches[state]
-            except IndexError:
-                response = None
-            return response
-
-        def _path_completions(self,text,state,extensions):
-            # get the path so far
-            if text.startswith('-I'):
-                path = text.replace('-I','',1).lstrip()
-            elif text.startswith('--init_img='):
-                path = text.replace('--init_img=','',1).lstrip()
-            else:
-                path = text
-
-            matches  = list()
-
-            path = os.path.expanduser(path)
-            if len(path)==0:
-                matches.append(text+'./')
-            else:
-                dir  = os.path.dirname(path)
-                dir_list = os.listdir(dir)
-                for n in dir_list:
-                    if n.startswith('.') and len(n)>1:
-                        continue
-                    full_path = os.path.join(dir,n)
-                    if full_path.startswith(path):
-                        if os.path.isdir(full_path):
-                            matches.append(os.path.join(os.path.dirname(text),n)+'/')
-                        elif n.endswith(extensions):
-                            matches.append(os.path.join(os.path.dirname(text),n))
-
-            try:
-                response = matches[state]
-            except IndexError:
-                response = None
-            return response
-
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
-

scripts/inpaint.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 from main import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
-
+from ldm.dream.devices import choose_torch_device
 
 def make_batch(image, mask, device):
     image = np.array(Image.open(image).convert("RGB"))
@@ -61,8 +61,8 @@ if __name__ == "__main__":
     model.load_state_dict(torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"],
                           strict=False)
 
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
+    device  = choose_torch_device()
+    model   = model.to(device)
     sampler = DDIMSampler(model)
 
     os.makedirs(opt.outdir, exist_ok=True)

scripts/merge_embeddings.py

@@ -1,4 +1,4 @@
-from ldm.modules.encoders.modules import BERTTokenizer
+from ldm.modules.encoders.modules import FrozenCLIPEmbedder, BERTEmbedder
 from ldm.modules.embedding_manager import EmbeddingManager
 
 import argparse, os
@@ -6,7 +6,7 @@ from functools import partial
 
 import torch
 
-def get_placeholder_loop(placeholder_string, tokenizer):
+def get_placeholder_loop(placeholder_string, embedder, use_bert):
     
     new_placeholder   = None
     
@@ -16,10 +16,36 @@ def get_placeholder_loop(placeholder_string, tokenizer):
         else:
             new_placeholder = input(f"Placeholder string '{new_placeholder}' maps to more than a single token. Please enter another string: ")
 
-        token = tokenizer(new_placeholder)
+        token = get_bert_token_for_string(embedder.tknz_fn, new_placeholder) if use_bert else get_clip_token_for_string(embedder.tokenizer, new_placeholder)
+
+        if token is not None:
+            return new_placeholder, token
+            
+def get_clip_token_for_string(tokenizer, string):
+    batch_encoding = tokenizer(
+        string,
+        truncation=True,
+        max_length=77,
+        return_length=True,
+        return_overflowing_tokens=False,
+        padding="max_length",
+        return_tensors="pt"
+    )
+
+    tokens = batch_encoding["input_ids"]
+
+    if torch.count_nonzero(tokens - 49407) == 2:
+        return tokens[0, 1]
+    
+    return None
+
+def get_bert_token_for_string(tokenizer, string):
+    token = tokenizer(string)
+    if torch.count_nonzero(token) == 3:
+        return token[0, 1]
+
+    return None
 
-        if torch.count_nonzero(token) == 3:
-            return new_placeholder, token[0, 1]
 
 if __name__ == "__main__":
 
@@ -40,10 +66,20 @@ if __name__ == "__main__":
         help="Output path for the merged manager",
     )
 
+    parser.add_argument(
+        "-sd", "--use_bert",
+        action="store_true",
+        help="Flag to denote that we are not merging stable diffusion embeddings"
+    )
+
     args = parser.parse_args()
 
