[nodes] Add cancelation to the API

.coveragerc

@@ -0,0 +1,6 @@
+[run]
+omit='.env/*'
+source='.'
+
+[report]
+show_missing = true

.github/CODEOWNERS

@@ -1,16 +1,16 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant
+/.github/workflows/ @mauwii @lstein
 
 # documentation
-/docs/ @lstein  @tildebyte @blessedcoolant
-/mkdocs.yml @lstein  @blessedcoolant
+/docs/ @lstein @mauwii @tildebyte
+/mkdocs.yml @lstein @mauwii
 
 # nodes
 /invokeai/app/ @Kyle0654 @blessedcoolant
 
 # installation and configuration
-/pyproject.toml  @lstein @blessedcoolant
-/docker/  @lstein @blessedcoolant
+/pyproject.toml @mauwii @lstein @blessedcoolant
+/docker/ @mauwii @lstein
 /scripts/ @ebr @lstein
 /installer/ @lstein @ebr
 /invokeai/assets @lstein @ebr
@@ -22,11 +22,11 @@
 /invokeai/backend @blessedcoolant @psychedelicious @lstein
 
 # generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @jpphoto @gregghelt2
+/invokeai/backend @keturn @damian0815 @lstein @blessedcoolant @jpphoto
 
 # front ends
 /invokeai/frontend/CLI @lstein
-/invokeai/frontend/install @lstein @ebr  
+/invokeai/frontend/install @lstein @ebr @mauwii 
 /invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
 /invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
 /invokeai/frontend/web @psychedelicious @blessedcoolant

.github/stale.yaml

@@ -1,19 +0,0 @@
-# Number of days of inactivity before an issue becomes stale
-daysUntilStale: 28
-# Number of days of inactivity before a stale issue is closed
-daysUntilClose: 14
-# Issues with these labels will never be considered stale
-exemptLabels:
-  - pinned
-  - security
-# Label to use when marking an issue as stale
-staleLabel: stale
-# Comment to post when marking an issue as stale. Set to `false` to disable
-markComment: >
-  This issue has been automatically marked as stale because it has not had
-  recent activity. It will be closed if no further activity occurs. Please
-  update the ticket if this is still a problem on the latest release.
-# Comment to post when closing a stale issue. Set to `false` to disable
-closeComment: >
-  Due to inactivity, this issue has been automatically closed. If this is
-  still a problem on the latest release, please recreate the issue.

.github/workflows/build-container.yml

@@ -16,10 +16,6 @@ on:
       - 'v*.*.*'
   workflow_dispatch:
 
-permissions:
-  contents: write
-  packages: write
-
 jobs:
   docker:
     if: github.event.pull_request.draft == false

.github/workflows/mkdocs-material.yml

@@ -2,19 +2,13 @@ name: mkdocs-material
 on:
   push:
     branches:
-      - 'refs/heads/v2.3'
-
-permissions:
-    contents: write
+      - 'main'
+      - 'development'
 
 jobs:
   mkdocs-material:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-latest
-    env:
-      REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
-      REPO_NAME: '${{ github.repository }}'
-      SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
     steps:
       - name: checkout sources
         uses: actions/checkout@v3
@@ -25,15 +19,11 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: '3.10'
-          cache: pip
-          cache-dependency-path: pyproject.toml
 
       - name: install requirements
-        env:
-          PIP_USE_PEP517: 1
         run: |
           python -m \
-            pip install ".[docs]"
+            pip install -r docs/requirements-mkdocs.txt
 
       - name: confirm buildability
         run: |
@@ -43,7 +33,7 @@ jobs:
             --verbose
 
       - name: deploy to gh-pages
-        if: ${{ github.ref == 'refs/heads/v2.3' }}
+        if: ${{ github.ref == 'refs/heads/main' }}
         run: |
           python -m \
             mkdocs gh-deploy \

.github/workflows/test-invoke-pip-skip.yml

@@ -6,6 +6,7 @@ on:
       - '!pyproject.toml'
       - '!invokeai/**'
       - 'invokeai/frontend/web/**'
+      - '!invokeai/frontend/web/dist/**'
   merge_group:
   workflow_dispatch:
 

.github/workflows/test-invoke-pip.yml

@@ -7,11 +7,13 @@ on:
       - 'pyproject.toml'
       - 'invokeai/**'
       - '!invokeai/frontend/web/**'
+      - 'invokeai/frontend/web/dist/**'
   pull_request:
     paths:
       - 'pyproject.toml'
       - 'invokeai/**'
       - '!invokeai/frontend/web/**'
+      - 'invokeai/frontend/web/dist/**'
     types:
       - 'ready_for_review'
       - 'opened'

.gitignore

@@ -9,8 +9,6 @@ models/ldm/stable-diffusion-v1/model.ckpt
 configs/models.user.yaml
 config/models.user.yml
 invokeai.init
-.version
-.last_model
 
 # ignore the Anaconda/Miniconda installer used while building Docker image
 anaconda.sh
@@ -65,7 +63,6 @@ pip-delete-this-directory.txt
 htmlcov/
 .tox/
 .nox/
-.coveragerc
 .coverage
 .coverage.*
 .cache
@@ -76,7 +73,6 @@ cov.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
-.pytest.ini
 cover/
 junit/
 

.pytest.ini

@@ -0,0 +1,5 @@
+[pytest]
+DJANGO_SETTINGS_MODULE = webtas.settings
+; python_files = tests.py test_*.py *_tests.py
+
+addopts = --cov=. --cov-config=.coveragerc --cov-report xml:cov.xml

README.md

@@ -33,8 +33,6 @@
 
 </div>
 
-_**Note: The UI is not fully functional on `main`. If you need a stable UI based on `main`, use the `pre-nodes` tag while we [migrate to a new backend](https://github.com/invoke-ai/InvokeAI/discussions/3246).**_
-
 InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.
 
 **Quick links**: [[How to Install](https://invoke-ai.github.io/InvokeAI/#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
@@ -86,7 +84,7 @@ installing lots of models.
 
 6. Wait while the installer does its thing. After installing the software,
 the installer will launch a script that lets you configure InvokeAI and
-select a set of starting image generation models.
+select a set of starting image generaiton models.
 
 7. Find the folder that InvokeAI was installed into (it is not the
 same as the unpacked zip file directory!) The default location of this
@@ -141,20 +139,15 @@ not supported.
     _For Windows/Linux with an NVIDIA GPU:_
 
     ```terminal
-    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
+    pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
     ```
 
     _For Linux with an AMD GPU:_
 
     ```sh
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
     ```
 
-    _For non-GPU systems:_
-    ```terminal
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
-    ``` 
-
     _For Macintoshes, either Intel or M1/M2:_
 
     ```sh

coverage/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-*
-# Except this file
-!.gitignore

docs/contributing/INVOCATIONS.md

@@ -1,18 +1,10 @@
 # Invocations
 
-Invocations represent a single operation, its inputs, and its outputs. These
-operations and their outputs can be chained together to generate and modify
-images.
+Invocations represent a single operation, its inputs, and its outputs. These operations and their outputs can be chained together to generate and modify images.
 
 ## Creating a new invocation
 
-To create a new invocation, either find the appropriate module file in
-`/ldm/invoke/app/invocations` to add your invocation to, or create a new one in
-that folder. All invocations in that folder will be discovered and made
-available to the CLI and API automatically. Invocations make use of
-[typing](https://docs.python.org/3/library/typing.html) and
-[pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration
-into the CLI and API.
+To create a new invocation, either find the appropriate module file in `/ldm/invoke/app/invocations` to add your invocation to, or create a new one in that folder. All invocations in that folder will be discovered and made available to the CLI and API automatically. Invocations make use of [typing](https://docs.python.org/3/library/typing.html) and [pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration into the CLI and API.
 
 An invocation looks like this:
 
@@ -49,54 +41,34 @@ class UpscaleInvocation(BaseInvocation):
 Each portion is important to implement correctly.
 
 ### Class definition and type
-
 ```py
 class UpscaleInvocation(BaseInvocation):
     """Upscales an image."""
     type: Literal['upscale'] = 'upscale'
 ```
-
-All invocations must derive from `BaseInvocation`. They should have a docstring
-that declares what they do in a single, short line. They should also have a
-`type` with a type hint that's `Literal["command_name"]`, where `command_name`
-is what the user will type on the CLI or use in the API to create this
-invocation. The `command_name` must be unique. The `type` must be assigned to
-the value of the literal in the type hint.
+All invocations must derive from `BaseInvocation`. They should have a docstring that declares what they do in a single, short line. They should also have a `type` with a type hint that's `Literal["command_name"]`, where `command_name` is what the user will type on the CLI or use in the API to create this invocation. The `command_name` must be unique. The `type` must be assigned to the value of the literal in the type hint.
 
 ### Inputs
-
 ```py
     # Inputs
     image: Union[ImageField,None] = Field(description="The input image")
     strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
     level: Literal[2,4]           = Field(default=2, description="The upscale level")
 ```
+Inputs consist of three parts: a name, a type hint, and a `Field` with default, description, and validation information. For example:
+| Part | Value | Description |
+| ---- | ----- | ----------- |
+| Name | `strength` | This field is referred to as `strength` |
+| Type Hint | `float` | This field must be of type `float` |
+| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
 
-Inputs consist of three parts: a name, a type hint, and a `Field` with default,
-description, and validation information. For example:
+Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this field to be parsed with `None` as a value, which enables linking to previous invocations. All fields should either provide a default value or allow `None` as a value, so that they can be overwritten with a linked output from another invocation.
 
-| Part      | Value                                                         | Description                                                                                                               |
-| --------- | ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
-| Name      | `strength`                                                    | This field is referred to as `strength`                                                                                   |
-| Type Hint | `float`                                                       | This field must be of type `float`                                                                                        |
-| Field     | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
+The special type `ImageField` is also used here. All images are passed as `ImageField`, which protects them from pydantic validation errors (since images only ever come from links).
 
-Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this
-field to be parsed with `None` as a value, which enables linking to previous
-invocations. All fields should either provide a default value or allow `None` as
-a value, so that they can be overwritten with a linked output from another
-invocation.
-
-The special type `ImageField` is also used here. All images are passed as
-`ImageField`, which protects them from pydantic validation errors (since images
-only ever come from links).
-
-Finally, note that for all linking, the `type` of the linked fields must match.
-If the `name` also matches, then the field can be **automatically linked** to a
-previous invocation by name and matching.
+Finally, note that for all linking, the `type` of the linked fields must match. If the `name` also matches, then the field can be **automatically linked** to a previous invocation by name and matching.
 
 ### Invoke Function
-
 ```py
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(self.image.image_type, self.image.image_name)
@@ -116,22 +88,13 @@ previous invocation by name and matching.
             image = ImageField(image_type = image_type, image_name = image_name)
         )
 ```
+The `invoke` function is the last portion of an invocation. It is provided an `InvocationContext` which contains services to perform work as well as a `session_id` for use as needed. It should return a class with output values that derives from `BaseInvocationOutput`.
 
-The `invoke` function is the last portion of an invocation. It is provided an
-`InvocationContext` which contains services to perform work as well as a
-`session_id` for use as needed. It should return a class with output values that
-derives from `BaseInvocationOutput`.
+Before being called, the invocation will have all of its fields set from defaults, inputs, and finally links (overriding in that order).
 
-Before being called, the invocation will have all of its fields set from
-defaults, inputs, and finally links (overriding in that order).
-
-Assume that this invocation may be running simultaneously with other
-invocations, may be running on another machine, or in other interesting
-scenarios. If you need functionality, please provide it as a service in the
-`InvocationServices` class, and make sure it can be overridden.
+Assume that this invocation may be running simultaneously with other invocations, may be running on another machine, or in other interesting scenarios. If you need functionality, please provide it as a service in the `InvocationServices` class, and make sure it can be overridden.
 
 ### Outputs
-
 ```py
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
@@ -139,64 +102,4 @@ class ImageOutput(BaseInvocationOutput):
 
     image: ImageField = Field(default=None, description="The output image")
 ```
-
-Output classes look like an invocation class without the invoke method. Prefer
-to use an existing output class if available, and prefer to name inputs the same
-as outputs when possible, to promote automatic invocation linking.
-
-## Schema Generation
-
-Invocation, output and related classes are used to generate an OpenAPI schema.
-
-### Required Properties
-
-The schema generation treat all properties with default values as optional. This
-makes sense internally, but when when using these classes via the generated
-schema, we end up with e.g. the `ImageOutput` class having its `image` property
-marked as optional.
-
-We know that this property will always be present, so the additional logic
-needed to always check if the property exists adds a lot of extraneous cruft.
-
-To fix this, we can leverage `pydantic`'s
-[schema customisation](https://docs.pydantic.dev/usage/schema/#schema-customization)
-to mark properties that we know will always be present as required.
-
-Here's that `ImageOutput` class, without the needed schema customisation:
-
-```python
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    type: Literal["image"] = "image"
-    image:      ImageField = Field(default=None, description="The output image")
-```
-
-The generated OpenAPI schema, and all clients/types generated from it, will have
-the `type` and `image` properties marked as optional, even though we know they
-will always have a value by the time we can interact with them via the API.
-
-Here's the same class, but with the schema customisation added:
-
-```python
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    type: Literal["image"] = "image"
-    image:      ImageField = Field(default=None, description="The output image")
-
-    class Config:
-        schema_extra = {
-            'required': [
-                'type',
-                'image',
-            ]
-        }
-```
-
-The resultant schema (and any API client or types generated from it) will now
-have see `type` as string literal `"image"` and `image` as an `ImageField`
-object.
-
-See this `pydantic` issue for discussion on this solution:
-<https://github.com/pydantic/pydantic/discussions/4577>
+Output classes look like an invocation class without the invoke method. Prefer to use an existing output class if available, and prefer to name inputs the same as outputs when possible, to promote automatic invocation linking.

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -1,83 +0,0 @@
-# Local Development
-
-If you are looking to contribute you will need to have a local development
-environment. See the
-[Developer Install](../installation/020_INSTALL_MANUAL.md#developer-install) for
-full details.
-
-Broadly this involves cloning the repository, installing the pre-reqs, and
-InvokeAI (in editable form). Assuming this is working, choose your area of
-focus.
-
-## Documentation
-
-We use [mkdocs](https://www.mkdocs.org) for our documentation with the
-[material theme](https://squidfunk.github.io/mkdocs-material/). Documentation is
-written in markdown files under the `./docs` folder and then built into a static
-website for hosting with GitHub Pages at
-[invoke-ai.github.io/InvokeAI](https://invoke-ai.github.io/InvokeAI).
-
-To contribute to the documentation you'll need to install the dependencies. Note
-the use of `"`.
-
-```zsh
-pip install ".[docs]"
-```
-
-Now, to run the documentation locally with hot-reloading for changes made.
-
-```zsh
-mkdocs serve
-```
-
-You'll then be prompted to connect to `http://127.0.0.1:8080` in order to
-access.
-
-## Backend
-
-The backend is contained within the `./invokeai/backend` folder structure. To
-get started however please install the development dependencies.
-
-From the root of the repository run the following command. Note the use of `"`.
-
-```zsh
-pip install ".[test]"
-```
-
-This in an optional group of packages which is defined within the
-`pyproject.toml` and will be required for testing the changes you make the the
-code.
-
-### Running Tests
-
-We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
-be found under the `./tests` folder and can be run with a single `pytest`
-command. Optionally, to review test coverage you can append `--cov`.
-
-```zsh
-pytest --cov
-```
-
-Test outcomes and coverage will be reported in the terminal. In addition a more
-detailed report is created in both XML and HTML format in the `./coverage`
-folder. The HTML one in particular can help identify missing statements
-requiring tests to ensure coverage. This can be run by opening
-`./coverage/html/index.html`.
-
-For example.
-
-```zsh
-pytest --cov; open ./coverage/html/index.html
-```
-
-??? info "HTML coverage report output"
-
-    ![html-overview](../assets/contributing/html-overview.png)
-
-    ![html-detail](../assets/contributing/html-detail.png)
-
-## Front End
-
-<!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
-
---8<-- "invokeai/frontend/web/README.md"

docs/features/INPAINTING.md

@@ -168,15 +168,11 @@ used by Stable Diffusion 1.4 and 1.5.
 After installation, your `models.yaml` should contain an entry that looks like
 this one:
 
-```yml
-inpainting-1.5:
-  weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
-  description: SD inpainting v1.5
-  config: configs/stable-diffusion/v1-inpainting-inference.yaml
-  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
-  width: 512
-  height: 512
-```
+inpainting-1.5: weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+description: SD inpainting v1.5 config:
+configs/stable-diffusion/v1-inpainting-inference.yaml vae:
+models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt width: 512
+height: 512
 
 As shown in the example, you may include a VAE fine-tuning weights file as well.
 This is strongly recommended.

docs/features/NSFW.md

@@ -32,7 +32,7 @@ turned on and off on the command line using `--nsfw_checker` and
 At installation time, InvokeAI will ask whether the checker should be
 activated by default (neither argument given on the command line). The
 response is stored in the InvokeAI initialization file (usually
-`invokeai.init` in your home directory). You can change the default at any
+`.invokeai` in your home directory). You can change the default at any
 time by opening this file in a text editor and commenting or
 uncommenting the line `--nsfw_checker`.
 

docs/features/PROMPTS.md

@@ -268,7 +268,7 @@ model is so good at inpainting, a good substitute is to use the `clipseg` text
 masking option:
 
 ```bash
-invoke> a fluffy cat eating a hotdog
+invoke> a fluffy cat eating a hotdot
 Outputs:
 [1010] outputs/000025.2182095108.png: a fluffy cat eating a hotdog
 invoke> a smiling dog eating a hotdog -I 000025.2182095108.png -tm cat

docs/features/TEXTUAL_INVERSION.md

@@ -17,7 +17,7 @@ notebooks.
 
 You will need a GPU to perform training in a reasonable length of
 time, and at least 12 GB of VRAM. We recommend using the [`xformers`
-library](../installation/070_INSTALL_XFORMERS.md) to accelerate the
+library](../installation/070_INSTALL_XFORMERS) to accelerate the
 training process further. During training, about ~8 GB is temporarily
 needed in order to store intermediate models, checkpoints and logs.
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -89,7 +89,7 @@ experimental versions later.
          sudo apt update
          sudo apt install -y software-properties-common
          sudo add-apt-repository -y ppa:deadsnakes/ppa
-         sudo apt install -y python3.10 python3-pip python3.10-venv
+         sudo apt install python3.10 python3-pip python3.10-venv
          sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
          ```
 
@@ -417,7 +417,7 @@ Then type the following commands:
 
 === "AMD System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.2
     ```
 
 ### Corrupted configuration file

docs/installation/020_INSTALL_MANUAL.md

@@ -154,7 +154,7 @@ manager, please follow these steps:
     === "ROCm (AMD)"
 
         ```bash
-        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
         ```
 
     === "CPU (Intel Macs & non-GPU systems)"
@@ -315,7 +315,7 @@ installation protocol (important!)
 
     === "ROCm (AMD)"
         ```bash
-        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
         ```
 
     === "CPU (Intel Macs & non-GPU systems)"

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -110,7 +110,7 @@ recipes are available
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
+https://download.pytorch.org/whl/rocm5.2` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 This will be done automatically for you if you use the installer

docs/installation/050_INSTALLING_MODELS.md

@@ -50,7 +50,7 @@ subset that are currently installed are found in
 |stable-diffusion-1.5|runwayml/stable-diffusion-v1-5|Stable Diffusion version 1.5 diffusers model (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-v1-5 |
 |sd-inpainting-1.5|runwayml/stable-diffusion-inpainting|RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)|https://huggingface.co/runwayml/stable-diffusion-inpainting |
 |stable-diffusion-2.1|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
-|sd-inpainting-2.0|stabilityai/stable-diffusion-2-inpainting|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-inpainting |
+|sd-inpainting-2.0|stabilityai/stable-diffusion-2-1|Stable Diffusion version 2.0 inpainting model (5.21 GB)|https://huggingface.co/stabilityai/stable-diffusion-2-1 |
 |analog-diffusion-1.0|wavymulder/Analog-Diffusion|An SD-1.5 model trained on diverse analog photographs (2.13 GB)|https://huggingface.co/wavymulder/Analog-Diffusion |
 |deliberate-1.0|XpucT/Deliberate|Versatile model that produces detailed images up to 768px (4.27 GB)|https://huggingface.co/XpucT/Deliberate |
 |d&d-diffusion-1.0|0xJustin/Dungeons-and-Diffusion|Dungeons & Dragons characters (2.13 GB)|https://huggingface.co/0xJustin/Dungeons-and-Diffusion |

docs/installation/060_INSTALL_PATCHMATCH.md

@@ -24,7 +24,7 @@ You need to have opencv installed so that pypatchmatch can be built:
 brew install opencv
 ```
 
-The next time you start `invoke`, after successfully installing opencv, pypatchmatch will be built.
+The next time you start `invoke`, after sucesfully installing opencv, pypatchmatch will be built.
 
 ## Linux
 
@@ -56,7 +56,7 @@ Prior to installing PyPatchMatch, you need to take the following steps:
 
 5. Confirm that pypatchmatch is installed. At the command-line prompt enter
    `python`, and then at the `>>>` line type
-   `from patchmatch import patch_match`: It should look like the following:
+   `from patchmatch import patch_match`: It should look like the follwing:
 
     ```py
     Python 3.9.5 (default, Nov 23 2021, 15:27:38)
@@ -108,4 +108,4 @@ Prior to installing PyPatchMatch, you need to take the following steps:
 
 [**Next, Follow Steps 4-6 from the Debian Section above**](#linux)
 
-If you see no errors you're ready to go!
+If you see no errors, then you're ready to go!

installer/lib/installer.py

@@ -456,7 +456,7 @@ def get_torch_source() -> (Union[str, None],str):
     optional_modules = None
     if OS == "Linux":
         if device == "rocm":
-            url = "https://download.pytorch.org/whl/rocm5.4.2"
+            url = "https://download.pytorch.org/whl/rocm5.2"
         elif device == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
 

installer/templates/invoke.sh.in

@@ -24,9 +24,9 @@ if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
+while true
+do
 if [ "$0" != "bash" ]; then
-  while true
-  do
     echo "Do you want to generate images using the"
     echo "1. command-line interface"
     echo "2. browser-based UI"
@@ -67,29 +67,29 @@ if [ "$0" != "bash" ]; then
             ;;
         7)
             invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
-            ;;
-        8)
-            echo "Developer Console:"
+	    ;;
+	8)
+	    echo "Developer Console:"
             file_name=$(basename "${BASH_SOURCE[0]}")
             bash --init-file "$file_name"
             ;;
         9)
-            echo "Update:"
+	    echo "Update:"
             invokeai-update
             ;;
         10)
             invokeai --help
             ;;
-        [qQ])
+	[qQ])
             exit 0
             ;;
         *)
             echo "Invalid selection"
             exit;;
     esac
- done
 else # in developer console
     python --version
     echo "Press ^D to exit"
     export PS1="(InvokeAI) \u@\h \w> "
 fi
+done

invokeai/app/api/dependencies.py

@@ -1,23 +1,18 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 import os
+from argparse import Namespace
 
-import invokeai.backend.util.logging as logger
-from typing import types
-
-from ..services.default_graphs import create_system_graphs
-from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
 from ...backend import Globals
 from ..services.model_manager_initializer import get_model_manager
 from ..services.restoration_services import RestorationServices
-from ..services.graph import GraphExecutionState, LibraryGraph
+from ..services.graph import GraphExecutionState
 from ..services.image_storage import DiskImageStorage
 from ..services.invocation_queue import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
 from ..services.invoker import Invoker
 from ..services.processor import DefaultInvocationProcessor
 from ..services.sqlite import SqliteItemStorage
-from ..services.metadata import PngMetadataService
 from .events import FastAPIEventService
 
 
@@ -43,16 +38,15 @@ class ApiDependencies:
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(config, event_handler_id: int, logger: types.ModuleType=logger):
+    def initialize(config, event_handler_id: int):
         Globals.try_patchmatch = config.patchmatch
         Globals.always_use_cpu = config.always_use_cpu
         Globals.internet_available = config.internet_available and check_internet()
         Globals.disable_xformers = not config.xformers
         Globals.ckpt_convert = config.ckpt_convert
 
-        # TO DO: Use the config to select the logger rather than use the default
-        # invokeai logging module
-        logger.info(f"Internet connectivity is {Globals.internet_available}")
+        # TODO: Use a logger
+        print(f">> Internet connectivity is {Globals.internet_available}")
 
         events = FastAPIEventService(event_handler_id)
 
@@ -60,35 +54,23 @@ class ApiDependencies:
             os.path.join(os.path.dirname(__file__), "../../../../outputs")
         )
 
-        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents'))
-
-        metadata = PngMetadataService()
-
-        images = DiskImageStorage(f'{output_folder}/images', metadata_service=metadata)
+        images = DiskImageStorage(output_folder)
 
         # TODO: build a file/path manager?
         db_location = os.path.join(output_folder, "invokeai.db")
 
         services = InvocationServices(
-            model_manager=get_model_manager(config,logger),
+            model_manager=get_model_manager(config),
             events=events,
-            logger=logger,
-            latents=latents,
             images=images,
-            metadata=metadata,
             queue=MemoryInvocationQueue(),
-            graph_library=SqliteItemStorage[LibraryGraph](
-                filename=db_location, table_name="graphs"
-            ),
             graph_execution_manager=SqliteItemStorage[GraphExecutionState](
                 filename=db_location, table_name="graph_executions"
             ),
             processor=DefaultInvocationProcessor(),
-            restoration=RestorationServices(config,logger),
+            restoration=RestorationServices(config),
         )
 
-        create_system_graphs(services.graph_library)
-
         ApiDependencies.invoker = Invoker(services)
 
     @staticmethod

invokeai/app/api/events.py

@@ -45,7 +45,7 @@ class FastAPIEventService(EventServiceBase):
                 )
 
             except Empty:
-                await asyncio.sleep(0.1)
+                await asyncio.sleep(0.001)
                 pass
 
             except asyncio.CancelledError as e:

invokeai/app/api/models/images.py

@@ -1,40 +0,0 @@
-from typing import Optional
-from pydantic import BaseModel, Field
-
-from invokeai.app.models.image import ImageType
-from invokeai.app.services.metadata import InvokeAIMetadata
-
-
-class ImageResponseMetadata(BaseModel):
-    """An image's metadata. Used only in HTTP responses."""
-
-    created: int = Field(description="The creation timestamp of the image")
-    width: int = Field(description="The width of the image in pixels")
-    height: int = Field(description="The height of the image in pixels")
-    invokeai: Optional[InvokeAIMetadata] = Field(
-        description="The image's InvokeAI-specific metadata"
-    )
-
-
-class ImageResponse(BaseModel):
-    """The response type for images"""
-
-    image_type: ImageType = Field(description="The type of the image")
-    image_name: str = Field(description="The name of the image")
-    image_url: str = Field(description="The url of the image")
-    thumbnail_url: str = Field(description="The url of the image's thumbnail")
-    metadata: ImageResponseMetadata = Field(description="The image's metadata")
-
-
-class ProgressImage(BaseModel):
-    """The progress image sent intermittently during processing"""
-
-    width: int = Field(description="The effective width of the image in pixels")
-    height: int = Field(description="The effective height of the image in pixels")
-    dataURL: str = Field(description="The image data as a b64 data URL")
-
-
-class SavedImage(BaseModel):
-    image_name: str = Field(description="The name of the saved image")
-    thumbnail_name: str = Field(description="The name of the saved thumbnail")
-    created: int = Field(description="The created timestamp of the saved image")

invokeai/app/api/routers/images.py

@@ -1,20 +1,11 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
-import io
-from datetime import datetime, timezone
-import json
-import os
-from typing import Any
-import uuid
 
-from fastapi import Body, HTTPException, Path, Query, Request, UploadFile
+from datetime import datetime, timezone
+
+from fastapi import Path, Request, UploadFile
 from fastapi.responses import FileResponse, Response
 from fastapi.routing import APIRouter
 from PIL import Image
-from invokeai.app.api.models.images import (
-    ImageResponse,
-    ImageResponseMetadata,
-)
-from invokeai.app.services.item_storage import PaginatedResults
 
 from ...services.image_storage import ImageType
 from ..dependencies import ApiDependencies
@@ -26,123 +17,40 @@ images_router = APIRouter(prefix="/v1/images", tags=["images"])
 async def get_image(
     image_type: ImageType = Path(description="The type of image to get"),
     image_name: str = Path(description="The name of the image to get"),
-) -> FileResponse:
-    """Gets an image"""
-
-    path = ApiDependencies.invoker.services.images.get_path(
-        image_type=image_type, image_name=image_name
-    )
-
-    if ApiDependencies.invoker.services.images.validate_path(path):
-        return FileResponse(path)
-    else:
-        raise HTTPException(status_code=404)
-
-
-@images_router.delete("/{image_type}/{image_name}", operation_id="delete_image")
-async def delete_image(
-    image_type: ImageType = Path(description="The type of image to delete"),
-    image_name: str = Path(description="The name of the image to delete"),
-) -> None:
-    """Deletes an image and its thumbnail"""
-
-    ApiDependencies.invoker.services.images.delete(
-        image_type=image_type, image_name=image_name
-    )
-
-
-@images_router.get(
-    "/{thumbnail_type}/thumbnails/{thumbnail_name}", operation_id="get_thumbnail"
-)
-async def get_thumbnail(
-    thumbnail_type: ImageType = Path(description="The type of thumbnail to get"),
-    thumbnail_name: str = Path(description="The name of the thumbnail to get"),
-) -> FileResponse | Response:
-    """Gets a thumbnail"""
-
-    path = ApiDependencies.invoker.services.images.get_path(
-        image_type=thumbnail_type, image_name=thumbnail_name, is_thumbnail=True
-    )
-
-    if ApiDependencies.invoker.services.images.validate_path(path):
-        return FileResponse(path)
-    else:
-        raise HTTPException(status_code=404)
+):
+    """Gets a result"""
+    # TODO: This is not really secure at all. At least make sure only output results are served
+    filename = ApiDependencies.invoker.services.images.get_path(image_type, image_name)
+    return FileResponse(filename)
 
 
 @images_router.post(
     "/uploads/",
     operation_id="upload_image",
     responses={
-        201: {
-            "description": "The image was uploaded successfully",
-            "model": ImageResponse,
-        },
-        415: {"description": "Image upload failed"},
+        201: {"description": "The image was uploaded successfully"},
+        404: {"description": "Session not found"},
     },
-    status_code=201,
 )
-async def upload_image(
-    file: UploadFile, request: Request, response: Response
-) -> ImageResponse:
+async def upload_image(file: UploadFile, request: Request):
     if not file.content_type.startswith("image"):
-        raise HTTPException(status_code=415, detail="Not an image")
+        return Response(status_code=415)
 
     contents = await file.read()
-
     try:
-        img = Image.open(io.BytesIO(contents))
+        im = Image.open(contents)
     except:
         # Error opening the image
-        raise HTTPException(status_code=415, detail="Failed to read image")
+        return Response(status_code=415)
 
-    filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
+    filename = f"{str(int(datetime.now(timezone.utc).timestamp()))}.png"
+    ApiDependencies.invoker.services.images.save(ImageType.UPLOAD, filename, im)
 
