updated README for version release

added assertion checks for out-of-bound arguments; added various copyright and license agreement files
confirmed that pip install -r requirements.txt is working as expected
2026-01-15 10:48:12 -05:00 · 2022-08-24 09:31:17 -04:00 · 2022-08-24 09:22:27 -04:00 · 2022-08-23 13:15:03 -04:00 · 2022-08-23 11:00:57 -04:00 · 2022-08-23 10:44:33 -04:00
8 changed files with 967 additions and 370 deletions
--- a/30
+++ b/30
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-All rights reserved by the authors.
-You must not distribute the weights provided to you directly or indirectly without explicit consent of the authors.
-You must not distribute harmful, offensive, dehumanizing content or otherwise harmful representations of people or their environments, cultures, religions, etc. produced with the model weights
-or other generated content described in the "Misuse and Malicious Use" section in the model card.
-The model weights are provided for research purposes only.
+MIT License

+Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
+
+This software is derived from a fork of the source code available from
+https://github.com/pesser/stable-diffusion and
+https://github.com/CompViz/stable-diffusion. They carry the following
+copyrights:
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
+
+Please see individual source code files for copyright and authorship
+attributions.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.

 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
@@ -11,4 +29,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.
--- a/LICENSE-ModelWeights.txt
+++ b/LICENSE-ModelWeights.txt
@@ -0,0 +1,294 @@
+Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
+
+CreativeML Open RAIL-M
+dated August 22, 2022
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and
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+
+In short, this license strives for both the open and responsible
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+Even though downstream derivative versions of the model could be
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+Attachment A
+
+Use Restrictions
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+  and arbitrarily-targeted use).
--- a/README-CompViz.md
+++ b/README-CompViz.md
@@ -0,0 +1,210 @@
+# Original README from CompViz/stable-diffusion
+*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
+
+[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
+[Robin Rombach](https://github.com/rromb)\*,
+[Andreas Blattmann](https://github.com/ablattmann)\*,
+[Dominik Lorenz](https://github.com/qp-qp)\,
+[Patrick Esser](https://github.com/pesser),
+[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
+
+**CVPR '22 Oral**
+
+which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).
+
+![txt2img-stable2](assets/stable-samples/txt2img/merged-0006.png)
+[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
+model.
+Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. 
+Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), 
+this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
+With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
+See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
+
+  
+## Requirements
+
+A suitable [conda](https://conda.io/) environment named `ldm` can be created
+and activated with:
+
+```
+conda env create -f environment.yaml
+conda activate ldm
+```
+
+You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
+
+```
+conda install pytorch torchvision -c pytorch
+pip install transformers==4.19.2
+pip install -e .
+```
+
+## Stable Diffusion v1
+
+Stable Diffusion v1 refers to a specific configuration of the model
+architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
+and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and 
+then finetuned on 512x512 images.
+
+*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
+in its training data. 
+Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).
+Research into the safe deployment of general text-to-image models is an ongoing effort. To prevent misuse and harm, we currently provide access to the checkpoints only for [academic research purposes upon request](https://stability.ai/academia-access-form).
+**This is an experiment in safe and community-driven publication of a capable and general text-to-image model. We are working on a public release with a more permissive license that also incorporates ethical considerations.***
+
+[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form) 
+
+### Weights
+
+We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
+which were trained as follows,
+
+- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
+  194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
+- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
+  515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
+filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
+- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
+
+Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
+5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
+steps show the relative improvements of the checkpoints:
+![sd evaluation results](assets/v1-variants-scores.jpg)
+
+
+
+### Text-to-Image with Stable Diffusion
+![txt2img-stable2](assets/stable-samples/txt2img/merged-0005.png)
+![txt2img-stable2](assets/stable-samples/txt2img/merged-0007.png)
+
+Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
+
+
+#### Sampling Script
+
+After [obtaining the weights](#weights), link them
+```
+mkdir -p models/ldm/stable-diffusion-v1/
+ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt 
+```
+and sample with
+```
+python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms 
+```
+
+By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, 
+and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).
+
+```commandline
+usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA] [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS]
+                  [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] [--seed SEED] [--precision {full,autocast}]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --prompt [PROMPT]     the prompt to render
+  --outdir [OUTDIR]     dir to write results to
+  --skip_grid           do not save a grid, only individual samples. Helpful when evaluating lots of samples
+  --skip_save           do not save individual samples. For speed measurements.
+  --ddim_steps DDIM_STEPS
+                        number of ddim sampling steps
+  --plms                use plms sampling
+  --laion400m           uses the LAION400M model
+  --fixed_code          if enabled, uses the same starting code across samples
+  --ddim_eta DDIM_ETA   ddim eta (eta=0.0 corresponds to deterministic sampling
+  --n_iter N_ITER       sample this often
+  --H H                 image height, in pixel space
+  --W W                 image width, in pixel space
+  --C C                 latent channels
+  --f F                 downsampling factor
+  --n_samples N_SAMPLES
+                        how many samples to produce for each given prompt. A.k.a. batch size
+                        (note that the seeds for each image in the batch will be unavailable)
+  --n_rows N_ROWS       rows in the grid (default: n_samples)
+  --scale SCALE         unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
+  --from-file FROM_FILE
+                        if specified, load prompts from this file
+  --config CONFIG       path to config which constructs model
+  --ckpt CKPT           path to checkpoint of model
+  --seed SEED           the seed (for reproducible sampling)
+  --precision {full,autocast}
+                        evaluate at this precision
+
+```
+Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints. 