-    tokenizer = BERTTokenizer(vq_interface=False, max_length=77)
-    EmbeddingManager = partial(EmbeddingManager, tokenizer, ["*"])
+    if args.use_bert:
+        embedder = BERTEmbedder(n_embed=1280, n_layer=32).cuda()
+    else:
+        embedder = FrozenCLIPEmbedder().cuda()
+
+    EmbeddingManager = partial(EmbeddingManager, embedder, ["*"])
 
     string_to_token_dict = {}    
     string_to_param_dict = torch.nn.ParameterDict()
@@ -63,7 +99,7 @@ if __name__ == "__main__":
 
                 placeholder_to_src[placeholder_string] = manager_ckpt
             else:
-                new_placeholder, new_token = get_placeholder_loop(placeholder_string, tokenizer)
+                new_placeholder, new_token = get_placeholder_loop(placeholder_string, embedder, use_bert=args.use_bert)
                 string_to_token_dict[new_placeholder] = new_token
                 string_to_param_dict[new_placeholder] = manager.string_to_param_dict[placeholder_string]
 
@@ -77,7 +113,3 @@ if __name__ == "__main__":
 
     print("Managers merged. Final list of placeholders: ")
     print(placeholder_to_src)
-
-
-
-                

scripts/orig_scripts/img2img.py

@@ -18,6 +18,7 @@ from pytorch_lightning import seed_everything
 from ldm.util import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
+from ldm.dream.devices         import choose_torch_device
 
 
 def chunk(it, size):
@@ -40,7 +41,7 @@ def load_model_from_config(config, ckpt, verbose=False):
         print("unexpected keys:")
         print(u)
 
-    model.cuda()
+    model.to(choose_torch_device())
     model.eval()
     return model
 
@@ -199,7 +200,7 @@ def main():
     config = OmegaConf.load(f"{opt.config}")
     model = load_model_from_config(config, f"{opt.ckpt}")
 
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    device = torch.device(choose_torch_device())
     model = model.to(device)
 
     if opt.plms:
@@ -241,8 +242,10 @@ def main():
     print(f"target t_enc is {t_enc} steps")
 
     precision_scope = autocast if opt.precision == "autocast" else nullcontext
+    if device.type in ['mps', 'cpu']:
+        precision_scope = nullcontext # have to use f32 on mps
     with torch.no_grad():
-        with precision_scope("cuda"):
+        with precision_scope(device.type):
             with model.ema_scope():
                 tic = time.time()
                 all_samples = list()

scripts/orig_scripts/txt2img.py

@@ -12,14 +12,13 @@ from pytorch_lightning import seed_everything
 from torch import autocast
 from contextlib import contextmanager, nullcontext
 
-import accelerate
 import k_diffusion as K
 import torch.nn as nn
 
-from ldm.util import instantiate_from_config
+from ldm.util                  import instantiate_from_config
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
-
+from ldm.dream.devices         import choose_torch_device
 
 def chunk(it, size):
     it = iter(it)
@@ -41,7 +40,7 @@ def load_model_from_config(config, ckpt, verbose=False):
         print("unexpected keys:")
         print(u)
 
-    model.cuda()
+    model.to(choose_torch_device())
     model.eval()
     return model
 
@@ -191,18 +190,17 @@ def main():
         opt.ckpt = "models/ldm/text2img-large/model.ckpt"
         opt.outdir = "outputs/txt2img-samples-laion400m"
 
-    seed_everything(opt.seed)
 
     config = OmegaConf.load(f"{opt.config}")
     model = load_model_from_config(config, f"{opt.ckpt}")
 
-    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-    model = model.to(device)
+    seed_everything(opt.seed)
+
+    device = torch.device(choose_torch_device())
+    model  = model.to(device)
 
     #for klms
     model_wrap = K.external.CompVisDenoiser(model)
-    accelerator = accelerate.Accelerator()
-    device = accelerator.device
     class CFGDenoiser(nn.Module):
         def __init__(self, model):
             super().__init__()
@@ -243,16 +241,22 @@ def main():
 
     start_code = None
     if opt.fixed_code:
-        start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
+        shape = [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f]
+        if device.type == 'mps':
+            start_code = torch.randn(shape, device='cpu').to(device)
+        else:
+            torch.randn(shape, device=device)
 