-    saved_image = ApiDependencies.invoker.services.images.save(
-        ImageType.UPLOAD, filename, img
+    return Response(
+        status_code=201,
+        headers={
+            "Location": request.url_for(
+                "get_image", image_type=ImageType.UPLOAD, image_name=filename
+            )
+        },
     )
-
-    invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
-
-    image_url = ApiDependencies.invoker.services.images.get_uri(
-        ImageType.UPLOAD, saved_image.image_name
-    )
-
-    thumbnail_url = ApiDependencies.invoker.services.images.get_uri(
-        ImageType.UPLOAD, saved_image.image_name, True
-    )
-
-    res = ImageResponse(
-        image_type=ImageType.UPLOAD,
-        image_name=saved_image.image_name,
-        image_url=image_url,
-        thumbnail_url=thumbnail_url,
-        metadata=ImageResponseMetadata(
-            created=saved_image.created,
-            width=img.width,
-            height=img.height,
-            invokeai=invokeai_metadata,
-        ),
-    )
-
-    response.status_code = 201
-    response.headers["Location"] = image_url
-
-    return res
-
-
-@images_router.get(
-    "/",
-    operation_id="list_images",
-    responses={200: {"model": PaginatedResults[ImageResponse]}},
-)
-async def list_images(
-    image_type: ImageType = Query(
-        default=ImageType.RESULT, description="The type of images to get"
-    ),
-    page: int = Query(default=0, description="The page of images to get"),
-    per_page: int = Query(default=10, description="The number of images per page"),
-) -> PaginatedResults[ImageResponse]:
-    """Gets a list of images"""
-    result = ApiDependencies.invoker.services.images.list(image_type, page, per_page)
-    return result

invokeai/app/api/routers/models.py

@@ -1,248 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and 2023 Kent Keirsey (https://github.com/hipsterusername)
-
-import shutil
-import asyncio
-from typing import Annotated, Any, List, Literal, Optional, Union
-
-from fastapi.routing import APIRouter, HTTPException
-from pydantic import BaseModel, Field, parse_obj_as
-from pathlib import Path
-from ..dependencies import ApiDependencies
-
-models_router = APIRouter(prefix="/v1/models", tags=["models"])
-
-
-class VaeRepo(BaseModel):
-    repo_id: str = Field(description="The repo ID to use for this VAE")
-    path: Optional[str] = Field(description="The path to the VAE")
-    subfolder: Optional[str] = Field(description="The subfolder to use for this VAE")
-
-class ModelInfo(BaseModel):
-    description: Optional[str] = Field(description="A description of the model")
-    
-class CkptModelInfo(ModelInfo):
-    format: Literal['ckpt'] = 'ckpt'
-
-    config: str = Field(description="The path to the model config")
-    weights: str = Field(description="The path to the model weights")
-    vae: str = Field(description="The path to the model VAE")
-    width: Optional[int] = Field(description="The width of the model")
-    height: Optional[int] = Field(description="The height of the model")
-
-class DiffusersModelInfo(ModelInfo):
-    format: Literal['diffusers'] = 'diffusers'
-
-    vae: Optional[VaeRepo] = Field(description="The VAE repo to use for this model")
-    repo_id: Optional[str] = Field(description="The repo ID to use for this model")
-    path: Optional[str] = Field(description="The path to the model")
-
-class CreateModelRequest(BaseModel):
-    name: str = Field(description="The name of the model")
-    info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info")
-
-class CreateModelResponse(BaseModel):
-    name: str = Field(description="The name of the new model")
-    info: Union[CkptModelInfo, DiffusersModelInfo] = Field(discriminator="format", description="The model info")
-    status: str = Field(description="The status of the API response")
-
-class ConversionRequest(BaseModel):
-    name: str = Field(description="The name of the new model")
-    info: CkptModelInfo = Field(description="The converted model info")
-    save_location: str = Field(description="The path to save the converted model weights")
-    
-
-class ConvertedModelResponse(BaseModel):
-    name: str = Field(description="The name of the new model")
-    info: DiffusersModelInfo = Field(description="The converted model info")
-
-class ModelsList(BaseModel):
-    models: dict[str, Annotated[Union[(CkptModelInfo,DiffusersModelInfo)], Field(discriminator="format")]]
-
-
-@models_router.get(
-    "/",
-    operation_id="list_models",
-    responses={200: {"model": ModelsList }},
-)
-async def list_models() -> ModelsList:
-    """Gets a list of models"""
-    models_raw = ApiDependencies.invoker.services.model_manager.list_models()
-    models = parse_obj_as(ModelsList, { "models": models_raw })
-    return models
-
-
-@models_router.post(
-    "/",
-    operation_id="update_model",
-    responses={200: {"status": "success"}},
-)
-async def update_model(
-    model_request: CreateModelRequest
-) -> CreateModelResponse:
-    """ Add Model """
-    model_request_info = model_request.info
-    info_dict = model_request_info.dict()
-    model_response = CreateModelResponse(name=model_request.name, info=model_request.info, status="success")
-
-    ApiDependencies.invoker.services.model_manager.add_model(
-        model_name=model_request.name,
-        model_attributes=info_dict,
-        clobber=True,
-    )
-
-    return model_response
-
-
-@models_router.delete(
-    "/{model_name}",
-    operation_id="del_model",
-    responses={
-        204: {
-        "description": "Model deleted successfully"
-        }, 
-        404: {
-        "description": "Model not found"
-        }
-    },
-)
-async def delete_model(model_name: str) -> None:
-    """Delete Model"""
-    model_names = ApiDependencies.invoker.services.model_manager.model_names()
-    logger = ApiDependencies.invoker.services.logger
-    model_exists = model_name in model_names
-
-    # check if model exists
-    logger.info(f"Checking for model {model_name}...")
-           
-    if model_exists:
-        logger.info(f"Deleting Model: {model_name}")
-        ApiDependencies.invoker.services.model_manager.del_model(model_name, delete_files=True)
-        logger.info(f"Model Deleted: {model_name}")
-        raise HTTPException(status_code=204, detail=f"Model '{model_name}' deleted successfully")
-    
-    else:
-        logger.error(f"Model not found")
-        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
-    
-
-            # @socketio.on("convertToDiffusers")
-        # def convert_to_diffusers(model_to_convert: dict):
-        #     try:
-        #         if model_info := self.generate.model_manager.model_info(
-        #             model_name=model_to_convert["model_name"]
-        #         ):
-        #             if "weights" in model_info:
-        #                 ckpt_path = Path(model_info["weights"])
-        #                 original_config_file = Path(model_info["config"])
-        #                 model_name = model_to_convert["model_name"]
-        #                 model_description = model_info["description"]
-        #             else:
-        #                 self.socketio.emit(
-        #                     "error", {"message": "Model is not a valid checkpoint file"}
-        #                 )
-        #         else:
-        #             self.socketio.emit(
-        #                 "error", {"message": "Could not retrieve model info."}
-        #             )
-
-        #         if not ckpt_path.is_absolute():
-        #             ckpt_path = Path(Globals.root, ckpt_path)
-
-        #         if original_config_file and not original_config_file.is_absolute():
-        #             original_config_file = Path(Globals.root, original_config_file)
-
-        #         diffusers_path = Path(
-        #             ckpt_path.parent.absolute(), f"{model_name}_diffusers"
-        #         )
-
-        #         if model_to_convert["save_location"] == "root":
-        #             diffusers_path = Path(
-        #                 global_converted_ckpts_dir(), f"{model_name}_diffusers"
-        #             )
-
-        #         if (
-        #             model_to_convert["save_location"] == "custom"
-        #             and model_to_convert["custom_location"] is not None
-        #         ):
-        #             diffusers_path = Path(
-        #                 model_to_convert["custom_location"], f"{model_name}_diffusers"
-        #             )
-
-        #         if diffusers_path.exists():
-        #             shutil.rmtree(diffusers_path)
-
-        #         self.generate.model_manager.convert_and_import(
-        #             ckpt_path,
-        #             diffusers_path,
-        #             model_name=model_name,
-        #             model_description=model_description,
-        #             vae=None,
-        #             original_config_file=original_config_file,
-        #             commit_to_conf=opt.conf,
-        #         )
-
-        #         new_model_list = self.generate.model_manager.list_models()
-        #         socketio.emit(
-        #             "modelConverted",
-        #             {
-        #                 "new_model_name": model_name,
-        #                 "model_list": new_model_list,
-        #                 "update": True,
-        #             },
-        #         )
-        #         print(f">> Model Converted: {model_name}")
-        #     except Exception as e:
-        #         self.handle_exceptions(e)
-
-        # @socketio.on("mergeDiffusersModels")
-        # def merge_diffusers_models(model_merge_info: dict):
-        #     try:
-        #         models_to_merge = model_merge_info["models_to_merge"]
-        #         model_ids_or_paths = [
-        #             self.generate.model_manager.model_name_or_path(x)
-        #             for x in models_to_merge
-        #         ]
-        #         merged_pipe = merge_diffusion_models(
-        #             model_ids_or_paths,
-        #             model_merge_info["alpha"],
-        #             model_merge_info["interp"],
-        #             model_merge_info["force"],
-        #         )
-
-        #         dump_path = global_models_dir() / "merged_models"
-        #         if model_merge_info["model_merge_save_path"] is not None:
-        #             dump_path = Path(model_merge_info["model_merge_save_path"])
-
-        #         os.makedirs(dump_path, exist_ok=True)
-        #         dump_path = dump_path / model_merge_info["merged_model_name"]
-        #         merged_pipe.save_pretrained(dump_path, safe_serialization=1)
-
-        #         merged_model_config = dict(
-        #             model_name=model_merge_info["merged_model_name"],
-        #             description=f'Merge of models {", ".join(models_to_merge)}',
-        #             commit_to_conf=opt.conf,
-        #         )
-
-        #         if vae := self.generate.model_manager.config[models_to_merge[0]].get(
-        #             "vae", None
-        #         ):
-        #             print(f">> Using configured VAE assigned to {models_to_merge[0]}")
-        #             merged_model_config.update(vae=vae)
-
-        #         self.generate.model_manager.import_diffuser_model(
-        #             dump_path, **merged_model_config
-        #         )
-        #         new_model_list = self.generate.model_manager.list_models()
-
-        #         socketio.emit(
-        #             "modelsMerged",
-        #             {
-        #                 "merged_models": models_to_merge,
-        #                 "merged_model_name": model_merge_info["merged_model_name"],
-        #                 "model_list": new_model_list,
-        #                 "update": True,
-        #             },
-        #         )
-        #         print(f">> Models Merged: {models_to_merge}")
-        #         print(f">> New Model Added: {model_merge_info['merged_model_name']}")
-        #     except Exception as e:

invokeai/app/api/routers/sessions.py

@@ -2,7 +2,8 @@
 
 from typing import Annotated, List, Optional, Union
 
-from fastapi import Body, HTTPException, Path, Query, Response
+from fastapi import Body, Path, Query
+from fastapi.responses import Response
 from fastapi.routing import APIRouter
 from pydantic.fields import Field
 
@@ -50,7 +51,7 @@ async def list_sessions(
     query: str = Query(default="", description="The query string to search for"),
 ) -> PaginatedResults[GraphExecutionState]:
     """Gets a list of sessions, optionally searching"""
-    if query == "":
+    if filter == "":
         result = ApiDependencies.invoker.services.graph_execution_manager.list(
             page, per_page
         )
@@ -75,7 +76,7 @@ async def get_session(
     """Gets a session"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
     else:
         return session
 
@@ -98,7 +99,7 @@ async def add_node(
     """Adds a node to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.add_node(node)
@@ -107,9 +108,9 @@ async def add_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session.id
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.put(
@@ -131,7 +132,7 @@ async def update_node(
     """Updates a node in the graph and removes all linked edges"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.update_node(node_path, node)
@@ -140,9 +141,9 @@ async def update_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.delete(
@@ -161,7 +162,7 @@ async def delete_node(
     """Deletes a node in the graph and removes all linked edges"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.delete_node(node_path)
@@ -170,9 +171,9 @@ async def delete_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.post(
@@ -191,7 +192,7 @@ async def add_edge(
     """Adds an edge to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.add_edge(edge)
@@ -200,9 +201,9 @@ async def add_edge(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 # TODO: the edge being in the path here is really ugly, find a better solution
@@ -225,7 +226,7 @@ async def delete_edge(
     """Deletes an edge from the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         edge = Edge(
@@ -238,9 +239,9 @@ async def delete_edge(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.put(
@@ -258,14 +259,14 @@ async def invoke_session(
     all: bool = Query(
         default=False, description="Whether or not to invoke all remaining invocations"
     ),
-) -> Response:
+) -> None:
     """Invokes a session"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     if session.is_complete():
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
     ApiDependencies.invoker.invoke(session, invoke_all=all)
     return Response(status_code=202)
@@ -280,7 +281,7 @@ async def invoke_session(
 )
 async def cancel_session_invoke(
     session_id: str = Path(description="The id of the session to cancel"),
-) -> Response:
+) -> None:
     """Invokes a session"""
     ApiDependencies.invoker.cancel(session_id)
     return Response(status_code=202)

invokeai/app/api_app.py

@@ -3,7 +3,6 @@ import asyncio
 from inspect import signature
 
 import uvicorn
-import invokeai.backend.util.logging as logger
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
@@ -15,8 +14,9 @@ from pydantic.schema import schema
 
 from ..backend import Args
 from .api.dependencies import ApiDependencies
-from .api.routers import images, sessions, models
+from .api.routers import images, sessions
 from .api.sockets import SocketIO
+from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 
 # Create the app
@@ -56,7 +56,7 @@ async def startup_event():
     config.parse_args()
 
     ApiDependencies.initialize(
-        config=config, event_handler_id=event_handler_id, logger=logger
+        config=config, event_handler_id=event_handler_id
     )
 
 
@@ -76,8 +76,6 @@ app.include_router(sessions.session_router, prefix="/api")
 
 app.include_router(images.images_router, prefix="/api")
 
-app.include_router(models.models_router, prefix="/api")
-
 
 # Build a custom OpenAPI to include all outputs
 # TODO: can outputs be included on metadata of invocation schemas somehow?

invokeai/app/cli/commands.py

@@ -2,46 +2,14 @@
 
 from abc import ABC, abstractmethod
 import argparse
-from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints
+from typing import Any, Callable, Iterable, Literal, get_args, get_origin, get_type_hints
 from pydantic import BaseModel, Field
-import networkx as nx
-import matplotlib.pyplot as plt
 
-import invokeai.backend.util.logging as logger
-from ..invocations.baseinvocation import BaseInvocation
 from ..invocations.image import ImageField
-from ..services.graph import GraphExecutionState, LibraryGraph, Edge
+from ..services.graph import GraphExecutionState
 from ..services.invoker import Invoker
 
 
-def add_field_argument(command_parser, name: str, field, default_override = None):
-    default = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
-    if get_origin(field.type_) == Literal:
-        allowed_values = get_args(field.type_)
-        allowed_types = set()
-        for val in allowed_values:
-            allowed_types.add(type(val))
-        allowed_types_list = list(allowed_types)
-        field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
-
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field_type,
-            default=default,
-            choices=allowed_values,
-            help=field.field_info.description,
-        )
-    else:
-        command_parser.add_argument(
-            f"--{name}",
-            dest=name,
-            type=field.type_,
-            default=default,
-            help=field.field_info.description,
-        )
-
-
 def add_parsers(
     subparsers,
     commands: list[type],
@@ -66,26 +34,30 @@ def add_parsers(
             if name in exclude_fields:
                 continue
 
-            add_field_argument(command_parser, name, field)
+            if get_origin(field.type_) == Literal:
+                allowed_values = get_args(field.type_)
+                allowed_types = set()
+                for val in allowed_values:
+                    allowed_types.add(type(val))
+                allowed_types_list = list(allowed_types)
+                field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
 
-
-def add_graph_parsers(
-    subparsers,
-    graphs: list[LibraryGraph],
-    add_arguments: Callable[[argparse.ArgumentParser], None]|None = None
-):
-    for graph in graphs:
-        command_parser = subparsers.add_parser(graph.name, help=graph.description)
-        
-        if add_arguments is not None:
-            add_arguments(command_parser)
-
-        # Add arguments for inputs
-        for exposed_input in graph.exposed_inputs:
-            node = graph.graph.get_node(exposed_input.node_path)
-            field = node.__fields__[exposed_input.field]
-            default_override = getattr(node, exposed_input.field)
-            add_field_argument(command_parser, exposed_input.alias, field, default_override)
+                command_parser.add_argument(
+                    f"--{name}",
+                    dest=name,
+                    type=field_type,
+                    default=field.default,
+                    choices=allowed_values,
+                    help=field.field_info.description,
+                )
+            else:
+                command_parser.add_argument(
+                    f"--{name}",
+                    dest=name,
+                    type=field.type_,
+                    default=field.default,
+                    help=field.field_info.description,
+                )
 
 
 class CliContext:
@@ -93,38 +65,17 @@ class CliContext:
     session: GraphExecutionState
     parser: argparse.ArgumentParser
     defaults: dict[str, Any]
-    graph_nodes: dict[str, str]
-    nodes_added: list[str]
 
     def __init__(self, invoker: Invoker, session: GraphExecutionState, parser: argparse.ArgumentParser):
         self.invoker = invoker
         self.session = session
         self.parser = parser
         self.defaults = dict()
-        self.graph_nodes = dict()
-        self.nodes_added = list()
 
     def get_session(self):
         self.session = self.invoker.services.graph_execution_manager.get(self.session.id)
         return self.session
 
-    def reset(self):
-        self.session = self.invoker.create_execution_state()
-        self.graph_nodes = dict()
-        self.nodes_added = list()
-        # Leave defaults unchanged
-
-    def add_node(self, node: BaseInvocation):
-        self.get_session()
-        self.session.graph.add_node(node)
-        self.nodes_added.append(node.id)
-        self.invoker.services.graph_execution_manager.set(self.session)
-
-    def add_edge(self, edge: Edge):
-        self.get_session()
-        self.session.add_edge(edge)
-        self.invoker.services.graph_execution_manager.set(self.session)
-
 
 class ExitCli(Exception):
     """Exception to exit the CLI"""
@@ -230,7 +181,7 @@ class HistoryCommand(BaseCommand):
         for i in range(min(self.count, len(history))):
             entry_id = history[-1 - i]
             entry = context.get_session().graph.get_node(entry_id)
-            logger.info(f"{entry_id}: {get_invocation_command(entry)}")
+            print(f"{entry_id}: {get_invocation_command(entry)}")
 
 
 class SetDefaultCommand(BaseCommand):
@@ -249,39 +200,3 @@ class SetDefaultCommand(BaseCommand):
                 del context.defaults[self.field]
         else:
             context.defaults[self.field] = self.value
-
-
-class DrawGraphCommand(BaseCommand):
-    """Debugs a graph"""
-    type: Literal['draw_graph'] = 'draw_graph'
-
-    def run(self, context: CliContext) -> None:
-        session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
-        nxgraph = session.graph.nx_graph_flat()
-
-        # Draw the networkx graph
-        plt.figure(figsize=(20, 20))
-        pos = nx.spectral_layout(nxgraph)
-        nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
-        nx.draw_networkx_edges(nxgraph, pos, width=2)
-        nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
-        plt.axis("off")
-        plt.show()
-
-
-class DrawExecutionGraphCommand(BaseCommand):
-    """Debugs an execution graph"""
-    type: Literal['draw_xgraph'] = 'draw_xgraph'
-
-    def run(self, context: CliContext) -> None:
-        session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
-        nxgraph = session.execution_graph.nx_graph_flat()
-
-        # Draw the networkx graph
-        plt.figure(figsize=(20, 20))
-        pos = nx.spectral_layout(nxgraph)
-        nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
-        nx.draw_networkx_edges(nxgraph, pos, width=2)
-        nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
-        plt.axis("off")
-        plt.show()

invokeai/app/cli/completer.py

@@ -1,168 +0,0 @@
-"""
-Readline helper functions for cli_app.py
-You may import the global singleton `completer` to get access to the
-completer object.
-"""
-import atexit
-import readline
-import shlex
-
-from pathlib import Path
-from typing import List, Dict, Literal, get_args, get_type_hints, get_origin
-
-import invokeai.backend.util.logging as logger
-from ...backend import ModelManager, Globals
-from ..invocations.baseinvocation import BaseInvocation
-from .commands import BaseCommand
-
-# singleton object, class variable
-completer = None
-
-class Completer(object):
-    
-    def __init__(self, model_manager: ModelManager):
-        self.commands = self.get_commands()
-        self.matches = None
-        self.linebuffer = None
-        self.manager = model_manager
-        return
-
-    def complete(self, text, state):
-        """
-        Complete commands and switches fromm the node CLI command line.
-        Switches are determined in a context-specific manner.
-        """
-
-        buffer = readline.get_line_buffer()
-        if state == 0:
-            options = None
-            try:
-                current_command, current_switch = self.get_current_command(buffer)
-                options = self.get_command_options(current_command, current_switch)
-            except IndexError:
-                pass
-            options = options or list(self.parse_commands().keys())
-            
-            if not text:  # first time
-                self.matches = options
-            else:
-                self.matches = [s for s in options if s and s.startswith(text)]
-
-        try:
-            match = self.matches[state]
-        except IndexError:
-            match = None
-        return match
-
-    @classmethod
-    def get_commands(self)->List[object]:
-        """
-        Return a list of all the client commands and invocations.
-        """
-        return BaseCommand.get_commands() + BaseInvocation.get_invocations()
-
-    def get_current_command(self, buffer: str)->tuple[str, str]:
-        """
-        Parse the readline buffer to find the most recent command and its switch.
-        """
-        if len(buffer)==0:
-            return None, None
-        tokens = shlex.split(buffer)
-        command = None
-        switch = None
-        for t in tokens:
-            if t[0].isalpha():
-                if switch is None:
-                    command = t
-            else:
-                switch = t
-        # don't try to autocomplete switches that are already complete
-        if switch and buffer.endswith(' '):
-            switch=None
-        return command or '', switch or ''
-
-    def parse_commands(self)->Dict[str, List[str]]:
-        """
-        Return a dict in which the keys are the command name
-        and the values are the parameters the command takes.
-        """
-        result = dict()
-        for command in self.commands:
-            hints = get_type_hints(command)
-            name = get_args(hints['type'])[0]
-            result.update({name:hints})
-        return result
-
-    def get_command_options(self, command: str, switch: str)->List[str]:
-        """
-        Return all the parameters that can be passed to the command as
-        command-line switches. Returns None if the command is unrecognized.
-        """
-        parsed_commands = self.parse_commands()
-        if command not in parsed_commands:
-            return None
-        
-        # handle switches in the format "-foo=bar"
-        argument = None
-        if switch and '=' in switch:
-            switch, argument = switch.split('=')
-            
-        parameter = switch.strip('-')
-        if parameter in parsed_commands[command]:
-            if argument is None:
-                return self.get_parameter_options(parameter, parsed_commands[command][parameter])
-            else:
-                return [f"--{parameter}={x}" for x in self.get_parameter_options(parameter, parsed_commands[command][parameter])]
-        else:
-            return [f"--{x}" for x in parsed_commands[command].keys()]
-
-    def get_parameter_options(self, parameter: str, typehint)->List[str]:
-        """
-        Given a parameter type (such as Literal), offers autocompletions.
-        """
-        if get_origin(typehint) == Literal:
-            return get_args(typehint)
-        if parameter == 'model':
-            return self.manager.model_names()
-        
-    def _pre_input_hook(self):
-        if self.linebuffer:
-            readline.insert_text(self.linebuffer)
-            readline.redisplay()
-            self.linebuffer = None
-    
-def set_autocompleter(model_manager: ModelManager) -> Completer:
-    global completer
-    
-    if completer:
-        return completer
-    
-    completer = Completer(model_manager)
-
-    readline.set_completer(completer.complete)
-    # pyreadline3 does not have a set_auto_history() method
-    try:
-        readline.set_auto_history(True)
-    except:
-        pass
-    readline.set_pre_input_hook(completer._pre_input_hook)
-    readline.set_completer_delims(" ")
-    readline.parse_and_bind("tab: complete")
-    readline.parse_and_bind("set print-completions-horizontally off")
-    readline.parse_and_bind("set page-completions on")
-    readline.parse_and_bind("set skip-completed-text on")
-    readline.parse_and_bind("set show-all-if-ambiguous on")
-
-    histfile = Path(Globals.root, ".invoke_history")
-    try:
-        readline.read_history_file(histfile)
-        readline.set_history_length(1000)
-    except FileNotFoundError:
-        pass
-    except OSError:  # file likely corrupted
-        newname = f"{histfile}.old"
-        logger.error(
-            f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
-        )
-        histfile.replace(Path(newname))
-    atexit.register(readline.write_history_file, histfile)

invokeai/app/cli_app.py

@@ -2,7 +2,6 @@
 
 import argparse
 import os
-import re
 import shlex
 import time
 from typing import (
@@ -13,21 +12,14 @@ from typing import (
 from pydantic import BaseModel
 from pydantic.fields import Field
 
-
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.metadata import PngMetadataService
-from .services.default_graphs import create_system_graphs
-from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
-
 from ..backend import Args
-from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers
-from .cli.completer import set_autocompleter
+from .cli.commands import BaseCommand, CliContext, ExitCli, add_parsers, get_graph_execution_history
+from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
 from .services.model_manager_initializer import get_model_manager
 from .services.restoration_services import RestorationServices
-from .services.graph import Edge, EdgeConnection, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
-from .services.default_graphs import default_text_to_image_graph_id
+from .services.graph import Edge, EdgeConnection, GraphExecutionState
 from .services.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
 from .services.invocation_services import InvocationServices
@@ -51,7 +43,7 @@ def add_invocation_args(command_parser):
         "-l",
         action="append",
         nargs=3,
-        help="A link in the format 'source_node source_field dest_field'. source_node can be relative to history (e.g. -1)",
+        help="A link in the format 'dest_field source_node source_field'. source_node can be relative to history (e.g. -1)",
     )
 
     command_parser.add_argument(
@@ -62,7 +54,7 @@ def add_invocation_args(command_parser):
     )
 
 
-def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
+def get_command_parser() -> argparse.ArgumentParser:
     # Create invocation parser
     parser = argparse.ArgumentParser()
 
@@ -80,72 +72,20 @@ def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
     commands = BaseCommand.get_all_subclasses()
     add_parsers(subparsers, commands, exclude_fields=["type"])
 
-    # Create subparsers for exposed CLI graphs
-    # TODO: add a way to identify these graphs
-    text_to_image = services.graph_library.get(default_text_to_image_graph_id)
-    add_graph_parsers(subparsers, [text_to_image], add_arguments=add_invocation_args)
-
     return parser
 
 
-class NodeField():
-    alias: str
-    node_path: str
-    field: str
-    field_type: type
-
-    def __init__(self, alias: str, node_path: str, field: str, field_type: type):
-        self.alias = alias
-        self.node_path = node_path
-        self.field = field
-        self.field_type = field_type
-
-
-def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str,NodeField]:
-    return {k:NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
-
-
-def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
-    """Gets the node field for the specified field alias"""
-    exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias)
-    node_type = type(graph.graph.get_node(exposed_input.node_path))
-    return NodeField(alias=exposed_input.alias, node_path=f'{node_id}.{exposed_input.node_path}', field=exposed_input.field, field_type=get_type_hints(node_type)[exposed_input.field])
-
-
-def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
-    """Gets the node field for the specified field alias"""
-    exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias)
-    node_type = type(graph.graph.get_node(exposed_output.node_path))
-    node_output_type = node_type.get_output_type()
-    return NodeField(alias=exposed_output.alias, node_path=f'{node_id}.{exposed_output.node_path}', field=exposed_output.field, field_type=get_type_hints(node_output_type)[exposed_output.field])
-
-
-def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
-    """Gets the inputs for the specified invocation from the context"""
-    node_type = type(invocation)
-    if node_type is not GraphInvocation:
-        return fields_from_type_hints(get_type_hints(node_type), invocation.id)
-    else:
-        graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
-        return {e.alias: get_node_input_field(graph, e.alias, invocation.id) for e in graph.exposed_inputs}
-
-
-def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
-    """Gets the outputs for the specified invocation from the context"""
-    node_type = type(invocation)
-    if node_type is not GraphInvocation:
-        return fields_from_type_hints(get_type_hints(node_type.get_output_type()), invocation.id)
-    else:
-        graph: LibraryGraph = context.invoker.services.graph_library.get(context.graph_nodes[invocation.id])
-        return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs}
-
-
 def generate_matching_edges(
-    a: BaseInvocation, b: BaseInvocation, context: CliContext
+    a: BaseInvocation, b: BaseInvocation
 ) -> list[Edge]:
     """Generates all possible edges between two invocations"""
-    afields = get_node_outputs(a, context)
-    bfields = get_node_inputs(b, context)
+    atype = type(a)
+    btype = type(b)
+
+    aoutputtype = atype.get_output_type()
+
+    afields = get_type_hints(aoutputtype)
+    bfields = get_type_hints(btype)
 
     matching_fields = set(afields.keys()).intersection(bfields.keys())
 
@@ -153,15 +93,12 @@ def generate_matching_edges(
     invalid_fields = set(["type", "id"])
     matching_fields = matching_fields.difference(invalid_fields)
 
-    # Validate types
-    matching_fields = [f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)]
-
     edges = [
         Edge(
-            source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
-            destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field)
+            source=EdgeConnection(node_id=a.id, field=field),
+            destination=EdgeConnection(node_id=b.id, field=field)
         )
-        for alias in matching_fields
+        for field in matching_fields
     ]
     return edges
 
@@ -181,7 +118,7 @@ def invoke_all(context: CliContext):
     # Print any errors
     if context.session.has_error():
         for n in context.session.errors:
-            context.invoker.services.logger.error(
+            print(
                 f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
             )
         
@@ -191,18 +128,10 @@ def invoke_all(context: CliContext):
 def invoke_cli():
     config = Args()
     config.parse_args()
-    model_manager = get_model_manager(config,logger=logger)
-
-    # This initializes the autocompleter and returns it.
-    # Currently nothing is done with the returned Completer
-    # object, but the object can be used to change autocompletion
-    # behavior on the fly, if desired.
-    set_autocompleter(model_manager)
+    model_manager = get_model_manager(config)
 
     events = EventServiceBase()
 
-    metadata = PngMetadataService()
-
     output_folder = os.path.abspath(
         os.path.join(os.path.dirname(__file__), "../../../outputs")
     )
@@ -213,29 +142,18 @@ def invoke_cli():
     services = InvocationServices(
         model_manager=model_manager,
         events=events,
-        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
-        images=DiskImageStorage(f'{output_folder}/images', metadata_service=metadata),
-        metadata=metadata,
+        images=DiskImageStorage(output_folder),
         queue=MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](
-            filename=db_location, table_name="graphs"
-        ),
         graph_execution_manager=SqliteItemStorage[GraphExecutionState](
             filename=db_location, table_name="graph_executions"
         ),
         processor=DefaultInvocationProcessor(),
-        restoration=RestorationServices(config,logger=logger),
-        logger=logger,
+        restoration=RestorationServices(config),
     )
 
-    system_graphs = create_system_graphs(services.graph_library)
-    system_graph_names = set([g.name for g in system_graphs])
-
     invoker = Invoker(services)
     session: GraphExecutionState = invoker.create_execution_state()
-    parser = get_command_parser(services)
-
-    re_negid = re.compile('^-[0-9]+$')
+    parser = get_command_parser()
 
     # Uncomment to print out previous sessions at startup
     # print(services.session_manager.list())
@@ -244,19 +162,18 @@ def invoke_cli():
 
     while True:
         try:
-            cmd_input = input("invoke> ")
-        except (KeyboardInterrupt, EOFError):
+            cmd_input = input("> ")
+        except KeyboardInterrupt:
             # Ctrl-c exits
             break
 
         try:
             # Refresh the state of the session
-            #history = list(get_graph_execution_history(context.session))
-            history = list(reversed(context.nodes_added))
+            history = list(get_graph_execution_history(context.session))
 
             # Split the command for piping
             cmds = cmd_input.split("|")
-            start_id = len(context.nodes_added)
+            start_id = len(history)
             current_id = start_id
             new_invocations = list()
             for cmd in cmds:
@@ -272,24 +189,8 @@ def invoke_cli():
                         args[field_name] = field_default
 
                 # Parse invocation
-                command: CliCommand = None # type:ignore
-                system_graph: LibraryGraph|None = None
-                if args['type'] in system_graph_names:
-                    system_graph = next(filter(lambda g: g.name == args['type'], system_graphs))
-                    invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))
-                    for exposed_input in system_graph.exposed_inputs:
-                        if exposed_input.alias in args:
-                            node = invocation.graph.get_node(exposed_input.node_path)
-                            field = exposed_input.field
-                            setattr(node, field, args[exposed_input.alias])
-                    command = CliCommand(command = invocation)
-                    context.graph_nodes[invocation.id] = system_graph.id
-                else:
-                    args["id"] = current_id
-                    command = CliCommand(command=args)
-
-                if command is None:
-                    continue
+                args["id"] = current_id
+                command = CliCommand(command=args)
 
                 # Run any CLI commands immediately
                 if isinstance(command.command, BaseCommand):
@@ -300,7 +201,6 @@ def invoke_cli():
                     command.command.run(context)
                     continue
 
-                # TODO: handle linking with library graphs
                 # Pipe previous command output (if there was a previous command)
                 edges: list[Edge] = list()
                 if len(history) > 0 or current_id != start_id:
@@ -313,20 +213,16 @@ def invoke_cli():
                         else context.session.graph.get_node(from_id)
                     )
                     matching_edges = generate_matching_edges(
-                        from_node, command.command, context
+                        from_node, command.command
                     )
                     edges.extend(matching_edges)
 
                 # Parse provided links
                 if "link_node" in args and args["link_node"]:
                     for link in args["link_node"]:
-                        node_id = link
-                        if re_negid.match(node_id):
-                            node_id = str(current_id + int(node_id))
-
-                        link_node = context.session.graph.get_node(node_id)
+                        link_node = context.session.graph.get_node(link)
                         matching_edges = generate_matching_edges(
-                            link_node, command.command, context
+                            link_node, command.command
                         )
                         matching_destinations = [e.destination for e in matching_edges]
                         edges = [e for e in edges if e.destination not in matching_destinations]
@@ -334,20 +230,13 @@ def invoke_cli():
 
                 if "link" in args and args["link"]:
                     for link in args["link"]:
-                        edges = [e for e in edges if e.destination.node_id != command.command.id or e.destination.field != link[2]]
-
-                        node_id = link[0]
-                        if re_negid.match(node_id):
-                            node_id = str(current_id + int(node_id))
-
-                        # TODO: handle missing input/output
-                        node_output = get_node_outputs(context.session.graph.get_node(node_id), context)[link[1]]
-                        node_input = get_node_inputs(command.command, context)[link[2]]
-
+                        edges = [e for e in edges if e.destination.node_id != command.command.id and e.destination.field != link[2]]
                         edges.append(
                             Edge(
-                                source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
-                                destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field)
+                                source=EdgeConnection(node_id=link[1], field=link[0]),
+                                destination=EdgeConnection(
+                                    node_id=command.command.id, field=link[2]
+                                )
                             )
                         )
 
@@ -356,22 +245,22 @@ def invoke_cli():
                 current_id = current_id + 1
 
                 # Add the node to the session
-                context.add_node(command.command)
+                context.session.add_node(command.command)
                 for edge in edges:
                     print(edge)
-                    context.add_edge(edge)
+                    context.session.add_edge(edge)
 
             # Execute all remaining nodes
             invoke_all(context)
 
         except InvalidArgs:
-            invoker.services.logger.warning('Invalid command, use "help" to list commands')
+            print('Invalid command, use "help" to list commands')
             continue
 
         except SessionError:
             # Start a new session
-            invoker.services.logger.warning("Session error: creating a new session")
-            context.reset()
+            print("Session error: creating a new session")
+            context.session = context.invoker.create_execution_state()
 
         except ExitCli:
             break

invokeai/app/invocations/baseinvocation.py

@@ -2,7 +2,7 @@
 
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict
+from typing import get_args, get_type_hints
 
 from pydantic import BaseModel, Field
 
@@ -76,56 +76,3 @@ class BaseInvocation(ABC, BaseModel):
     #fmt: off
     id: str = Field(description="The id of this node. Must be unique among all nodes.")
     #fmt: on
-
-
-# TODO: figure out a better way to provide these hints
-# TODO: when we can upgrade to python 3.11, we can use the`NotRequired` type instead of `total=False`
-class UIConfig(TypedDict, total=False):
-    type_hints: Dict[
-        str,
-        Literal[
-            "integer",
-            "float",
-            "boolean",
-            "string",
-            "enum",
-            "image",
-            "latents",
-            "model",
-        ],
-    ]
-    tags: List[str]
-    title: str
-
-class CustomisedSchemaExtra(TypedDict):
-    ui: UIConfig
-
-
-class InvocationConfig(BaseModel.Config):
-    """Customizes pydantic's BaseModel.Config class for use by Invocations.
-
-    Provide `schema_extra` a `ui` dict to add hints for generated UIs.
-
-    `tags`
-    - A list of strings, used to categorise invocations.
-
-    `type_hints`
-    - A dict of field types which override the types in the invocation definition.
-    - Each key should be the name of one of the invocation's fields.
-    - Each value should be one of the valid types:
-      - `integer`, `float`, `boolean`, `string`, `enum`, `image`, `latents`, `model`
-
-    ```python
-    class Config(InvocationConfig):
-      schema_extra = {
-          "ui": {
-              "tags": ["stable-diffusion", "image"],
-              "type_hints": {
-                  "initial_image": "image",
-              },
-          },
-      }
-    ```
-    """
-
-    schema_extra: CustomisedSchemaExtra

invokeai/app/invocations/collections.py

@@ -1,64 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal, Optional
-
-import numpy as np
-import numpy.random
-from pydantic import Field
-
-from .baseinvocation import (
-    BaseInvocation,
-    InvocationConfig,
-    InvocationContext,
-    BaseInvocationOutput,
-)
-
-
-class IntCollectionOutput(BaseInvocationOutput):
-    """A collection of integers"""
-
-    type: Literal["int_collection"] = "int_collection"
-
-    # Outputs
-    collection: list[int] = Field(default=[], description="The int collection")
-
-
-class RangeInvocation(BaseInvocation):
-    """Creates a range"""
-
-    type: Literal["range"] = "range"
-
-    # Inputs
-    start: int = Field(default=0, description="The start of the range")
-    stop: int = Field(default=10, description="The stop of the range")
-    step: int = Field(default=1, description="The step of the range")
-
-    def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        return IntCollectionOutput(
-            collection=list(range(self.start, self.stop, self.step))
-        )
-
-
-class RandomRangeInvocation(BaseInvocation):
-    """Creates a collection of random numbers"""
-
-    type: Literal["random_range"] = "random_range"
-
-    # Inputs
-    low: int = Field(default=0, description="The inclusive low value")
-    high: int = Field(
-        default=np.iinfo(np.int32).max, description="The exclusive high value"
-    )
-    size: int = Field(default=1, description="The number of values to generate")
-    seed: Optional[int] = Field(
-        ge=0,
-        le=np.iinfo(np.int32).max,
-        description="The seed for the RNG",
-        default_factory=lambda: numpy.random.randint(0, np.iinfo(np.int32).max),
-    )
-
-    def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        rng = np.random.default_rng(self.seed)
-        return IntCollectionOutput(
-            collection=list(rng.integers(low=self.low, high=self.high, size=self.size))
-        )

invokeai/app/invocations/compel.py

@@ -1,245 +0,0 @@
-from typing import Literal, Optional, Union
-from pydantic import BaseModel, Field
-
-from invokeai.app.invocations.util.choose_model import choose_model
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-
-from ...backend.util.devices import choose_torch_device, torch_dtype
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
-from ...backend.stable_diffusion.textual_inversion_manager import TextualInversionManager
-
-from compel import Compel
-from compel.prompt_parser import (
-    Blend,
-    CrossAttentionControlSubstitute,
-    FlattenedPrompt,
-    Fragment,
-)
-
-from invokeai.backend.globals import Globals
-
-
-class ConditioningField(BaseModel):
-    conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
-    class Config:
-        schema_extra = {"required": ["conditioning_name"]}
-
-
-class CompelOutput(BaseInvocationOutput):
-    """Compel parser output"""
-
-    #fmt: off
-    type: Literal["compel_output"] = "compel_output"
-
-    conditioning: ConditioningField = Field(default=None, description="Conditioning")
-    #fmt: on
-
-
-class CompelInvocation(BaseInvocation):
-    """Parse prompt using compel package to conditioning."""
-
-    type: Literal["compel"] = "compel"
-
-    prompt: str = Field(default="", description="Prompt")
-    model: str = Field(default="", description="Model to use")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "title": "Prompt (Compel)",
-                "tags": ["prompt", "compel"],
-                "type_hints": {
-                  "model": "model"
-                }
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> CompelOutput:
-
-        # TODO: load without model
-        model = choose_model(context.services.model_manager, self.model)
-        pipeline = model["model"]
-        tokenizer = pipeline.tokenizer
-        text_encoder = pipeline.text_encoder
-
-        # TODO: global? input?
-        #use_full_precision = precision == "float32" or precision == "autocast"
-        #use_full_precision = False
-
-        # TODO: redo TI when separate model loding implemented
-        #textual_inversion_manager = TextualInversionManager(
-        #    tokenizer=tokenizer,
-        #    text_encoder=text_encoder,
-        #    full_precision=use_full_precision,
-        #)
-
-        def load_huggingface_concepts(concepts: list[str]):
-            pipeline.textual_inversion_manager.load_huggingface_concepts(concepts)
-
-        # apply the concepts library to the prompt
-        prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
-            self.prompt,
-            lambda concepts: load_huggingface_concepts(concepts),
-            pipeline.textual_inversion_manager.get_all_trigger_strings(),
-        )
-
-        # lazy-load any deferred textual inversions.
-        # this might take a couple of seconds the first time a textual inversion is used.
-        pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
-            prompt_str
-        )
-
-        compel = Compel(
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            textual_inversion_manager=pipeline.textual_inversion_manager,
-            dtype_for_device_getter=torch_dtype,
-            truncate_long_prompts=True, # TODO:
-        )
-
-        # TODO: support legacy blend?
-
-        prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str)
-
-        if getattr(Globals, "log_tokenization", False):
-            log_tokenization_for_prompt_object(prompt, tokenizer)
-
-        c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
-
-        # TODO: long prompt support
-        #if not self.truncate_long_prompts:
-        #    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
-
-        ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
-            tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt),
-            cross_attention_control_args=options.get("cross_attention_control", None),
-        )
-
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-
-        # TODO: hacky but works ;D maybe rename latents somehow?
-        context.services.latents.set(conditioning_name, (c, ec))
-
-        return CompelOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
-
-
-def get_max_token_count(
-    tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
-) -> int:
-    if type(prompt) is Blend:
-        blend: Blend = prompt
-        return max(
-            [
-                get_max_token_count(tokenizer, c, truncate_if_too_long)
-                for c in blend.prompts
-            ]
-        )
-    else:
-        return len(
-            get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long)
-        )
-
-
-def get_tokens_for_prompt_object(
-    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
-) -> [str]:
-    if type(parsed_prompt) is Blend:
-        raise ValueError(
-            "Blend is not supported here - you need to get tokens for each of its .children"
-        )
-
-    text_fragments = [
-        x.text
-        if type(x) is Fragment
-        else (
-            " ".join([f.text for f in x.original])
-            if type(x) is CrossAttentionControlSubstitute
-            else str(x)
-        )
-        for x in parsed_prompt.children
-    ]
-    text = " ".join(text_fragments)
-    tokens = tokenizer.tokenize(text)
-    if truncate_if_too_long:
-        max_tokens_length = tokenizer.model_max_length - 2  # typically 75
-        tokens = tokens[0:max_tokens_length]
-    return tokens
-
-
-def log_tokenization_for_prompt_object(
-    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
-):
-    display_label_prefix = display_label_prefix or ""
-    if type(p) is Blend:
-        blend: Blend = p
-        for i, c in enumerate(blend.prompts):
-            log_tokenization_for_prompt_object(
-                c,
-                tokenizer,
-                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
-            )
-    elif type(p) is FlattenedPrompt:
-        flattened_prompt: FlattenedPrompt = p
-        if flattened_prompt.wants_cross_attention_control:
-            original_fragments = []
-            edited_fragments = []
-            for f in flattened_prompt.children:
-                if type(f) is CrossAttentionControlSubstitute:
-                    original_fragments += f.original
-                    edited_fragments += f.edited
-                else:
-                    original_fragments.append(f)
-                    edited_fragments.append(f)
-
-            original_text = " ".join([x.text for x in original_fragments])
-            log_tokenization_for_text(
-                original_text,
-                tokenizer,
-                display_label=f"{display_label_prefix}(.swap originals)",
-            )
-            edited_text = " ".join([x.text for x in edited_fragments])
-            log_tokenization_for_text(
-                edited_text,
-                tokenizer,
-                display_label=f"{display_label_prefix}(.swap replacements)",
-            )
-        else:
-            text = " ".join([x.text for x in flattened_prompt.children])
-            log_tokenization_for_text(
-                text, tokenizer, display_label=display_label_prefix
-            )
-
-
-def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
-    """shows how the prompt is tokenized
-    # usually tokens have '</w>' to indicate end-of-word,
-    # but for readability it has been replaced with ' '
-    """
-    tokens = tokenizer.tokenize(text)
-    tokenized = ""
-    discarded = ""
-    usedTokens = 0
-    totalTokens = len(tokens)
-
-    for i in range(0, totalTokens):
-        token = tokens[i].replace("</w>", " ")
-        # alternate color
-        s = (usedTokens % 6) + 1
-        if truncate_if_too_long and i >= tokenizer.model_max_length:
-            discarded = discarded + f"\x1b[0;3{s};40m{token}"
-        else:
-            tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
-            usedTokens += 1
-
-    if usedTokens > 0:
-        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        print(f"{tokenized}\x1b[0m")
-
-    if discarded != "":
-        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        print(f"{discarded}\x1b[0m")

invokeai/app/invocations/cv.py

@@ -5,26 +5,14 @@ from typing import Literal
 import cv2 as cv
 import numpy
 from PIL import Image, ImageOps
-from pydantic import BaseModel, Field
+from pydantic import Field
 