+For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
+non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
+which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
+
+
+#### Diffusers Integration
+
+Another way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers)
+```py
+# make sure you're logged in with `huggingface-cli login`
+from torch import autocast
+from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
+
+pipe = StableDiffusionPipeline.from_pretrained(
+	"CompVis/stable-diffusion-v1-3-diffusers", 
+	use_auth_token=True
+)
+
+prompt = "a photo of an astronaut riding a horse on mars"
+with autocast("cuda"):
+    image = pipe(prompt)["sample"][0]  
+    
+image.save("astronaut_rides_horse.png")
+```
+
+
+
+### Image Modification with Stable Diffusion
+
+By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different 
+tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script, 
+we provide a script to perform image modification with Stable Diffusion.  
+
+The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
+```
+python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8
+```
+Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
+Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example.
+
+**Input**
+
+![sketch-in](assets/stable-samples/img2img/sketch-mountains-input.jpg)
+
+**Outputs**
+
+![out3](assets/stable-samples/img2img/mountains-3.png)
+![out2](assets/stable-samples/img2img/mountains-2.png)
+
+This procedure can, for example, also be used to upscale samples from the base model.
+
+
+## Comments 
+
+- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
+and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). 
+Thanks for open-sourcing!
+
+- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). 
+
+
+## BibTeX
+
+```
+@misc{rombach2021highresolution,
+      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
+      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
+      year={2021},
+      eprint={2112.10752},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
+```
+
+
--- a/README.md
+++ b/README.md
@@ -57,16 +57,16 @@ dream> q
 00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
 ~~~~

-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 type -h (or --help) at the dream> prompt.
+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
+type -h (or --help) at the dream> prompt.

-The script itself also recognizes a series of command-line switches that will change
-important global defaults, such as the directory for image outputs and the location
-of the model weight files.
+The script itself also recognizes a series of command-line switches
+that will change important global defaults, such as the directory for
+image outputs and the location of the model weight files.

 ## Image-to-Image

@@ -84,8 +84,56 @@ The --init_img (-I) option gives the path to the seed picture. --strength (-f) c
 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.

+## 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
+(surprisingly, this example actually works). The prompt weights can
+use any combination of integers and floating point numbers, and they
+do not need to add up to 1.
+
 ## Changes

+* 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
@@ -110,6 +158,8 @@ completely). The default is 0.75, and ranges from 0.25-0.75 give interesting res

 ## Installation

+There are separate installation walkthroughs for [Linux/Mac](#linuxmac) and [Windows](#windows).
+
 ### Linux/Mac

 1. You will need to install the following prerequisites if they are not already available. Use your
@@ -149,24 +199,27 @@ After these steps, your command prompt will be prefixed by "(ldm)" as shown abov
 (ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
 ```

+Note that this step is necessary because I modified the original
+just-in-time model loading scheme to allow the script to work on GPU
+machines that are not internet connected. See [Workaround for machines with limited internet connectivity](#workaround-for-machines-with-limited-internet-connectivity)
+
 7. Now you need to install the weights for the 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 credentials to log in, and then point browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
+Use your credentials to log in, and then point your browser at 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.
+to a page that prompts you to click the "download" link. Save the file somewhere safe on your local machine.

-Now run the following commands from within the stable-diffusion directory to point it to the weights file.
+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
 ```

-The weight file is >4 GB in size, so downloading may take a while.
-
 8. Start generating images!
 ```
 # for the pre-release weights use the -l or --liaon400m switch
@@ -181,7 +234,7 @@ The weight file is >4 GB in size, so downloading may take a while.
 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
+#### 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:
 ```
@@ -191,34 +244,31 @@ This will bring your local copy into sync with the remote one.

 ### Windows

-1. Install Python version 3.8.5 from here: https://www.python.org/downloads/windows/
-   (note that several users have reported that later versions do not work properly)
+1. Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/

-2. Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/
+2. Install Git from here: https://git-scm.com/download/win

-3. Install Git from here: https://git-scm.com/download/win
+3. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.

-4. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
-
-5. Run the command:
+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.

-6. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
+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
 ```

-7. Run the following two commands:
+6. Run the following two commands:
 ```
-conda env create -f environment.yaml    (step 7a)
-conda activate ldm                      (step 7b)
+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.

-8. Run the command:
+7. Run the command:
 ```
 python scripts\preload_models.py
 ```
@@ -228,11 +278,11 @@ requires. (Note that this step is required. I created it because some people
 are using GPU systems that are behind a firewall and the models can't be
 downloaded just-in-time)

-9. Now you need to install the weights for the big stable diffusion model.
+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
-credentials to log in, and then point browser at
+credentials to log in, and then point your browser at
 https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.  You
 may be asked to sign a license agreement at this point.

@@ -243,17 +293,18 @@ 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 point it to the weights file:
+directory** to copy the weights file to the right place:
   
 ```
-mkdir -p models/ldm/stable-diffusion-v1
+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 may instead create a shortcut to it from within
+"models\ldm\stable-diffusion-v1\".

-Instead of copying the file, you may instead create a shortcut within the
-models\ldm\stable-diffusion-v1\ directory that points to it.
-
-10. Start generating images!
+9. Start generating images!
 ```
 # for the pre-release weights
 python scripts\dream.py -l
@@ -261,9 +312,9 @@ python scripts\dream.py -l
 # for the post-release weights
 python scripts\dream.py
 ```
-11. Subsequently, to relaunch the script, first activate the Anaconda command window (step 4), enter the stable-diffusion directory (step 6, "cd \path\to\stable-diffusion"), run "conda activate ldm" (step 7b), and then launch the dream script (step 10).