     precision_scope = autocast if opt.precision=="autocast" else nullcontext
+    if device.type in ['mps', 'cpu']:
+        precision_scope = nullcontext # have to use f32 on mps
     with torch.no_grad():
-        with precision_scope("cuda"):
+        with precision_scope(device.type):
             with model.ema_scope():
                 tic = time.time()
                 all_samples = list()
-                for n in trange(opt.n_iter, desc="Sampling", disable =not accelerator.is_main_process):
-                    for prompts in tqdm(data, desc="data", disable =not accelerator.is_main_process):
+                for n in trange(opt.n_iter, desc="Sampling"):
+                    for prompts in tqdm(data, desc="data"):
                         uc = None
                         if opt.scale != 1.0:
                             uc = model.get_learned_conditioning(batch_size * [""])
@@ -279,13 +283,10 @@ def main():
                                 x = torch.randn([opt.n_samples, *shape], device=device) * sigmas[0] # for GPU draw
                             model_wrap_cfg = CFGDenoiser(model_wrap)
                             extra_args = {'cond': c, 'uncond': uc, 'cond_scale': opt.scale}
-                            samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not accelerator.is_main_process)
+                            samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args)
                         
                         x_samples_ddim = model.decode_first_stage(samples_ddim)
                         x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-                        
-                        if opt.klms:
-                            x_sample = accelerator.gather(x_samples_ddim)
 
                         if not opt.skip_save:
                             for x_sample in x_samples_ddim:

scripts/preload_models.py

@@ -3,32 +3,84 @@
 # Before running stable-diffusion on an internet-isolated machine,
 # run this script from one with internet connectivity. The
 # two machines must share a common .cache directory.
+from transformers import CLIPTokenizer, CLIPTextModel
+import clip
+from transformers import BertTokenizerFast
 import sys
 import transformers
+import os
+import warnings
 
 transformers.logging.set_verbosity_error()
 
 # this will preload the Bert tokenizer fles
-print("preloading bert tokenizer...")
-from transformers import BertTokenizerFast
-tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
-print("...success")
+print('preloading bert tokenizer...')
+
+tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
+print('...success')
 
 # this will download requirements for Kornia
-print("preloading Kornia requirements (ignore the warnings)...")
-import kornia
-print("...success")
+print('preloading Kornia requirements (ignore the deprecation warnings)...')
+with warnings.catch_warnings():
+    warnings.filterwarnings('ignore', category=DeprecationWarning)
+    import kornia
+print('...success')
 
-# doesn't work - probably wrong logger
-# logging.getLogger('transformers.tokenization_utils').setLevel(logging.ERROR)
-version='openai/clip-vit-large-patch14'
+version = 'openai/clip-vit-large-patch14'
 
-print('preloading CLIP model (Ignore the warnings)...')
+print('preloading CLIP model (Ignore the deprecation warnings)...')
 sys.stdout.flush()
-import clip
-from transformers import CLIPTokenizer, CLIPTextModel
-tokenizer  =CLIPTokenizer.from_pretrained(version)
-transformer=CLIPTextModel.from_pretrained(version)
+
+tokenizer = CLIPTokenizer.from_pretrained(version)
+transformer = CLIPTextModel.from_pretrained(version)
 print('\n\n...success')
 
+# In the event that the user has installed GFPGAN and also elected to use
+# RealESRGAN, this will attempt to download the model needed by RealESRGANer
+gfpgan = False
+try:
+    from realesrgan import RealESRGANer
 
+    gfpgan = True
+except ModuleNotFoundError:
+    pass
+
+if gfpgan:
+    print('Loading models from RealESRGAN and facexlib')
+    try:
+        from basicsr.archs.rrdbnet_arch import RRDBNet
+        from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+
+        RealESRGANer(
+            scale=2,
+            model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
+            model=RRDBNet(
+                num_in_ch=3,
+                num_out_ch=3,
+                num_feat=64,
+                num_block=23,
+                num_grow_ch=32,
+                scale=2,
+            ),
+        )
+
+        RealESRGANer(
+            scale=4,
+            model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
+            model=RRDBNet(
+                num_in_ch=3,
+                num_out_ch=3,
+                num_feat=64,
+                num_block=23,
+                num_grow_ch=32,
+                scale=4,
+            ),
+        )
+
+        FaceRestoreHelper(1, det_model='retinaface_resnet50')
+        print('...success')
+    except Exception:
+        import traceback
+
+        print('Error loading GFPGAN:')
+        print(traceback.format_exc())