-from invokeai.app.models.image import ImageField, ImageType
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput, build_image_output
+from ..services.image_storage import ImageType
+from .baseinvocation import BaseInvocation, InvocationContext
+from .image import ImageField, ImageOutput
 
 
-class CvInvocationConfig(BaseModel):
-    """Helper class to provide all OpenCV invocations with additional config"""
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["cv", "image"],
-            },
-        }
-
-
-class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
+class CvInpaintInvocation(BaseInvocation):
     """Simple inpaint using opencv."""
     #fmt: off
     type: Literal["cv_inpaint"] = "cv_inpaint"
@@ -56,14 +44,7 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
+        context.services.images.save(image_type, image_name, image_inpainted)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
-        
-        context.services.images.save(image_type, image_name, image_inpainted, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=image_inpainted,
-        )

invokeai/app/invocations/generate.py

@@ -1,41 +1,29 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from functools import partial
-from typing import Literal, Optional, Union
+from datetime import datetime, timezone
+from typing import Any, Literal, Optional, Union
 
 import numpy as np
+
 from torch import Tensor
+from PIL import Image
+from pydantic import Field
+from skimage.exposure.histogram_matching import match_histograms
 
-from pydantic import BaseModel, Field
-
-from invokeai.app.models.image import ImageField, ImageType
-from invokeai.app.invocations.util.choose_model import choose_model
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput, build_image_output
-from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+from .baseinvocation import BaseInvocation, InvocationContext
+from .image import ImageField, ImageOutput
+from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator, Generator
 from ...backend.stable_diffusion import PipelineIntermediateState
-from ..util.step_callback import stable_diffusion_step_callback
-
-SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
-
-
-class SDImageInvocation(BaseModel):
-    """Helper class to provide all Stable Diffusion raster image invocations with additional config"""
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["stable-diffusion", "image"],
-                "type_hints": {
-                    "model": "model",
-                },
-            },
-        }
+from ...backend.util.util import image_to_dataURL
 
+SAMPLER_NAME_VALUES = Literal[
+    tuple(InvokeAIGenerator.schedulers())
+]
 
 # Text to image
-class TextToImageInvocation(BaseInvocation, SDImageInvocation):
+class TextToImageInvocation(BaseInvocation):
     """Generates an image using text2img."""
 
     type: Literal["txt2img"] = "txt2img"
@@ -46,10 +34,10 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     prompt: Optional[str] = Field(description="The prompt to generate an image from")
     seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
     steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
-    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
+    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
+    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
+    sampler_name: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The sampler to use" )
     seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     model:       str = Field(default="", description="The model to use (currently ignored)")
     progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
@@ -57,31 +45,41 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
     def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
+        self, context: InvocationContext, sample: Tensor, step: int
+    ) -> None:  
+        # TODO: only output a preview image when requested
+        image = Generator.sample_to_lowres_estimated_image(sample)
+
+        (width, height) = image.size
+        width *= 8
+        height *= 8
+
+        dataURL = image_to_dataURL(image, image_format="JPEG")
+
+        context.services.events.emit_generator_progress(
+            context.graph_execution_state_id,
+            self.id,
+            {
+                "width": width,
+                "height": height,
+                "dataURL": dataURL
+            },
+            step,
+            self.steps,
         )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
+        def step_callback(state: PipelineIntermediateState):
+            self.dispatch_progress(context, state.latents, state.step)
+
         # Handle invalid model parameter
-        model = choose_model(context.services.model_manager, self.model)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        #       (right now uses whatever current model is set in model manager)
+        model= context.services.model_manager.get_model()
         outputs = Txt2Img(model).generate(
             prompt=self.prompt,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
+            step_callback=step_callback,
             **self.dict(
                 exclude={"prompt"}
             ),  # Shorthand for passing all of the parameters above manually
@@ -97,18 +95,9 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(
-            image_type, image_name, generate_output.image, metadata
-        )
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=generate_output.image,
+        context.services.images.save(image_type, image_name, generate_output.image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
@@ -127,19 +116,6 @@ class ImageToImageInvocation(TextToImageInvocation):
         description="Whether or not the result should be fit to the aspect ratio of the input image",
     )
 
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
             None
@@ -150,31 +126,24 @@ class ImageToImageInvocation(TextToImageInvocation):
         )
         mask = None
 
-        if self.fit:
-            image = image.resize((self.width, self.height))
+        def step_callback(sample, step=0):
+            self.dispatch_progress(context, sample, step)
 
         # Handle invalid model parameter
-        model = choose_model(context.services.model_manager, self.model)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        model = context.services.model_manager.get_model()
+        generator_output = next(
+            Img2Img(model).generate(
+                prompt=self.prompt,
+                init_image=image,
+                init_mask=mask,
+                step_callback=step_callback,
+                **self.dict(
+                    exclude={"prompt", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
         )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        outputs = Img2Img(model).generate(
-            prompt=self.prompt,
-            init_image=image,
-            init_mask=mask,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
-
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generator_output = next(outputs)
 
         result_image = generator_output.image
 
@@ -185,19 +154,11 @@ class ImageToImageInvocation(TextToImageInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
+        context.services.images.save(image_type, image_name, result_image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
-        context.services.images.save(image_type, image_name, result_image, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=result_image,
-        )
-
-
 class InpaintInvocation(ImageToImageInvocation):
     """Generates an image using inpaint."""
 
@@ -212,19 +173,6 @@ class InpaintInvocation(ImageToImageInvocation):
         description="The amount by which to replace masked areas with latent noise",
     )
 
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        intermediate_state: PipelineIntermediateState,
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = (
             None
@@ -239,28 +187,24 @@ class InpaintInvocation(ImageToImageInvocation):
             else context.services.images.get(self.mask.image_type, self.mask.image_name)
         )
 
+        def step_callback(sample, step=0):
+            self.dispatch_progress(context, sample, step)
+
         # Handle invalid model parameter
-        model = choose_model(context.services.model_manager, self.model)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        manager = context.services.model_manager.get_model()
+        generator_output = next(
+            Inpaint(model).generate(
+                prompt=self.prompt,
+                init_image=image,
+                mask_image=mask,
+                step_callback=step_callback,
+                **self.dict(
+                    exclude={"prompt", "image", "mask"}
+                ),  # Shorthand for passing all of the parameters above manually
+            )
         )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        outputs = Inpaint(model).generate(
-            prompt=self.prompt,
-            init_image=image,
-            mask_image=mask,
-            step_callback=partial(self.dispatch_progress, context, source_node_id),
-            **self.dict(
-                exclude={"prompt", "image", "mask"}
-            ),  # Shorthand for passing all of the parameters above manually
-        )
-
-        # Outputs is an infinite iterator that will return a new InvokeAIGeneratorOutput object
-        # each time it is called. We only need the first one.
-        generator_output = next(outputs)
 
         result_image = generator_output.image
 
@@ -271,14 +215,7 @@ class InpaintInvocation(ImageToImageInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, result_image, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=result_image,
+        context.services.images.save(image_type, image_name, result_image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )

invokeai/app/invocations/image.py

@@ -1,97 +1,54 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from datetime import datetime, timezone
 from typing import Literal, Optional
 
 import numpy
 from PIL import Image, ImageFilter, ImageOps
 from pydantic import BaseModel, Field
 
-from ..models.image import ImageField, ImageType
-from .baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InvocationContext,
-    InvocationConfig,
-)
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
 
 
-class PILInvocationConfig(BaseModel):
-    """Helper class to provide all PIL invocations with additional config"""
+class ImageField(BaseModel):
+    """An image field used for passing image objects between invocations"""
 
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["PIL", "image"],
-            },
-        }
+    image_type: str = Field(
+        default=ImageType.RESULT, description="The type of the image"
+    )
+    image_name: Optional[str] = Field(default=None, description="The name of the image")
 
 
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["image"] = "image"
     image:      ImageField = Field(default=None, description="The output image")
-    width:   Optional[int] = Field(default=None, description="The width of the image in pixels")
-    height:  Optional[int] = Field(default=None, description="The height of the image in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {
-            "required": ["type", "image", "width", "height", "mode"]
-        }
-
-
-def build_image_output(
-    image_type: ImageType, image_name: str, image: Image.Image
-) -> ImageOutput:
-    """Builds an ImageOutput and its ImageField"""
-    image_field = ImageField(
-        image_name=image_name,
-        image_type=image_type,
-    )
-    return ImageOutput(
-        image=image_field,
-        width=image.width,
-        height=image.height,
-        mode=image.mode,
-    )
-
+    #fmt: on
 
 class MaskOutput(BaseInvocationOutput):
     """Base class for invocations that output a mask"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["mask"] = "mask"
     mask:      ImageField = Field(default=None, description="The output mask")
-    # fmt: on
-
-    class Config:
-        schema_extra = {
-            "required": [
-                "type",
-                "mask",
-            ]
-        }
-
+    #fomt: on
 
+# TODO: this isn't really necessary anymore
 class LoadImageInvocation(BaseInvocation):
-    """Load an image and provide it as output."""
-
-    # fmt: off
+    """Load an image from a filename and provide it as output."""
+    #fmt: off
     type: Literal["load_image"] = "load_image"
 
     # Inputs
     image_type: ImageType = Field(description="The type of the image")
     image_name:       str = Field(description="The name of the image")
-    # fmt: on
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get(self.image_type, self.image_name)
+    #fmt: on
 
-        return build_image_output(
-            image_type=self.image_type,
-            image_name=self.image_name,
-            image=image,
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        return ImageOutput(
+            image=ImageField(image_type=self.image_type, image_name=self.image_name)
         )
 
 
@@ -112,17 +69,16 @@ class ShowImageInvocation(BaseInvocation):
 
         # TODO: how to handle failure?
 
-        return build_image_output(
-            image_type=self.image.image_type,
-            image_name=self.image.image_name,
-            image=image,
+        return ImageOutput(
+            image=ImageField(
+                image_type=self.image.image_type, image_name=self.image.image_name
+            )
         )
 
 
-class CropImageInvocation(BaseInvocation, PILInvocationConfig):
+class CropImageInvocation(BaseInvocation):
     """Crops an image to a specified box. The box can be outside of the image."""
-
-    # fmt: off
+    #fmt: off
     type: Literal["crop"] = "crop"
 
     # Inputs
@@ -131,7 +87,7 @@ class CropImageInvocation(BaseInvocation, PILInvocationConfig):
     y:      int = Field(default=0, description="The top y coordinate of the crop rectangle")
     width:  int = Field(default=512, gt=0, description="The width of the crop rectangle")
     height: int = Field(default=512, gt=0, description="The height of the crop rectangle")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
@@ -147,23 +103,15 @@ class CropImageInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-        
-        context.services.images.save(image_type, image_name, image_crop, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=image_crop,
+        context.services.images.save(image_type, image_name, image_crop)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
-class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
+class PasteImageInvocation(BaseInvocation):
     """Pastes an image into another image."""
-
-    # fmt: off
+    #fmt: off
     type: Literal["paste"] = "paste"
 
     # Inputs
@@ -172,7 +120,7 @@ class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
     mask: Optional[ImageField] = Field(default=None, description="The mask to use when pasting")
     x:                     int = Field(default=0, description="The left x coordinate at which to paste the image")
     y:                     int = Field(default=0, description="The top y coordinate at which to paste the image")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         base_image = context.services.images.get(
@@ -185,7 +133,7 @@ class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
             None
             if self.mask is None
             else ImageOps.invert(
-                context.services.images.get(self.mask.image_type, self.mask.image_name)
+                services.images.get(self.mask.image_type, self.mask.image_name)
             )
         )
         # TODO: probably shouldn't invert mask here... should user be required to do it?
@@ -205,29 +153,21 @@ class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-        
-        context.services.images.save(image_type, image_name, new_image, metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=new_image,
+        context.services.images.save(image_type, image_name, new_image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
-class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
+class MaskFromAlphaInvocation(BaseInvocation):
     """Extracts the alpha channel of an image as a mask."""
-
-    # fmt: off
+    #fmt: off
     type: Literal["tomask"] = "tomask"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to create the mask from")
     invert:      bool = Field(default=False, description="Whether or not to invert the mask")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
         image = context.services.images.get(
@@ -242,27 +182,22 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, image_mask, metadata)
+        context.services.images.save(image_type, image_name, image_mask)
         return MaskOutput(mask=ImageField(image_type=image_type, image_name=image_name))
 
 
-class BlurInvocation(BaseInvocation, PILInvocationConfig):
+class BlurInvocation(BaseInvocation):
     """Blurs an image"""
 
-    # fmt: off
+    #fmt: off
     type: Literal["blur"] = "blur"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to blur")
     radius:     float = Field(default=8.0, ge=0, description="The blur radius")
     blur_type: Literal["gaussian", "box"] = Field(default="gaussian", description="The type of blur")
-    # fmt: on
-
+    #fmt: on
+    
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
@@ -279,28 +214,22 @@ class BlurInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, blur_image, metadata)
-        return build_image_output(
-            image_type=image_type, image_name=image_name, image=blur_image
+        context.services.images.save(image_type, image_name, blur_image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
-class LerpInvocation(BaseInvocation, PILInvocationConfig):
+class LerpInvocation(BaseInvocation):
     """Linear interpolation of all pixels of an image"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["lerp"] = "lerp"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to lerp")
     min: int = Field(default=0, ge=0, le=255, description="The minimum output value")
     max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
@@ -316,29 +245,23 @@ class LerpInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, lerp_image, metadata)
-        return build_image_output(
-            image_type=image_type, image_name=image_name, image=lerp_image
+        context.services.images.save(image_type, image_name, lerp_image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
 
 
-class InverseLerpInvocation(BaseInvocation, PILInvocationConfig):
+class InverseLerpInvocation(BaseInvocation):
     """Inverse linear interpolation of all pixels of an image"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["ilerp"] = "ilerp"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to lerp")
     min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
     max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
-    # fmt: on
-
+    #fmt: on
+    
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
@@ -358,12 +281,7 @@ class InverseLerpInvocation(BaseInvocation, PILInvocationConfig):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
-        )
-
-        context.services.images.save(image_type, image_name, ilerp_image, metadata)
-        return build_image_output(
-            image_type=image_type, image_name=image_name, image=ilerp_image
+        context.services.images.save(image_type, image_name, ilerp_image)
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )

invokeai/app/invocations/latent.py

@@ -1,435 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-import random
-from typing import Literal, Optional
-from pydantic import BaseModel, Field
-import torch
-
-from invokeai.app.invocations.util.choose_model import choose_model
-
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
-
-from ...backend.model_management.model_manager import ModelManager
-from ...backend.util.devices import choose_torch_device, torch_dtype
-from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
-from ...backend.image_util.seamless import configure_model_padding
-from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-import numpy as np
-from ..services.image_storage import ImageType
-from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput, build_image_output
-from .compel import ConditioningField
-from ...backend.stable_diffusion import PipelineIntermediateState
-from diffusers.schedulers import SchedulerMixin as Scheduler
-import diffusers
-from diffusers import DiffusionPipeline
-
-
-class LatentsField(BaseModel):
-    """A latents field used for passing latents between invocations"""
-
-    latents_name: Optional[str] = Field(default=None, description="The name of the latents")
-
-    class Config:
-        schema_extra = {"required": ["latents_name"]}
-
-class LatentsOutput(BaseInvocationOutput):
-    """Base class for invocations that output latents"""
-    #fmt: off
-    type: Literal["latent_output"] = "latent_output"
-    latents: LatentsField            = Field(default=None, description="The output latents")
-    #fmt: on
-
-class NoiseOutput(BaseInvocationOutput):
-    """Invocation noise output"""
-    #fmt: off
-    type: Literal["noise_output"] = "noise_output"
-    noise: LatentsField            = Field(default=None, description="The output noise")
-    #fmt: on
-
-
-# TODO: this seems like a hack
-scheduler_map = dict(
-    ddim=diffusers.DDIMScheduler,
-    dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_dpm_2=diffusers.KDPM2DiscreteScheduler,
-    k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
-    k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
-    k_euler=diffusers.EulerDiscreteScheduler,
-    k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
-    k_heun=diffusers.HeunDiscreteScheduler,
-    k_lms=diffusers.LMSDiscreteScheduler,
-    plms=diffusers.PNDMScheduler,
-)
-
-
-SAMPLER_NAME_VALUES = Literal[
-    tuple(list(scheduler_map.keys()))
-]
-
-
-def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-    scheduler_class = scheduler_map.get(scheduler_name,'ddim')
-    scheduler = scheduler_class.from_config(model.scheduler.config)
-    # hack copied over from generate.py
-    if not hasattr(scheduler, 'uses_inpainting_model'):
-        scheduler.uses_inpainting_model = lambda: False
-    return scheduler
-
-
-def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_channels:int=4, use_mps_noise:bool=False, downsampling_factor:int = 8):
-    # limit noise to only the diffusion image channels, not the mask channels
-    input_channels = min(latent_channels, 4)
-    use_device = "cpu" if (use_mps_noise or device.type == "mps") else device
-    generator = torch.Generator(device=use_device).manual_seed(seed)
-    x = torch.randn(
-        [
-            1,
-            input_channels,
-            height // downsampling_factor,
-            width //  downsampling_factor,
-        ],
-        dtype=torch_dtype(device),
-        device=use_device,
-        generator=generator,
-    ).to(device)
-    # if self.perlin > 0.0:
-    #     perlin_noise = self.get_perlin_noise(
-    #         width // self.downsampling_factor, height // self.downsampling_factor
-    #     )
-    #     x = (1 - self.perlin) * x + self.perlin * perlin_noise
-    return x
-
-
-def random_seed():
-    return random.randint(0, np.iinfo(np.uint32).max)
-
-
-class NoiseInvocation(BaseInvocation):
-    """Generates latent noise."""
-
-    type: Literal["noise"] = "noise"
-
-    # Inputs
-    seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
-    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting noise", )
-
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "noise"],
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> NoiseOutput:
-        device = torch.device(choose_torch_device())
-        noise = get_noise(self.width, self.height, device, self.seed)
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, noise)
-        return NoiseOutput(
-            noise=LatentsField(latents_name=name)
-        )
-
-
-# Text to image
-class TextToLatentsInvocation(BaseInvocation):
-    """Generates latents from conditionings."""
-
-    type: Literal["t2l"] = "t2l"
-
-    # Inputs
-    # fmt: off
-    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
-    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
-    noise: Optional[LatentsField] = Field(description="The noise to use")
-    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
-    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-    seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
-    model:       str = Field(default="", description="The model to use (currently ignored)")
-    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
-    # fmt: on
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "image"],
-                "type_hints": {
-                  "model": "model"
-                }
-            },
-        }
-
-    # TODO: pass this an emitter method or something? or a session for dispatching?
-    def dispatch_progress(
-        self, context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState
-    ) -> None:
-        stable_diffusion_step_callback(
-            context=context,
-            intermediate_state=intermediate_state,
-            node=self.dict(),
-            source_node_id=source_node_id,
-        )
-
-    def get_model(self, model_manager: ModelManager) -> StableDiffusionGeneratorPipeline:
-        model_info = choose_model(model_manager, self.model)
-        model_name = model_info['model_name']
-        model_hash = model_info['hash']
-        model: StableDiffusionGeneratorPipeline = model_info['model']
-        model.scheduler = get_scheduler(
-            model=model,
-            scheduler_name=self.scheduler
-        )
-
-        if isinstance(model, DiffusionPipeline):
-            for component in [model.unet, model.vae]:
-                configure_model_padding(component,
-                                        self.seamless,
-                                        self.seamless_axes
-                                        )
-        else:
-            configure_model_padding(model,
-                                    self.seamless,
-                                    self.seamless_axes
-                                    )
-
-        return model
-
-
-    def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
-        c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
-        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
-
-        conditioning_data = ConditioningData(
-            uc,
-            c,
-            self.cfg_scale,
-            extra_conditioning_info,
-            postprocessing_settings=PostprocessingSettings(
-                threshold=0.0,#threshold,
-                warmup=0.2,#warmup,
-                h_symmetry_time_pct=None,#h_symmetry_time_pct,
-                v_symmetry_time_pct=None#v_symmetry_time_pct,
-            ),
-        ).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta)
-        return conditioning_data
-
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
-
-        model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(context, model)
-
-        # TODO: Verify the noise is the right size
-
-        result_latents, result_attention_map_saver = model.latents_from_embeddings(
-            latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)),
-            noise=noise,
-            num_inference_steps=self.steps,
-            conditioning_data=conditioning_data,
-            callback=step_callback
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
-
-
-class LatentsToLatentsInvocation(TextToLatentsInvocation):
-    """Generates latents using latents as base image."""
-
-    type: Literal["l2l"] = "l2l"
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents"],
-                "type_hints": {
-                    "model": "model"
-                }
-            },
-        }
-
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
-    strength: float = Field(default=0.5, description="The strength of the latents to use")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        noise = context.services.latents.get(self.noise.latents_name)
-        latent = context.services.latents.get(self.latents.latents_name)
-
-        # Get the source node id (we are invoking the prepared node)
-        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
-
-        def step_callback(state: PipelineIntermediateState):
-            self.dispatch_progress(context, source_node_id, state)
-
-        model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
-
-        # TODO: Verify the noise is the right size
-
-        initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
-            latent, device=model.device, dtype=latent.dtype
-        )
-
-        timesteps, _ = model.get_img2img_timesteps(
-            self.steps,
-            self.strength,
-            device=model.device,
-        )
-
-        result_latents, result_attention_map_saver = model.latents_from_embeddings(
-            latents=initial_latents,
-            timesteps=timesteps,
-            noise=noise,
-            num_inference_steps=self.steps,
-            conditioning_data=conditioning_data,
-            callback=step_callback
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.set(name, result_latents)
-        return LatentsOutput(
-            latents=LatentsField(latents_name=name)
-        )
-
-
-# Latent to image
-class LatentsToImageInvocation(BaseInvocation):
-    """Generates an image from latents."""
-
-    type: Literal["l2i"] = "l2i"
-
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
-    model: str = Field(default="", description="The model to use")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "image"],
-                "type_hints": {
-                  "model": "model"
-                }
-            },
-        }
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        # TODO: this only really needs the vae
-        model_info = choose_model(context.services.model_manager, self.model)
-        model: StableDiffusionGeneratorPipeline = model_info['model']
-
-        with torch.inference_mode():
-            np_image = model.decode_latents(latents)
-            image = model.numpy_to_pil(np_image)[0]
-
-            image_type = ImageType.RESULT
-            image_name = context.services.images.create_name(
-                context.graph_execution_state_id, self.id
-            )
-
-            metadata = context.services.metadata.build_metadata(
-                session_id=context.graph_execution_state_id, node=self
-            )
-
-            torch.cuda.empty_cache()
-
-            context.services.images.save(image_type, image_name, image, metadata)
-            return build_image_output(
-                image_type=image_type, image_name=image_name, image=image
-            )
-
-
-LATENTS_INTERPOLATION_MODE = Literal[
-    "nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"
-]
-
-
-class ResizeLatentsInvocation(BaseInvocation):
-    """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
-
-    type: Literal["lresize"] = "lresize"
-
-    # Inputs
-    latents: Optional[LatentsField]             = Field(description="The latents to resize")
-    width: int                                  = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
-    height: int                                 = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
-    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
-    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        resized_latents = torch.nn.functional.interpolate(
-            latents,
-            size=(self.height // 8, self.width // 8),
-            mode=self.mode,
-            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.set(name, resized_latents)
-        return LatentsOutput(latents=LatentsField(latents_name=name))
-
-
-class ScaleLatentsInvocation(BaseInvocation):
-    """Scales latents by a given factor."""
-
-    type: Literal["lscale"] = "lscale"
-
-    # Inputs
-    latents: Optional[LatentsField]             = Field(description="The latents to scale")
-    scale_factor: float                         = Field(gt=0, description="The factor by which to scale the latents")
-    mode: Optional[LATENTS_INTERPOLATION_MODE]  = Field(default="bilinear", description="The interpolation mode")
-    antialias: Optional[bool]                   = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        # resizing
-        resized_latents = torch.nn.functional.interpolate(
-            latents,
-            scale_factor=self.scale_factor,
-            mode=self.mode,
-            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        context.services.latents.set(name, resized_latents)
-        return LatentsOutput(latents=LatentsField(latents_name=name))

invokeai/app/invocations/math.py

@@ -1,75 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal
-
-from pydantic import BaseModel, Field
-
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-
-
-class MathInvocationConfig(BaseModel):
-    """Helper class to provide all math invocations with additional config"""
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["math"],
-            }
-        }
-
-
-class IntOutput(BaseInvocationOutput):
-    """An integer output"""
-    #fmt: off
-    type: Literal["int_output"] = "int_output"
-    a: int = Field(default=None, description="The output integer")
-    #fmt: on
-
-
-class AddInvocation(BaseInvocation, MathInvocationConfig):
-    """Adds two numbers"""
-    #fmt: off
-    type: Literal["add"] = "add"
-    a: int = Field(default=0, description="The first number")
-    b: int = Field(default=0, description="The second number")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a + self.b)
-
-
-class SubtractInvocation(BaseInvocation, MathInvocationConfig):
-    """Subtracts two numbers"""
-    #fmt: off
-    type: Literal["sub"] = "sub"
-    a: int = Field(default=0, description="The first number")
-    b: int = Field(default=0, description="The second number")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a - self.b)
-
-
-class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
-    """Multiplies two numbers"""
-    #fmt: off
-    type: Literal["mul"] = "mul"
-    a: int = Field(default=0, description="The first number")
-    b: int = Field(default=0, description="The second number")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a * self.b)
-
-
-class DivideInvocation(BaseInvocation, MathInvocationConfig):
-    """Divides two numbers"""
-    #fmt: off
-    type: Literal["div"] = "div"
-    a: int = Field(default=0, description="The first number")
-    b: int = Field(default=0, description="The second number")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=int(self.a / self.b))

invokeai/app/invocations/params.py

@@ -1,18 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-from typing import Literal
-from pydantic import Field
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
-from .math import IntOutput
-
-# Pass-through parameter nodes - used by subgraphs
-
-class ParamIntInvocation(BaseInvocation):
-    """An integer parameter"""
-    #fmt: off
-    type: Literal["param_int"] = "param_int"
-    a: int = Field(default=0, description="The integer value")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=self.a)

invokeai/app/invocations/prompt.py

@@ -12,11 +12,3 @@ class PromptOutput(BaseInvocationOutput):
 
     prompt: str = Field(default=None, description="The output prompt")
     #fmt: on
-
-    class Config:
-        schema_extra = {
-            'required': [
-                'type',
-                'prompt',
-            ]
-        }

invokeai/app/invocations/reconstruct.py

@@ -1,11 +1,12 @@
+from datetime import datetime, timezone
 from typing import Literal, Union
 
 from pydantic import Field
 
-from invokeai.app.models.image import ImageField, ImageType
-
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput, build_image_output
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+from .baseinvocation import BaseInvocation, InvocationContext
+from .image import ImageField, ImageOutput
 
 class RestoreFaceInvocation(BaseInvocation):
     """Restores faces in an image."""
@@ -17,14 +18,6 @@ class RestoreFaceInvocation(BaseInvocation):
     strength:                float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration"  )
     #fmt: on
     
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["restoration", "image"],
-            },
-        }
-
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
@@ -43,14 +36,7 @@ class RestoreFaceInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
-
-        context.services.images.save(image_type, image_name, results[0][0], metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=results[0][0]
-        )

invokeai/app/invocations/upscale.py

@@ -1,12 +1,14 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+from datetime import datetime, timezone
 from typing import Literal, Union
 
 from pydantic import Field
 
-from invokeai.app.models.image import ImageField, ImageType
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput, build_image_output
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+from .baseinvocation import BaseInvocation, InvocationContext
+from .image import ImageField, ImageOutput
 
 
 class UpscaleInvocation(BaseInvocation):
@@ -20,15 +22,6 @@ class UpscaleInvocation(BaseInvocation):
     level: Literal[2, 4] = Field(default=2, description="The upscale level")
     #fmt: on
 
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["upscaling", "image"],
-            },
-        }
-
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.services.images.get(
             self.image.image_type, self.image.image_name
@@ -47,14 +40,7 @@ class UpscaleInvocation(BaseInvocation):
         image_name = context.services.images.create_name(
             context.graph_execution_state_id, self.id
         )
-
-        metadata = context.services.metadata.build_metadata(
-            session_id=context.graph_execution_state_id, node=self
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image=ImageField(image_type=image_type, image_name=image_name)
         )
-
-        context.services.images.save(image_type, image_name, results[0][0], metadata)
-        return build_image_output(
-            image_type=image_type,
-            image_name=image_name,
-            image=results[0][0]
-        )

invokeai/app/invocations/util/choose_model.py

@@ -1,13 +0,0 @@
-from invokeai.backend.model_management.model_manager import ModelManager
-
-
-def choose_model(model_manager: ModelManager, model_name: str):
-    """Returns the default model if the `model_name` not a valid model, else returns the selected model."""
-    logger = model_manager.logger
-    if model_manager.valid_model(model_name):
-        model = model_manager.get_model(model_name)
-    else:
-        model = model_manager.get_model()
-        logger.warning(f"{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead.")
-
-    return model

invokeai/app/models/exceptions.py

@@ -1,3 +0,0 @@
-class CanceledException(Exception):
-    """Execution canceled by user."""
-    pass

invokeai/app/models/image.py

@@ -1,29 +0,0 @@
-from enum import Enum
-from typing import Optional
-from pydantic import BaseModel, Field
-
-
-class ImageType(str, Enum):
-    RESULT = "results"
-    INTERMEDIATE = "intermediates"
-    UPLOAD = "uploads"
-
-
-def is_image_type(obj):
-    try:
-        ImageType(obj)
-    except ValueError:
-        return False
-    return True
-
-
-class ImageField(BaseModel):
-    """An image field used for passing image objects between invocations"""
-
-    image_type: ImageType = Field(
-        default=ImageType.RESULT, description="The type of the image"
-    )
-    image_name: Optional[str] = Field(default=None, description="The name of the image")
-
-    class Config:
-        schema_extra = {"required": ["image_type", "image_name"]}

invokeai/app/services/default_graphs.py

@@ -1,63 +0,0 @@
-from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
-from ..invocations.compel import CompelInvocation
-from ..invocations.params import ParamIntInvocation
-from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
-from .item_storage import ItemStorageABC
-
-
-default_text_to_image_graph_id = '539b2af5-2b4d-4d8c-8071-e54a3255fc74'
-
-
-def create_text_to_image() -> LibraryGraph:
-    return LibraryGraph(
-        id=default_text_to_image_graph_id,
-        name='t2i',
-        description='Converts text to an image',
-        graph=Graph(
-            nodes={
-                'width': ParamIntInvocation(id='width', a=512),
-                'height': ParamIntInvocation(id='height', a=512),
-                'seed': ParamIntInvocation(id='seed', a=-1),
-                '3': NoiseInvocation(id='3'),
-                '4': CompelInvocation(id='4'),
-                '5': CompelInvocation(id='5'),
-                '6': TextToLatentsInvocation(id='6'),
-                '7': LatentsToImageInvocation(id='7'),
-            },
-            edges=[
-                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
-                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
-                Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
-                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='6', field='noise')),
-                Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
-                Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
-                Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
-            ]
-        ),
-        exposed_inputs=[
-            ExposedNodeInput(node_path='4', field='prompt', alias='positive_prompt'),
-            ExposedNodeInput(node_path='5', field='prompt', alias='negative_prompt'),
-            ExposedNodeInput(node_path='width', field='a', alias='width'),
-            ExposedNodeInput(node_path='height', field='a', alias='height'),
-            ExposedNodeInput(node_path='seed', field='a', alias='seed'),
-        ],
-        exposed_outputs=[
-            ExposedNodeOutput(node_path='7', field='image', alias='image')
-        ])
-
-
-def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
-    """Creates the default system graphs, or adds new versions if the old ones don't match"""
-
-    graphs: list[LibraryGraph] = list()
-
-    text_to_image = graph_library.get(default_text_to_image_graph_id)
-    
-    # TODO: Check if the graph is the same as the default one, and if not, update it
-    #if text_to_image is None:
-    text_to_image = create_text_to_image()
-    graph_library.set(text_to_image)
-
-    graphs.append(text_to_image)
-
-    return graphs

invokeai/app/services/events.py

@@ -1,9 +1,10 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Any
-from invokeai.app.api.models.images import ProgressImage
-from invokeai.app.util.misc import get_timestamp
+from typing import Any, Dict, TypedDict
 