+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
+#### 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:
 ```
@@ -280,7 +331,7 @@ lets you create images from a prompt in just three lines of code:
 ~~~~
 from ldm.simplet2i import T2I
 model   = T2I()
-outputs = model.text2image("a unicorn in manhattan")
+outputs = model.txt2img("a unicorn in manhattan")
 ~~~~

 Outputs is a list of lists in the format [[filename1,seed1],[filename2,seed2]...]
@@ -332,212 +383,9 @@ to send me an email if you use and like the script.

 *Contributions by:* [Peter Kowalczyk](https://github.com/slix), [Henry Harrison](https://github.com/hwharrison), [xraxra](https://github.com/xraxra), and [bmaltais](https://github.com/bmaltais)

-# Original README from CompViz/stable-diffusion
-*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
-
-[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
-[Robin Rombach](https://github.com/rromb)\*,
-[Andreas Blattmann](https://github.com/ablattmann)\*,
-[Dominik Lorenz](https://github.com/qp-qp)\,
-[Patrick Esser](https://github.com/pesser),
-[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
-
-**CVPR '22 Oral**
-
-which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).
-
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0006.png)
-[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
-model.
-Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. 
-Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487), 
-this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
-With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
-See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
-
-  
-## Requirements
-
-A suitable [conda](https://conda.io/) environment named `ldm` can be created
-and activated with:
-
-```
-conda env create -f environment.yaml
-conda activate ldm
-```
-
-You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
-
-```
-conda install pytorch torchvision -c pytorch
-pip install transformers==4.19.2
-pip install -e .
-```
-
-## Stable Diffusion v1
-
-Stable Diffusion v1 refers to a specific configuration of the model
-architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
-and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and 
-then finetuned on 512x512 images.
-
-*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
-in its training data. 
-Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).
-Research into the safe deployment of general text-to-image models is an ongoing effort. To prevent misuse and harm, we currently provide access to the checkpoints only for [academic research purposes upon request](https://stability.ai/academia-access-form).
-**This is an experiment in safe and community-driven publication of a capable and general text-to-image model. We are working on a public release with a more permissive license that also incorporates ethical considerations.***
-
-[Request access to Stable Diffusion v1 checkpoints for academic research](https://stability.ai/academia-access-form) 
-
-### Weights
-
-We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
-which were trained as follows,
-
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
-  194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
-  515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
-filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
-
-Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
-steps show the relative improvements of the checkpoints:
-![sd evaluation results](assets/v1-variants-scores.jpg)
-
-
-
-### Text-to-Image with Stable Diffusion
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0005.png)
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0007.png)
-
-Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder.
-
-
-#### Sampling Script
-
-After [obtaining the weights](#weights), link them
-```
-mkdir -p models/ldm/stable-diffusion-v1/
-ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt 
-```
-and sample with
-```
-python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms 
-```
-
-By default, this uses a guidance scale of `--scale 7.5`, [Katherine Crowson's implementation](https://github.com/CompVis/latent-diffusion/pull/51) of the [PLMS](https://arxiv.org/abs/2202.09778) sampler, 
-and renders images of size 512x512 (which it was trained on) in 50 steps. All supported arguments are listed below (type `python scripts/txt2img.py --help`).
-
-```commandline
-usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA] [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS]
-                  [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT] [--seed SEED] [--precision {full,autocast}]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --prompt [PROMPT]     the prompt to render
-  --outdir [OUTDIR]     dir to write results to
-  --skip_grid           do not save a grid, only individual samples. Helpful when evaluating lots of samples
-  --skip_save           do not save individual samples. For speed measurements.
-  --ddim_steps DDIM_STEPS
-                        number of ddim sampling steps
-  --plms                use plms sampling
-  --laion400m           uses the LAION400M model
-  --fixed_code          if enabled, uses the same starting code across samples
-  --ddim_eta DDIM_ETA   ddim eta (eta=0.0 corresponds to deterministic sampling
-  --n_iter N_ITER       sample this often
-  --H H                 image height, in pixel space
-  --W W                 image width, in pixel space
-  --C C                 latent channels
-  --f F                 downsampling factor
-  --n_samples N_SAMPLES
-                        how many samples to produce for each given prompt. A.k.a. batch size
-  --n_rows N_ROWS       rows in the grid (default: n_samples)
-  --scale SCALE         unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
-  --from-file FROM_FILE
-                        if specified, load prompts from this file
-  --config CONFIG       path to config which constructs model
-  --ckpt CKPT           path to checkpoint of model
-  --seed SEED           the seed (for reproducible sampling)
-  --precision {full,autocast}
-                        evaluate at this precision
-
-```
-Note: The inference config for all v1 versions is designed to be used with EMA-only checkpoints. 
-For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
-non-EMA to EMA weights. If you want to examine the effect of EMA vs no EMA, we provide "full" checkpoints
-which contain both types of weights. For these, `use_ema=False` will load and use the non-EMA weights.