static/dream_web/index.css

@@ -0,0 +1,97 @@
+* {
+    font-family: 'Arial';
+}
+#header {
+    text-decoration: dotted underline;
+}
+#search {
+    margin-top: 20vh;
+    margin-left: auto;
+    margin-right: auto;
+    max-width: 1024px;
+    text-align: center;
+}
+fieldset {
+    border: none;
+}
+div {
+    padding: 10px 10px 10px 10px;
+}
+#fieldset-search {
+    display: flex;
+}
+#scaling-inprocess-message{
+    font-weight: bold;
+    font-style: italic;
+    display: none;
+}
+#prompt {
+    flex-grow: 1;
+
+    border-radius: 20px 0px 0px 20px;
+    padding: 5px 10px 5px 10px;
+    border: 1px solid black;
+    border-right: none;
+    outline: none;
+}
+#submit {
+    border-radius: 0px 20px 20px 0px;
+    padding: 5px 10px 5px 10px;
+    border: 1px solid black;
+}
+#reset-all {
+    background-color: pink;
+}
+#results {
+    text-align: center;
+//    max-width: 1024px;
+    margin: auto;
+    padding-top: 10px;
+}
+#results img {
+    cursor: pointer;
+    height: 30vh;
+    border-radius: 5px;
+    margin: 10px;
+}
+#fieldset-config {
+    line-height:2em;
+}
+input[type="number"] {
+    width: 60px;
+}
+#seed {
+    width: 150px;
+}
+hr {
+//    width: 200px;
+}
+label {
+    white-space: nowrap;
+}
+#progress-section {
+    display: none;
+}
+#progress-image {
+    width: 30vh;
+    height: 30vh;
+}
+#cancel-button {
+    cursor: pointer;
+    color: red;
+}
+#txt2img {
+    background-color: #DCDCDC;
+}
+#img2img {
+    background-color: #F5F5F5;
+}
+#gfpgan {
+    background-color: #DCDCDC;
+}
+#progress-section {
+    background-color: #F5F5F5;
+}
+#about {
+    background-color: #DCDCDC;
+}