+ProgressImage = TypedDict(
+    "ProgressImage", {"dataURL": str, "width": int, "height": int}
+)
 
 class EventServiceBase:
     session_event: str = "session_event"
@@ -13,8 +14,7 @@ class EventServiceBase:
     def dispatch(self, event_name: str, payload: Any) -> None:
         pass
 
-    def __emit_session_event(self, event_name: str, payload: dict) -> None:
-        payload["timestamp"] = get_timestamp()
+    def __emit_session_event(self, event_name: str, payload: Dict) -> None:
         self.dispatch(
             event_name=EventServiceBase.session_event,
             payload=dict(event=event_name, data=payload),
@@ -25,8 +25,7 @@ class EventServiceBase:
     def emit_generator_progress(
         self,
         graph_execution_state_id: str,
-        node: dict,
-        source_node_id: str,
+        invocation_id: str,
         progress_image: ProgressImage | None,
         step: int,
         total_steps: int,
@@ -36,60 +35,48 @@ class EventServiceBase:
             event_name="generator_progress",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
-                progress_image=progress_image.dict() if progress_image is not None else None,
+                invocation_id=invocation_id,
+                progress_image=progress_image,
                 step=step,
                 total_steps=total_steps,
             ),
         )
 
     def emit_invocation_complete(
-        self,
-        graph_execution_state_id: str,
-        result: dict,
-        node: dict,
-        source_node_id: str,
+        self, graph_execution_state_id: str, invocation_id: str, result: Dict
     ) -> None:
         """Emitted when an invocation has completed"""
         self.__emit_session_event(
             event_name="invocation_complete",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
+                invocation_id=invocation_id,
                 result=result,
             ),
         )
 
     def emit_invocation_error(
-        self,
-        graph_execution_state_id: str,
-        node: dict,
-        source_node_id: str,
-        error: str,
+        self, graph_execution_state_id: str, invocation_id: str, error: str
     ) -> None:
         """Emitted when an invocation has completed"""
         self.__emit_session_event(
             event_name="invocation_error",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
+                invocation_id=invocation_id,
                 error=error,
             ),
         )
 
     def emit_invocation_started(
-        self, graph_execution_state_id: str, node: dict, source_node_id: str
+        self, graph_execution_state_id: str, invocation_id: str
     ) -> None:
         """Emitted when an invocation has started"""
         self.__emit_session_event(
             event_name="invocation_started",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
-                node=node,
-                source_node_id=source_node_id,
+                invocation_id=invocation_id,
             ),
         )
 
@@ -97,7 +84,5 @@ class EventServiceBase:
         """Emitted when a session has completed all invocations"""
         self.__emit_session_event(
             event_name="graph_execution_state_complete",
-            payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-            ),
+            payload=dict(graph_execution_state_id=graph_execution_state_id),
         )

invokeai/app/services/graph.py

@@ -2,6 +2,7 @@
 
 import copy
 import itertools
+import traceback
 import uuid
 from types import NoneType
 from typing import (
@@ -16,7 +17,7 @@ from typing import (
 )
 
 import networkx as nx
-from pydantic import BaseModel, root_validator, validator
+from pydantic import BaseModel, validator
 from pydantic.fields import Field
 
 from ..invocations import *
@@ -25,6 +26,7 @@ from ..invocations.baseinvocation import (
     BaseInvocationOutput,
     InvocationContext,
 )
+from .invocation_services import InvocationServices
 
 
 class EdgeConnection(BaseModel):
@@ -125,13 +127,6 @@ class NodeAlreadyExecutedError(Exception):
 class GraphInvocationOutput(BaseInvocationOutput):
     type: Literal["graph_output"] = "graph_output"
 
-    class Config:
-        schema_extra = {
-            'required': [
-                'type',
-                'image',
-            ]
-        }
 
 # TODO: Fill this out and move to invocations
 class GraphInvocation(BaseInvocation):
@@ -152,13 +147,6 @@ class IterateInvocationOutput(BaseInvocationOutput):
 
     item: Any = Field(description="The item being iterated over")
 
-    class Config:
-        schema_extra = {
-            'required': [
-                'type',
-                'item',
-            ]
-        }
 
 # TODO: Fill this out and move to invocations
 class IterateInvocation(BaseInvocation):
@@ -181,13 +169,6 @@ class CollectInvocationOutput(BaseInvocationOutput):
 
     collection: list[Any] = Field(description="The collection of input items")
 
-    class Config:
-        schema_extra = {
-            'required': [
-                'type',
-                'collection',
-            ]
-        }
 
 class CollectInvocation(BaseInvocation):
     """Collects values into a collection"""
@@ -213,7 +194,7 @@ InvocationOutputsUnion = Union[BaseInvocationOutput.get_all_subclasses_tuple()]
 
 
 class Graph(BaseModel):
-    id: str = Field(description="The id of this graph", default_factory=lambda: uuid.uuid4().__str__())
+    id: str = Field(description="The id of this graph", default_factory=uuid.uuid4)
     # TODO: use a list (and never use dict in a BaseModel) because pydantic/fastapi hates me
     nodes: dict[str, Annotated[InvocationsUnion, Field(discriminator="type")]] = Field(
         description="The nodes in this graph", default_factory=dict
@@ -281,8 +262,7 @@ class Graph(BaseModel):
         :raises InvalidEdgeError: the provided edge is invalid.
         """
 
-        self._validate_edge(edge)
-        if edge not in self.edges:
+        if self._is_edge_valid(edge) and edge not in self.edges:
             self.edges.append(edge)
         else:
             raise InvalidEdgeError()
@@ -353,7 +333,7 @@ class Graph(BaseModel):
 
         return True
 
-    def _validate_edge(self, edge: Edge):
+    def _is_edge_valid(self, edge: Edge) -> bool:
         """Validates that a new edge doesn't create a cycle in the graph"""
 
         # Validate that the nodes exist (edges may contain node paths, so we can't just check for nodes directly)
@@ -361,53 +341,54 @@ class Graph(BaseModel):
             from_node = self.get_node(edge.source.node_id)
             to_node = self.get_node(edge.destination.node_id)
         except NodeNotFoundError:
-            raise InvalidEdgeError("One or both nodes don't exist")
+            return False
 
         # Validate that an edge to this node+field doesn't already exist
         input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
         if len(input_edges) > 0 and not isinstance(to_node, CollectInvocation):
-            raise InvalidEdgeError(f'Edge to node {edge.destination.node_id} field {edge.destination.field} already exists')
+            return False
 
         # Validate that no cycles would be created
         g = self.nx_graph_flat()
         g.add_edge(edge.source.node_id, edge.destination.node_id)
         if not nx.is_directed_acyclic_graph(g):
-            raise InvalidEdgeError(f'Edge creates a cycle in the graph')
+            return False
 
         # Validate that the field types are compatible
         if not are_connections_compatible(
             from_node, edge.source.field, to_node, edge.destination.field
         ):
-            raise InvalidEdgeError(f'Fields are incompatible')
+            return False
 
         # Validate if iterator output type matches iterator input type (if this edge results in both being set)
         if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
             if not self._is_iterator_connection_valid(
                 edge.destination.node_id, new_input=edge.source
             ):
-                raise InvalidEdgeError(f'Iterator input type does not match iterator output type')
+                return False
 
         # Validate if iterator input type matches output type (if this edge results in both being set)
         if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
             if not self._is_iterator_connection_valid(
                 edge.source.node_id, new_output=edge.destination
             ):
-                raise InvalidEdgeError(f'Iterator output type does not match iterator input type')
+                return False
 
         # Validate if collector input type matches output type (if this edge results in both being set)
         if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
             if not self._is_collector_connection_valid(
                 edge.destination.node_id, new_input=edge.source
             ):
-                raise InvalidEdgeError(f'Collector output type does not match collector input type')
+                return False
 
         # Validate if collector output type matches input type (if this edge results in both being set)
         if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
             if not self._is_collector_connection_valid(
                 edge.source.node_id, new_output=edge.destination
             ):
-                raise InvalidEdgeError(f'Collector input type does not match collector output type')
+                return False
 
+        return True
 
     def has_node(self, node_path: str) -> bool:
         """Determines whether or not a node exists in the graph."""
@@ -731,7 +712,7 @@ class Graph(BaseModel):
         for sgn in (
             gn for gn in self.nodes.values() if isinstance(gn, GraphInvocation)
         ):
-            g = sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
+            sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
 
         # TODO: figure out if iteration nodes need to be expanded
 
@@ -748,7 +729,9 @@ class Graph(BaseModel):
 class GraphExecutionState(BaseModel):
     """Tracks the state of a graph execution"""
 
-    id: str = Field(description="The id of the execution state", default_factory=lambda: uuid.uuid4().__str__())
+    id: str = Field(
+        description="The id of the execution state", default_factory=uuid.uuid4
+    )
 
     # TODO: Store a reference to the graph instead of the actual graph?
     graph: Graph = Field(description="The graph being executed")
@@ -790,6 +773,9 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
+    # Declare all fields as required; necessary for OpenAPI schema generation build.
+    # Technically only fields without a `default_factory` need to be listed here.
+    # See: https://github.com/pydantic/pydantic/discussions/4577
     class Config:
         schema_extra = {
             'required': [
@@ -854,8 +840,7 @@ class GraphExecutionState(BaseModel):
 
     def is_complete(self) -> bool:
         """Returns true if the graph is complete"""
-        node_ids = set(self.graph.nx_graph_flat().nodes)
-        return self.has_error() or all((k in self.executed for k in node_ids))
+        return self.has_error() or all((k in self.executed for k in self.graph.nodes))
 
     def has_error(self) -> bool:
         """Returns true if the graph has any errors"""
@@ -943,11 +928,11 @@ class GraphExecutionState(BaseModel):
 
     def _iterator_graph(self) -> nx.DiGraph:
         """Gets a DiGraph with edges to collectors removed so an ancestor search produces all active iterators for any node"""
-        g = self.graph.nx_graph_flat()
+        g = self.graph.nx_graph()
         collectors = (
             n
             for n in self.graph.nodes
-            if isinstance(self.graph.get_node(n), CollectInvocation)
+            if isinstance(self.graph.nodes[n], CollectInvocation)
         )
         for c in collectors:
             g.remove_edges_from(list(g.in_edges(c)))
@@ -959,7 +944,7 @@ class GraphExecutionState(BaseModel):
         iterators = [
             n
             for n in nx.ancestors(g, node_id)
-            if isinstance(self.graph.get_node(n), IterateInvocation)
+            if isinstance(self.graph.nodes[n], IterateInvocation)
         ]
         return iterators
 
@@ -1063,8 +1048,9 @@ class GraphExecutionState(BaseModel):
                 n
                 for n in prepared_nodes
                 if all(
-                    nx.has_path(execution_graph, pit[0], n)
+                    pit
                     for pit in parent_iterators
+                    if nx.has_path(execution_graph, pit[0], n)
                 )
             ),
             None,
@@ -1095,9 +1081,7 @@ class GraphExecutionState(BaseModel):
 
     # TODO: Add API for modifying underlying graph that checks if the change will be valid given the current execution state
     def _is_edge_valid(self, edge: Edge) -> bool:
-        try:
-            self.graph._validate_edge(edge)
-        except InvalidEdgeError:
+        if not self._is_edge_valid(edge):
             return False
 
         # Invalid if destination has already been prepared or executed
@@ -1143,52 +1127,4 @@ class GraphExecutionState(BaseModel):
         self.graph.delete_edge(edge)
 
 
-class ExposedNodeInput(BaseModel):
-    node_path: str = Field(description="The node path to the node with the input")
-    field: str = Field(description="The field name of the input")
-    alias: str = Field(description="The alias of the input")
-
-
-class ExposedNodeOutput(BaseModel):
-    node_path: str = Field(description="The node path to the node with the output")
-    field: str = Field(description="The field name of the output")
-    alias: str = Field(description="The alias of the output")
-
-class LibraryGraph(BaseModel):
-    id: str = Field(description="The unique identifier for this library graph", default_factory=uuid.uuid4)
-    graph: Graph = Field(description="The graph")
-    name: str = Field(description="The name of the graph")
-    description: str = Field(description="The description of the graph")
-    exposed_inputs: list[ExposedNodeInput] = Field(description="The inputs exposed by this graph", default_factory=list)
-    exposed_outputs: list[ExposedNodeOutput] = Field(description="The outputs exposed by this graph", default_factory=list)
-
-    @validator('exposed_inputs', 'exposed_outputs')
-    def validate_exposed_aliases(cls, v):
-        if len(v) != len(set(i.alias for i in v)):
-            raise ValueError("Duplicate exposed alias")
-        return v
-
-    @root_validator
-    def validate_exposed_nodes(cls, values):
-        graph = values['graph']
-
-        # Validate exposed inputs
-        for exposed_input in values['exposed_inputs']:
-            if not graph.has_node(exposed_input.node_path):
-                raise ValueError(f"Exposed input node {exposed_input.node_path} does not exist")
-            node = graph.get_node(exposed_input.node_path)
-            if get_input_field(node, exposed_input.field) is None:
-                raise ValueError(f"Exposed input field {exposed_input.field} does not exist on node {exposed_input.node_path}")
-
-        # Validate exposed outputs
-        for exposed_output in values['exposed_outputs']:
-            if not graph.has_node(exposed_output.node_path):
-                raise ValueError(f"Exposed output node {exposed_output.node_path} does not exist")
-            node = graph.get_node(exposed_output.node_path)
-            if get_output_field(node, exposed_output.field) is None:
-                raise ValueError(f"Exposed output field {exposed_output.field} does not exist on node {exposed_output.node_path}")
-
-        return values
-
-
 GraphInvocation.update_forward_refs()

invokeai/app/services/image_storage.py

@@ -1,29 +1,22 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
+import datetime
 import os
-from glob import glob
 from abc import ABC, abstractmethod
+from enum import Enum
 from pathlib import Path
 from queue import Queue
-from typing import Dict, List
+from typing import Dict
 
 from PIL.Image import Image
-import PIL.Image as PILImage
-from send2trash import send2trash
-from invokeai.app.api.models.images import (
-    ImageResponse,
-    ImageResponseMetadata,
-    SavedImage,
-)
-from invokeai.app.models.image import ImageType
-from invokeai.app.services.metadata import (
-    InvokeAIMetadata,
-    MetadataServiceBase,
-    build_invokeai_metadata_pnginfo,
-)
-from invokeai.app.services.item_storage import PaginatedResults
-from invokeai.app.util.misc import get_timestamp
-from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
+
+from invokeai.backend.image_util import PngWriter
+
+
+class ImageType(str, Enum):
+    RESULT = "results"
+    INTERMEDIATE = "intermediates"
+    UPLOAD = "uploads"
 
 
 class ImageStorageBase(ABC):
@@ -31,74 +24,40 @@ class ImageStorageBase(ABC):
 
     @abstractmethod
     def get(self, image_type: ImageType, image_name: str) -> Image:
-        """Retrieves an image as PIL Image."""
-        pass
-
-    @abstractmethod
-    def list(
-        self, image_type: ImageType, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[ImageResponse]:
-        """Gets a paginated list of images."""
         pass
 
     # TODO: make this a bit more flexible for e.g. cloud storage
     @abstractmethod
-    def get_path(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        """Gets the internal path to an image or its thumbnail."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def get_uri(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        """Gets the external URI to an image or its thumbnail."""
-        pass
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates an image path."""
+    def get_path(self, image_type: ImageType, image_name: str) -> str:
         pass
 
     @abstractmethod
-    def save(
-        self,
-        image_type: ImageType,
-        image_name: str,
-        image: Image,
-        metadata: InvokeAIMetadata | None = None,
-    ) -> SavedImage:
-        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
+    def save(self, image_type: ImageType, image_name: str, image: Image) -> None:
         pass
 
     @abstractmethod
     def delete(self, image_type: ImageType, image_name: str) -> None:
-        """Deletes an image and its thumbnail (if one exists)."""
         pass
 
     def create_name(self, context_id: str, node_id: str) -> str:
-        """Creates a unique contextual image filename."""
-        return f"{context_id}_{node_id}_{str(get_timestamp())}.png"
+        return f"{context_id}_{node_id}_{str(int(datetime.datetime.now(datetime.timezone.utc).timestamp()))}.png"
 
 
 class DiskImageStorage(ImageStorageBase):
     """Stores images on disk"""
 
     __output_folder: str
+    __pngWriter: PngWriter
     __cache_ids: Queue  # TODO: this is an incredibly naive cache
     __cache: Dict[str, Image]
     __max_cache_size: int
-    __metadata_service: MetadataServiceBase
 
-    def __init__(self, output_folder: str, metadata_service: MetadataServiceBase):
+    def __init__(self, output_folder: str):
         self.__output_folder = output_folder
+        self.__pngWriter = PngWriter(output_folder)
         self.__cache = dict()
         self.__cache_ids = Queue()
         self.__max_cache_size = 10  # TODO: get this from config
-        self.__metadata_service = metadata_service
 
         Path(output_folder).mkdir(parents=True, exist_ok=True)
 
@@ -107,61 +66,6 @@ class DiskImageStorage(ImageStorageBase):
             Path(os.path.join(output_folder, image_type)).mkdir(
                 parents=True, exist_ok=True
             )
-            Path(os.path.join(output_folder, image_type, "thumbnails")).mkdir(
-                parents=True, exist_ok=True
-            )
-
-    def list(
-        self, image_type: ImageType, page: int = 0, per_page: int = 10
-    ) -> PaginatedResults[ImageResponse]:
-        dir_path = os.path.join(self.__output_folder, image_type)
-        image_paths = glob(f"{dir_path}/*.png")
-        count = len(image_paths)
-
-        sorted_image_paths = sorted(
-            glob(f"{dir_path}/*.png"), key=os.path.getctime, reverse=True
-        )
-
-        page_of_image_paths = sorted_image_paths[
-            page * per_page : (page + 1) * per_page
-        ]
-
-        page_of_images: List[ImageResponse] = []
-
-        for path in page_of_image_paths:
-            filename = os.path.basename(path)
-            img = PILImage.open(path)
-
-            invokeai_metadata = self.__metadata_service.get_metadata(img)
-
-            page_of_images.append(
-                ImageResponse(
-                    image_type=image_type.value,
-                    image_name=filename,
-                    # TODO: DiskImageStorage should not be building URLs...?
-                    image_url=self.get_uri(image_type, filename),
-                    thumbnail_url=self.get_uri(image_type, filename, True),
-                    # TODO: Creation of this object should happen elsewhere (?), just making it fit here so it works
-                    metadata=ImageResponseMetadata(
-                        created=int(os.path.getctime(path)),
-                        width=img.width,
-                        height=img.height,
-                        invokeai=invokeai_metadata,
-                    ),
-                )
-            )
-
-        page_count_trunc = int(count / per_page)
-        page_count_mod = count % per_page
-        page_count = page_count_trunc if page_count_mod == 0 else page_count_trunc + 1
-
-        return PaginatedResults[ImageResponse](
-            items=page_of_images,
-            page=page,
-            pages=page_count,
-            per_page=per_page,
-            total=count,
-        )
 
     def get(self, image_type: ImageType, image_name: str) -> Image:
         image_path = self.get_path(image_type, image_name)
@@ -169,97 +73,33 @@ class DiskImageStorage(ImageStorageBase):
         if cache_item:
             return cache_item
 
-        image = PILImage.open(image_path)
+        image = Image.open(image_path)
         self.__set_cache(image_path, image)
         return image
 
     # TODO: make this a bit more flexible for e.g. cloud storage
-    def get_path(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        # strip out any relative path shenanigans
-        basename = os.path.basename(image_name)
+    def get_path(self, image_type: ImageType, image_name: str) -> str:
+        path = os.path.join(self.__output_folder, image_type, image_name)
+        return path
 
-        if is_thumbnail:
-            path = os.path.join(
-                self.__output_folder, image_type, "thumbnails", basename
-            )
-        else:
-            path = os.path.join(self.__output_folder, image_type, basename)
+    def save(self, image_type: ImageType, image_name: str, image: Image) -> None:
+        image_subpath = os.path.join(image_type, image_name)
+        self.__pngWriter.save_image_and_prompt_to_png(
+            image, "", image_subpath, None
+        )  # TODO: just pass full path to png writer
 
-        abspath = os.path.abspath(path)
-
-        return abspath
-
-    def get_uri(
-        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
-    ) -> str:
-        # strip out any relative path shenanigans
-        basename = os.path.basename(image_name)
-
-        if is_thumbnail:
-            thumbnail_basename = get_thumbnail_name(basename)
-            uri = f"api/v1/images/{image_type.value}/thumbnails/{thumbnail_basename}"
-        else:
-            uri = f"api/v1/images/{image_type.value}/{basename}"
-
-        return uri
-
-    def validate_path(self, path: str) -> bool:
-        try:
-            os.stat(path)
-            return True
-        except Exception:
-            return False
-
-    def save(
-        self,
-        image_type: ImageType,
-        image_name: str,
-        image: Image,
-        metadata: InvokeAIMetadata | None = None,
-    ) -> SavedImage:
         image_path = self.get_path(image_type, image_name)
-
-        # TODO: Reading the image and then saving it strips the metadata...
-        if metadata:
-            pnginfo = build_invokeai_metadata_pnginfo(metadata=metadata)
-            image.save(image_path, "PNG", pnginfo=pnginfo)
-        else:
-            image.save(image_path)  # this saved image has an empty info
-
-        thumbnail_name = get_thumbnail_name(image_name)
-        thumbnail_path = self.get_path(image_type, thumbnail_name, is_thumbnail=True)
-        thumbnail_image = make_thumbnail(image)
-        thumbnail_image.save(thumbnail_path)
-
         self.__set_cache(image_path, image)
-        self.__set_cache(thumbnail_path, thumbnail_image)
-
-        return SavedImage(
-            image_name=image_name,
-            thumbnail_name=thumbnail_name,
-            created=int(os.path.getctime(image_path)),
-        )
 
     def delete(self, image_type: ImageType, image_name: str) -> None:
-        basename = os.path.basename(image_name)
-        image_path = self.get_path(image_type, basename)
-
+        image_path = self.get_path(image_type, image_name)
         if os.path.exists(image_path):
-            send2trash(image_path)
+            os.remove(image_path)
+
         if image_path in self.__cache:
             del self.__cache[image_path]
 
-        thumbnail_name = get_thumbnail_name(image_name)
-        thumbnail_path = self.get_path(image_type, thumbnail_name, True)
-
-        if os.path.exists(thumbnail_path):
-            send2trash(thumbnail_path)
-        if thumbnail_path in self.__cache:
-            del self.__cache[thumbnail_path]
-
-    def __get_cache(self, image_name: str) -> Image | None:
+    def __get_cache(self, image_name: str) -> Image:
         return None if image_name not in self.__cache else self.__cache[image_name]
 
     def __set_cache(self, image_name: str, image: Image):

invokeai/app/services/invocation_queue.py

@@ -1,17 +1,30 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-import time
 from abc import ABC, abstractmethod
 from queue import Queue
-
-from pydantic import BaseModel, Field
+import time
 
 
-class InvocationQueueItem(BaseModel):
-    graph_execution_state_id: str = Field(description="The ID of the graph execution state")
-    invocation_id: str = Field(description="The ID of the node being invoked")
-    invoke_all: bool = Field(default=False)
-    timestamp: float = Field(default_factory=time.time)
+# TODO: make this serializable
+class InvocationQueueItem:
+    # session_id: str
+    graph_execution_state_id: str
+    invocation_id: str
+    invoke_all: bool
+    timestamp: float
+
+    def __init__(
+        self,
+        # session_id: str,
+        graph_execution_state_id: str,
+        invocation_id: str,
+        invoke_all: bool = False,
+    ):
+        # self.session_id = session_id
+        self.graph_execution_state_id = graph_execution_state_id
+        self.invocation_id = invocation_id
+        self.invoke_all = invoke_all
+        self.timestamp = time.time()
 
 
 class InvocationQueueABC(ABC):

invokeai/app/services/invocation_services.py

@@ -1,11 +1,7 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
-
-from typing import types
-from invokeai.app.services.metadata import MetadataServiceBase
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 from invokeai.backend import ModelManager
 
 from .events import EventServiceBase
-from .latent_storage import LatentsStorageBase
 from .image_storage import ImageStorageBase
 from .restoration_services import RestorationServices
 from .invocation_queue import InvocationQueueABC
@@ -15,15 +11,12 @@ class InvocationServices:
     """Services that can be used by invocations"""
 
     events: EventServiceBase
-    latents: LatentsStorageBase
     images: ImageStorageBase
-    metadata: MetadataServiceBase
     queue: InvocationQueueABC
     model_manager: ModelManager
     restoration: RestorationServices
 
     # NOTE: we must forward-declare any types that include invocations, since invocations can use services
-    graph_library: ItemStorageABC["LibraryGraph"]
     graph_execution_manager: ItemStorageABC["GraphExecutionState"]
     processor: "InvocationProcessorABC"
 
@@ -31,24 +24,16 @@ class InvocationServices:
             self,
             model_manager: ModelManager,
             events: EventServiceBase,
-            logger: types.ModuleType,
-            latents: LatentsStorageBase,
             images: ImageStorageBase,
-            metadata: MetadataServiceBase,
             queue: InvocationQueueABC,
-            graph_library: ItemStorageABC["LibraryGraph"],
             graph_execution_manager: ItemStorageABC["GraphExecutionState"],
             processor: "InvocationProcessorABC",
             restoration: RestorationServices,
     ):
         self.model_manager = model_manager
         self.events = events
-        self.logger = logger
-        self.latents = latents
         self.images = images
-        self.metadata = metadata
         self.queue = queue
-        self.graph_library = graph_library
         self.graph_execution_manager = graph_execution_manager
         self.processor = processor
         self.restoration = restoration

invokeai/app/services/invoker.py

@@ -33,6 +33,7 @@ class Invoker:
         self.services.graph_execution_manager.set(graph_execution_state)
 
         # Queue the invocation
+        print(f"queueing item {invocation.id}")
         self.services.queue.put(
             InvocationQueueItem(
                 # session_id    = session.id,
@@ -49,7 +50,7 @@ class Invoker:
         new_state = GraphExecutionState(graph=Graph() if graph is None else graph)
         self.services.graph_execution_manager.set(new_state)
         return new_state
-
+    
     def cancel(self, graph_execution_state_id: str) -> None:
         """Cancels the given execution state"""
         self.services.queue.cancel(graph_execution_state_id)
@@ -71,12 +72,18 @@ class Invoker:
         for service in vars(self.services):
             self.__start_service(getattr(self.services, service))
 
+        for service in vars(self.services):
+            self.__start_service(getattr(self.services, service))
+
     def stop(self) -> None:
         """Stops the invoker. A new invoker will have to be created to execute further."""
         # First stop all services
         for service in vars(self.services):
             self.__stop_service(getattr(self.services, service))
 
+        for service in vars(self.services):
+            self.__stop_service(getattr(self.services, service))
+
         self.services.queue.put(None)
 
 

invokeai/app/services/latent_storage.py

@@ -1,93 +0,0 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
-
-import os
-from abc import ABC, abstractmethod
-from pathlib import Path
-from queue import Queue
-from typing import Dict
-
-import torch
-
-class LatentsStorageBase(ABC):
-    """Responsible for storing and retrieving latents."""
-
-    @abstractmethod
-    def get(self, name: str) -> torch.Tensor:
-        pass
-
-    @abstractmethod
-    def set(self, name: str, data: torch.Tensor) -> None:
-        pass
-
-    @abstractmethod
-    def delete(self, name: str) -> None:
-        pass
-
-
-class ForwardCacheLatentsStorage(LatentsStorageBase):
-    """Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
-    
-    __cache: Dict[str, torch.Tensor]
-    __cache_ids: Queue
-    __max_cache_size: int
-    __underlying_storage: LatentsStorageBase
-
-    def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
-        self.__underlying_storage = underlying_storage
-        self.__cache = dict()
-        self.__cache_ids = Queue()
-        self.__max_cache_size = max_cache_size
-
-    def get(self, name: str) -> torch.Tensor:
-        cache_item = self.__get_cache(name)
-        if cache_item is not None:
-            return cache_item
-
-        latent = self.__underlying_storage.get(name)
-        self.__set_cache(name, latent)
-        return latent
-
-    def set(self, name: str, data: torch.Tensor) -> None:
-        self.__underlying_storage.set(name, data)
-        self.__set_cache(name, data)
-
-    def delete(self, name: str) -> None:
-        self.__underlying_storage.delete(name)
-        if name in self.__cache:
-            del self.__cache[name]
-
-    def __get_cache(self, name: str) -> torch.Tensor|None:
-        return None if name not in self.__cache else self.__cache[name]
-
-    def __set_cache(self, name: str, data: torch.Tensor):
-        if not name in self.__cache:
-            self.__cache[name] = data
-            self.__cache_ids.put(name)
-            if self.__cache_ids.qsize() > self.__max_cache_size:
-                self.__cache.pop(self.__cache_ids.get())
-
-
-class DiskLatentsStorage(LatentsStorageBase):
-    """Stores latents in a folder on disk without caching"""
-
-    __output_folder: str
-
-    def __init__(self, output_folder: str):
-        self.__output_folder = output_folder
-        Path(output_folder).mkdir(parents=True, exist_ok=True)
-
-    def get(self, name: str) -> torch.Tensor:
-        latent_path = self.get_path(name)
-        return torch.load(latent_path)
-
-    def set(self, name: str, data: torch.Tensor) -> None:
-        latent_path = self.get_path(name)
-        torch.save(data, latent_path)
-
-    def delete(self, name: str) -> None:
-        latent_path = self.get_path(name)
-        os.remove(latent_path)
-
-    def get_path(self, name: str) -> str:
-        return os.path.join(self.__output_folder, name)
-    

invokeai/app/services/metadata.py

@@ -1,96 +0,0 @@
-import json
-from abc import ABC, abstractmethod
-from typing import Any, Dict, Optional, TypedDict
-from PIL import Image, PngImagePlugin
-from pydantic import BaseModel
-
-from invokeai.app.models.image import ImageType, is_image_type
-
-
-class MetadataImageField(TypedDict):
-    """Pydantic-less ImageField, used for metadata parsing."""
-
-    image_type: ImageType
-    image_name: str
-
-
-class MetadataLatentsField(TypedDict):
-    """Pydantic-less LatentsField, used for metadata parsing."""
-
-    latents_name: str
-
-
-# TODO: This is a placeholder for `InvocationsUnion` pending resolution of circular imports
-NodeMetadata = Dict[
-    str, str | int | float | bool | MetadataImageField | MetadataLatentsField
-]
-
-
-class InvokeAIMetadata(TypedDict, total=False):
-    """InvokeAI-specific metadata format."""
-
-    session_id: Optional[str]
-    node: Optional[NodeMetadata]
-
-
-def build_invokeai_metadata_pnginfo(
-    metadata: InvokeAIMetadata | None,
-) -> PngImagePlugin.PngInfo:
-    """Builds a PngInfo object with key `"invokeai"` and value `metadata`"""
-    pnginfo = PngImagePlugin.PngInfo()
-
-    if metadata is not None:
-        pnginfo.add_text("invokeai", json.dumps(metadata))
-
-    return pnginfo
-
-
-class MetadataServiceBase(ABC):
-    @abstractmethod
-    def get_metadata(self, image: Image.Image) -> InvokeAIMetadata | None:
-        """Gets the InvokeAI metadata from a PIL Image, skipping invalid values"""
-        pass
-
-    @abstractmethod
-    def build_metadata(
-        self, session_id: str, node: BaseModel
-    ) -> InvokeAIMetadata | None:
-        """Builds an InvokeAIMetadata object"""
-        pass
-
-
-class PngMetadataService(MetadataServiceBase):
-    """Handles loading and building metadata for images."""
-
-    # TODO: Use `InvocationsUnion` to **validate** metadata as representing a fully-functioning node
-    def _load_metadata(self, image: Image.Image) -> dict | None:
-        """Loads a specific info entry from a PIL Image."""
-
-        try:
-            info = image.info.get("invokeai")
-
-            if type(info) is not str:
-                return None
-
-            loaded_metadata = json.loads(info)
-
-            if type(loaded_metadata) is not dict:
-                return None
-
-            if len(loaded_metadata.items()) == 0:
-                return None
-
-            return loaded_metadata
-        except:
-            return None
-
-    def get_metadata(self, image: Image.Image) -> dict | None:
-        """Retrieves an image's metadata as a dict"""
-        loaded_metadata = self._load_metadata(image)
-
-        return loaded_metadata
-
-    def build_metadata(self, session_id: str, node: BaseModel) -> InvokeAIMetadata:
-        metadata = InvokeAIMetadata(session_id=session_id, node=node.dict())
-
-        return metadata

invokeai/app/services/model_manager_initializer.py

@@ -5,7 +5,6 @@ from argparse import Namespace
 from invokeai.backend import Args
 from omegaconf import OmegaConf
 from pathlib import Path
-from typing import types
 
 import invokeai.version
 from ...backend import ModelManager
@@ -13,16 +12,16 @@ from ...backend.util import choose_precision, choose_torch_device
 from ...backend import Globals
 
 # TODO: Replace with an abstract class base ModelManagerBase
-def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
+def get_model_manager(config: Args) -> ModelManager:
     if not config.conf:
         config_file = os.path.join(Globals.root, "configs", "models.yaml")
         if not os.path.exists(config_file):
             report_model_error(
-                config, FileNotFoundError(f"The file {config_file} could not be found."), logger
+                config, FileNotFoundError(f"The file {config_file} could not be found.")
             )
 
-    logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}")
-    logger.info(f'InvokeAI runtime directory is "{Globals.root}"')
+    print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
+    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
@@ -63,12 +62,11 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
             device_type=device,
             max_loaded_models=config.max_loaded_models,
             embedding_path = Path(embedding_path),
-            logger = logger,
         )
     except (FileNotFoundError, TypeError, AssertionError) as e:
-        report_model_error(config, e, logger)
+        report_model_error(config, e)
     except (IOError, KeyError) as e:
-        logger.error(f"{e}. Aborting.")
+        print(f"{e}. Aborting.")
         sys.exit(-1)
 
     # try to autoconvert new models
@@ -78,18 +76,18 @@ def get_model_manager(config: Args, logger: types.ModuleType) -> ModelManager:
             conf_path=config.conf,
             weights_directory=path,
         )
-    logger.info('Model manager initialized')
+
     return model_manager
 
-def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType):
-    logger.error(f'An error occurred while attempting to initialize the model: "{str(e)}"')
-    logger.error(
-        "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
+def report_model_error(opt: Namespace, e: Exception):
+    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
+    print(
+        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
     )
     yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
     if yes_to_all:
-        logger.warning(
-            "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
+        print(
+            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
         )
     else:
         response = input(
@@ -98,12 +96,13 @@ def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType):
         if response.startswith(("n", "N")):
             return
 
-    logger.info("invokeai-configure is launching....\n")
+    print("invokeai-configure is launching....\n")
 
     # Match arguments that were set on the CLI
     # only the arguments accepted by the configuration script are parsed
     root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
     config = ["--config", opt.conf] if opt.conf is not None else []
+    previous_config = sys.argv
     sys.argv = ["invokeai-configure"]
     sys.argv.extend(root_dir)
     sys.argv.extend(config.to_dict())

invokeai/app/services/processor.py

@@ -1,20 +1,17 @@
 import traceback
-from threading import Event, Thread, BoundedSemaphore
+from threading import Event, Thread
 