-
-
-#### Diffusers Integration
-
-Another way to download and sample Stable Diffusion is by using the [diffusers library](https://github.com/huggingface/diffusers/tree/main#new--stable-diffusion-is-now-fully-compatible-with-diffusers)
-```py
-# make sure you're logged in with `huggingface-cli login`
-from torch import autocast
-from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
-
-pipe = StableDiffusionPipeline.from_pretrained(
-	"CompVis/stable-diffusion-v1-3-diffusers", 
-	use_auth_token=True
-)
-
-prompt = "a photo of an astronaut riding a horse on mars"
-with autocast("cuda"):
-    image = pipe(prompt)["sample"][0]  
-    
-image.save("astronaut_rides_horse.png")
-```
-
-
-
-### Image Modification with Stable Diffusion
-
-By using a diffusion-denoising mechanism as first proposed by [SDEdit](https://arxiv.org/abs/2108.01073), the model can be used for different 
-tasks such as text-guided image-to-image translation and upscaling. Similar to the txt2img sampling script, 
-we provide a script to perform image modification with Stable Diffusion.  
-
-The following describes an example where a rough sketch made in [Pinta](https://www.pinta-project.com/) is converted into a detailed artwork.
-```
-python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation" --init-img <path-to-img.jpg> --strength 0.8
-```
-Here, strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. 
-Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. See the following example.
-
-**Input**
-
-![sketch-in](assets/stable-samples/img2img/sketch-mountains-input.jpg)
-
-**Outputs**
-
-![out3](assets/stable-samples/img2img/mountains-3.png)
-![out2](assets/stable-samples/img2img/mountains-2.png)
-
-This procedure can, for example, also be used to upscale samples from the base model.
-
-
-## Comments 
-
- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
-and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). 
-Thanks for open-sourcing!
-
- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). 
-
-
-## BibTeX
-
-```
-@misc{rombach2021highresolution,
-      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
-      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
-      year={2021},
-      eprint={2112.10752},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-
-```
+Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)

+#Further Reading

+Please see the original README for more information on this software
+and underlying algorithm, located in the file README-CompViz.md.
--- a/ldm/simplet2i.py
+++ b/ldm/simplet2i.py
@@ -1,3 +1,10 @@
+# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
+
+# Derived from source code carrying the following copyrights
+# Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+# Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
+
+
 """Simplified text to image API for stable diffusion/latent diffusion

 Example Usage:
@@ -60,6 +67,7 @@ from torch import autocast
 from contextlib import contextmanager, nullcontext
 import time
 import math
+import re

 from ldm.util import instantiate_from_config
 from ldm.models.diffusion.ddim     import DDIMSampler
@@ -103,7 +111,7 @@ The vast majority of these arguments default to reasonable values.
                 seed=None,
                 cfg_scale=7.5,
                 weights="models/ldm/stable-diffusion-v1/model.ckpt",
-                 config = "configs/latent-diffusion/txt2img-1p4B-eval.yaml",
+                 config = "configs/stable-diffusion/v1-inference.yaml",
                 sampler_name="klms",
                 latent_channels=4,
                 downsampling_factor=8,
@@ -142,7 +150,7 @@ The vast majority of these arguments default to reasonable values.

    def txt2img(self,prompt,outdir=None,batch_size=None,iterations=None,
                steps=None,seed=None,grid=None,individual=None,width=None,height=None,
-                cfg_scale=None,ddim_eta=None,strength=None,init_img=None):
+                cfg_scale=None,ddim_eta=None,strength=None,init_img=None,skip_normalize=False):
        """
        Generate an image from the prompt, writing iteration images into the outdir
        The output is a list of lists in the format: [[filename1,seed1], [filename2,seed2],...]
@@ -160,6 +168,9 @@ The vast majority of these arguments default to reasonable values.

        model = self.load_model()  # will instantiate the model or return it from cache

+        assert strength<1.0 and strength>=0.0, "strength (-f) must be >=0.0 and <1.0"
+        assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
+
        # grid and individual are mutually exclusive, with individual taking priority.
        # not necessary, but needed for compatability with dream bot
        if (grid is None):
@@ -171,7 +182,6 @@ The vast majority of these arguments default to reasonable values.

        # make directories and establish names for the output files
        os.makedirs(outdir, exist_ok=True)
-        base_count = len(os.listdir(outdir))-1

        start_code = None
        if self.fixed_code:
@@ -185,65 +195,90 @@ The vast majority of these arguments default to reasonable values.
        sampler         = self.sampler
        images = list()
        seeds  = list()
-
+        filename = None
+        image_count = 0
        tic    = time.time()
-        
-        with torch.no_grad():
-            with precision_scope("cuda"):
-                with model.ema_scope():
-                    all_samples = list()
-                    for n in trange(iterations, desc="Sampling"):
-                        seed_everything(seed)
-                        for prompts in tqdm(data, desc="data", dynamic_ncols=True):
-                            uc = None
-                            if cfg_scale != 1.0:
-                                uc = model.get_learned_conditioning(batch_size * [""])
-                            if isinstance(prompts, tuple):
-                                prompts = list(prompts)
-                            c = model.get_learned_conditioning(prompts)
-                            shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
-                            samples_ddim, _ = sampler.sample(S=steps,
-                                                             conditioning=c,
-                                                             batch_size=batch_size,
-                                                             shape=shape,
-                                                             verbose=False,
-                                                             unconditional_guidance_scale=cfg_scale,
-                                                             unconditional_conditioning=uc,
-                                                             eta=ddim_eta,
-                                                             x_T=start_code)

-                            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)
+        # Gawd. Too many levels of indent here. Need to refactor into smaller routines!