static/dream_web/index.html

@@ -0,0 +1,111 @@
+<html lang="en">
+  <head>
+    <title>Stable Diffusion Dream Server</title>
+    <meta charset="utf-8">
+    <link rel="icon" href="data:,">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+    <link rel="stylesheet" href="static/dream_web/index.css">
+    <script src="config.js"></script>
+    <script src="static/dream_web/index.js"></script>
+  </head>
+  <body>
+    <div id="search">
+      <h2 id="header">Stable Diffusion Dream Server</h2>
+
+      <form id="generate-form" method="post" action="#">
+	<div id="txt2img">
+          <fieldset id="fieldset-search">
+            <input type="text" id="prompt" name="prompt">
+            <input type="submit" id="submit" value="Generate">
+          </fieldset>
+          <fieldset id="fieldset-config">
+            <label for="iterations">Images to generate:</label>
+            <input value="1" type="number" id="iterations" name="iterations" size="4">
+            <label for="steps">Steps:</label>
+            <input value="50" type="number" id="steps" name="steps">
+            <label for="cfgscale">Cfg Scale:</label>
+            <input value="7.5" type="number" id="cfgscale" name="cfgscale" step="any">
+            <label for="sampler">Sampler:</label>
+            <select id="sampler" name="sampler" value="k_lms">
+              <option value="ddim">DDIM</option>
+              <option value="plms">PLMS</option>
+              <option value="k_lms" selected>KLMS</option>
+              <option value="k_dpm_2">KDPM_2</option>
+              <option value="k_dpm_2_a">KDPM_2A</option>
+              <option value="k_euler">KEULER</option>
+	      <option value="k_euler_a">KEULER_A</option>
+              <option value="k_heun">KHEUN</option>
+            </select>
+            <br>
+            <label title="Set to multiple of 64" for="width">Width:</label>
+            <select id="width" name="width" value="512">
+              <option value="64">64</option> <option value="128">128</option>
+              <option value="192">192</option> <option value="256">256</option>
+              <option value="320">320</option> <option value="384">384</option>
+              <option value="448">448</option> <option value="512" selected>512</option>
+              <option value="576">576</option> <option value="640">640</option>
+              <option value="704">704</option> <option value="768">768</option>
+              <option value="832">832</option> <option value="896">896</option>
+              <option value="960">960</option> <option value="1024">1024</option>
+            </select>
+            <label title="Set to multiple of 64" for="height">Height:</label>
+            <select id="height" name="height" value="512">
+              <option value="64">64</option> <option value="128">128</option>
+              <option value="192">192</option> <option value="256">256</option>
+              <option value="320">320</option> <option value="384">384</option>
+              <option value="448">448</option> <option value="512" selected>512</option>
+              <option value="576">576</option> <option value="640">640</option>
+              <option value="704">704</option> <option value="768">768</option>
+              <option value="832">832</option> <option value="896">896</option>
+              <option value="960">960</option> <option value="1024">1024</option>
+            </select>
+            <label title="Set to -1 for random seed" for="seed">Seed:</label>
+            <input value="-1" type="number" id="seed" name="seed">
+            <button type="button" id="reset-seed">&olarr;</button>
+            <input type="checkbox" name="progress_images" id="progress_images">
+	    <label for="progress_images">Display in-progress images (slows down generation):</label>
+	    <button type="button" id="reset-all">Reset to Defaults</button>
+	</div>
+	<div id="img2img">
+          <label title="Upload an image to use img2img" for="initimg">Initial image:</label>
+          <input type="file" id="initimg" name="initimg" accept=".jpg, .jpeg, .png">
+	  <br>
+          <label for="strength">Img2Img Strength:</label>
+          <input value="0.75" type="number" id="strength" name="strength" step="0.01" min="0" max="1">
+          <input type="checkbox" id="fit" name="fit" checked>
+          <label title="Rescale image to fit within requested width and height" for="fit">Fit to width/height:</label>
+	</div>
+        <div id="gfpgan">
+          <label title="Strength of the gfpgan (face fixing) algorithm." for="gfpgan_strength">GPFGAN Strength (0 to disable):</label>
+          <input value="0.8" min="0" max="1" type="number" id="gfpgan_strength" name="gfpgan_strength" step="0.05">
+          <label title="Upscaling to perform using ESRGAN." for="upscale_level">Upscaling Level</label>
+          <select id="upscale_level" name="upscale_level" value="">
+            <option value="" selected>None</option>
+            <option value="2">2x</option>
+            <option value="4">4x</option>
+          </select>
+          <label title="Strength of the esrgan (upscaling) algorithm." for="upscale_strength">Upscale Strength:</label>
+          <input value="0.75" min="0" max="1" type="number" id="upscale_strength" name="upscale_strength" step="0.05">
+        </div>
+        </fieldset>
+      </form>
+      <div id="about">For news and support for this web service, visit our <a href="http://github.com/lstein/stable-diffusion">GitHub site</a></div>
+      <br>
+      <div id="progress-section">
+        <progress id="progress-bar" value="0" max="1"></progress>
+        <span id="cancel-button" title="Cancel">&#10006;</span>
+        <br>
+        <img id="progress-image" src='data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>'></img>
+        <div id="scaling-inprocess-message">
+          <i><span>Postprocessing...</span><span id="processing_cnt">1/3</span></i>
+        </div>
+      </div>
+    </div>
+    <div id="results">
+      <div id="no-results-message">
+        <i><p>No results...</p></i>
+      </div>
+    </div>
+  </body>
+</html>