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
-from ..models.exceptions import CanceledException
+
 
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
     __stop_event: Event
     __invoker: Invoker
-    __threadLimit: BoundedSemaphore
 
     def start(self, invoker) -> None:
-        # if we do want multithreading at some point, we could make this configurable
-        self.__threadLimit = BoundedSemaphore(1)
         self.__invoker = invoker
         self.__stop_event = Event()
         self.__invoker_thread = Thread(
@@ -23,7 +20,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
             kwargs=dict(stop_event=self.__stop_event),
         )
         self.__invoker_thread.daemon = (
-            True  # TODO: make async and do not use threads
+            True  # TODO: probably better to just not use threads?
         )
         self.__invoker_thread.start()
 
@@ -32,7 +29,6 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
 
     def __process(self, stop_event: Event):
         try:
-            self.__threadLimit.acquire()
             while not stop_event.is_set():
                 queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
                 if not queue_item:  # Probably stopping
@@ -47,14 +43,10 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     queue_item.invocation_id
                 )
 
-                # get the source node id to provide to clients (the prepared node id is not as useful)
-                source_node_id = graph_execution_state.prepared_source_mapping[invocation.id]
-
                 # Send starting event
                 self.__invoker.services.events.emit_invocation_started(
                     graph_execution_state_id=graph_execution_state.id,
-                    node=invocation.dict(),
-                    source_node_id=source_node_id
+                    invocation_id=invocation.id,
                 )
 
                 # Invoke
@@ -83,17 +75,13 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     # Send complete event
                     self.__invoker.services.events.emit_invocation_complete(
                         graph_execution_state_id=graph_execution_state.id,
-                        node=invocation.dict(),
-                        source_node_id=source_node_id,
+                        invocation_id=invocation.id,
                         result=outputs.dict(),
                     )
 
                 except KeyboardInterrupt:
                     pass
 
-                except CanceledException:
-                    pass
-
                 except Exception as e:
                     error = traceback.format_exc()
 
@@ -108,13 +96,12 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     # Send error event
                     self.__invoker.services.events.emit_invocation_error(
                         graph_execution_state_id=graph_execution_state.id,
-                        node=invocation.dict(),
-                        source_node_id=source_node_id,
+                        invocation_id=invocation.id,
                         error=error,
                     )
 
                     pass
-
+                
                 # Check queue to see if this is canceled, and skip if so
                 if self.__invoker.services.queue.is_canceled(
                     graph_execution_state.id
@@ -131,6 +118,4 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     )
 
         except KeyboardInterrupt:
-            pass  # Log something? KeyboardInterrupt is probably not going to be seen by the processor
-        finally:
-            self.__threadLimit.release()
+            ...  # Log something?

invokeai/app/services/restoration_services.py

@@ -1,7 +1,6 @@
 import sys
 import traceback
 import torch
-from typing import types
 from ...backend.restoration import Restoration
 from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
 
@@ -11,7 +10,7 @@ from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
 class RestorationServices:
     '''Face restoration and upscaling'''
     
-    def __init__(self,args,logger:types.ModuleType):
+    def __init__(self,args):
         try:
             gfpgan, codeformer, esrgan = None, None, None
             if args.restore or args.esrgan:
@@ -21,22 +20,20 @@ class RestorationServices:
                         args.gfpgan_model_path
                     )
                 else:
-                    logger.info("Face restoration disabled")
+                    print(">> Face restoration disabled")
                     if args.esrgan:
                         esrgan = restoration.load_esrgan(args.esrgan_bg_tile)
                     else:
-                        logger.info("Upscaling disabled")
+                        print(">> Upscaling disabled")
             else:
-                logger.info("Face restoration and upscaling disabled")
+                print(">> Face restoration and upscaling disabled")
         except (ModuleNotFoundError, ImportError):
             print(traceback.format_exc(), file=sys.stderr)
-            logger.info("You may need to install the ESRGAN and/or GFPGAN modules")
+            print(">> You may need to install the ESRGAN and/or GFPGAN modules")
         self.device = torch.device(choose_torch_device())
         self.gfpgan = gfpgan
         self.codeformer = codeformer
         self.esrgan = esrgan
-        self.logger = logger
-        self.logger.info('Face restoration initialized')
 
     # note that this one method does gfpgan and codepath reconstruction, as well as
     # esrgan upscaling
@@ -61,15 +58,15 @@ class RestorationServices:
                     if self.gfpgan is not None or self.codeformer is not None:
                         if facetool == "gfpgan":
                             if self.gfpgan is None:
-                                self.logger.info(
-                                    "GFPGAN not found. Face restoration is disabled."
+                                print(
+                                    ">> GFPGAN not found. Face restoration is disabled."
                                 )
                             else:
                                 image = self.gfpgan.process(image, strength, seed)
                         if facetool == "codeformer":
                             if self.codeformer is None:
-                                self.logger.info(
-                                    "CodeFormer not found. Face restoration is disabled."
+                                print(
+                                    ">> CodeFormer not found. Face restoration is disabled."
                                 )
                             else:
                                 cf_device = (
@@ -83,7 +80,7 @@ class RestorationServices:
                                     fidelity=codeformer_fidelity,
                                 )
                     else:
-                        self.logger.info("Face Restoration is disabled.")
+                        print(">> Face Restoration is disabled.")
                 if upscale is not None:
                     if self.esrgan is not None:
                         if len(upscale) < 2:
@@ -96,10 +93,10 @@ class RestorationServices:
                             denoise_str=upscale_denoise_str,
                         )
                     else:
-                        self.logger.info("ESRGAN is disabled. Image not upscaled.")
+                        print(">> ESRGAN is disabled. Image not upscaled.")
             except Exception as e:
-                self.logger.info(
-                    f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
+                print(
+                    f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
                 )
 
             if image_callback is not None:

invokeai/app/services/sqlite.py

@@ -59,7 +59,6 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
                 f"""INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);""",
                 (item.json(),),
             )
-            self._conn.commit()
         finally:
             self._lock.release()
         self._on_changed(item)
@@ -85,7 +84,6 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
             self._cursor.execute(
                 f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),)
             )
-            self._conn.commit()
         finally:
             self._lock.release()
         self._on_deleted(id)

invokeai/app/util/misc.py

@@ -1,5 +0,0 @@
-import datetime
-
-
-def get_timestamp():
-    return int(datetime.datetime.now(datetime.timezone.utc).timestamp())

invokeai/app/util/step_callback.py

@@ -1,55 +0,0 @@
-from invokeai.app.api.models.images import ProgressImage
-from invokeai.app.models.exceptions import CanceledException
-from ..invocations.baseinvocation import InvocationContext
-from ...backend.util.util import image_to_dataURL
-from ...backend.generator.base import Generator
-from ...backend.stable_diffusion import PipelineIntermediateState
-
-
-def stable_diffusion_step_callback(
-    context: InvocationContext,
-    intermediate_state: PipelineIntermediateState,
-    node: dict,
-    source_node_id: str,
-):
-    if context.services.queue.is_canceled(context.graph_execution_state_id):
-        raise CanceledException
-
-    # Some schedulers report not only the noisy latents at the current timestep,
-    # but also their estimate so far of what the de-noised latents will be. Use
-    # that estimate if it is available.
-    if intermediate_state.predicted_original is not None:
-        sample = intermediate_state.predicted_original
-    else:
-        sample = intermediate_state.latents
-
-    # TODO: This does not seem to be needed any more?
-    # # txt2img provides a Tensor in the step_callback
-    # # img2img provides a PipelineIntermediateState
-    # if isinstance(sample, PipelineIntermediateState):
-    #     # this was an img2img
-    #     print('img2img')
-    #     latents = sample.latents
-    #     step = sample.step
-    # else:
-    #     print('txt2img')
-    #     latents = sample
-    #     step = intermediate_state.step
-
-    # TODO: only output a preview image when requested
-    image = Generator.sample_to_lowres_estimated_image(sample)
-
-    (width, height) = image.size
-    width *= 8
-    height *= 8
-
-    dataURL = image_to_dataURL(image, image_format="JPEG")
-
-    context.services.events.emit_generator_progress(
-        graph_execution_state_id=context.graph_execution_state_id,
-        node=node,
-        source_node_id=source_node_id,
-        progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
-        step=intermediate_state.step,
-        total_steps=node["steps"],
-    )

invokeai/app/util/thumbnails.py

@@ -1,15 +0,0 @@
-import os
-from PIL import Image
-
-
-def get_thumbnail_name(image_name: str) -> str:
-    """Formats given an image name, returns the appropriate thumbnail image name"""
-    thumbnail_name = os.path.splitext(image_name)[0] + ".webp"
-    return thumbnail_name
-
-
-def make_thumbnail(image: Image.Image, size: int = 256) -> Image.Image:
-    """Makes a thumbnail from a PIL Image"""
-    thumbnail = image.copy()
-    thumbnail.thumbnail(size=(size, size))
-    return thumbnail

invokeai/backend/__init__.py

@@ -10,7 +10,7 @@ from .generator import (
     Img2Img,
     Inpaint
 )
-from .model_management import ModelManager, SDModelComponent
+from .model_management import ModelManager
 from .safety_checker import SafetyChecker
 from .args import Args
 from .globals import Globals

invokeai/backend/args.py

@@ -96,7 +96,6 @@ from pathlib import Path
 from typing import List
 
 import invokeai.version
-import invokeai.backend.util.logging as logger
 from invokeai.backend.image_util import retrieve_metadata
 
 from .globals import Globals
@@ -190,7 +189,7 @@ class Args(object):
                 print(f"{APP_NAME} {APP_VERSION}")
                 sys.exit(0)
 
-            logger.info("Initializing, be patient...")
+            print("* Initializing, be patient...")
             Globals.root = Path(os.path.abspath(switches.root_dir or Globals.root))
             Globals.try_patchmatch = switches.patchmatch
 
@@ -198,13 +197,14 @@ class Args(object):
             initfile = os.path.expanduser(os.path.join(Globals.root, Globals.initfile))
             legacyinit = os.path.expanduser("~/.invokeai")
             if os.path.exists(initfile):
-                logger.info(
-                    f"Initialization file {initfile} found. Loading...",
+                print(
+                    f">> Initialization file {initfile} found. Loading...",
+                    file=sys.stderr,
                 )
                 sysargs.insert(0, f"@{initfile}")
             elif os.path.exists(legacyinit):
-                logger.warning(
-                    f"Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init."
+                print(
+                    f">> WARNING: Old initialization file found at {legacyinit}. This location is deprecated. Please move it to {Globals.root}/invokeai.init."
                 )
                 sysargs.insert(0, f"@{legacyinit}")
             Globals.log_tokenization = self._arg_parser.parse_args(
@@ -214,7 +214,7 @@ class Args(object):
             self._arg_switches = self._arg_parser.parse_args(sysargs)
             return self._arg_switches
         except Exception as e:
-            logger.error(f"An exception has occurred: {e}")
+            print(f"An exception has occurred: {e}")
             return None
 
     def parse_cmd(self, cmd_string):
@@ -561,7 +561,7 @@ class Args(object):
             "--autoimport",
             default=None,
             type=str,
-            help="(DEPRECATED - NONFUNCTIONAL). Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly",
+            help="Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly",
         )
         model_group.add_argument(
             "--autoconvert",
@@ -1154,7 +1154,7 @@ class Args(object):
 
 
 def format_metadata(**kwargs):
-    logger.warning("format_metadata() is deprecated. Please use metadata_dumps()")
+    print("format_metadata() is deprecated. Please use metadata_dumps()")
     return metadata_dumps(kwargs)
 
 
@@ -1326,7 +1326,7 @@ def metadata_loads(metadata) -> list:
         import sys
         import traceback
 
-        logger.error("Could not read metadata")
+        print(">> could not read metadata", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
     return results
 

invokeai/backend/config/model_install_backend.py

@@ -67,6 +67,7 @@ def install_requested_models(
     scan_directory: Path = None,
     external_models: List[str] = None,
     scan_at_startup: bool = False,
+    convert_to_diffusers: bool = False,
     precision: str = "float16",
     purge_deleted: bool = False,
     config_file_path: Path = None,
@@ -112,6 +113,7 @@ def install_requested_models(
             try:
                 model_manager.heuristic_import(
                     path_url_or_repo,
+                    convert=convert_to_diffusers,
                     commit_to_conf=config_file_path,
                 )
             except KeyboardInterrupt:
@@ -120,7 +122,7 @@ def install_requested_models(
                 pass
 
     if scan_at_startup and scan_directory.is_dir():
-        argument = "--autoconvert"
+        argument = "--autoconvert" if convert_to_diffusers else "--autoimport"
         initfile = Path(Globals.root, Globals.initfile)
         replacement = Path(Globals.root, f"{Globals.initfile}.new")
         directory = str(scan_directory).replace("\\", "/")

invokeai/backend/generate.py

@@ -27,7 +27,6 @@ from diffusers.utils.import_utils import is_xformers_available
 from omegaconf import OmegaConf
 from pathlib import Path
 
-import invokeai.backend.util.logging as logger
 from .args import metadata_from_png
 from .generator import infill_methods
 from .globals import Globals, global_cache_dir
@@ -196,12 +195,12 @@ class Generate:
         # device to Generate(). However the device was then ignored, so
         # it wasn't actually doing anything. This logic could be reinstated.
         self.device = torch.device(choose_torch_device())
-        logger.info(f"Using device_type {self.device.type}")
+        print(f">> Using device_type {self.device.type}")
         if full_precision:
             if self.precision != "auto":
                 raise ValueError("Remove --full_precision / -F if using --precision")
-            logger.warning("Please remove deprecated --full_precision / -F")
-            logger.warning("If auto config does not work you can use --precision=float32")
+            print("Please remove deprecated --full_precision / -F")
+            print("If auto config does not work you can use --precision=float32")
             self.precision = "float32"
         if self.precision == "auto":
             self.precision = choose_precision(self.device)
@@ -209,13 +208,13 @@ class Generate:
 
         if is_xformers_available():
             if torch.cuda.is_available() and not Globals.disable_xformers:
-                logger.info("xformers memory-efficient attention is available and enabled")
+                print(">> xformers memory-efficient attention is available and enabled")
             else:
-                logger.info(
-                    "xformers memory-efficient attention is available but disabled"
+                print(
+                    ">> xformers memory-efficient attention is available but disabled"
                 )
         else:
-            logger.info("xformers not installed")
+            print(">> xformers not installed")
 
         # model caching system for fast switching
         self.model_manager = ModelManager(
@@ -230,8 +229,8 @@ class Generate:
         fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
         model = model or fallback
         if not self.model_manager.valid_model(model):
-            logger.warning(
-                f'"{model}" is not a known model name; falling back to {fallback}.'
+            print(
+                f'** "{model}" is not a known model name; falling back to {fallback}.'
             )
             model = None
         self.model_name = model or fallback
@@ -247,10 +246,10 @@ class Generate:
 
         # load safety checker if requested
         if safety_checker:
-            logger.info("Initializing NSFW checker")
+            print(">> Initializing NSFW checker")
             self.safety_checker = SafetyChecker(self.device)
         else:
-            logger.info("NSFW checker is disabled")
+            print(">> NSFW checker is disabled")
 
     def prompt2png(self, prompt, outdir, **kwargs):
         """
@@ -568,7 +567,7 @@ class Generate:
             self.clear_cuda_cache()
 
             if catch_interrupts:
-                logger.warning("Interrupted** Partial results will be returned.")
+                print("**Interrupted** Partial results will be returned.")
             else:
                 raise KeyboardInterrupt
         except RuntimeError:
@@ -576,11 +575,11 @@ class Generate:
             self.clear_cuda_cache()
 
             print(traceback.format_exc(), file=sys.stderr)
-            logger.info("Could not generate image.")
+            print(">> Could not generate image.")
 
         toc = time.time()
-        logger.info("Usage stats:")
-        logger.info(f"{len(results)} image(s) generated in "+"%4.2fs" % (toc - tic))
+        print("\n>> Usage stats:")
+        print(f">>   {len(results)} image(s) generated in", "%4.2fs" % (toc - tic))
         self.print_cuda_stats()
         return results
 
@@ -610,16 +609,16 @@ class Generate:
     def print_cuda_stats(self):
         if self._has_cuda():
             self.gather_cuda_stats()
-            logger.info(
-                "Max VRAM used for this generation: "+
-                "%4.2fG. " % (self.max_memory_allocated / 1e9)+
-                "Current VRAM utilization: "+
-                "%4.2fG" % (self.memory_allocated / 1e9)
+            print(
+                ">>   Max VRAM used for this generation:",
+                "%4.2fG." % (self.max_memory_allocated / 1e9),
+                "Current VRAM utilization:",
+                "%4.2fG" % (self.memory_allocated / 1e9),
             )
 
-            logger.info(
-                "Max VRAM used since script start: " +
-                "%4.2fG" % (self.session_peakmem / 1e9)
+            print(
+                ">>   Max VRAM used since script start: ",
+                "%4.2fG" % (self.session_peakmem / 1e9),
             )
 
     # this needs to be generalized to all sorts of postprocessors, which should be wrapped
@@ -648,7 +647,7 @@ class Generate:
             seed = random.randrange(0, np.iinfo(np.uint32).max)
 
         prompt = opt.prompt or args.prompt or ""
-        logger.info(f'using seed {seed} and prompt "{prompt}" for {image_path}')
+        print(f'>> using seed {seed} and prompt "{prompt}" for {image_path}')
 
         # try to reuse the same filename prefix as the original file.
         # we take everything up to the first period
@@ -697,8 +696,8 @@ class Generate:
                 try:
                     extend_instructions[direction] = int(pixels)
                 except ValueError:
-                    logger.warning(
-                        'invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"'
+                    print(
+                        '** invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"'
                     )
 
             opt.seed = seed
@@ -721,8 +720,8 @@ class Generate:
             # fetch the metadata from the image
             generator = self.select_generator(embiggen=True)
             opt.strength = opt.embiggen_strength or 0.40
-            logger.info(
-                f"Setting img2img strength to {opt.strength} for happy embiggening"
+            print(
+                f">> Setting img2img strength to {opt.strength} for happy embiggening"
             )
             generator.generate(
                 prompt,
@@ -749,12 +748,12 @@ class Generate:
             return restorer.process(opt, args, image_callback=callback, prefix=prefix)
 
         elif tool is None:
-            logger.warning(
-                "please provide at least one postprocessing option, such as -G or -U"
+            print(
+                "* please provide at least one postprocessing option, such as -G or -U"
             )
             return None
         else:
-            logger.warning(f"postprocessing tool {tool} is not yet supported")
+            print(f"* postprocessing tool {tool} is not yet supported")
             return None
 
     def select_generator(
@@ -798,8 +797,8 @@ class Generate:
         image = self._load_img(img)
 
         if image.width < self.width and image.height < self.height:
-            logger.warning(
-                f"img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions"
+            print(
+                f">> WARNING: img2img and inpainting may produce unexpected results with initial images smaller than {self.width}x{self.height} in both dimensions"
             )
 
         # if image has a transparent area and no mask was provided, then try to generate mask
@@ -810,8 +809,8 @@ class Generate:
         if (image.width * image.height) > (
             self.width * self.height
         ) and self.size_matters:
-            logger.info(
-                "This input is larger than your defaults. If you run out of memory, please use a smaller image."
+            print(
+                ">> This input is larger than your defaults. If you run out of memory, please use a smaller image."
             )
             self.size_matters = False
 
@@ -892,11 +891,11 @@ class Generate:
         try:
             model_data = cache.get_model(model_name)
         except Exception as e:
-            logger.warning(f"model {model_name} could not be loaded: {str(e)}")
+            print(f"** model {model_name} could not be loaded: {str(e)}")
             print(traceback.format_exc(), file=sys.stderr)
             if previous_model_name is None:
                 raise e
-            logger.warning("trying to reload previous model")
+            print("** trying to reload previous model")
             model_data = cache.get_model(previous_model_name)  # load previous
             if model_data is None:
                 raise e
@@ -963,15 +962,15 @@ class Generate:
                     if self.gfpgan is not None or self.codeformer is not None:
                         if facetool == "gfpgan":
                             if self.gfpgan is None:
-                                logger.info(
-                                    "GFPGAN not found. Face restoration is disabled."
+                                print(
+                                    ">> GFPGAN not found. Face restoration is disabled."
                                 )
                             else:
                                 image = self.gfpgan.process(image, strength, seed)
                         if facetool == "codeformer":
                             if self.codeformer is None:
-                                logger.info(
-                                    "CodeFormer not found. Face restoration is disabled."
+                                print(
+                                    ">> CodeFormer not found. Face restoration is disabled."
                                 )
                             else:
                                 cf_device = (
@@ -985,7 +984,7 @@ class Generate:
                                     fidelity=codeformer_fidelity,
                                 )
                     else:
-                        logger.info("Face Restoration is disabled.")
+                        print(">> Face Restoration is disabled.")
                 if upscale is not None:
                     if self.esrgan is not None:
                         if len(upscale) < 2:
@@ -998,10 +997,10 @@ class Generate:
                             denoise_str=upscale_denoise_str,
                         )
                     else:
-                        logger.info("ESRGAN is disabled. Image not upscaled.")
+                        print(">> ESRGAN is disabled. Image not upscaled.")
             except Exception as e:
-                logger.info(
-                    f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
+                print(
+                    f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
                 )
 
             if image_callback is not None:
@@ -1067,17 +1066,17 @@ class Generate:
         if self.sampler_name in scheduler_map:
             sampler_class = scheduler_map[self.sampler_name]
             msg = (
-                f"Setting Sampler to {self.sampler_name} ({sampler_class.__name__})"
+                f">> Setting Sampler to {self.sampler_name} ({sampler_class.__name__})"
             )
             self.sampler = sampler_class.from_config(self.model.scheduler.config)
         else:
             msg = (
-                f" Unsupported Sampler: {self.sampler_name} "+
+                f">> Unsupported Sampler: {self.sampler_name} "
                 f"Defaulting to {default}"
             )
             self.sampler = default
 
-        logger.info(msg)
+        print(msg)
 
         if not hasattr(self.sampler, "uses_inpainting_model"):
             # FIXME: terrible kludge!
@@ -1086,17 +1085,17 @@ class Generate:
     def _load_img(self, img) -> Image:
         if isinstance(img, Image.Image):
             image = img
-            logger.info(f"using provided input image of size {image.width}x{image.height}")
+            print(f">> using provided input image of size {image.width}x{image.height}")
         elif isinstance(img, str):
-            assert os.path.exists(img), f"{img}: File not found"
+            assert os.path.exists(img), f">> {img}: File not found"
 
             image = Image.open(img)
-            logger.info(
-                f"loaded input image of size {image.width}x{image.height} from {img}"
+            print(
+                f">> loaded input image of size {image.width}x{image.height} from {img}"
             )
         else:
             image = Image.open(img)
-            logger.info(f"loaded input image of size {image.width}x{image.height}")
+            print(f">> loaded input image of size {image.width}x{image.height}")
         image = ImageOps.exif_transpose(image)
         return image
 
@@ -1184,14 +1183,14 @@ class Generate:
 
     def _transparency_check_and_warning(self, image, mask, force_outpaint=False):
         if not mask:
-           logger.info(
-                "Initial image has transparent areas. Will inpaint in these regions."
+            print(
+                ">> Initial image has transparent areas. Will inpaint in these regions."
             )
-           if (not force_outpaint) and self._check_for_erasure(image):
-                logger.info(
-                    "Colors underneath the transparent region seem to have been erased.\n" +
-                    "Inpainting will be suboptimal. Please preserve the colors when making\n" +
-                    "a transparency mask, or provide mask explicitly using --init_mask (-M)."
+            if (not force_outpaint) and self._check_for_erasure(image):
+                print(
+                    ">> WARNING: Colors underneath the transparent region seem to have been erased.\n",
+                    ">>          Inpainting will be suboptimal. Please preserve the colors when making\n",
+                    ">>          a transparency mask, or provide mask explicitly using --init_mask (-M).",
                 )
 
     def _squeeze_image(self, image):
@@ -1202,11 +1201,11 @@ class Generate:
 
     def _fit_image(self, image, max_dimensions):
         w, h = max_dimensions
-        logger.info(f"image will be resized to fit inside a box {w}x{h} in size.")
+        print(f">> image will be resized to fit inside a box {w}x{h} in size.")
         # note that InitImageResizer does the multiple of 64 truncation internally
         image = InitImageResizer(image).resize(width=w, height=h)
-        logger.info(
-            f"after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}"
+        print(
+            f">> after adjusting image dimensions to be multiples of 64, init image is {image.width}x{image.height}"
         )
         return image
 
@@ -1217,8 +1216,8 @@ class Generate:
         )  # resize to integer multiple of 64
         if h != height or w != width:
             if log:
-                logger.info(
-                    f"Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}"
+                print(
+                    f">> Provided width and height must be multiples of 64. Auto-resizing to {w}x{h}"
                 )
             height = h
             width = w

invokeai/backend/generator/base.py

@@ -21,11 +21,10 @@ from PIL import Image, ImageChops, ImageFilter
 from accelerate.utils import set_seed
 from diffusers import DiffusionPipeline
 from tqdm import trange
-from typing import Callable, List, Iterator, Optional, Type
+from typing import List, Iterator, Type
 from dataclasses import dataclass, field
 from diffusers.schedulers import SchedulerMixin as Scheduler
 
-import invokeai.backend.util.logging as logger
 from ..image_util import configure_model_padding
 from ..util.util import rand_perlin_2d
 from ..safety_checker import SafetyChecker
@@ -36,23 +35,23 @@ downsampling = 8
 
 @dataclass
 class InvokeAIGeneratorBasicParams:
-    seed: Optional[int]=None
+    seed: int=None
     width: int=512
     height: int=512
-    cfg_scale: float=7.5
+    cfg_scale: int=7.5
     steps: int=20
     ddim_eta: float=0.0
-    scheduler: str='ddim'
+    scheduler: int='ddim'
     precision: str='float16'
     perlin: float=0.0
-    threshold: float=0.0
+    threshold: int=0.0
     seamless: bool=False
     seamless_axes: List[str]=field(default_factory=lambda: ['x', 'y'])
-    h_symmetry_time_pct: Optional[float]=None
-    v_symmetry_time_pct: Optional[float]=None
+    h_symmetry_time_pct: float=None
+    v_symmetry_time_pct: float=None
     variation_amount: float = 0.0
     with_variations: list=field(default_factory=list)
-    safety_checker: Optional[SafetyChecker]=None
+    safety_checker: SafetyChecker=None
 
 @dataclass
 class InvokeAIGeneratorOutput:
@@ -62,10 +61,10 @@ class InvokeAIGeneratorOutput:
     and the model hash, as well as all the generate() parameters that went into
     generating the image (in .params, also available as attributes)
     '''
-    image: Image.Image
+    image: Image
     seed: int
     model_hash: str
-    attention_maps_images: List[Image.Image]
+    attention_maps_images: List[Image]
     params: Namespace
 
 # we are interposing a wrapper around the original Generator classes so that
@@ -93,8 +92,8 @@ class InvokeAIGenerator(metaclass=ABCMeta):
 
     def generate(self,
                  prompt: str='',
-                 callback: Optional[Callable]=None,
-                 step_callback: Optional[Callable]=None,
+                 callback: callable=None,
+                 step_callback: callable=None,
                  iterations: int=1,
                  **keyword_args,
                  )->Iterator[InvokeAIGeneratorOutput]:
@@ -155,7 +154,6 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         for i in iteration_count:
             results = generator.generate(prompt,
                                          conditioning=(uc, c, extra_conditioning_info),
-                                         step_callback=step_callback,
                                          sampler=scheduler,
                                          **generator_args,
                                          )
@@ -207,10 +205,10 @@ class Txt2Img(InvokeAIGenerator):
 # ------------------------------------
 class Img2Img(InvokeAIGenerator):
     def generate(self,
-               init_image: Image.Image | torch.FloatTensor,
+               init_image: Image | torch.FloatTensor,
                strength: float=0.75,
                **keyword_args
-               )->Iterator[InvokeAIGeneratorOutput]:
+               )->List[InvokeAIGeneratorOutput]:
         return super().generate(init_image=init_image,
                                 strength=strength,
                                 **keyword_args
@@ -224,7 +222,7 @@ class Img2Img(InvokeAIGenerator):
 # Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff
 class Inpaint(Img2Img):
     def generate(self,
-                 mask_image: Image.Image | torch.FloatTensor,
+                 mask_image: Image | torch.FloatTensor,
                  # Seam settings - when 0, doesn't fill seam
                  seam_size: int = 0,
                  seam_blur: int = 0,
@@ -237,7 +235,7 @@ class Inpaint(Img2Img):
                  inpaint_height=None,
                  inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
                  **keyword_args
-                 )->Iterator[InvokeAIGeneratorOutput]:
+                 )->List[InvokeAIGeneratorOutput]:
         return super().generate(
             mask_image=mask_image,
             seam_size=seam_size,
@@ -264,7 +262,7 @@ class Embiggen(Txt2Img):
             embiggen: list=None,
             embiggen_tiles: list = None,
             strength: float=0.75,
-            **kwargs)->Iterator[InvokeAIGeneratorOutput]:
+            **kwargs)->List[InvokeAIGeneratorOutput]:
         return super().generate(embiggen=embiggen,
                                 embiggen_tiles=embiggen_tiles,
                                 strength=strength,
@@ -373,7 +371,7 @@ class Generator:
                     try:
                         x_T = self.get_noise(width, height)
                     except:
-                        logger.error("An error occurred while getting initial noise")
+                        print("** An error occurred while getting initial noise **")
                         print(traceback.format_exc())
 
                 # Pass on the seed in case a layer beneath us needs to generate noise on its own.
@@ -608,7 +606,7 @@ class Generator:
         image = self.sample_to_image(sample)
         dirname = os.path.dirname(filepath) or "."
         if not os.path.exists(dirname):
-            logger.info(f"creating directory {dirname}")
+            print(f"** creating directory {dirname}")
             os.makedirs(dirname, exist_ok=True)
         image.save(filepath, "PNG")
 

invokeai/backend/generator/embiggen.py

@@ -8,11 +8,10 @@ import torch
 from PIL import Image
 from tqdm import trange
 
-import invokeai.backend.util.logging as logger
-
 from .base import Generator
 from .img2img import Img2Img
 
+
 class Embiggen(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -73,22 +72,22 @@ class Embiggen(Generator):
             embiggen = [1.0]  # If not specified, assume no scaling
         elif embiggen[0] < 0:
             embiggen[0] = 1.0
-            logger.warning(
-                "Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
+            print(
+                ">> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
             )
         if len(embiggen) < 2:
             embiggen.append(0.75)
         elif embiggen[1] > 1.0 or embiggen[1] < 0:
             embiggen[1] = 0.75
-            logger.warning(
-                "Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
+            print(
+                ">> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
             )
         if len(embiggen) < 3:
             embiggen.append(0.25)
         elif embiggen[2] < 0:
             embiggen[2] = 0.25
-            logger.warning(
-                "Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
+            print(
+                ">> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
             )
 
         # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
@@ -98,8 +97,8 @@ class Embiggen(Generator):
             embiggen_tiles.sort()
 
         if strength >= 0.5:
-            logger.warning(
-                f"Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
+            print(
+                f"* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
             )
 
         # Prep img2img generator, since we wrap over it
@@ -122,8 +121,8 @@ class Embiggen(Generator):
                 from ..restoration.realesrgan import ESRGAN
 
                 esrgan = ESRGAN()
-                logger.info(
-                    f"ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
+                print(
+                    f">> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
                 )
                 if embiggen[0] > 2:
                     initsuperimage = esrgan.process(
@@ -313,10 +312,10 @@ class Embiggen(Generator):
         def make_image():
             # Make main tiles -------------------------------------------------
             if embiggen_tiles:
-                logger.info(f"Making {len(embiggen_tiles)} Embiggen tiles...")
+                print(f">> Making {len(embiggen_tiles)} Embiggen tiles...")
             else:
-                logger.info(
-                    f"Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
+                print(
+                    f">> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
                 )
 
             emb_tile_store = []
@@ -362,11 +361,11 @@ class Embiggen(Generator):
                 # newinitimage.save(newinitimagepath)
 
                 if embiggen_tiles:
-                    logger.debug(
+                    print(
                         f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)"
                     )
                 else:
-                    logger.debug(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
+                    print(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
 
                 # create a torch tensor from an Image
                 newinitimage = np.array(newinitimage).astype(np.float32) / 255.0
@@ -548,8 +547,8 @@ class Embiggen(Generator):
                     # Layer tile onto final image
                     outputsuperimage.alpha_composite(intileimage, (left, top))
             else:
-                logger.error(
-                    "Could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
+                print(
+                    "Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
                 )
 
             # after internal loops and patching up return Embiggen image

invokeai/backend/generator/txt2img2img.py

@@ -14,8 +14,6 @@ from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeli
 from ..stable_diffusion.diffusers_pipeline import ConditioningData
 from ..stable_diffusion.diffusers_pipeline import trim_to_multiple_of
 
-import invokeai.backend.util.logging as logger
-
 class Txt2Img2Img(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -79,8 +77,8 @@ class Txt2Img2Img(Generator):
             # the message below is accurate.
             init_width = first_pass_latent_output.size()[3] * self.downsampling_factor
             init_height = first_pass_latent_output.size()[2] * self.downsampling_factor
-            logger.info(
-                f"Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
+            print(
+                f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
             )
 
             # resizing

invokeai/backend/image_util/patchmatch.py

@@ -5,9 +5,10 @@ wraps the actual patchmatch object. It respects the global
 be suppressed or deferred
 """
 import numpy as np
-import invokeai.backend.util.logging as logger
+
 from invokeai.backend.globals import Globals
 
+
 class PatchMatch:
     """
     Thin class wrapper around the patchmatch function.
@@ -27,12 +28,12 @@ class PatchMatch:
             from patchmatch import patch_match as pm
 
             if pm.patchmatch_available:
-                logger.info("Patchmatch initialized")
+                print(">> Patchmatch initialized")
             else:
-                logger.info("Patchmatch not loaded (nonfatal)")
+                print(">> Patchmatch not loaded (nonfatal)")
             self.patch_match = pm
         else:
-            logger.info("Patchmatch loading disabled")
+            print(">> Patchmatch loading disabled")
         self.tried_load = True
 
     @classmethod

invokeai/backend/image_util/txt2mask.py

@@ -30,9 +30,9 @@ work fine.
 import numpy as np
 import torch
 from PIL import Image, ImageOps
+from torchvision import transforms
 from transformers import AutoProcessor, CLIPSegForImageSegmentation
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import global_cache_dir
 
 CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
@@ -83,7 +83,7 @@ class Txt2Mask(object):
     """
 
     def __init__(self, device="cpu", refined=False):
-        logger.info("Initializing clipseg model for text to mask inference")
+        print(">> Initializing clipseg model for text to mask inference")
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device
@@ -101,6 +101,18 @@ class Txt2Mask(object):
         provided image and returns a SegmentedGrayscale object in which the brighter
         pixels indicate where the object is inferred to be.
         """
+        transform = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                ),
+                transforms.Resize(
+                    (CLIPSEG_SIZE, CLIPSEG_SIZE)
+                ),  # must be multiple of 64...
+            ]
+        )
+
         if type(image) is str:
             image = Image.open(image).convert("RGB")
 

invokeai/backend/model_management/__init__.py

@@ -5,7 +5,5 @@ from .convert_ckpt_to_diffusers import (
     convert_ckpt_to_diffusers,
     load_pipeline_from_original_stable_diffusion_ckpt,
 )
-from .model_manager import ModelManager,SDModelComponent
-
-
+from .model_manager import ModelManager
 