+        try:
+            with torch.no_grad():
+                with precision_scope("cuda"):
+                    with model.ema_scope():
+                        all_samples = list()
+                        for n in trange(iterations, desc="Sampling"):
+                            seed_everything(seed)
+                            for prompts in tqdm(data, desc="data", dynamic_ncols=True):
+                                uc = None
+                                if cfg_scale != 1.0:
+                                    uc = model.get_learned_conditioning(batch_size * [""])
+                                if isinstance(prompts, tuple):
+                                    prompts = list(prompts)

-                            if not grid:
-                                for x_sample in x_samples_ddim:
-                                    x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                                    filename = os.path.join(outdir, f"{base_count:05}.png")
-                                    Image.fromarray(x_sample.astype(np.uint8)).save(filename)
-                                    images.append([filename,seed])
-                                    base_count += 1
-                            else:
-                                all_samples.append(x_samples_ddim)
-                                seeds.append(seed)
+                                # weighted sub-prompts
+                                subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
+                                if len(subprompts) > 1:
+                                    # i dont know if this is correct.. but it works
+                                    c = torch.zeros_like(uc)
+                                    # get total weight for normalizing
+                                    totalWeight = sum(weights)
+                                    # normalize each "sub prompt" and add it
+                                    for i in range(0,len(subprompts)):
+                                        weight = weights[i]
+                                        if not skip_normalize:
+                                            weight = weight / totalWeight
+                                        c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
+                                else: # just standard 1 prompt
+                                    c = model.get_learned_conditioning(prompts)

-                        seed = self._new_seed()
- 
-                    if grid:
-                        images = self._make_grid(samples=all_samples,
-                                                 seeds=seeds,
-                                                 batch_size=batch_size,
-                                                 iterations=iterations,
-                                                 outdir=outdir)
+                                shape = [self.latent_channels, height // self.downsampling_factor, width // self.downsampling_factor]
+                                samples_ddim, _ = sampler.sample(S=steps,
+                                                                 conditioning=c,
+                                                                 batch_size=batch_size,
+                                                                 shape=shape,
+                                                                 verbose=False,
+                                                                 unconditional_guidance_scale=cfg_scale,
+                                                                 unconditional_conditioning=uc,
+                                                                 eta=ddim_eta,
+                                                                 x_T=start_code)
+
+                                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 not grid:
+                                    for x_sample in x_samples_ddim:
+                                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                                        filename = self._unique_filename(outdir,previousname=filename,
+                                                                         seed=seed,isbatch=(batch_size>1))
+                                        assert not os.path.exists(filename)
+                                        Image.fromarray(x_sample.astype(np.uint8)).save(filename)
+                                        images.append([filename,seed])
+                                else:
+                                    all_samples.append(x_samples_ddim)
+                                    seeds.append(seed)
+
+                            image_count += 1
+                            seed = self._new_seed()
+                        if grid:
+                            images = self._make_grid(samples=all_samples,
+                                                     seeds=seeds,
+                                                     batch_size=batch_size,
+                                                     iterations=iterations,
+                                                     outdir=outdir)
+        except KeyboardInterrupt:
+            print('*interrupted*')
+            print('Partial results will be returned; if --grid was requested, nothing will be returned.')
+        except RuntimeError as e:
+            print(str(e))

        toc = time.time()
-        print(f'{batch_size * iterations} images generated in',"%4.2fs"% (toc-tic))
+        print(f'{image_count} images generated in',"%4.2fs"% (toc-tic))

        return images
        
    # There is lots of shared code between this and txt2img and should be refactored.
    def img2img(self,prompt,outdir=None,init_img=None,batch_size=None,iterations=None,
                steps=None,seed=None,grid=None,individual=None,width=None,height=None,
-                cfg_scale=None,ddim_eta=None,strength=None):
+                cfg_scale=None,ddim_eta=None,strength=None,skip_normalize=False):
        """
        Generate an image from the prompt and the initial image, writing iteration images into the outdir
        The output is a list of lists in the format: [[filename1,seed1], [filename2,seed2],...]
@@ -257,6 +292,9 @@ The vast majority of these arguments default to reasonable values.
        iterations = iterations or self.iterations
        strength   = strength   or self.strength

+        assert strength<1.0 and strength>=0.0, "strength (-f) must be >=0.0 and <1.0"
+        assert cfg_scale>1.0, "CFG_Scale (-C) must be >1.0"
+
        if init_img is None:
            print("no init_img provided!")
            return []
@@ -283,7 +321,6 @@ The vast majority of these arguments default to reasonable values.

        # make directories and establish names for the output files
        os.makedirs(outdir, exist_ok=True)
-        base_count = len(os.listdir(outdir))-1

        assert os.path.isfile(init_img)
        init_image = self._load_img(init_img).to(self.device)
@@ -304,60 +341,83 @@ The vast majority of these arguments default to reasonable values.

        images = list()
        seeds  = list()
-
+        filename = None
+        image_count = 0 # actual number of iterations performed
        tic    = time.time()
-        
-        with torch.no_grad():
-            with precision_scope("cuda"):
-                with model.ema_scope():
-                    all_samples = list()
-                    for n in trange(iterations, desc="Sampling"):
-                        seed_everything(seed)
-                        for prompts in tqdm(data, desc="data", dynamic_ncols=True):
-                            uc = None
-                            if cfg_scale != 1.0:
-                                uc = model.get_learned_conditioning(batch_size * [""])
-                            if isinstance(prompts, tuple):
-                                prompts = list(prompts)
-                            c = model.get_learned_conditioning(prompts)

-                            # encode (scaled latent)
-                            z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
-                            # decode it
-                            samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
-                                                     unconditional_conditioning=uc,)
+        # Gawd. Too many levels of indent here. Need to refactor into smaller routines!