static/dream_web/index.js

@@ -0,0 +1,161 @@
+function toBase64(file) {
+    return new Promise((resolve, reject) => {
+        const r = new FileReader();
+        r.readAsDataURL(file);
+        r.onload = () => resolve(r.result);
+        r.onerror = (error) => reject(error);
+    });
+}
+
+function appendOutput(src, seed, config) {
+    let outputNode = document.createElement("img");
+    outputNode.src = src;
+
+    let altText = seed.toString() + " | " + config.prompt;
+    outputNode.alt = altText;
+    outputNode.title = altText;
+
+    // Reload image config
+    outputNode.addEventListener('click', () => {
+        let form = document.querySelector("#generate-form");
+        for (const [k, v] of new FormData(form)) {
+            form.querySelector(`*[name=${k}]`).value = config[k];
+        }
+        document.querySelector("#seed").value = seed;
+
+        saveFields(document.querySelector("#generate-form"));
+    });
+
+    document.querySelector("#results").prepend(outputNode);
+}
+
+function saveFields(form) {
+    for (const [k, v] of new FormData(form)) {
+        if (typeof v !== 'object') { // Don't save 'file' type
+            localStorage.setItem(k, v);
+        }
+    }
+}
+
+function loadFields(form) {
+    for (const [k, v] of new FormData(form)) {
+        const item = localStorage.getItem(k);
+        if (item != null) {
+            form.querySelector(`*[name=${k}]`).value = item;
+        }
+    }
+}
+
+function clearFields(form) {
+    localStorage.clear();
+    let prompt = form.prompt.value;
+    form.reset();
+    form.prompt.value = prompt;
+}
+
+const BLANK_IMAGE_URL = 'data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg"/>';
+async function generateSubmit(form) {
+    const prompt = document.querySelector("#prompt").value;
+
+    // Convert file data to base64
+    let formData = Object.fromEntries(new FormData(form));
+    formData.initimg = formData.initimg.name !== '' ? await toBase64(formData.initimg) : null;
+
+    let strength = formData.strength;
+    let totalSteps = formData.initimg ? Math.floor(strength * formData.steps) : formData.steps;
+
+    let progressSectionEle = document.querySelector('#progress-section');
+    progressSectionEle.style.display = 'initial';
+    let progressEle = document.querySelector('#progress-bar');
+    progressEle.setAttribute('max', totalSteps);
+    let progressImageEle = document.querySelector('#progress-image');
+    progressImageEle.src = BLANK_IMAGE_URL;
+
+    progressImageEle.style.display = {}.hasOwnProperty.call(formData, 'progress_images') ? 'initial': 'none';
+
+    // Post as JSON, using Fetch streaming to get results
+    fetch(form.action, {
+        method: form.method,
+        body: JSON.stringify(formData),
+    }).then(async (response) => {
+        const reader = response.body.getReader();
+
+        let noOutputs = true;
+        while (true) {
+            let {value, done} = await reader.read();
+            value = new TextDecoder().decode(value);
+            if (done) {
+                progressSectionEle.style.display = 'none';
+                break;
+            }
+
+            for (let event of value.split('\n').filter(e => e !== '')) {
+                const data = JSON.parse(event);
+
+                if (data.event === 'result') {
+                    noOutputs = false;
+                    document.querySelector("#no-results-message")?.remove();
+                    appendOutput(data.url, data.seed, data.config);
+                    progressEle.setAttribute('value', 0);
+                    progressEle.setAttribute('max', totalSteps);
+                } else if (data.event === 'upscaling-started') {
+                    document.getElementById("processing_cnt").textContent=data.processed_file_cnt;
+                    document.getElementById("scaling-inprocess-message").style.display = "block";
+                } else if (data.event === 'upscaling-done') {
+                    document.getElementById("scaling-inprocess-message").style.display = "none";
+                } else if (data.event === 'step') {
+                    progressEle.setAttribute('value', data.step);
+                    if (data.url) {
+                        progressImageEle.src = data.url;
+                    }
+                } else if (data.event === 'canceled') {
+                    // avoid alerting as if this were an error case
+                    noOutputs = false;
+                }
+            }
+        }
+
+        // Re-enable form, remove no-results-message
+        form.querySelector('fieldset').removeAttribute('disabled');
+        document.querySelector("#prompt").value = prompt;
+        document.querySelector('progress').setAttribute('value', '0');
+
+        if (noOutputs) {
+            alert("Error occurred while generating.");
+        }
+    });
+
+    // Disable form while generating
+    form.querySelector('fieldset').setAttribute('disabled','');
+    document.querySelector("#prompt").value = `Generating: "${prompt}"`;
+}
+
+window.onload = () => {
+    document.querySelector("#generate-form").addEventListener('submit', (e) => {
+        e.preventDefault();
+        const form = e.target;
+
+        generateSubmit(form);
+    });
+    document.querySelector("#generate-form").addEventListener('change', (e) => {
+        saveFields(e.target.form);
+    });
+    document.querySelector("#reset-seed").addEventListener('click', (e) => {
+        document.querySelector("#seed").value = -1;
+        saveFields(e.target.form);
+    });
+    document.querySelector("#reset-all").addEventListener('click', (e) => {
+        clearFields(e.target.form);
+    });
+    loadFields(document.querySelector("#generate-form"));
+
+    document.querySelector('#cancel-button').addEventListener('click', () => {
+        fetch('/cancel').catch(e => {
+            console.error(e);
+        });
+    });
+
+    if (!config.gfpgan_model_exists) {
+        document.querySelector("#gfpgan").style.display = 'none';
+    }
+};