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -25,7 +25,6 @@ from typing import Union
 import torch
 from safetensors.torch import load_file
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import global_cache_dir, global_config_dir
 
 from .model_manager import ModelManager, SDLegacyType
@@ -373,32 +372,22 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
     unet_key = "model.diffusion_model."
     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith("model_ema") for k in keys) > 100:
-        logger.debug(f"Checkpoint {path} has both EMA and non-EMA weights.")
+        print(f"  | Checkpoint {path} has both EMA and non-EMA weights.")
         if extract_ema:
-            logger.debug("Extracting EMA weights (usually better for inference)")
+            print("  | Extracting EMA weights (usually better for inference)")
             for key in keys:
                 if key.startswith("model.diffusion_model"):
                     flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
-                    flat_ema_key_alt = "model_ema." + "".join(key.split(".")[2:])
-                    if flat_ema_key in checkpoint:
-                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
-                            flat_ema_key
-                        )
-                    elif flat_ema_key_alt in checkpoint:
-                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
-                            flat_ema_key_alt
-                        )
-                    else:
-                        unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
-                            key
-                        )
+                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
+                        flat_ema_key
+                    )
         else:
-            logger.debug(
-                "Extracting only the non-EMA weights (usually better for fine-tuning)"
+            print(
+                "  | Extracting only the non-EMA weights (usually better for fine-tuning)"
             )
 
     for key in keys:
-        if key.startswith("model.diffusion_model") and key in checkpoint:
+        if key.startswith(unet_key):
             unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
 
     new_checkpoint = {}
@@ -1037,15 +1026,6 @@ def convert_open_clip_checkpoint(checkpoint):
 
     return text_model
 
-def replace_checkpoint_vae(checkpoint, vae_path:str):
-    if vae_path.endswith(".safetensors"):
-        vae_ckpt = load_file(vae_path)
-    else:
-        vae_ckpt = torch.load(vae_path, map_location="cpu")
-    state_dict = vae_ckpt['state_dict'] if "state_dict" in vae_ckpt else vae_ckpt
-    for vae_key in state_dict:
-        new_key = f'first_stage_model.{vae_key}'
-        checkpoint[new_key] = state_dict[vae_key]
 
 def load_pipeline_from_original_stable_diffusion_ckpt(
     checkpoint_path: str,
@@ -1058,10 +1038,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     extract_ema: bool = True,
     upcast_attn: bool = False,
     vae: AutoencoderKL = None,
-    vae_path: str = None,
     precision: torch.dtype = torch.float32,
     return_generator_pipeline: bool = False,
-    scan_needed:bool=True,
 ) -> Union[StableDiffusionPipeline, StableDiffusionGeneratorPipeline]:
     """
     Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml`
@@ -1089,8 +1067,6 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     :param precision: precision to use - torch.float16, torch.float32 or torch.autocast
     :param upcast_attention: Whether the attention computation should always be upcasted. This is necessary when
     running stable diffusion 2.1.
-    :param vae: A diffusers VAE to load into the pipeline.
-    :param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
     """
 
     with warnings.catch_warnings():
@@ -1098,13 +1074,12 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         verbosity = dlogging.get_verbosity()
         dlogging.set_verbosity_error()
 
-        if Path(checkpoint_path).suffix == '.ckpt':
-            if scan_needed:
-                ModelManager.scan_model(checkpoint_path,checkpoint_path)
-            checkpoint = torch.load(checkpoint_path)
-        else:
-            checkpoint = load_file(checkpoint_path)
-
+        checkpoint = (
+            torch.load(checkpoint_path)
+            if Path(checkpoint_path).suffix == ".ckpt"
+            else load_file(checkpoint_path)
+            
+        )
         cache_dir = global_cache_dir("hub")
         pipeline_class = (
             StableDiffusionGeneratorPipeline
@@ -1116,7 +1091,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         if "global_step" in checkpoint:
             global_step = checkpoint["global_step"]
         else:
-            logger.debug("global_step key not found in model")
+            print("  | global_step key not found in model")
             global_step = None
 
         # sometimes there is a state_dict key and sometimes not
@@ -1227,19 +1202,9 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
 
         unet.load_state_dict(converted_unet_checkpoint)
 
-        # If a replacement VAE path was specified, we'll incorporate that into
-        # the checkpoint model and then convert it
-        if vae_path:
-            logger.debug(f"Converting VAE {vae_path}")
-            replace_checkpoint_vae(checkpoint,vae_path)
-        # otherwise we use the original VAE, provided that
-        # an externally loaded diffusers VAE was not passed
-        elif not vae:
-            logger.debug("Using checkpoint model's original VAE")
-
-        if vae:
-            logger.debug("Using replacement diffusers VAE")
-        else:  # convert the original or replacement VAE
+        # Convert the VAE model, or use the one passed
+        if not vae:
+            print("  | Using checkpoint model's original VAE")
             vae_config = create_vae_diffusers_config(
                 original_config, image_size=image_size
             )
@@ -1249,6 +1214,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
 
             vae = AutoencoderKL(**vae_config)
             vae.load_state_dict(converted_vae_checkpoint)
+        else:
+            print("  | Using external VAE specified in config")
 
         # Convert the text model.
         model_type = pipeline_type
@@ -1265,10 +1232,10 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
                 cache_dir=cache_dir,
             )
             pipe = pipeline_class(
-                vae=vae.to(precision),
-                text_encoder=text_model.to(precision),
+                vae=vae,
+                text_encoder=text_model,
                 tokenizer=tokenizer,
-                unet=unet.to(precision),
+                unet=unet,
                 scheduler=scheduler,
                 safety_checker=None,
                 feature_extractor=None,

invokeai/backend/model_management/model_manager.py

@@ -1,4 +1,4 @@
-"""enum
+"""
 Manage a cache of Stable Diffusion model files for fast switching.
 They are moved between GPU and CPU as necessary. If CPU memory falls
 below a preset minimum, the least recently used model will be
@@ -15,22 +15,17 @@ import sys
 import textwrap
 import time
 import warnings
-from enum import Enum, auto
+from enum import Enum
 from pathlib import Path
 from shutil import move, rmtree
-from typing import Any, Optional, Union, Callable, types
+from typing import Any, Optional, Union
 
 import safetensors
 import safetensors.torch
 import torch
 import transformers
-import invokeai.backend.util.logging as logger
-from diffusers import (
-    AutoencoderKL,
-    UNet2DConditionModel,
-    SchedulerMixin,
-    logging as dlogging,
-)    
+from diffusers import AutoencoderKL
+from diffusers import logging as dlogging
 from huggingface_hub import scan_cache_dir
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
@@ -38,61 +33,40 @@ from picklescan.scanner import scan_file_path
 
 from invokeai.backend.globals import Globals, global_cache_dir
 
-from transformers import (
-    CLIPTextModel,
-    CLIPTokenizer,
-    CLIPFeatureExtractor,
-)
-from diffusers.pipelines.stable_diffusion.safety_checker import (
-    StableDiffusionSafetyChecker,
-    )
-from ..stable_diffusion import (
-    StableDiffusionGeneratorPipeline,
-)
-from ..util import CUDA_DEVICE, ask_user, download_with_resume
-
+from ..stable_diffusion import StableDiffusionGeneratorPipeline
+from ..util import CUDA_DEVICE, CPU_DEVICE, ask_user, download_with_resume
 
 class SDLegacyType(Enum):
-    V1 = auto()
-    V1_INPAINT = auto()
-    V2 = auto()
-    V2_e = auto()
-    V2_v = auto()
-    UNKNOWN = auto()
+    V1 = 1
+    V1_INPAINT = 2
+    V2   = 3
+    V2_e = 4
+    V2_v = 5
+    UNKNOWN = 99
 
-class SDModelComponent(Enum):
-    vae="vae"
-    text_encoder="text_encoder"
-    tokenizer="tokenizer"
-    unet="unet"
-    scheduler="scheduler"
-    safety_checker="safety_checker"
-    feature_extractor="feature_extractor"
-    
 DEFAULT_MAX_MODELS = 2
+VAE_TO_REPO_ID = {  # hack, see note in convert_and_import()
+    "vae-ft-mse-840000-ema-pruned": "stabilityai/sd-vae-ft-mse",
+}
 
 class ModelManager(object):
-    """
+    '''
     Model manager handles loading, caching, importing, deleting, converting, and editing models.
-    """
-
-    logger: types.ModuleType = logger
-
+    '''
     def __init__(
-        self,
-        config: OmegaConf | Path,
-        device_type: torch.device = CUDA_DEVICE,
-        precision: str = "float16",
-        max_loaded_models=DEFAULT_MAX_MODELS,
-        sequential_offload=False,
-        embedding_path: Path = None,
-        logger: types.ModuleType = logger,
+            self,
+            config: OmegaConf|Path,
+            device_type: torch.device = CUDA_DEVICE,
+            precision: str = "float16",
+            max_loaded_models=DEFAULT_MAX_MODELS,
+            sequential_offload=False,
+            embedding_path: Path=None,
     ):
         """
         Initialize with the path to the models.yaml config file or
         an initialized OmegaConf dictionary. Optional parameters
         are the torch device type, precision, max_loaded_models,
-        and sequential_offload boolean. Note that the default device
+        and sequential_offload boolean. Note that the default device 
         type and precision are set up for a CUDA system running at half precision.
         """
         # prevent nasty-looking CLIP log message
@@ -108,7 +82,6 @@ class ModelManager(object):
         self.current_model = None
         self.sequential_offload = sequential_offload
         self.embedding_path = embedding_path
-        self.logger = logger
 
     def valid_model(self, model_name: str) -> bool:
         """
@@ -117,28 +90,18 @@ class ModelManager(object):
         """
         return model_name in self.config
 
-    def get_model(self, model_name: str = None) -> dict:
-        """Given a model named identified in models.yaml, return a dict
-        containing the model object and some of its key features. If
-        in RAM will load into GPU VRAM.  If on disk, will load from
-        there.
-        The dict has the following keys:
-        'model': The StableDiffusionGeneratorPipeline object
-        'model_name': The name of the model in models.yaml
-        'width': The width of images trained by this model
-        'height': The height of images trained by this model
-        'hash': A unique hash of this model's files on disk.
+    def get_model(self, model_name: str=None)->dict:
+        """
+        Given a model named identified in models.yaml, return
+        the model object. If in RAM will load into GPU VRAM.
+        If on disk, will load from there.
         """
         if not model_name:
-            return (
-                self.get_model(self.current_model)
-                if self.current_model
-                else self.get_model(self.default_model())
-            )
-
+            return self.get_model(self.current_model) if self.current_model else self.get_model(self.default_model())
+        
         if not self.valid_model(model_name):
-            self.logger.error(
-                f'"{model_name}" is not a known model name. Please check your models.yaml file'
+            print(
+                f'** "{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return self.current_model
 
@@ -149,7 +112,7 @@ class ModelManager(object):
 
         if model_name in self.models:
             requested_model = self.models[model_name]["model"]
-            self.logger.info(f"Retrieving model {model_name} from system RAM cache")
+            print(f">> Retrieving model {model_name} from system RAM cache")
             requested_model.ready()
             width = self.models[model_name]["width"]
             height = self.models[model_name]["height"]
@@ -175,81 +138,6 @@ class ModelManager(object):
             "hash": hash,
         }
 
-    def get_model_vae(self, model_name: str=None)->AutoencoderKL:
-        """Given a model name identified in models.yaml, load the model into
-        GPU if necessary and return its assigned VAE as an
-        AutoencoderKL object. If no model name is provided, return the
-        vae from the model currently in the GPU.
-        """
-        return self._get_sub_model(model_name, SDModelComponent.vae)
-                                   
-    def get_model_tokenizer(self, model_name: str=None)->CLIPTokenizer:
-        """Given a model name identified in models.yaml, load the model into
-        GPU if necessary and return its assigned CLIPTokenizer. If no
-        model name is provided, return the tokenizer from the model
-        currently in the GPU.
-        """
-        return self._get_sub_model(model_name, SDModelComponent.tokenizer)
-                                   
-    def get_model_unet(self, model_name: str=None)->UNet2DConditionModel:
-        """Given a model name identified in models.yaml, load the model into
-        GPU if necessary and return its assigned UNet2DConditionModel. If no model
-        name is provided, return the UNet from the model
-        currently in the GPU.  
-        """
-        return self._get_sub_model(model_name, SDModelComponent.unet)
-
-    def get_model_text_encoder(self, model_name: str=None)->CLIPTextModel:
-        """Given a model name identified in models.yaml, load the model into
-        GPU if necessary and return its assigned CLIPTextModel. If no
-        model name is provided, return the text encoder from the model
-        currently in the GPU.
-        """
-        return self._get_sub_model(model_name, SDModelComponent.text_encoder)
-
-    def get_model_feature_extractor(self, model_name: str=None)->CLIPFeatureExtractor:
-        """Given a model name identified in models.yaml, load the model into
-        GPU if necessary and return its assigned CLIPFeatureExtractor. If no
-        model name is provided, return the text encoder from the model
-        currently in the GPU.
-        """
-        return self._get_sub_model(model_name, SDModelComponent.feature_extractor)
-
-    def get_model_scheduler(self, model_name: str=None)->SchedulerMixin:
-        """Given a model name identified in models.yaml, load the model into
-        GPU if necessary and return its assigned scheduler. If no
-        model name is provided, return the text encoder from the model
-        currently in the GPU.
-        """
-        return self._get_sub_model(model_name, SDModelComponent.scheduler)
-    
-    def _get_sub_model(
-            self,
-            model_name: str=None,
-            model_part: SDModelComponent=SDModelComponent.vae,
-    ) -> Union[
-        AutoencoderKL,
-        CLIPTokenizer,
-        CLIPFeatureExtractor,
-        UNet2DConditionModel,
-        CLIPTextModel,
-        StableDiffusionSafetyChecker,
-    ]:
-        """Given a model name identified in models.yaml, and the part of the
-        model you wish to retrieve, return that part. Parts are in an Enum
-        class named SDModelComponent, and consist of:
-        SDModelComponent.vae
-        SDModelComponent.text_encoder
-        SDModelComponent.tokenizer
-        SDModelComponent.unet
-        SDModelComponent.scheduler
-        SDModelComponent.safety_checker
-        SDModelComponent.feature_extractor
-        """
-        model_dict = self.get_model(model_name)
-        model = model_dict["model"]
-        return getattr(model, model_part.value)
-
     def default_model(self) -> str | None:
         """
         Returns the name of the default model, or None
@@ -384,7 +272,7 @@ class ModelManager(object):
         """
         omega = self.config
         if model_name not in omega:
-            self.logger.error(f"Unknown model {model_name}")
+            print(f"** Unknown model {model_name}")
             return
         # save these for use in deletion later
         conf = omega[model_name]
@@ -397,13 +285,13 @@ class ModelManager(object):
             self.stack.remove(model_name)
         if delete_files:
             if weights:
-                self.logger.info(f"Deleting file {weights}")
+                print(f"** deleting file {weights}")
                 Path(weights).unlink(missing_ok=True)
             elif path:
-                self.logger.info(f"Deleting directory {path}")
+                print(f"** deleting directory {path}")
                 rmtree(path, ignore_errors=True)
             elif repo_id:
-                self.logger.info(f"Deleting the cached model directory for {repo_id}")
+                print(f"** deleting the cached model directory for {repo_id}")
                 self._delete_model_from_cache(repo_id)
 
     def add_model(
@@ -444,7 +332,7 @@ class ModelManager(object):
     def _load_model(self, model_name: str):
         """Load and initialize the model from configuration variables passed at object creation time"""
         if model_name not in self.config:
-            self.logger.error(
+            print(
                 f'"{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return
@@ -462,7 +350,7 @@ class ModelManager(object):
         model_format = mconfig.get("format", "ckpt")
         if model_format == "ckpt":
             weights = mconfig.weights
-            self.logger.info(f"Loading {model_name} from {weights}")
+            print(f">> Loading {model_name} from {weights}")
             model, width, height, model_hash = self._load_ckpt_model(
                 model_name, mconfig
             )
@@ -474,19 +362,16 @@ class ModelManager(object):
             raise NotImplementedError(
                 f"Unknown model format {model_name}: {model_format}"
             )
-        self._add_embeddings_to_model(model)
-
+        
         # usage statistics
         toc = time.time()
-        self.logger.info("Model loaded in " + "%4.2fs" % (toc - tic))
+        print(">> Model loaded in", "%4.2fs" % (toc - tic))
         if self._has_cuda():
-            self.logger.info(
-                "Max VRAM used to load the model: "+
-                "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9)
-            )
-            self.logger.info(
-                "Current VRAM usage: "+
-                "%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
+            print(
+                ">> Max VRAM used to load the model:",
+                "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9),
+                "\n>> Current VRAM usage:"
+                "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
             )
         return model, width, height, model_hash
 
@@ -494,11 +379,11 @@ class ModelManager(object):
         name_or_path = self.model_name_or_path(mconfig)
         using_fp16 = self.precision == "float16"
 
-        self.logger.info(f"Loading diffusers model from {name_or_path}")
+        print(f">> Loading diffusers model from {name_or_path}")
         if using_fp16:
-            self.logger.debug("Using faster float16 precision")
+            print("  | Using faster float16 precision")
         else:
-            self.logger.debug("Using more accurate float32 precision")
+            print("  | Using more accurate float32 precision")
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
@@ -530,8 +415,8 @@ class ModelManager(object):
                 if str(e).startswith("fp16 is not a valid"):
                     pass
                 else:
-                    self.logger.error(
-                        f"An unexpected error occurred while downloading the model: {e})"
+                    print(
+                        f"** An unexpected error occurred while downloading the model: {e})"
                     )
             if pipeline:
                 break
@@ -549,7 +434,9 @@ class ModelManager(object):
         # square images???
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
-        self.logger.debug(f"Default image dimensions = {width} x {height}")
+
+        print(f"  | Default image dimensions = {width} x {height}")
+        self._add_embeddings_to_model(pipeline)
 
         return pipeline, width, height, model_hash
 
@@ -566,29 +453,19 @@ class ModelManager(object):
             weights = os.path.normpath(os.path.join(Globals.root, weights))
 
         # Convert to diffusers and return a diffusers pipeline
-        self.logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
+        print(f">> Converting legacy checkpoint {model_name} into a diffusers model...")
 
         from . import load_pipeline_from_original_stable_diffusion_ckpt
 
-        try:
-            if self.list_models()[self.current_model]["status"] == "active":
-                self.offload_model(self.current_model)
-        except Exception:
-            pass
-
-        vae_path = None
-        if vae:
-            vae_path = (
-                vae
-                if os.path.isabs(vae)
-                else os.path.normpath(os.path.join(Globals.root, vae))
-            )
+        self.offload_model(self.current_model)
+        if vae_config := self._choose_diffusers_vae(model_name):
+            vae = self._load_vae(vae_config)
         if self._has_cuda():
             torch.cuda.empty_cache()
         pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
             checkpoint_path=weights,
             original_config_file=config,
-            vae_path=vae_path,
+            vae=vae,
             return_generator_pipeline=True,
             precision=torch.float16 if self.precision == "float16" else torch.float32,
         )
@@ -596,6 +473,7 @@ class ModelManager(object):
             pipeline.enable_offload_submodels(self.device)
         else:
             pipeline.to(self.device)
+
         return (
             pipeline,
             width,
@@ -631,42 +509,44 @@ class ModelManager(object):
         if model_name not in self.models:
             return
 
-        self.logger.info(f"Offloading {model_name} to CPU")
+        print(f">> Offloading {model_name} to CPU")
         model = self.models[model_name]["model"]
         model.offload_all()
-        self.current_model = None
 
         gc.collect()
         if self._has_cuda():
             torch.cuda.empty_cache()
 
-    @classmethod
     def scan_model(self, model_name, checkpoint):
         """
         Apply picklescanner to the indicated checkpoint and issue a warning
         and option to exit if an infected file is identified.
         """
         # scan model
-        self.logger.debug(f"Scanning Model: {model_name}")
+        print(f">> Scanning Model: {model_name}")
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
             if scan_result.infected_files == 1:
-                self.logger.critical(f"Issues Found In Model: {scan_result.issues_count}")
-                self.logger.critical("The model you are trying to load seems to be infected.")
-                self.logger.critical("For your safety, InvokeAI will not load this model.")
-                self.logger.critical("Please use checkpoints from trusted sources.")
-                self.logger.critical("Exiting InvokeAI")
+                print(f"\n### Issues Found In Model: {scan_result.issues_count}")
+                print(
+                    "### WARNING: The model you are trying to load seems to be infected."
+                )
+                print("### For your safety, InvokeAI will not load this model.")
+                print("### Please use checkpoints from trusted sources.")
+                print("### Exiting InvokeAI")
                 sys.exit()
             else:
-                self.logger.warning("InvokeAI was unable to scan the model you are using.")
+                print(
+                    "\n### WARNING: InvokeAI was unable to scan the model you are using."
+                )
                 model_safe_check_fail = ask_user(
                     "Do you want to to continue loading the model?", ["y", "n"]
                 )
                 if model_safe_check_fail.lower() != "y":
-                    self.logger.critical("Exiting InvokeAI")
+                    print("### Exiting InvokeAI")
                     sys.exit()
         else:
-            self.logger.debug("Model scanned ok")
+            print(">> Model scanned ok")
 
     def import_diffuser_model(
         self,
@@ -688,7 +568,9 @@ class ModelManager(object):
         models.yaml file.
         """
         model_name = model_name or Path(repo_or_path).stem
-        model_description = description or f"Imported diffusers model {model_name}"
+        model_description = (
+            description or f"Imported diffusers model {model_name}"
+        )
         new_config = dict(
             description=model_description,
             vae=vae,
@@ -717,7 +599,7 @@ class ModelManager(object):
         SDLegacyType.V2_v   (V2 using 'v_prediction' prediction type)
         SDLegacyType.UNKNOWN
         """
-        global_step = checkpoint.get("global_step")
+        global_step = checkpoint.get('global_step')
         state_dict = checkpoint.get("state_dict") or checkpoint
 
         try:
@@ -743,15 +625,16 @@ class ModelManager(object):
             return SDLegacyType.UNKNOWN
 
     def heuristic_import(
-        self,
-        path_url_or_repo: str,
-        model_name: str = None,
-        description: str = None,
-        model_config_file: Path = None,
-        commit_to_conf: Path = None,
-        config_file_callback: Callable[[Path], Path] = None,
+            self,
+            path_url_or_repo: str,
+            convert: bool = True,
+            model_name: str = None,
+            description: str = None,
+            model_config_file: Path = None,
+            commit_to_conf: Path = None,
     ) -> str:
-        """Accept a string which could be:
+        """
+        Accept a string which could be:
            - a HF diffusers repo_id
            - a URL pointing to a legacy .ckpt or .safetensors file
            - a local path pointing to a legacy .ckpt or .safetensors file
@@ -765,42 +648,40 @@ class ModelManager(object):
         The model_name and/or description can be provided. If not, they will
         be generated automatically.
 
+        If convert is true, legacy models will be converted to diffusers
+        before importing.
+
         If commit_to_conf is provided, the newly loaded model will be written
         to the `models.yaml` file at the indicated path. Otherwise, the changes
         will only remain in memory.
 
-        The routine will do its best to figure out the config file
-        needed to convert legacy checkpoint file, but if it can't it
-        will call the config_file_callback routine, if provided. The
-        callback accepts a single argument, the Path to the checkpoint
-        file, and returns a Path to the config file to use.
-
-        The (potentially derived) name of the model is returned on
-        success, or None on failure. When multiple models are added
-        from a directory, only the last imported one is returned.
-
+        The (potentially derived) name of the model is returned on success, or None
+        on failure. When multiple models are added from a directory, only the last
+        imported one is returned.
         """
         model_path: Path = None
         thing = path_url_or_repo  # to save typing
 
-        self.logger.info(f"Probing {thing} for import")
+        print(f">> Probing {thing} for import")
 
         if thing.startswith(("http:", "https:", "ftp:")):
-            self.logger.info(f"{thing} appears to be a URL")
+            print(f"  | {thing} appears to be a URL")
             model_path = self._resolve_path(
                 thing, "models/ldm/stable-diffusion-v1"
             )  # _resolve_path does a download if needed
 
         elif Path(thing).is_file() and thing.endswith((".ckpt", ".safetensors")):
             if Path(thing).stem in ["model", "diffusion_pytorch_model"]:
-                self.logger.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import")
+                print(
+                    f"  | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
+                )
                 return
             else:
-                self.logger.debug(f"{thing} appears to be a checkpoint file on disk")
+                print(f"  | {thing} appears to be a checkpoint file on disk")
                 model_path = self._resolve_path(thing, "models/ldm/stable-diffusion-v1")
 
         elif Path(thing).is_dir() and Path(thing, "model_index.json").exists():
-            self.logger.debug(f"{thing} appears to be a diffusers file on disk")
+            print(f"  | {thing} appears to be a diffusers file on disk")
             model_name = self.import_diffuser_model(
                 thing,
                 vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
@@ -811,30 +692,34 @@ class ModelManager(object):
 
         elif Path(thing).is_dir():
             if (Path(thing) / "model_index.json").exists():
-                self.logger.debug(f"{thing} appears to be a diffusers model.")
+                print(f"  | {thing} appears to be a diffusers model.")
                 model_name = self.import_diffuser_model(
                     thing, commit_to_conf=commit_to_conf
                 )
             else:
-                self.logger.debug(f"{thing} appears to be a directory. Will scan for models to import")
+                print(
+                    f"  |{thing} appears to be a directory. Will scan for models to import"
+                )
                 for m in list(Path(thing).rglob("*.ckpt")) + list(
                     Path(thing).rglob("*.safetensors")
                 ):
                     if model_name := self.heuristic_import(
-                        str(m), commit_to_conf=commit_to_conf
+                        str(m), convert, commit_to_conf=commit_to_conf
                     ):
-                        self.logger.info(f"{model_name} successfully imported")
+                        print(f" >> {model_name} successfully imported")
                 return model_name
 
         elif re.match(r"^[\w.+-]+/[\w.+-]+$", thing):
-            self.logger.debug(f"{thing} appears to be a HuggingFace diffusers repo_id")
+            print(f"  | {thing} appears to be a HuggingFace diffusers repo_id")
             model_name = self.import_diffuser_model(
                 thing, commit_to_conf=commit_to_conf
             )
             pipeline, _, _, _ = self._load_diffusers_model(self.config[model_name])
             return model_name
         else:
-            self.logger.warning(f"{thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id")
+            print(
+                f"** {thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id"
+            )
 
         # Model_path is set in the event of a legacy checkpoint file.
         # If not set, we're all done
@@ -842,72 +727,55 @@ class ModelManager(object):
             return
 
         if model_path.stem in self.config:  # already imported
-            self.logger.debug("Already imported. Skipping")
+            print("  | Already imported. Skipping")
             return model_path.stem
 
         # another round of heuristics to guess the correct config file.
-        checkpoint = None
-        if model_path.suffix in [".ckpt", ".pt"]:
-            self.scan_model(model_path, model_path)
-            checkpoint = torch.load(model_path)
-        else:
-            checkpoint = safetensors.torch.load_file(model_path)
+        checkpoint = (
+            torch.load(model_path)
+            if model_path.suffix == ".ckpt"
+            else safetensors.torch.load_file(model_path)
+        )
 
         # additional probing needed if no config file provided
         if model_config_file is None:
-            # look for a like-named .yaml file in same directory
-            if model_path.with_suffix(".yaml").exists():
-                model_config_file = model_path.with_suffix(".yaml")
-                self.logger.debug(f"Using config file {model_config_file.name}")
-
+            model_type = self.probe_model_type(checkpoint)
+            if model_type == SDLegacyType.V1:
+                print("  | SD-v1 model detected")
+                model_config_file = Path(
+                    Globals.root, "configs/stable-diffusion/v1-inference.yaml"
+                )
+            elif model_type == SDLegacyType.V1_INPAINT:
+                print("  | SD-v1 inpainting model detected")
+                model_config_file = Path(
+                    Globals.root, "configs/stable-diffusion/v1-inpainting-inference.yaml"
+                )
+            elif model_type == SDLegacyType.V2_v:
+                print(
+                    "  | SD-v2-v model detected; model will be converted to diffusers format"
+                )
+                model_config_file = Path(
+                    Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
+                )
+                convert = True
+            elif model_type == SDLegacyType.V2_e:
+                print(
+                    "  | SD-v2-e model detected; model will be converted to diffusers format"
+                )
+                model_config_file = Path(
+                    Globals.root, "configs/stable-diffusion/v2-inference.yaml"
+                )
+                convert = True
+            elif model_type == SDLegacyType.V2:
+                print(
+                    f"** {thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
+                )
+                return
             else:
-                model_type = self.probe_model_type(checkpoint)
-                if model_type == SDLegacyType.V1:
-                    self.logger.debug("SD-v1 model detected")
-                    model_config_file = Path(
-                        Globals.root, "configs/stable-diffusion/v1-inference.yaml"
-                    )
-                elif model_type == SDLegacyType.V1_INPAINT:
-                    self.logger.debug("SD-v1 inpainting model detected")
-                    model_config_file = Path(
-                        Globals.root,
-                        "configs/stable-diffusion/v1-inpainting-inference.yaml",
-                    )
-                elif model_type == SDLegacyType.V2_v:
-                    self.logger.debug("SD-v2-v model detected")
-                    model_config_file = Path(
-                        Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
-                    )
-                elif model_type == SDLegacyType.V2_e:
-                    self.logger.debug("SD-v2-e model detected")
-                    model_config_file = Path(
-                        Globals.root, "configs/stable-diffusion/v2-inference.yaml"
-                    )
-                elif model_type == SDLegacyType.V2:
-                    self.logger.warning(
-                        f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
-                    )
-                    return
-                else:
-                    self.logger.warning(
-                        f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
-                    )
-                    return
-
-        if not model_config_file and config_file_callback:
-            model_config_file = config_file_callback(model_path)
-
-        # despite our best efforts, we could not find a model config file, so give up
-        if not model_config_file:
-            return
-
-        # look for a custom vae, a like-named file ending with .vae in the same directory
-        vae_path = None
-        for suffix in ["pt", "ckpt", "safetensors"]:
-            if (model_path.with_suffix(f".vae.{suffix}")).exists():
-                vae_path = model_path.with_suffix(f".vae.{suffix}")
-                self.logger.debug(f"Using VAE file {vae_path.name}")
-        vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
+                print(
+                    f"** {thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
+                )
+                return
 
         diffuser_path = Path(
             Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
@@ -915,13 +783,11 @@ class ModelManager(object):
         model_name = self.convert_and_import(
             model_path,
             diffusers_path=diffuser_path,
-            vae=vae,
-            vae_path=str(vae_path),
+            vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
             model_name=model_name,
             model_description=description,
             original_config_file=model_config_file,
             commit_to_conf=commit_to_conf,
-            scan_needed=False,
         )
         return model_name
 
@@ -931,11 +797,9 @@ class ModelManager(object):
         diffusers_path: Path,
         model_name=None,
         model_description=None,
-        vae: dict = None,
-        vae_path: Path = None,
+        vae=None,
         original_config_file: Path = None,
         commit_to_conf: Path = None,
-        scan_needed: bool = True,
     ) -> str:
         """
         Convert a legacy ckpt weights file to diffuser model and import
@@ -952,34 +816,29 @@ class ModelManager(object):
         from . import convert_ckpt_to_diffusers
 
         if diffusers_path.exists():
-            self.logger.error(
-                f"The path {str(diffusers_path)} already exists. Please move or remove it and try again."
+            print(
+                f"ERROR: The path {str(diffusers_path)} already exists. Please move or remove it and try again."
             )
             return
 
         model_name = model_name or diffusers_path.name
-        model_description = model_description or f"Converted version of {model_name}"
-        self.logger.debug(f"Converting {model_name} to diffusers (30-60s)")
+        model_description = model_description or f"Optimized version of {model_name}"
+        print(f">> Optimizing {model_name} (30-60s)")
         try:
             # By passing the specified VAE to the conversion function, the autoencoder
             # will be built into the model rather than tacked on afterward via the config file
-            vae_model = None
-            if vae:
-                vae_model = self._load_vae(vae)
-                vae_path = None
+            vae_model = self._load_vae(vae) if vae else None
             convert_ckpt_to_diffusers(
                 ckpt_path,
                 diffusers_path,
                 extract_ema=True,
                 original_config_file=original_config_file,
                 vae=vae_model,
-                vae_path=vae_path,
-                scan_needed=scan_needed,
             )
-            self.logger.debug(
-                f"Success. Converted model is now located at {str(diffusers_path)}"
+            print(
+                f"  | Success. Optimized model is now located at {str(diffusers_path)}"
             )
-            self.logger.debug(f"Writing new config file entry for {model_name}")
+            print(f"  | Writing new config file entry for {model_name}")
             new_config = dict(
                 path=str(diffusers_path),
                 description=model_description,
@@ -990,17 +849,17 @@ class ModelManager(object):
             self.add_model(model_name, new_config, True)
             if commit_to_conf:
                 self.commit(commit_to_conf)
-            self.logger.debug("Conversion succeeded")
+            print(">> Conversion succeeded")
         except Exception as e:
-            self.logger.warning(f"Conversion failed: {str(e)}")
-            self.logger.warning(
-                "If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
+            print(f"** Conversion failed: {str(e)}")
+            print(
+                "** If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
             )
 
         return model_name
 
     def search_models(self, search_folder):
-        self.logger.info(f"Finding Models In: {search_folder}")
+        print(f">> Finding Models In: {search_folder}")
         models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
         models_folder_safetensors = Path(search_folder).glob("**/*.safetensors")
 
@@ -1020,12 +879,42 @@ class ModelManager(object):
 
         return search_folder, found_models
 
+    def _choose_diffusers_vae(
+        self, model_name: str, vae: str = None
+    ) -> Union[dict, str]:
+        # In the event that the original entry is using a custom ckpt VAE, we try to
+        # map that VAE onto a diffuser VAE using a hard-coded dictionary.
+        # I would prefer to do this differently: We load the ckpt model into memory, swap the
+        # VAE in memory, and then pass that to convert_ckpt_to_diffuser() so that the swapped
+        # VAE is built into the model. However, when I tried this I got obscure key errors.
+        if vae:
+            return vae
+        if model_name in self.config and (
+            vae_ckpt_path := self.model_info(model_name).get("vae", None)
+        ):
+            vae_basename = Path(vae_ckpt_path).stem
+            diffusers_vae = None
+            if diffusers_vae := VAE_TO_REPO_ID.get(vae_basename, None):
+                print(
+                    f">> {vae_basename} VAE corresponds to known {diffusers_vae} diffusers version"
+                )
+                vae = {"repo_id": diffusers_vae}
+            else:
+                print(
+                    f'** Custom VAE "{vae_basename}" found, but corresponding diffusers model unknown'
+                )
+                print(
+                    '** Using "stabilityai/sd-vae-ft-mse"; If this isn\'t right, please edit the model config'
+                )
+                vae = {"repo_id": "stabilityai/sd-vae-ft-mse"}
+        return vae
+
     def _make_cache_room(self) -> None:
         num_loaded_models = len(self.models)
         if num_loaded_models >= self.max_loaded_models:
             least_recent_model = self._pop_oldest_model()
-            self.logger.info(
-                f"Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
+            print(
+                f">> Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
             )
             if least_recent_model is not None:
                 del self.models[least_recent_model]
@@ -1033,8 +922,8 @@ class ModelManager(object):
 
     def print_vram_usage(self) -> None:
         if self._has_cuda:
-            self.logger.info(
-                "Current VRAM usage:"+
+            print(
+                ">> Current VRAM usage: ",
                 "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
             )
 
@@ -1082,16 +971,16 @@ class ModelManager(object):
         legacy_locations = [
             Path(
                 models_dir,
-                "CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker",
+                "CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker"
             ),
             Path(models_dir, "bert-base-uncased/models--bert-base-uncased"),
             Path(
                 models_dir,
-                "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14",
+                "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14"
             ),
         ]
-        legacy_locations.extend(list(global_cache_dir("diffusers").glob("*")))
-
+        legacy_locations.extend(list(global_cache_dir("diffusers").glob('*')))
+        
         legacy_layout = False
         for model in legacy_locations:
             legacy_layout = legacy_layout or model.exists()
@@ -1109,7 +998,7 @@ class ModelManager(object):
 >> make adjustments, please press ctrl-C now to abort and relaunch InvokeAI when you are ready.
 >> Otherwise press <enter> to continue."""
         )
-        input("continue> ")
+        input('continue> ')
 