+        try:
+            with torch.no_grad():
+                with precision_scope("cuda"):
+                    with model.ema_scope():
+                        all_samples = list()
+                        for n in trange(iterations, desc="Sampling"):
+                            seed_everything(seed)
+                            for prompts in tqdm(data, desc="data", dynamic_ncols=True):
+                                uc = None
+                                if cfg_scale != 1.0:
+                                    uc = model.get_learned_conditioning(batch_size * [""])
+                                if isinstance(prompts, tuple):
+                                    prompts = list(prompts)

-                            x_samples = model.decode_first_stage(samples)
-                            x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+                                # weighted sub-prompts
+                                subprompts,weights = T2I._split_weighted_subprompts(prompts[0])
+                                if len(subprompts) > 1:
+                                    # i dont know if this is correct.. but it works
+                                    c = torch.zeros_like(uc)
+                                    # get total weight for normalizing
+                                    totalWeight = sum(weights)
+                                    # normalize each "sub prompt" and add it
+                                    for i in range(0,len(subprompts)):
+                                        weight = weights[i]
+                                        if not skip_normalize:
+                                            weight = weight / totalWeight
+                                        c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
+                                else: # just standard 1 prompt
+                                    c = model.get_learned_conditioning(prompts)

-                            if not grid:
-                                for x_sample in x_samples:
-                                    x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                                    filename = os.path.join(outdir, f"{base_count:05}.png")
-                                    Image.fromarray(x_sample.astype(np.uint8)).save(filename)
-                                    images.append([filename,seed])
-                                    base_count += 1
-                            else:
-                                all_samples.append(x_samples)
-                                seeds.append(seed)
+                                # encode (scaled latent)
+                                z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(self.device))
+                                # decode it
+                                samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=cfg_scale,
+                                                         unconditional_conditioning=uc,)

-                        seed = self._new_seed()
+                                x_samples = model.decode_first_stage(samples)
+                                x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)

-                    if grid:
-                        images = self._make_grid(samples=all_samples,
-                                                 seeds=seeds,
-                                                 batch_size=batch_size,
-                                                 iterations=iterations,
-                                                 outdir=outdir)
+                                if not grid:
+                                    for x_sample in x_samples:
+                                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                                        filename = self._unique_filename(outdir,previousname=filename,
+                                                                         seed=seed,isbatch=(batch_size>1))
+                                        assert not os.path.exists(filename)
+                                        Image.fromarray(x_sample.astype(np.uint8)).save(filename)
+                                        images.append([filename,seed])
+                                else:
+                                    all_samples.append(x_samples)
+                                    seeds.append(seed)
+                            image_count +=1
+                            seed = self._new_seed()
+                        if grid:
+                            images = self._make_grid(samples=all_samples,
+                                                     seeds=seeds,
+                                                     batch_size=batch_size,
+                                                     iterations=iterations,
+                                                     outdir=outdir)
+
+        except KeyboardInterrupt:
+            print('*interrupted*')
+            print('Partial results will be returned; if --grid was requested, nothing will be returned.')
+        except RuntimeError as e:
+            print(str(e))

        toc = time.time()
-        print(f'{batch_size * iterations} images generated in',"%4.2fs"% (toc-tic))
+        print(f'{image_count} images generated in',"%4.2fs"% (toc-tic))

        return images

    def _make_grid(self,samples,seeds,batch_size,iterations,outdir):
        images = list()
-        base_count = len(os.listdir(outdir))-1
        n_rows = batch_size if batch_size>1 else int(math.sqrt(batch_size * iterations))
        # save as grid
        grid = torch.stack(samples, 0)
@@ -366,7 +426,7 @@ The vast majority of these arguments default to reasonable values.

        # to image
        grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-        filename = os.path.join(outdir, f"{base_count:05}.png")
+        filename = self._unique_filename(outdir,seed=seeds[0],grid_count=batch_size*iterations)
        Image.fromarray(grid.astype(np.uint8)).save(filename)
        for s in seeds:
            images.append([filename,s])
@@ -430,3 +490,85 @@ The vast majority of these arguments default to reasonable values.
        image = image[None].transpose(0, 3, 1, 2)
        image = torch.from_numpy(image)
        return 2.*image - 1.