         # transformer files get moved into the hub directory
         if cls._is_huggingface_hub_directory_present():
@@ -1123,10 +1012,10 @@ class ModelManager(object):
             dest = hub / model.stem
             if dest.exists() and not source.exists():
                 continue
-            cls.logger.info(f"{source} => {dest}")
+            print(f"** {source} => {dest}")
             if source.exists():
                 if dest.is_symlink():
-                    logger.warning(f"Found symlink at {dest.name}. Not migrating.")
+                    print(f"** Found symlink at {dest.name}. Not migrating.")
                 elif dest.exists():
                     if source.is_dir():
                         rmtree(source)
@@ -1143,7 +1032,7 @@ class ModelManager(object):
         ]
         for d in empty:
             os.rmdir(d)
-        cls.logger.info("Migration is done. Continuing...")
+        print("** Migration is done. Continuing...")
 
     def _resolve_path(
         self, source: Union[str, Path], dest_directory: str
@@ -1186,22 +1075,22 @@ class ModelManager(object):
 
     def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline):
         if self.embedding_path is not None:
-            self.logger.info(f"Loading embeddings from {self.embedding_path}")
+            print(f">> Loading embeddings from {self.embedding_path}")
             for root, _, files in os.walk(self.embedding_path):
                 for name in files:
                     ti_path = os.path.join(root, name)
                     model.textual_inversion_manager.load_textual_inversion(
                         ti_path, defer_injecting_tokens=True
                     )
-            self.logger.info(
-                f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
+            print(
+                f'>> Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
             )
-
+            
     def _has_cuda(self) -> bool:
         return self.device.type == "cuda"
 
     def _diffuser_sha256(
-        self, name_or_path: Union[str, Path], chunksize=16777216
+        self, name_or_path: Union[str, Path], chunksize=4096
     ) -> Union[str, bytes]:
         path = None
         if isinstance(name_or_path, Path):
@@ -1216,7 +1105,7 @@ class ModelManager(object):
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        self.logger.debug("Calculating sha256 hash of model files")
+        print("  | Calculating sha256 hash of model files")
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
@@ -1228,7 +1117,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        self.logger.debug(f"sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
+        print(f"  | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1246,13 +1135,13 @@ class ModelManager(object):
                 hash = f.read()
             return hash
 
-        self.logger.debug("Calculating sha256 hash of weights file")
+        print("   | Calculating sha256 hash of weights file")
         tic = time.time()
         sha = hashlib.sha256()
         sha.update(data)
         hash = sha.hexdigest()
         toc = time.time()
-        self.logger.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
+        print(f">> sha256 = {hash}", "(%4.2fs)" % (toc - tic))
 
         with open(hashpath, "w") as f:
             f.write(hash)
@@ -1273,12 +1162,12 @@ class ModelManager(object):
             local_files_only=not Globals.internet_available,
         )
 
-        self.logger.debug(f"Loading diffusers VAE from {name_or_path}")
+        print(f"  | Loading diffusers VAE from {name_or_path}")
         if using_fp16:
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
         else:
-            self.logger.debug("Using more accurate float32 precision")
+            print("  | Using more accurate float32 precision")
             fp_args_list = [{}]
 
         vae = None
@@ -1302,12 +1191,12 @@ class ModelManager(object):
                 break
 
         if not vae and deferred_error:
-            self.logger.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
+            print(f"** Could not load VAE {name_or_path}: {str(deferred_error)}")
 
         return vae
 
-    @classmethod
-    def _delete_model_from_cache(cls,repo_id):
+    @staticmethod
+    def _delete_model_from_cache(repo_id):
         cache_info = scan_cache_dir(global_cache_dir("hub"))
 
         # I'm sure there is a way to do this with comprehensions
@@ -1318,8 +1207,8 @@ class ModelManager(object):
                 for revision in repo.revisions:
                     hashes_to_delete.add(revision.commit_hash)
         strategy = cache_info.delete_revisions(*hashes_to_delete)
-        cls.logger.warning(
-            f"Deletion of this model is expected to free {strategy.expected_freed_size_str}"
+        print(
+            f"** deletion of this model is expected to free {strategy.expected_freed_size_str}"
         )
         strategy.execute()
 

invokeai/backend/prompting/conditioning.py

@@ -18,7 +18,6 @@ from compel.prompt_parser import (
     PromptParser,
 )
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from ..stable_diffusion import InvokeAIDiffuserComponent
@@ -163,8 +162,8 @@ def log_tokenization(
     negative_prompt: Union[Blend, FlattenedPrompt],
     tokenizer,
 ):
-    logger.info(f"[TOKENLOG] Parsed Prompt: {positive_prompt}")
-    logger.info(f"[TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
+    print(f"\n>> [TOKENLOG] Parsed Prompt: {positive_prompt}")
+    print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
 
     log_tokenization_for_prompt_object(positive_prompt, tokenizer)
     log_tokenization_for_prompt_object(
@@ -238,12 +237,12 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t
             usedTokens += 1
 
     if usedTokens > 0:
-        logger.info(f'[TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        logger.debug(f"{tokenized}\x1b[0m")
+        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        print(f"{tokenized}\x1b[0m")
 
     if discarded != "":
-        logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        logger.debug(f"{discarded}\x1b[0m")
+        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
+        print(f"{discarded}\x1b[0m")
 
 
 def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]:
@@ -296,8 +295,8 @@ def split_weighted_subprompts(text, skip_normalize=False) -> list:
         return parsed_prompts
     weight_sum = sum(map(lambda x: x[1], parsed_prompts))
     if weight_sum == 0:
-        logger.warning(
-            "Subprompt weights add up to zero. Discarding and using even weights instead."
+        print(
+            "* Warning: Subprompt weights add up to zero. Discarding and using even weights instead."
         )
         equal_weight = 1 / max(len(parsed_prompts), 1)
         return [(x[0], equal_weight) for x in parsed_prompts]

invokeai/backend/restoration/base.py

@@ -1,5 +1,3 @@
-import invokeai.backend.util.logging as logger
-
 class Restoration:
     def __init__(self) -> None:
         pass
@@ -10,17 +8,17 @@ class Restoration:
         # Load GFPGAN
         gfpgan = self.load_gfpgan(gfpgan_model_path)
         if gfpgan.gfpgan_model_exists:
-            logger.info("GFPGAN Initialized")
+            print(">> GFPGAN Initialized")
         else:
-            logger.info("GFPGAN Disabled")
+            print(">> GFPGAN Disabled")
             gfpgan = None
 
         # Load CodeFormer
         codeformer = self.load_codeformer()
         if codeformer.codeformer_model_exists:
-            logger.info("CodeFormer Initialized")
+            print(">> CodeFormer Initialized")
         else:
-            logger.info("CodeFormer Disabled")
+            print(">> CodeFormer Disabled")
             codeformer = None
 
         return gfpgan, codeformer
@@ -41,5 +39,5 @@ class Restoration:
         from .realesrgan import ESRGAN
 
         esrgan = ESRGAN(esrgan_bg_tile)
-        logger.info("ESRGAN Initialized")
+        print(">> ESRGAN Initialized")
         return esrgan

invokeai/backend/restoration/codeformer.py

@@ -5,7 +5,6 @@ import warnings
 import numpy as np
 import torch
 
-import invokeai.backend.util.logging as logger
 from ..globals import Globals
 
 pretrained_model_url = (
@@ -24,12 +23,12 @@ class CodeFormerRestoration:
         self.codeformer_model_exists = os.path.isfile(self.model_path)
 
         if not self.codeformer_model_exists:
-            logger.error("NOT FOUND: CodeFormer model not found at " + self.model_path)
+            print("## NOT FOUND: CodeFormer model not found at " + self.model_path)
         sys.path.append(os.path.abspath(codeformer_dir))
 
     def process(self, image, strength, device, seed=None, fidelity=0.75):
         if seed is not None:
-            logger.info(f"CodeFormer - Restoring Faces for image seed:{seed}")
+            print(f">> CodeFormer - Restoring Faces for image seed:{seed}")
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=UserWarning)
@@ -98,7 +97,7 @@ class CodeFormerRestoration:
                     del output
                     torch.cuda.empty_cache()
                 except RuntimeError as error:
-                    logger.error(f"Failed inference for CodeFormer: {error}.")
+                    print(f"\tFailed inference for CodeFormer: {error}.")
                     restored_face = cropped_face
 
                 restored_face = restored_face.astype("uint8")

invokeai/backend/restoration/gfpgan.py

@@ -6,9 +6,9 @@ import numpy as np
 import torch
 from PIL import Image
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
+
 class GFPGAN:
     def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
         if not os.path.isabs(gfpgan_model_path):
@@ -19,7 +19,7 @@ class GFPGAN:
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
 
         if not self.gfpgan_model_exists:
-            logger.error("NOT FOUND: GFPGAN model not found at " + self.model_path)
+            print("## NOT FOUND: GFPGAN model not found at " + self.model_path)
             return None
 
     def model_exists(self):
@@ -27,7 +27,7 @@ class GFPGAN:
 
     def process(self, image, strength: float, seed: str = None):
         if seed is not None:
-            logger.info(f"GFPGAN - Restoring Faces for image seed:{seed}")
+            print(f">> GFPGAN - Restoring Faces for image seed:{seed}")
 
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -47,14 +47,14 @@ class GFPGAN:
             except Exception:
                 import traceback
 
-                logger.error("Error loading GFPGAN:", file=sys.stderr)
+                print(">> Error loading GFPGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
             os.chdir(cwd)
 
         if self.gfpgan is None:
-            logger.warning("WARNING: GFPGAN not initialized.")
-            logger.warning(
-                f"Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
+            print(f">> WARNING: GFPGAN not initialized.")
+            print(
+                f">> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
             )
 
         image = image.convert("RGB")

invokeai/backend/restoration/outcrop.py

@@ -1,7 +1,7 @@
 import math
 
 from PIL import Image
-import invokeai.backend.util.logging as logger
+
 
 class Outcrop(object):
     def __init__(
@@ -82,7 +82,7 @@ class Outcrop(object):
             pixels = extents[direction]
             # round pixels up to the nearest 64
             pixels = math.ceil(pixels / 64) * 64
-            logger.info(f"extending image {direction}ward by {pixels} pixels")
+            print(f">> extending image {direction}ward by {pixels} pixels")
             image = self._rotate(image, direction)
             image = self._extend(image, pixels)
             image = self._rotate(image, direction, reverse=True)

invokeai/backend/restoration/realesrgan.py

@@ -6,13 +6,18 @@ import torch
 from PIL import Image
 from PIL.Image import Image as ImageType
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
+
 class ESRGAN:
     def __init__(self, bg_tile_size=400) -> None:
         self.bg_tile_size = bg_tile_size
 
+        if not torch.cuda.is_available():  # CPU or MPS on M1
+            use_half_precision = False
+        else:
+            use_half_precision = True
+
     def load_esrgan_bg_upsampler(self, denoise_str):
         if not torch.cuda.is_available():  # CPU or MPS on M1
             use_half_precision = False
@@ -69,16 +74,16 @@ class ESRGAN:
                 import sys
                 import traceback
 
-                logger.error("Error loading Real-ESRGAN:")
+                print(">> Error loading Real-ESRGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
         if upsampler_scale == 0:
-            logger.warning("Real-ESRGAN: Invalid scaling option. Image not upscaled.")
+            print(">> Real-ESRGAN: Invalid scaling option. Image not upscaled.")
             return image
 
         if seed is not None:
-            logger.info(
-                f"Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
+            print(
+                f">> Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
             )
         # ESRGAN outputs images with partial transparency if given RGBA images; convert to RGB
         image = image.convert("RGB")

invokeai/backend/safety_checker.py

@@ -14,7 +14,6 @@ from PIL import Image, ImageFilter
 from transformers import AutoFeatureExtractor
 
 import invokeai.assets.web as web_assets
-import invokeai.backend.util.logging as logger
 from .globals import global_cache_dir
 from .util import CPU_DEVICE
 
@@ -41,8 +40,8 @@ class SafetyChecker(object):
                 cache_dir=safety_model_path,
             )
         except Exception:
-            logger.error(
-                "An error was encountered while installing the safety checker:"
+            print(
+                "** An error was encountered while installing the safety checker:"
             )
             print(traceback.format_exc())
 
@@ -66,8 +65,8 @@ class SafetyChecker(object):
         )
         self.safety_checker.to(CPU_DEVICE) # offload
         if has_nsfw_concept[0]:
-            logger.warning(
-                "An image with potential non-safe content has been detected. A blurred image will be returned."
+            print(
+                "** An image with potential non-safe content has been detected. A blurred image will be returned. **"
             )
             return self.blur(image)
         else:

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -6,6 +6,7 @@ The interface is through the Concepts() object.
 """
 import os
 import re
+import traceback
 from typing import Callable
 from urllib import error as ul_error
 from urllib import request
@@ -14,10 +15,10 @@ from huggingface_hub import (
     HfApi,
     HfFolder,
     ModelFilter,
+    ModelSearchArguments,
     hf_hub_url,
 )
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 
@@ -58,7 +59,7 @@ class HuggingFaceConceptsLibrary(object):
                 self.concept_list.extend(list(local_concepts_to_add))
                 return self.concept_list
             return self.concept_list
-        elif Globals.internet_available is True:
+        else:
             try:
                 models = self.hf_api.list_models(
                     filter=ModelFilter(model_name="sd-concepts-library/")
@@ -67,15 +68,13 @@ class HuggingFaceConceptsLibrary(object):
                 # when init, add all in dir. when not init, add only concepts added between init and now
                 self.concept_list.extend(list(local_concepts_to_add))
             except Exception as e:
-                logger.warning(
-                    f"Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
+                print(
+                    f" ** WARNING: Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
                 )
-                logger.warning(
-                    "You may load .bin and .pt file(s) manually using the --embedding_directory argument."
+                print(
+                    " ** You may load .bin and .pt file(s) manually using the --embedding_directory argument."
                 )
             return self.concept_list
-        else: 
-            return self.concept_list
 
     def get_concept_model_path(self, concept_name: str) -> str:
         """
@@ -84,8 +83,8 @@ class HuggingFaceConceptsLibrary(object):
         be downloaded.
         """
         if not concept_name in self.list_concepts():
-            logger.warning(
-                f"{concept_name} is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept."
+            print(
+                f"This concept is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept."
             )
             return None
         return self.get_concept_file(concept_name.lower(), "learned_embeds.bin")
@@ -222,7 +221,7 @@ class HuggingFaceConceptsLibrary(object):
             if chunk == 0:
                 bytes += total
 
-        logger.info(f"Downloading {repo_id}...", end="")
+        print(f">> Downloading {repo_id}...", end="")
         try:
             for file in (
                 "README.md",
@@ -236,22 +235,22 @@ class HuggingFaceConceptsLibrary(object):
                 )
         except ul_error.HTTPError as e:
             if e.code == 404:
-                logger.warning(
-                    f"Concept {concept_name} is not known to the Hugging Face library. Generation will continue without the concept."
+                print(
+                    f"This concept is not known to the Hugging Face library. Generation will continue without the concept."
                 )
             else:
-                logger.warning(
+                print(
                     f"Failed to download {concept_name}/{file} ({str(e)}. Generation will continue without the concept.)"
                 )
             os.rmdir(dest)
             return False
         except ul_error.URLError as e:
-            logger.error(
-                f"an error occurred while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
+            print(
+                f"ERROR: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
             )
             os.rmdir(dest)
             return False
-        logger.info("...{:.2f}Kb".format(bytes / 1024))
+        print("...{:.2f}Kb".format(bytes / 1024))
         return succeeded
 
     def _concept_id(self, concept_name: str) -> str:

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -445,15 +445,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     @property
     def _submodels(self) -> Sequence[torch.nn.Module]:
         module_names, _, _ = self.extract_init_dict(dict(self.config))
-        submodels = []
-        for name in module_names.keys():
-            if hasattr(self, name):
-                value = getattr(self, name)
-            else:
-                value = getattr(self.config, name)
-            if isinstance(value, torch.nn.Module):
-                submodels.append(value)
-        return submodels
+        values = [getattr(self, name) for name in module_names.keys()]
+        return [m for m in values if isinstance(m, torch.nn.Module)]
 
     def image_from_embeddings(
         self,
@@ -551,7 +544,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             yield PipelineIntermediateState(
                 run_id=run_id,
                 step=-1,
-                timestep=self.scheduler.config.num_train_timesteps,
+                timestep=self.scheduler.num_train_timesteps,
                 latents=latents,
             )
 
@@ -922,7 +915,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     @property
     def channels(self) -> int:
         """Compatible with DiffusionWrapper"""
-        return self.unet.config.in_channels
+        return self.unet.in_channels
 
     def decode_latents(self, latents):
         # Explicit call to get the vae loaded, since `decode` isn't the forward method.

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -10,12 +10,13 @@ import diffusers
 import psutil
 import torch
 from compel.cross_attention_control import Arguments
-from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.cross_attention import AttnProcessor
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from torch import nn
 
-import invokeai.backend.util.logging as logger
 from ...util import torch_dtype
 
+
 class CrossAttentionType(enum.Enum):
     SELF = 1
     TOKENS = 2
@@ -187,7 +188,7 @@ class Context:
 
 class InvokeAICrossAttentionMixin:
     """
-    Enable InvokeAI-flavoured Attention calculation, which does aggressive low-memory slicing and calls
+    Enable InvokeAI-flavoured CrossAttention calculation, which does aggressive low-memory slicing and calls
     through both to an attention_slice_wrangler and a slicing_strategy_getter for custom attention map wrangling
     and dymamic slicing strategy selection.
     """
@@ -208,7 +209,7 @@ class InvokeAICrossAttentionMixin:
         Set custom attention calculator to be called when attention is calculated
         :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
         which returns either the suggested_attention_slice or an adjusted equivalent.
-            `module` is the current Attention module for which the callback is being invoked.
+            `module` is the current CrossAttention module for which the callback is being invoked.
             `suggested_attention_slice` is the default-calculated attention slice
             `dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
                 If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
@@ -344,11 +345,11 @@ class InvokeAICrossAttentionMixin:
 def restore_default_cross_attention(
     model,
     is_running_diffusers: bool,
-    restore_attention_processor: Optional[AttentionProcessor] = None,
+    restore_attention_processor: Optional[AttnProcessor] = None,
 ):
     if is_running_diffusers:
         unet = model
-        unet.set_attn_processor(restore_attention_processor or AttnProcessor())
+        unet.set_attn_processor(restore_attention_processor or CrossAttnProcessor())
     else:
         remove_attention_function(model)
 
@@ -407,9 +408,12 @@ def override_cross_attention(model, context: Context, is_running_diffusers=False
 def get_cross_attention_modules(
     model, which: CrossAttentionType
 ) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
+    from ldm.modules.attention import CrossAttention  # avoid circular import
 
     cross_attention_class: type = (
         InvokeAIDiffusersCrossAttention
+        if isinstance(model, UNet2DConditionModel)
+        else CrossAttention
     )
     which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
     attention_module_tuples = [
@@ -421,13 +425,13 @@ def get_cross_attention_modules(
     expected_count = 16
     if cross_attention_modules_in_model_count != expected_count:
         # non-fatal error but .swap() won't work.
-        logger.error(
+        print(
             f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model "
             + f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed "
-            + "or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "
+            + f"or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "
             + f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows "
-            + "what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not "
-            + "work properly until it is fixed."
+            + f"what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not "
+            + f"work properly until it is fixed."
         )
     return attention_module_tuples
 
@@ -546,7 +550,7 @@ def get_mem_free_total(device):
 
 
 class InvokeAIDiffusersCrossAttention(
-    diffusers.models.attention.Attention, InvokeAICrossAttentionMixin
+    diffusers.models.attention.CrossAttention, InvokeAICrossAttentionMixin
 ):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -568,8 +572,8 @@ class InvokeAIDiffusersCrossAttention(
 """
 # base implementation
 
-class AttnProcessor:
-    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
+class CrossAttnProcessor:
+    def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None):
         batch_size, sequence_length, _ = hidden_states.shape
         attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
 
@@ -597,9 +601,9 @@ class AttnProcessor:
 from dataclasses import dataclass, field
 
 import torch
-from diffusers.models.attention_processor import (
-    Attention,
-    AttnProcessor,
+from diffusers.models.cross_attention import (
+    CrossAttention,
+    CrossAttnProcessor,
     SlicedAttnProcessor,
 )
 
@@ -649,7 +653,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
 
     def __call__(
         self,
-        attn: Attention,
+        attn: CrossAttention,
         hidden_states,
         encoder_hidden_states=None,
         attention_mask=None,

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -5,10 +5,9 @@ from typing import Any, Callable, Dict, Optional, Union
 
 import numpy as np
 import torch
-from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.cross_attention import AttnProcessor
 from typing_extensions import TypeAlias
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals
 
 from .cross_attention_control import (
@@ -102,7 +101,7 @@ class InvokeAIDiffuserComponent:
 
     def override_cross_attention(
         self, conditioning: ExtraConditioningInfo, step_count: int
-    ) -> Dict[str, AttentionProcessor]:
+    ) -> Dict[str, AttnProcessor]:
         """
         setup cross attention .swap control. for diffusers this replaces the attention processor, so
         the previous attention processor is returned so that the caller can restore it later.
@@ -119,7 +118,7 @@ class InvokeAIDiffuserComponent:
         )
 
     def restore_default_cross_attention(
-        self, restore_attention_processor: Optional["AttentionProcessor"] = None
+        self, restore_attention_processor: Optional["AttnProcessor"] = None
     ):
         self.conditioning = None
         self.cross_attention_control_context = None
@@ -263,7 +262,7 @@ class InvokeAIDiffuserComponent:
             # TODO remove when compvis codepath support is dropped
             if step_index is None and sigma is None:
                 raise ValueError(
-                    "Either step_index or sigma is required when doing cross attention control, but both are None."
+                    f"Either step_index or sigma is required when doing cross attention control, but both are None."
                 )
             percent_through = self.estimate_percent_through(step_index, sigma)
         return percent_through
@@ -467,14 +466,10 @@ class InvokeAIDiffuserComponent:
             outside = torch.count_nonzero(
                 (latents < -current_threshold) | (latents > current_threshold)
             )
-            logger.info(
-                f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})"
-                )
-            logger.debug(
-                f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}"
-            )
-            logger.debug(   
-                f"{outside / latents.numel() * 100:.2f}% values outside threshold"
+            print(
+                f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
+                f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
+                f"  | {outside / latents.numel() * 100:.2f}% values outside threshold"
             )
 
         if maxval < current_threshold and minval > -current_threshold:
@@ -501,11 +496,9 @@ class InvokeAIDiffuserComponent:
             )
 
         if self.debug_thresholding:
-            logger.debug(
-                f"min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})"
-            )
-            logger.debug(
-                f"{num_altered / latents.numel() * 100:.2f}% values altered"
+            print(
+                f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
+                f"  | {num_altered / latents.numel() * 100:.2f}% values altered"
             )
 
         return latents
@@ -606,6 +599,7 @@ class InvokeAIDiffuserComponent:
         )
 
         # below is fugly omg
+        num_actual_conditionings = len(c_or_weighted_c_list)
         conditionings = [uc] + [c for c, weight in weighted_cond_list]
         weights = [1] + [weight for c, weight in weighted_cond_list]
         chunk_count = ceil(len(conditionings) / 2)

invokeai/backend/stable_diffusion/image_degradation/utils_image.py

@@ -10,7 +10,7 @@ from torchvision.utils import make_grid
 
 # import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
 
-import invokeai.backend.util.logging as logger
+
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 
@@ -191,7 +191,7 @@ def mkdirs(paths):
 def mkdir_and_rename(path):
     if os.path.exists(path):
         new_name = path + "_archived_" + get_timestamp()
-        logger.error("Path already exists. Rename it to [{:s}]".format(new_name))
+        print("Path already exists. Rename it to [{:s}]".format(new_name))
         os.replace(path, new_name)
     os.makedirs(path)
 

invokeai/backend/stable_diffusion/textual_inversion_manager.py

@@ -1,27 +1,16 @@
+import os
 import traceback
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional, Union, List
+from typing import Optional, Union
 
-import safetensors.torch
 import torch
-
 from compel.embeddings_provider import BaseTextualInversionManager
 from picklescan.scanner import scan_file_path
 from transformers import CLIPTextModel, CLIPTokenizer
 
-import invokeai.backend.util.logging as logger
 from .concepts_lib import HuggingFaceConceptsLibrary
 
-@dataclass
-class EmbeddingInfo:
-    name: str
-    embedding: torch.Tensor
-    num_vectors_per_token: int
-    token_dim: int 
-    trained_steps: int = None
-    trained_model_name: str = None
-    trained_model_checksum: str = None
 
 @dataclass
 class TextualInversion:
@@ -60,12 +49,12 @@ class TextualInversionManager(BaseTextualInversionManager):
                 or self.has_textual_inversion_for_trigger_string(concept_name)
                 or self.has_textual_inversion_for_trigger_string(f"<{concept_name}>")
             ):  # in case a token with literal angle brackets encountered
-                logger.info(f"Loaded local embedding for trigger {concept_name}")
+                print(f">> Loaded local embedding for trigger {concept_name}")
                 continue
             bin_file = self.hf_concepts_library.get_concept_model_path(concept_name)
             if not bin_file:
                 continue
-            logger.info(f"Loaded remote embedding for trigger {concept_name}")
+            print(f">> Loaded remote embedding for trigger {concept_name}")
             self.load_textual_inversion(bin_file)
             self.hf_concepts_library.concepts_loaded[concept_name] = True
 
@@ -83,46 +72,66 @@ class TextualInversionManager(BaseTextualInversionManager):
         if str(ckpt_path).endswith(".DS_Store"):
             return
 
-        embedding_list = self._parse_embedding(str(ckpt_path))
-        for embedding_info in embedding_list:
-            if (self.text_encoder.get_input_embeddings().weight.data[0].shape[0] != embedding_info.token_dim):
-                logger.warning(
-                    f"Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info.token_dim}."
+        try:
+            scan_result = scan_file_path(str(ckpt_path))
+            if scan_result.infected_files == 1:
+                print(
+                    f"\n### Security Issues Found in Model: {scan_result.issues_count}"
                 )
-                continue
-
-            # Resolve the situation in which an earlier embedding has claimed the same
-            # trigger string. We replace the trigger with '<source_file>', as we used to.
-            trigger_str = embedding_info.name
-            sourcefile = (
-                f"{ckpt_path.parent.name}/{ckpt_path.name}"
-                if ckpt_path.name == "learned_embeds.bin"
-                else ckpt_path.name
+                print("### For your safety, InvokeAI will not load this embed.")
+                return
+        except Exception:
+            print(
+                f"### {ckpt_path.parents[0].name}/{ckpt_path.name} is damaged or corrupt."
             )
+            return
 
-            if trigger_str in self.trigger_to_sourcefile:
-                replacement_trigger_str = (
-                    f"<{ckpt_path.parent.name}>"
-                    if ckpt_path.name == "learned_embeds.bin"
-                    else f"<{ckpt_path.stem}>"
-                )
-                logger.info(
-                    f"{sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}"
-                )
-                trigger_str = replacement_trigger_str
+        embedding_info = self._parse_embedding(str(ckpt_path))
 
-            try:
-                self._add_textual_inversion(
-                    trigger_str,
-                    embedding_info.embedding,
-                    defer_injecting_tokens=defer_injecting_tokens,
-                )
-                # remember which source file claims this trigger
-                self.trigger_to_sourcefile[trigger_str] = sourcefile
+        if embedding_info is None:
+            # We've already put out an error message about the bad embedding in _parse_embedding, so just return.
+            return
+        elif (
+            self.text_encoder.get_input_embeddings().weight.data[0].shape[0]
+            != embedding_info["token_dim"]
+        ):
+            print(
+                f"** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info['token_dim']}."
+            )
+            return
 
-            except ValueError as e:
-                logger.debug(f'Ignoring incompatible embedding {embedding_info["name"]}')
-                logger.debug(f"The error was {str(e)}")
+        # Resolve the situation in which an earlier embedding has claimed the same
+        # trigger string. We replace the trigger with '<source_file>', as we used to.
+        trigger_str = embedding_info["name"]
+        sourcefile = (
+            f"{ckpt_path.parent.name}/{ckpt_path.name}"
+            if ckpt_path.name == "learned_embeds.bin"
+            else ckpt_path.name
+        )
+
+        if trigger_str in self.trigger_to_sourcefile:
+            replacement_trigger_str = (
+                f"<{ckpt_path.parent.name}>"
+                if ckpt_path.name == "learned_embeds.bin"
+                else f"<{ckpt_path.stem}>"
+            )
+            print(
+                f">> {sourcefile}: Trigger token '{trigger_str}' is already claimed by '{self.trigger_to_sourcefile[trigger_str]}'. Trigger this concept with {replacement_trigger_str}"
+            )
+            trigger_str = replacement_trigger_str
+
+        try:
+            self._add_textual_inversion(
+                trigger_str,
+                embedding_info["embedding"],
+                defer_injecting_tokens=defer_injecting_tokens,
+            )
+            # remember which source file claims this trigger
+            self.trigger_to_sourcefile[trigger_str] = sourcefile
+
+        except ValueError as e:
+            print(f'   | Ignoring incompatible embedding {embedding_info["name"]}')
+            print(f"   | The error was {str(e)}")
 
     def _add_textual_inversion(
         self, trigger_str, embedding, defer_injecting_tokens=False
@@ -134,8 +143,8 @@ class TextualInversionManager(BaseTextualInversionManager):
         :return: The token id for the added embedding, either existing or newly-added.
         """
         if trigger_str in [ti.trigger_string for ti in self.textual_inversions]:
-            logger.warning(
-                f"TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'"
+            print(
+                f"** TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'"
             )
             return
         if not self.full_precision:
@@ -156,11 +165,11 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         except ValueError as e:
             if str(e).startswith("Warning"):
-                logger.warning(f"{str(e)}")
+                print(f">> {str(e)}")
             else:
                 traceback.print_exc()
-                logger.error(
-                    f"TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}."
+                print(
+                    f"** TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}."
                 )
                 raise
 
@@ -220,16 +229,16 @@ class TextualInversionManager(BaseTextualInversionManager):
         for ti in self.textual_inversions:
             if ti.trigger_token_id is None and ti.trigger_string in prompt_string:
                 if ti.embedding_vector_length > 1:
-                    logger.info(
-                        f"Preparing tokens for textual inversion {ti.trigger_string}..."
+                    print(
+                        f">> Preparing tokens for textual inversion {ti.trigger_string}..."
                     )
                 try:
                     self._inject_tokens_and_assign_embeddings(ti)
                 except ValueError as e:
-                    logger.debug(
-                        f"Ignoring incompatible embedding trigger {ti.trigger_string}"
+                    print(
+                        f"   | Ignoring incompatible embedding trigger {ti.trigger_string}"
                     )
-                    logger.debug(f"The error was {str(e)}")
+                    print(f"   | The error was {str(e)}")
                     continue
                 injected_token_ids.append(ti.trigger_token_id)
                 injected_token_ids.extend(ti.pad_token_ids)
@@ -300,130 +309,111 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         return token_id
 
-
-    def _parse_embedding(self, embedding_file: str)->List[EmbeddingInfo]:
-        suffix = Path(embedding_file).suffix
-        try:
-            if suffix in [".pt",".ckpt",".bin"]:
-                scan_result = scan_file_path(embedding_file)
-                if scan_result.infected_files > 0:
-                    logger.critical(
-                        f"Security Issues Found in Model: {scan_result.issues_count}"
-                    )
-                    logger.critical("For your safety, InvokeAI will not load this embed.")
-                    return list()
-                ckpt = torch.load(embedding_file,map_location="cpu")
-            else:
-                ckpt = safetensors.torch.load_file(embedding_file)
-        except Exception as e:
-            logger.warning(f"Notice: unrecognized embedding file format: {embedding_file}: {e}")
-            return list()
-        
-        # try to figure out what kind of embedding file it is and parse accordingly
-        keys = list(ckpt.keys())
-        if all(x in keys for x in ['string_to_token','string_to_param','name','step']):
-            return self._parse_embedding_v1(ckpt, embedding_file)     # example rem_rezero.pt
-        
-        elif all(x in keys for x in ['string_to_token','string_to_param']):
-            return self._parse_embedding_v2(ckpt, embedding_file)     # example midj-strong.pt
-        
-        elif 'emb_params' in keys:
-            return self._parse_embedding_v3(ckpt, embedding_file)     # example easynegative.safetensors
-        
+    def _parse_embedding(self, embedding_file: str):
+        file_type = embedding_file.split(".")[-1]
+        if file_type == "pt":
+            return self._parse_embedding_pt(embedding_file)
+        elif file_type == "bin":
+            return self._parse_embedding_bin(embedding_file)
         else:
-            return self._parse_embedding_v4(ckpt, embedding_file)     # usually a '.bin' file
+            print(f"** Notice: unrecognized embedding file format: {embedding_file}")
+            return None
 
-    def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
-        basename = Path(file_path).stem
-        logger.debug(f'Loading v1 embedding file: {basename}')
+    def _parse_embedding_pt(self, embedding_file):
+        embedding_ckpt = torch.load(embedding_file, map_location="cpu")
+        embedding_info = {}
 
-        embeddings = list()
-        token_counter = -1
-        for token,embedding in embedding_ckpt["string_to_param"].items():
-            if token_counter < 0:
-                trigger = embedding_ckpt["name"]
-            elif token_counter == 0:
-                trigger = '<basename>'
-            else:
-                trigger = f'<{basename}-{int(token_counter:=token_counter)}>'
-            token_counter += 1
-            embedding_info = EmbeddingInfo(
-                name = trigger,
-                embedding = embedding,
-                num_vectors_per_token = embedding.size()[0],
-                token_dim = embedding.size()[1],
-                trained_steps = embedding_ckpt["step"],
-                trained_model_name = embedding_ckpt["sd_checkpoint_name"],
-                trained_model_checksum = embedding_ckpt["sd_checkpoint"]
-            )
-            embeddings.append(embedding_info)
-        return embeddings
+        # Check if valid embedding file
+        if "string_to_token" and "string_to_param" in embedding_ckpt:
+            # Catch variants that do not have the expected keys or values.
+            try:
+                embedding_info["name"] = embedding_ckpt["name"] or os.path.basename(
+                    os.path.splitext(embedding_file)[0]
+                )
 
-    def _parse_embedding_v2 (
-        self, embedding_ckpt: dict, file_path: str
-    ) -> List[EmbeddingInfo]:
+                # Check num of embeddings and warn user only the first will be used
+                embedding_info["num_of_embeddings"] = len(
+                    embedding_ckpt["string_to_token"]
+                )
+                if embedding_info["num_of_embeddings"] > 1:
+                    print(">> More than 1 embedding found. Will use the first one")
+
+                embedding = list(embedding_ckpt["string_to_param"].values())[0]
+            except (AttributeError, KeyError):
+                return self._handle_broken_pt_variants(embedding_ckpt, embedding_file)
+
+            embedding_info["embedding"] = embedding
+            embedding_info["num_vectors_per_token"] = embedding.size()[0]
+            embedding_info["token_dim"] = embedding.size()[1]
+
+            try:
+                embedding_info["trained_steps"] = embedding_ckpt["step"]
+                embedding_info["trained_model_name"] = embedding_ckpt[
+                    "sd_checkpoint_name"
+                ]
+                embedding_info["trained_model_checksum"] = embedding_ckpt[
+                    "sd_checkpoint"
+                ]
+            except AttributeError:
+                print(">> No Training Details Found. Passing ...")
+
+        # .pt files found at https://cyberes.github.io/stable-diffusion-textual-inversion-models/
+        # They are actually .bin files
+        elif len(embedding_ckpt.keys()) == 1:
+            embedding_info = self._parse_embedding_bin(embedding_file)
+
+        else:
+            print(">> Invalid embedding format")
+            embedding_info = None
+
+        return embedding_info
+
+    def _parse_embedding_bin(self, embedding_file):
+        embedding_ckpt = torch.load(embedding_file, map_location="cpu")
+        embedding_info = {}
+
+        if list(embedding_ckpt.keys()) == 0:
+            print(">> Invalid concepts file")
+            embedding_info = None
+        else:
+            for token in list(embedding_ckpt.keys()):
+                embedding_info["name"] = (
+                    token
+                    or f"<{os.path.basename(os.path.splitext(embedding_file)[0])}>"
+                )
+                embedding_info["embedding"] = embedding_ckpt[token]
+                embedding_info[
+                    "num_vectors_per_token"
+                ] = 1  # All Concepts seem to default to 1
+                embedding_info["token_dim"] = embedding_info["embedding"].size()[0]
+
+        return embedding_info
+
+    def _handle_broken_pt_variants(
+        self, embedding_ckpt: dict, embedding_file: str
+    ) -> dict:
         """
-        This handles embedding .pt file variant #2.
+        This handles the broken .pt file variants. We only know of one at present.
         """
-        basename = Path(file_path).stem
-        logger.debug(f'Loading v2 embedding file: {basename}')
-        embeddings = list()
-        
+        embedding_info = {}
         if isinstance(
             list(embedding_ckpt["string_to_token"].values())[0], torch.Tensor
         ):
-            token_counter = 0
-            for token,embedding in embedding_ckpt["string_to_param"].items():
-                trigger = token if token != '*' \
-                    else f'<{basename}>' if token_counter == 0 \
-                         else f'<{basename}-{int(token_counter:=token_counter+1)}>'
-                embedding_info = EmbeddingInfo(
-                    name = trigger,
-                    embedding = embedding,
-                    num_vectors_per_token = embedding.size()[0],
-                    token_dim = embedding.size()[1],
+            for token in list(embedding_ckpt["string_to_token"].keys()):
+                embedding_info["name"] = (
+                    token
+                    if token != "*"
+                    else f"<{os.path.basename(os.path.splitext(embedding_file)[0])}>"
                 )
-                embeddings.append(embedding_info)
+                embedding_info["embedding"] = embedding_ckpt[
+                    "string_to_param"
+                ].state_dict()[token]
+                embedding_info["num_vectors_per_token"] = embedding_info[
+                    "embedding"
+                ].shape[0]
+                embedding_info["token_dim"] = embedding_info["embedding"].size()[1]
         else:
-            logger.warning(f"{basename}: Unrecognized embedding format")
+            print(">> Invalid embedding format")
+            embedding_info = None
 