+
+    def _unique_filename(self,outdir,previousname=None,seed=0,isbatch=False,grid_count=None):
+        revision = 1
+
+        if previousname is None:
+            # sort reverse alphabetically until we find max+1
+            dirlist   = sorted(os.listdir(outdir),reverse=True)
+            # find the first filename that matches our pattern or return 000000.0.png
+            filename   = next((f for f in dirlist if re.match('^(\d+)\..*\.png',f)),'0000000.0.png')
+            basecount  = int(filename.split('.',1)[0])
+            basecount += 1
+            if grid_count is not None:
+                grid_label = f'grid#1-{grid_count}'
+                filename = f'{basecount:06}.{seed}.{grid_label}.png'
+            elif isbatch:
+                filename = f'{basecount:06}.{seed}.01.png'
+            else:
+                filename = f'{basecount:06}.{seed}.png'
+            
+            return os.path.join(outdir,filename)
+
+        else:
+            previousname = os.path.basename(previousname)
+            x = re.match('^(\d+)\..*\.png',previousname)
+            if not x:
+                return self._unique_filename(outdir,previousname,seed)
+
+            basecount = int(x.groups()[0])
+            series = 0 
+            finished = False
+            while not finished:
+                series += 1
+                filename = f'{basecount:06}.{seed}.png'
+                if isbatch or os.path.exists(os.path.join(outdir,filename)):
+                    filename = f'{basecount:06}.{seed}.{series:02}.png'
+                finished = not os.path.exists(os.path.join(outdir,filename))
+            return os.path.join(outdir,filename)
+
+    def _split_weighted_subprompts(text):
+        """
+        grabs all text up to the first occurrence of ':' 
+        uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
+        if ':' has no value defined, defaults to 1.0
+        repeats until no text remaining
+        """
+        remaining = len(text)
+        prompts = []
+        weights = []
+        while remaining > 0:
+            if ":" in text:
+                idx = text.index(":") # first occurrence from start
+                # grab up to index as sub-prompt
+                prompt = text[:idx]
+                remaining -= idx
+                # remove from main text
+                text = text[idx+1:]
+                # find value for weight 
+                if " " in text:
+                    idx = text.index(" ") # first occurence
+                else: # no space, read to end
+                    idx = len(text)
+                if idx != 0:
+                    try:
+                        weight = float(text[:idx])
+                    except: # couldn't treat as float
+                        print(f"Warning: '{text[:idx]}' is not a value, are you missing a space?")
+                        weight = 1.0
+                else: # no value found
+                    weight = 1.0
+                # remove from main text
+                remaining -= idx
+                text = text[idx+1:]
+                # append the sub-prompt and its weight
+                prompts.append(prompt)
+                weights.append(weight)
+            else: # no : found
+                if len(text) > 0: # there is still text though
+                    # take remainder as weight 1
+                    prompts.append(text)
+                    weights.append(1.0)
+                remaining = 0
+        return prompts, weights
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,23 @@
+accelerate==0.12.0
+albumentations==0.4.3
+clip==1.0
+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
+omegaconf==2.1.1
+opencv-python==4.1.2.30
+pudb==2019.2
+pytorch
+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
--- a/scripts/dream.py
+++ b/scripts/dream.py
@@ -1,4 +1,6 @@
 #!/usr/bin/env python3
+# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
+
 import argparse
 import shlex
 import atexit
@@ -67,36 +69,71 @@ def main():
    # gets rid of annoying messages about random seed
    logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)

+    infile = None
+    try:
+        if opt.infile is not None:
+            infile = open(opt.infile,'r')
+    except FileNotFoundError as e:
+        print(e)
+        exit(-1)
+
    # preload the model
    if not debugging:
        t2i.load_model()
-    print("\n* Initialization done! Awaiting your command (-h for help, q to quit)...")
+    print("\n* Initialization done! Awaiting your command (-h for help, 'q' to quit, 'cd' to change output dir, 'pwd' to print output dir)...")

-    log_path   = os.path.join(opt.outdir,"dream_log.txt")
+    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)
+        main_loop(t2i,cmd_parser,log,infile)
        log.close()
+    if infile:
+        infile.close()


-def main_loop(t2i,parser,log):
+def main_loop(t2i,parser,log,infile):
    ''' prompt/read/execute loop '''
    done = False
    
    while not done:
        try:
-            command = input("dream> ")
+            command = infile.readline() if infile else input("dream> ") 
        except EOFError:
            done = True
            break

-        elements = shlex.split(command)
-        if len(elements)==0:
-            continue
-        
-        if elements[0]=='q':  # 
+        if infile and len(command)==0:
            done = True
            break
+
+        if command.startswith(('#','//')):
+            continue
+
+        try:
+            elements = shlex.split(command)
+        except ValueError as e:
+            print(str(e))
+            continue
+        
+        if len(elements)==0:
+            continue
+
+        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
            elements.pop(0)
            
@@ -128,11 +165,12 @@ def main_loop(t2i,parser,log):
                results = t2i.txt2img(**vars(opt))
            else:
                results = t2i.img2img(**vars(opt))
-            print("Outputs:")
-            write_log_message(t2i,opt,results,log)
-        except KeyboardInterrupt:
-            print('*interrupted*')
+        except AssertionError as e:
+            print(e)
            continue
+        print("Outputs:")
+        write_log_message(t2i,opt,results,log)
+            

    print("goodbye!")

@@ -147,7 +185,13 @@ def write_log_message(t2i,opt,results,logfile):
    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})"
+    else:
+        seeds = f"(seeds for individual images: {seeds})"
+
    for r in results:
        seed = r[1]
        log_message = (f'{r[0]}: {prompt_str} -S{seed}')
@@ -166,7 +210,10 @@ def write_log_message(t2i,opt,results,logfile):
        if r[0] not in seenit:
            seenit[r[0]] = True
            try:
-                _write_prompt_to_png(r[0],f'{prompt_str} -S{seed}')
+                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")

@@ -201,6 +248,10 @@ def create_argv_parser():
                        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,
@@ -218,8 +269,8 @@ def create_argv_parser():
                        choices=['plms','ddim', 'klms'],
                        default='klms',
                        help="which sampler to use (klms) - can only be set on command line")
-    parser.add_argument('-o',
-                        '--outdir',
+    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")
@@ -231,8 +282,8 @@ def create_cmd_parser():
    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")
-    parser.add_argument('-b','--batch_size',type=int,default=1,help="number of images to produce per sampling")
+    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")
@@ -240,11 +291,13 @@ def create_cmd_parser():
    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 (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('-x','--skip_normalize',action='store_true',help="skip subprompt weight normalization")
    return parser

 if readline_available:
    def setup_readline():
-        readline.set_completer(Completer(['--steps','-s','--seed','-S','--iterations','-n','--batch_size','-b',
+        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']).complete)
        readline.set_completer_delims(" ")
@@ -266,8 +319,13 @@ if readline_available:
            return

        def complete(self,text,state):
-            if text.startswith('-I') or text.startswith('--init_img'):
-                return self._image_completions(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:
@@ -287,12 +345,14 @@ if readline_available:
                response = None
            return response

-        def _image_completions(self,text,state):
+        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()

@@ -309,7 +369,7 @@ if readline_available:
                    if full_path.startswith(path):
                        if os.path.isdir(full_path):
                            matches.append(os.path.join(os.path.dirname(text),n)+'/')
-                        elif n.endswith('.png'):
+                        elif n.endswith(extensions):
                            matches.append(os.path.join(os.path.dirname(text),n))

            try:
@@ -317,7 +377,7 @@ if readline_available:
            except IndexError:
                response = None
            return response
-        

 if __name__ == "__main__":
    main()
+
--- a/scripts/preload_models.py
+++ b/scripts/preload_models.py
@@ -1,4 +1,5 @@
 #!/usr/bin/env python3
+# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
 # 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.
@@ -30,3 +31,4 @@ tokenizer  =CLIPTokenizer.from_pretrained(version)
 transformer=CLIPTextModel.from_pretrained(version)
 print('\n\n...success')

+
Author	SHA1	Message	Date
Lincoln Stein	d495bac307	updated README for version release	2022-08-24 09:31:17 -04:00
Lincoln Stein	3393b8cad1	added assertion checks for out-of-bound arguments; added various copyright and license agreement files	2022-08-24 09:22:27 -04:00
Lincoln Stein	7a67d3d837	confirmed that pip install -r requirements.txt is working as expected	2022-08-23 13:15:03 -04:00
Lincoln Stein	9050f3d399	Merge branch 'main' into pip-install-test	2022-08-23 11:00:57 -04:00
Lincoln Stein	a21156e3e3	removed the reference to the mod-deleted Reddit walkthru of prompt weighting usage	2022-08-23 10:44:33 -04:00
Lincoln Stein	716dbbdf8c	resolved conflicts in README changelog	2022-08-23 10:40:22 -04:00
Lincoln Stein	1f2e52a1d6	fixed filename generation so that newer files are always chronologically later	2022-08-23 10:39:18 -04:00
Lincoln Stein	dc788f92b3	in output directory, new image files always start with the number higher than the previous maximum filename to ensure alphabetic sort==chronological sort	2022-08-23 10:19:11 -04:00
Lincoln Stein	13774912f4	generated requirements.txt to test pip-only (non-conda) install	2022-08-23 09:46:27 -04:00
Lincoln Stein	cb9e6d544a	Update README.md Fixed a forward/backward slash error.	2022-08-23 09:35:28 -04:00
Lincoln Stein	a6d6bafd13	updated README with info on weighted partial prompts	2022-08-23 01:58:47 -04:00
Lincoln Stein	9d1343dce3	resolved conflicts and tested	2022-08-23 01:44:43 -04:00
Lincoln Stein	11c0df07b7	prompt weighting not working	2022-08-23 01:23:14 -04:00
Lincoln Stein	ca8a799373	Merge pull request #24 from bakkot/patch-1 Fix usage of simplified API in readme	2022-08-23 01:02:13 -04:00
Lincoln Stein	710b908290	Keyboard interrupt retains seed and log information in files produced prior to interrupt. Closes #21	2022-08-23 00:51:38 -04:00
Kevin Gibbons	c80ce4fff5	fix default config to match docs / dream.py	2022-08-22 21:46:22 -07:00
Lincoln Stein	bc7b1fdd37	Added --from_file argument to load input from a file. Closes #23	2022-08-23 00:30:06 -04:00
Kevin Gibbons	1b7d414784	Fix usage of simplified API in readme	2022-08-22 21:01:15 -07:00
Lincoln Stein	6d1219deec	fixed filenames	2022-08-22 23:56:36 -04:00
Lincoln Stein	e019de34ac	can now change output directories in mid-session using cd and pwd commands	2022-08-22 21:14:31 -04:00
Lincoln Stein	88563fd27a	added support for cd command in path completer	2022-08-22 21:01:06 -04:00
Lincoln Stein	18289dabcb	better exception handling for out of memory errors and badly formatted prompts	2022-08-22 16:55:18 -04:00
Lincoln Stein	e70169257e	better exception handling for out of memory errors and badly formatted prompts	2022-08-22 16:55:06 -04:00
Lincoln Stein	2afa87e911	Update README.md	2022-08-22 15:45:44 -04:00
Lincoln Stein	281e381cfc	clarify use of preload_models.py	2022-08-22 15:42:06 -04:00
xra	e4eb775b63	added optional parameter to skip subprompt weight normalization allows more control when fine-tuning	2022-08-23 00:03:32 +09:00
xra	a3632f5b4f	improved comments & added warning if value couldn't be parsed correctly	2022-08-22 23:32:01 +09:00
xra	2736d7e15e	optional weighting for creative blending of prompts example: "an apple: a banana:0 a watermelon:0.5" the above example turns into 3 sub-prompts: "an apple" 1.0 (default if no value) "a banana" 0.0 "a watermelon" 0.5 The weights are added and normalized The resulting image will be: apple 66%, banana 0%, watermelon 33%	2022-08-22 22:59:06 +09:00