-        return embeddings
-
-    def _parse_embedding_v3(self, embedding_ckpt: dict, file_path: str)->List[EmbeddingInfo]:
-        """
-        Parse 'version 3' of the .pt textual inversion embedding files.
-        """
-        basename = Path(file_path).stem
-        logger.debug(f'Loading v3 embedding file: {basename}')
-        embedding = embedding_ckpt['emb_params']
-        embedding_info = EmbeddingInfo(
-            name = f'<{basename}>',
-            embedding = embedding,
-            num_vectors_per_token = embedding.size()[0],
-            token_dim = embedding.size()[1],
-        )
-        return [embedding_info]
-    
-    def _parse_embedding_v4(self, embedding_ckpt: dict, filepath: str)->List[EmbeddingInfo]:
-        """
-        Parse 'version 4' of the textual inversion embedding files. This one
-        is usually associated with .bin files trained by HuggingFace diffusers.
-        """
-        basename = Path(filepath).stem
-        short_path = Path(filepath).parents[0].name+'/'+Path(filepath).name
-        
-        logger.debug(f'Loading v4 embedding file: {short_path}')
-        
-        embeddings = list()
-        if list(embedding_ckpt.keys()) == 0:
-            logger.warning(f"Invalid embeddings file: {short_path}")
-        else:
-            for token,embedding in embedding_ckpt.items():
-                embedding_info = EmbeddingInfo(
-                    name = token or f"<{basename}>",
-                    embedding = embedding,
-                    num_vectors_per_token = 1,  # All Concepts seem to default to 1
-                    token_dim = embedding.size()[0],
-                )
-                embeddings.append(embedding_info)
-        return embeddings
+        return embedding_info

invokeai/backend/util/logging.py

@@ -1,109 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
-
-"""invokeai.util.logging
-
-Logging class for InvokeAI that produces console messages that follow
-the conventions established in InvokeAI 1.X through 2.X.
-
-
-One way to use it:
-
-from invokeai.backend.util.logging import InvokeAILogger
-
-logger = InvokeAILogger.getLogger(__name__)
-logger.critical('this is critical')
-logger.error('this is an error')
-logger.warning('this is a warning')
-logger.info('this is info')
-logger.debug('this is debugging')
-
-Console messages:
-     ### this is critical
-     *** this is an error ***
-     ** this is a warning
-     >> this is info
-        | this is debugging
-
-Another way:
-import invokeai.backend.util.logging as ialog
-ialogger.debug('this is a debugging message')
-"""
-import logging
-
-# module level functions
-def debug(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().debug(msg, *args, **kwargs)
-
-def info(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().info(msg, *args, **kwargs)
-
-def warning(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().warning(msg, *args, **kwargs)
-
-def error(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().error(msg, *args, **kwargs)
-    
-def critical(msg, *args, **kwargs):
-    InvokeAILogger.getLogger().critical(msg, *args, **kwargs)
-
-def log(level, msg, *args, **kwargs):
-    InvokeAILogger.getLogger().log(level, msg, *args, **kwargs)
-
-def disable(level=logging.CRITICAL):
-    InvokeAILogger.getLogger().disable(level)
-
-def basicConfig(**kwargs):
-    InvokeAILogger.getLogger().basicConfig(**kwargs)
-
-def getLogger(name: str=None)->logging.Logger:
-    return InvokeAILogger.getLogger(name)
-
-class InvokeAILogFormatter(logging.Formatter):
-    '''
-    Repurposed from:
-    https://stackoverflow.com/questions/14844970/modifying-logging-message-format-based-on-message-logging-level-in-python3
-    '''
-    crit_fmt = "### %(msg)s"
-    err_fmt = "*** %(msg)s"
-    warn_fmt = "** %(msg)s"
-    info_fmt = ">> %(msg)s"
-    dbg_fmt = "   | %(msg)s"
-
-    def __init__(self):
-        super().__init__(fmt="%(levelno)d: %(msg)s", datefmt=None, style='%')
-
-    def format(self, record):
-        # Remember the format used when the logging module
-        # was installed (in the event that this formatter is
-        # used with the vanilla logging module.
-        format_orig = self._style._fmt
-        if record.levelno == logging.DEBUG:
-            self._style._fmt = InvokeAILogFormatter.dbg_fmt
-        if record.levelno == logging.INFO:
-            self._style._fmt = InvokeAILogFormatter.info_fmt
-        if record.levelno == logging.WARNING:
-            self._style._fmt = InvokeAILogFormatter.warn_fmt
-        if record.levelno == logging.ERROR:
-            self._style._fmt = InvokeAILogFormatter.err_fmt
-        if record.levelno == logging.CRITICAL:
-            self._style._fmt = InvokeAILogFormatter.crit_fmt
-
-        # parent class does the work
-        result = super().format(record)
-        self._style._fmt = format_orig
-        return result
-
-class InvokeAILogger(object):
-    loggers = dict()
-    
-    @classmethod
-    def getLogger(self, name:str='invokeai')->logging.Logger:
-        if name not in self.loggers:
-            logger = logging.getLogger(name)
-            logger.setLevel(logging.DEBUG)
-            ch = logging.StreamHandler()
-            fmt = InvokeAILogFormatter()
-            ch.setFormatter(fmt)
-            logger.addHandler(ch)
-            self.loggers[name] = logger
-        return self.loggers[name]

invokeai/backend/util/util.py

@@ -18,7 +18,6 @@ import torch
 from PIL import Image, ImageDraw, ImageFont
 from tqdm import tqdm
 
-import invokeai.backend.util.logging as logger
 from .devices import torch_dtype
 
 
@@ -39,7 +38,7 @@ def log_txt_as_img(wh, xc, size=10):
         try:
             draw.text((0, 0), lines, fill="black", font=font)
         except UnicodeEncodeError:
-            logger.warning("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)
@@ -81,8 +80,8 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        logger.debug(
-            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
 
@@ -133,8 +132,8 @@ def parallel_data_prefetch(
         raise ValueError("list expected but function got ndarray.")
     elif isinstance(data, abc.Iterable):
         if isinstance(data, dict):
-            logger.warning(
-                '"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
+            print(
+                '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":
@@ -176,7 +175,7 @@ def parallel_data_prefetch(
         processes += [p]
 
     # start processes
-    logger.info("Start prefetching...")
+    print("Start prefetching...")
     import time
 
     start = time.time()
@@ -195,7 +194,7 @@ def parallel_data_prefetch(
                 gather_res[res[0]] = res[1]
 
     except Exception as e:
-        logger.error("Exception: ", e)
+        print("Exception: ", e)
         for p in processes:
             p.terminate()
 
@@ -203,7 +202,7 @@ def parallel_data_prefetch(
     finally:
         for p in processes:
             p.join()
-        logger.info(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):
@@ -319,23 +318,23 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
         resp = requests.get(url, headers=header, stream=True)  # new request with range
 
     if exist_size > content_length:
-        logger.warning("corrupt existing file found. re-downloading")
+        print("* corrupt existing file found. re-downloading")
         os.remove(dest)
         exist_size = 0
 
     if resp.status_code == 416 or exist_size == content_length:
-        logger.warning(f"{dest}: complete file found. Skipping.")
+        print(f"* {dest}: complete file found. Skipping.")
         return dest
     elif resp.status_code == 206 or exist_size > 0:
-        logger.warning(f"{dest}: partial file found. Resuming...")
+        print(f"* {dest}: partial file found. Resuming...")
     elif resp.status_code != 200:
-        logger.error(f"An error occurred during downloading {dest}: {resp.reason}")
+        print(f"** An error occurred during downloading {dest}: {resp.reason}")
     else:
-        logger.error(f"{dest}: Downloading...")
+        print(f"* {dest}: Downloading...")
 
     try:
         if content_length < 2000:
-            logger.error(f"ERROR DOWNLOADING {url}: {resp.text}")
+            print(f"*** ERROR DOWNLOADING {url}: {resp.text}")
             return None
 
         with open(dest, open_mode) as file, tqdm(
@@ -350,7 +349,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
                 size = file.write(data)
                 bar.update(size)
     except Exception as e:
-        logger.error(f"An error occurred while downloading {dest}: {str(e)}")
+        print(f"An error occurred while downloading {dest}: {str(e)}")
         return None
 
     return dest

invokeai/backend/web/invoke_ai_web_server.py

@@ -19,7 +19,6 @@ from PIL import Image
 from PIL.Image import Image as ImageType
 from werkzeug.utils import secure_filename
 
-import invokeai.backend.util.logging as logger
 import invokeai.frontend.web.dist as frontend
 
 from .. import Generate
@@ -78,6 +77,7 @@ class InvokeAIWebServer:
         mimetypes.add_type("application/javascript", ".js")
         mimetypes.add_type("text/css", ".css")
         # Socket IO
+        logger = True if args.web_verbose else False
         engineio_logger = True if args.web_verbose else False
         max_http_buffer_size = 10000000
 
@@ -213,7 +213,7 @@ class InvokeAIWebServer:
         self.load_socketio_listeners(self.socketio)
 
         if args.gui:
-            logger.info("Launching Invoke AI GUI")
+            print(">> Launching Invoke AI GUI")
             try:
                 from flaskwebgui import FlaskUI
 
@@ -231,17 +231,17 @@ class InvokeAIWebServer:
                 sys.exit(0)
         else:
             useSSL = args.certfile or args.keyfile
-            logger.info("Started Invoke AI Web Server")
+            print(">> Started Invoke AI Web Server")
             if self.host == "0.0.0.0":
-                logger.info(
+                print(
                     f"Point your browser at http{'s' if useSSL else ''}://localhost:{self.port} or use the host's DNS name or IP address."
                 )
             else:
-                logger.info(
-                    "Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address."
+                print(
+                    ">> Default host address now 127.0.0.1 (localhost). Use --host 0.0.0.0 to bind any address."
                 )
-                logger.info(
-                    f"Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}"
+                print(
+                    f">> Point your browser at http{'s' if useSSL else ''}://{self.host}:{self.port}"
                 )
             if not useSSL:
                 self.socketio.run(app=self.app, host=self.host, port=self.port)
@@ -273,7 +273,7 @@ class InvokeAIWebServer:
         # path for thumbnail images
         self.thumbnail_image_path = os.path.join(self.result_path, "thumbnails/")
         # txt log
-        self.log_path = os.path.join(self.result_path, "invoke_logger.txt")
+        self.log_path = os.path.join(self.result_path, "invoke_log.txt")
         # make all output paths
         [
             os.makedirs(path, exist_ok=True)
@@ -290,7 +290,7 @@ class InvokeAIWebServer:
     def load_socketio_listeners(self, socketio):
         @socketio.on("requestSystemConfig")
         def handle_request_capabilities():
-            logger.info("System config requested")
+            print(">> System config requested")
             config = self.get_system_config()
             config["model_list"] = self.generate.model_manager.list_models()
             config["infill_methods"] = infill_methods()
@@ -330,7 +330,7 @@ class InvokeAIWebServer:
                 if model_name in current_model_list:
                     update = True
 
-                logger.info(f"Adding New Model: {model_name}")
+                print(f">> Adding New Model: {model_name}")
 
                 self.generate.model_manager.add_model(
                     model_name=model_name,
@@ -348,14 +348,14 @@ class InvokeAIWebServer:
                         "update": update,
                     },
                 )
-                logger.info(f"New Model Added: {model_name}")
+                print(f">> New Model Added: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
         @socketio.on("deleteModel")
         def handle_delete_model(model_name: str):
             try:
-                logger.info(f"Deleting Model: {model_name}")
+                print(f">> Deleting Model: {model_name}")
                 self.generate.model_manager.del_model(model_name)
                 self.generate.model_manager.commit(opt.conf)
                 updated_model_list = self.generate.model_manager.list_models()
@@ -366,14 +366,14 @@ class InvokeAIWebServer:
                         "model_list": updated_model_list,
                     },
                 )
-                logger.info(f"Model Deleted: {model_name}")
+                print(f">> Model Deleted: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
         @socketio.on("requestModelChange")
         def handle_set_model(model_name: str):
             try:
-                logger.info(f"Model change requested: {model_name}")
+                print(f">> Model change requested: {model_name}")
                 model = self.generate.set_model(model_name)
                 model_list = self.generate.model_manager.list_models()
                 if model is None:
@@ -454,7 +454,7 @@ class InvokeAIWebServer:
                         "update": True,
                     },
                 )
-                logger.info(f"Model Converted: {model_name}")
+                print(f">> Model Converted: {model_name}")
             except Exception as e:
                 self.handle_exceptions(e)
 
@@ -490,7 +490,7 @@ class InvokeAIWebServer:
                 if vae := self.generate.model_manager.config[models_to_merge[0]].get(
                     "vae", None
                 ):
-                    logger.info(f"Using configured VAE assigned to {models_to_merge[0]}")
+                    print(f">> Using configured VAE assigned to {models_to_merge[0]}")
                     merged_model_config.update(vae=vae)
 
                 self.generate.model_manager.import_diffuser_model(
@@ -507,8 +507,8 @@ class InvokeAIWebServer:
                         "update": True,
                     },
                 )
-                logger.info(f"Models Merged: {models_to_merge}")
-                logger.info(f"New Model Added: {model_merge_info['merged_model_name']}")
+                print(f">> Models Merged: {models_to_merge}")
+                print(f">> New Model Added: {model_merge_info['merged_model_name']}")
             except Exception as e:
                 self.handle_exceptions(e)
 
@@ -698,7 +698,7 @@ class InvokeAIWebServer:
                             }
                         )
                     except Exception as e:
-                        logger.info(f"Unable to load {path}")
+                        print(f">> Unable to load {path}")
                         socketio.emit(
                             "error", {"message": f"Unable to load {path}: {str(e)}"}
                         )
@@ -735,9 +735,9 @@ class InvokeAIWebServer:
                         printable_parameters["init_mask"][:64] + "..."
                     )
 
-                logger.info(f"Image Generation Parameters:\n\n{printable_parameters}\n")
-                logger.info(f"ESRGAN Parameters: {esrgan_parameters}")
-                logger.info(f"Facetool Parameters: {facetool_parameters}")
+                print(f"\n>> Image Generation Parameters:\n\n{printable_parameters}\n")
+                print(f">> ESRGAN Parameters: {esrgan_parameters}")
+                print(f">> Facetool Parameters: {facetool_parameters}")
 
                 self.generate_images(
                     generation_parameters,
@@ -750,8 +750,8 @@ class InvokeAIWebServer:
         @socketio.on("runPostprocessing")
         def handle_run_postprocessing(original_image, postprocessing_parameters):
             try:
-                logger.info(
-                    f'Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}'
+                print(
+                    f'>> Postprocessing requested for "{original_image["url"]}": {postprocessing_parameters}'
                 )
 
                 progress = Progress()
@@ -861,14 +861,14 @@ class InvokeAIWebServer:
 
         @socketio.on("cancel")
         def handle_cancel():
-            logger.info("Cancel processing requested")
+            print(">> Cancel processing requested")
             self.canceled.set()
 
         # TODO: I think this needs a safety mechanism.
         @socketio.on("deleteImage")
         def handle_delete_image(url, thumbnail, uuid, category):
             try:
-                logger.info(f'Delete requested "{url}"')
+                print(f'>> Delete requested "{url}"')
                 from send2trash import send2trash
 
                 path = self.get_image_path_from_url(url)
@@ -1022,7 +1022,7 @@ class InvokeAIWebServer:
                     "RGB"
                 )
 
-            def image_progress(intermediate_state: PipelineIntermediateState):
+            def image_progress(sample, step):
                 if self.canceled.is_set():
                     raise CanceledException
 
@@ -1030,14 +1030,6 @@ class InvokeAIWebServer:
                 nonlocal generation_parameters
                 nonlocal progress
 
-                step = intermediate_state.step
-                if intermediate_state.predicted_original is not None:
-                    # Some schedulers report not only the noisy latents at the current timestep,
-                    # but also their estimate so far of what the de-noised latents will be.
-                    sample = intermediate_state.predicted_original
-                else:
-                    sample = intermediate_state.latents
-
                 generation_messages = {
                     "txt2img": "common.statusGeneratingTextToImage",
                     "img2img": "common.statusGeneratingImageToImage",
@@ -1263,7 +1255,7 @@ class InvokeAIWebServer:
                     image, os.path.basename(path), self.thumbnail_image_path
                 )
 
-                logger.info(f'Image generated: "{path}"\n')
+                print(f'\n\n>> Image generated: "{path}"\n')
                 self.write_log_message(f'[Generated] "{path}": {command}')
 
                 if progress.total_iterations > progress.current_iteration:
@@ -1310,9 +1302,16 @@ class InvokeAIWebServer:
 
                 progress.set_current_iteration(progress.current_iteration + 1)
 
+            def diffusers_step_callback_adapter(*cb_args, **kwargs):
+                if isinstance(cb_args[0], PipelineIntermediateState):
+                    progress_state: PipelineIntermediateState = cb_args[0]
+                    return image_progress(progress_state.latents, progress_state.step)
+                else:
+                    return image_progress(*cb_args, **kwargs)
+
             self.generate.prompt2image(
                 **generation_parameters,
-                step_callback=image_progress,
+                step_callback=diffusers_step_callback_adapter,
                 image_callback=image_done,
             )
 
@@ -1329,7 +1328,7 @@ class InvokeAIWebServer:
         except Exception as e:
             # Clear the CUDA cache on an exception
             self.empty_cuda_cache()
-            logger.error(e)
+            print(e)
             self.handle_exceptions(e)
 
     def empty_cuda_cache(self):

invokeai/frontend/CLI/CLI.py

@@ -16,7 +16,6 @@ if sys.platform == "darwin":
 import pyparsing  # type: ignore
 
 import invokeai.version as invokeai
-import invokeai.backend.util.logging as logger
 
 from ...backend import Generate, ModelManager
 from ...backend.args import Args, dream_cmd_from_png, metadata_dumps, metadata_from_png
@@ -70,7 +69,7 @@ def main():
     # run any post-install patches needed
     run_patches()
 
-    logger.info(f"Internet connectivity is {Globals.internet_available}")
+    print(f">> Internet connectivity is {Globals.internet_available}")
 
     if not args.conf:
         config_file = os.path.join(Globals.root, "configs", "models.yaml")
@@ -79,8 +78,8 @@ def main():
                 opt, FileNotFoundError(f"The file {config_file} could not be found.")
             )
 
-    logger.info(f"{invokeai.__app_name__}, version {invokeai.__version__}")
-    logger.info(f'InvokeAI runtime directory is "{Globals.root}"')
+    print(f">> {invokeai.__app_name__}, version {invokeai.__version__}")
+    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # loading here to avoid long delays on startup
     # these two lines prevent a horrible warning message from appearing
@@ -122,7 +121,7 @@ def main():
             else:
                 raise FileNotFoundError(f"{opt.infile} not found.")
         except (FileNotFoundError, IOError) as e:
-            logger.critical('Aborted',exc_info=True)
+            print(f"{e}. Aborting.")
             sys.exit(-1)
 
     # creating a Generate object:
@@ -143,12 +142,12 @@ def main():
         )
     except (FileNotFoundError, TypeError, AssertionError) as e:
         report_model_error(opt, e)
-    except (IOError, KeyError):
-        logger.critical("Aborted",exc_info=True)
+    except (IOError, KeyError) as e:
+        print(f"{e}. Aborting.")
         sys.exit(-1)
 
     if opt.seamless:
-        logger.info("Changed to seamless tiling mode")
+        print(">> changed to seamless tiling mode")
 
     # preload the model
     try:
@@ -159,9 +158,14 @@ def main():
         report_model_error(opt, e)
 
     # try to autoconvert new models
+    if path := opt.autoimport:
+        gen.model_manager.heuristic_import(
+            str(path), convert=False, commit_to_conf=opt.conf
+        )
+
     if path := opt.autoconvert:
         gen.model_manager.heuristic_import(
-            str(path), commit_to_conf=opt.conf
+            str(path), convert=True, commit_to_conf=opt.conf
         )
 
     # web server loops forever
@@ -181,7 +185,9 @@ def main():
             f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}'
         )
     except Exception:
-        logger.error("An error occurred",exc_info=True)
+        print(">> An error occurred:")
+        traceback.print_exc()
+
 
 # TODO: main_loop() has gotten busy. Needs to be refactored.
 def main_loop(gen, opt):
@@ -247,7 +253,7 @@ def main_loop(gen, opt):
                 if not opt.prompt:
                     oldargs = metadata_from_png(opt.init_img)
                     opt.prompt = oldargs.prompt
-                    logger.info(f'Retrieved old prompt "{opt.prompt}" from {opt.init_img}')
+                    print(f'>> Retrieved old prompt "{opt.prompt}" from {opt.init_img}')
             except (OSError, AttributeError, KeyError):
                 pass
 
@@ -264,9 +270,9 @@ def main_loop(gen, opt):
         if opt.init_img is not None and re.match("^-\\d+$", opt.init_img):
             try:
                 opt.init_img = last_results[int(opt.init_img)][0]
-                logger.info(f"Reusing previous image {opt.init_img}")
+                print(f">> Reusing previous image {opt.init_img}")
             except IndexError:
-                logger.info(f"No previous initial image at position {opt.init_img} found")
+                print(f">> No previous initial image at position {opt.init_img} found")
                 opt.init_img = None
                 continue
 
@@ -287,9 +293,9 @@ def main_loop(gen, opt):
         if opt.seed is not None and opt.seed < 0 and operation != "postprocess":
             try:
                 opt.seed = last_results[opt.seed][1]
-                logger.info(f"Reusing previous seed {opt.seed}")
+                print(f">> Reusing previous seed {opt.seed}")
             except IndexError:
-                logger.info(f"No previous seed at position {opt.seed} found")
+                print(f">> No previous seed at position {opt.seed} found")
                 opt.seed = None
                 continue
 
@@ -308,7 +314,7 @@ def main_loop(gen, opt):
             subdir = subdir[: (path_max - 39 - len(os.path.abspath(opt.outdir)))]
             current_outdir = os.path.join(opt.outdir, subdir)
 
-            logger.info('Writing files to directory: "' + current_outdir + '"')
+            print('Writing files to directory: "' + current_outdir + '"')
 
             # make sure the output directory exists
             if not os.path.exists(current_outdir):
@@ -437,14 +443,15 @@ def main_loop(gen, opt):
                         catch_interrupts=catch_ctrl_c,
                         **vars(opt),
                     )
-                except (PromptParser.ParsingException, pyparsing.ParseException):
-                    logger.error("An error occurred while processing your prompt",exc_info=True)
+                except (PromptParser.ParsingException, pyparsing.ParseException) as e:
+                    print("** An error occurred while processing your prompt **")
+                    print(f"** {str(e)} **")
             elif operation == "postprocess":
-                logger.info(f"fixing {opt.prompt}")
+                print(f">> fixing {opt.prompt}")
                 opt.last_operation = do_postprocess(gen, opt, image_writer)
 
             elif operation == "mask":
-                logger.info(f"generating masks from {opt.prompt}")
+                print(f">> generating masks from {opt.prompt}")
                 do_textmask(gen, opt, image_writer)
 
             if opt.grid and len(grid_images) > 0:
@@ -467,12 +474,12 @@ def main_loop(gen, opt):
                 )
                 results = [[path, formatted_dream_prompt]]
 
-        except AssertionError:
-            logger.error(e)
+        except AssertionError as e:
+            print(e)
             continue
 
         except OSError as e:
-            logger.error(e)
+            print(e)
             continue
 
         print("Outputs:")
@@ -511,7 +518,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
             gen.set_model(model_name)
             add_embedding_terms(gen, completer)
         except KeyError as e:
-            logger.error(e)
+            print(str(e))
         except Exception as e:
             report_model_error(opt, e)
         completer.add_history(command)
@@ -525,8 +532,8 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!import"):
         path = shlex.split(command)
         if len(path) < 2:
-            logger.warning(
-                "please provide (1) a URL to a .ckpt file to import; (2) a local path to a .ckpt file; or (3) a diffusers repository id in the form stabilityai/stable-diffusion-2-1"
+            print(
+                "** please provide (1) a URL to a .ckpt file to import; (2) a local path to a .ckpt file; or (3) a diffusers repository id in the form stabilityai/stable-diffusion-2-1"
             )
         else:
             try:
@@ -539,7 +546,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith(("!convert", "!optimize")):
         path = shlex.split(command)
         if len(path) < 2:
-            logger.warning("please provide the path to a .ckpt or .safetensors model")
+            print("** please provide the path to a .ckpt or .safetensors model")
         else:
             try:
                 convert_model(path[1], gen, opt, completer)
@@ -551,7 +558,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!edit"):
         path = shlex.split(command)
         if len(path) < 2:
-            logger.warning("please provide the name of a model")
+            print("** please provide the name of a model")
         else:
             edit_model(path[1], gen, opt, completer)
         completer.add_history(command)
@@ -560,7 +567,7 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
     elif command.startswith("!del"):
         path = shlex.split(command)
         if len(path) < 2:
-            logger.warning("please provide the name of a model")
+            print("** please provide the name of a model")
         else:
             del_config(path[1], gen, opt, completer)
         completer.add_history(command)
@@ -574,7 +581,6 @@ def do_command(command: str, gen, opt: Args, completer) -> tuple:
 
     elif command.startswith("!replay"):
         file_path = command.replace("!replay", "", 1).strip()
-        file_path = os.path.join(opt.outdir, file_path)
         if infile is None and os.path.isfile(file_path):
             infile = open(file_path, "r", encoding="utf-8")
         completer.add_history(command)
@@ -620,7 +626,7 @@ def set_default_output_dir(opt: Args, completer: Completer):
     completer.set_default_dir(opt.outdir)
 
 
-def import_model(model_path: str, gen, opt, completer):
+def import_model(model_path: str, gen, opt, completer, convert=False):
     """
     model_path can be (1) a URL to a .ckpt file; (2) a local .ckpt file path;
     (3) a huggingface repository id; or (4) a local directory containing a
@@ -640,8 +646,8 @@ def import_model(model_path: str, gen, opt, completer):
             try:
                 default_name = url_attachment_name(model_path)
                 default_name = Path(default_name).stem
-            except Exception:
-                logger.warning(f"A problem occurred while assigning the name of the downloaded model",exc_info=True)
+            except Exception as e:
+                print(f"** URL: {str(e)}")
             model_name, model_desc = _get_model_name_and_desc(
                 gen.model_manager,
                 completer,
@@ -651,6 +657,7 @@ def import_model(model_path: str, gen, opt, completer):
         model_path,
         model_name=model_name,
         description=model_desc,
+        convert=convert,
     )
 
     if not imported_name:
@@ -659,14 +666,15 @@ def import_model(model_path: str, gen, opt, completer):
                 model_path,
                 model_name=model_name,
                 description=model_desc,
+                convert=convert,
                 model_config_file=config_file,
             )
     if not imported_name:
-        logger.error("Aborting import.")
+        print("** Aborting import.")
         return
 
     if not _verify_load(imported_name, gen):
-        logger.error("model failed to load. Discarding configuration entry")
+        print("** model failed to load. Discarding configuration entry")
         gen.model_manager.del_model(imported_name)
         return
     if click.confirm("Make this the default model?", default=False):
@@ -674,7 +682,7 @@ def import_model(model_path: str, gen, opt, completer):
 
     gen.model_manager.commit(opt.conf)
     completer.update_models(gen.model_manager.list_models())
-    logger.info(f"{imported_name} successfully installed")
+    print(f">> {imported_name} successfully installed")
 
 def _pick_configuration_file(completer)->Path:
     print(
@@ -718,21 +726,21 @@ Please select the type of this model:
     return choice
 
 def _verify_load(model_name: str, gen) -> bool:
-    logger.info("Verifying that new model loads...")
+    print(">> Verifying that new model loads...")
     current_model = gen.model_name
     try:
         if not gen.set_model(model_name):
             return
     except Exception as e:
-        logger.warning(f"model failed to load: {str(e)}")
-        logger.warning(
+        print(f"** model failed to load: {str(e)}")
+        print(
             "** note that importing 2.X checkpoints is not supported. Please use !convert_model instead."
         )
         return False
     if click.confirm("Keep model loaded?", default=True):
         gen.set_model(model_name)
     else:
-        logger.info("Restoring previous model")
+        print(">> Restoring previous model")
         gen.set_model(current_model)
     return True
 
@@ -749,13 +757,14 @@ def _get_model_name_and_desc(
     )
     return model_name, model_description
 
+
 def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
     model_name_or_path = model_name_or_path.replace("\\", "/")  # windows
     manager = gen.model_manager
     ckpt_path = None
     original_config_file = None
     if model_name_or_path == gen.model_name:
-        logger.warning("Can't convert the active model. !switch to another model first. **")
+        print("** Can't convert the active model. !switch to another model first. **")
         return
     elif model_info := manager.model_info(model_name_or_path):
         if "weights" in model_info:
@@ -763,10 +772,16 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             original_config_file = Path(model_info["config"])
             model_name = model_name_or_path
             model_description = model_info["description"]
-            vae_path = model_info.get("vae")
+            vae = model_info["vae"]
         else:
-            logger.warning(f"{model_name_or_path} is not a legacy .ckpt weights file")
+            print(f"** {model_name_or_path} is not a legacy .ckpt weights file")
             return
+        if vae_repo := invokeai.backend.model_management.model_manager.VAE_TO_REPO_ID.get(
+            Path(vae).stem
+        ):
+            vae_repo = dict(repo_id=vae_repo)
+        else:
+            vae_repo = None
         model_name = manager.convert_and_import(
             ckpt_path,
             diffusers_path=Path(
@@ -775,27 +790,27 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             model_name=model_name,
             model_description=model_description,
             original_config_file=original_config_file,
-            vae_path=vae_path,
+            vae=vae_repo,
         )
     else:
         try:
-            import_model(model_name_or_path, gen, opt, completer)
+            import_model(model_name_or_path, gen, opt, completer, convert=True)
         except KeyboardInterrupt:
             return
 
     manager.commit(opt.conf)
     if click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False):
         ckpt_path.unlink(missing_ok=True)
-        logger.warning(f"{ckpt_path} deleted")
+        print(f"{ckpt_path} deleted")
 
 
 def del_config(model_name: str, gen, opt, completer):
     current_model = gen.model_name
     if model_name == current_model:
-        logger.warning("Can't delete active model. !switch to another model first. **")
+        print("** Can't delete active model. !switch to another model first. **")
         return
     if model_name not in gen.model_manager.config:
-        logger.warning(f"Unknown model {model_name}")
+        print(f"** Unknown model {model_name}")
         return
 
     if not click.confirm(
@@ -808,17 +823,17 @@ def del_config(model_name: str, gen, opt, completer):
     )
     gen.model_manager.del_model(model_name, delete_files=delete_completely)
     gen.model_manager.commit(opt.conf)
-    logger.warning(f"{model_name} deleted")
+    print(f"** {model_name} deleted")
     completer.update_models(gen.model_manager.list_models())
 
 
 def edit_model(model_name: str, gen, opt, completer):
     manager = gen.model_manager
     if not (info := manager.model_info(model_name)):
-        logger.warning(f"** Unknown model {model_name}")
+        print(f"** Unknown model {model_name}")
         return
-    print()
-    logger.info(f"Editing model {model_name} from configuration file {opt.conf}")
+
+    print(f"\n>> Editing model {model_name} from configuration file {opt.conf}")
     new_name = _get_model_name(manager.list_models(), completer, model_name)
 
     for attribute in info.keys():
@@ -856,7 +871,7 @@ def edit_model(model_name: str, gen, opt, completer):
         manager.set_default_model(new_name)
     manager.commit(opt.conf)
     completer.update_models(manager.list_models())
-    logger.info("Model successfully updated")
+    print(">> Model successfully updated")
 
 
 def _get_model_name(existing_names, completer, default_name: str = "") -> str:
@@ -867,11 +882,11 @@ def _get_model_name(existing_names, completer, default_name: str = "") -> str:
         if len(model_name) == 0:
             model_name = default_name
         if not re.match("^[\w._+:/-]+$", model_name):
-            logger.warning(
-                'model name must contain only words, digits and the characters "._+:/-" **'
+            print(
+                '** model name must contain only words, digits and the characters "._+:/-" **'
             )
         elif model_name != default_name and model_name in existing_names:
-            logger.warning(f"the name {model_name} is already in use. Pick another.")
+            print(f"** the name {model_name} is already in use. Pick another.")
         else:
             done = True
     return model_name
@@ -938,10 +953,11 @@ def do_postprocess(gen, opt, callback):
             opt=opt,
         )
     except OSError:
-        logger.error(f"{file_path}: file could not be read",exc_info=True)
+        print(traceback.format_exc(), file=sys.stderr)
+        print(f"** {file_path}: file could not be read")
         return
     except (KeyError, AttributeError):
-        logger.error(f"an error occurred while applying the {tool} postprocessor",exc_info=True)
+        print(traceback.format_exc(), file=sys.stderr)
         return
     return opt.last_operation
 
@@ -996,13 +1012,13 @@ def prepare_image_metadata(
         try:
             filename = opt.fnformat.format(**wildcards)
         except KeyError as e:
-            logger.error(
-                f"The filename format contains an unknown key '{e.args[0]}'. Will use {{prefix}}.{{seed}}.png' instead"
+            print(
+                f"** The filename format contains an unknown key '{e.args[0]}'. Will use {{prefix}}.{{seed}}.png' instead"
             )
             filename = f"{prefix}.{seed}.png"
         except IndexError:
-            logger.error(
-                "The filename format is broken or complete. Will use '{prefix}.{seed}.png' instead"
+            print(
+                "** The filename format is broken or complete. Will use '{prefix}.{seed}.png' instead"
             )
             filename = f"{prefix}.{seed}.png"
 
@@ -1091,14 +1107,14 @@ def split_variations(variations_string) -> list:
     for part in variations_string.split(","):
         seed_and_weight = part.split(":")
         if len(seed_and_weight) != 2:
-            logger.warning(f'Could not parse with_variation part "{part}"')
+            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:
-            logger.warning(f'Could not parse with_variation part "{part}"')
+            print(f'** Could not parse with_variation part "{part}"')
             broken = True
             break
         parts.append([seed, weight])
@@ -1122,23 +1138,23 @@ def load_face_restoration(opt):
                     opt.gfpgan_model_path
                 )
             else:
-                logger.info("Face restoration disabled")
+                print(">> Face restoration disabled")
             if opt.esrgan:
                 esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
             else:
-                logger.info("Upscaling disabled")
+                print(">> Upscaling disabled")
         else:
-            logger.info("Face restoration and upscaling disabled")
+            print(">> Face restoration and upscaling disabled")
     except (ModuleNotFoundError, ImportError):
         print(traceback.format_exc(), file=sys.stderr)
-        logger.info("You may need to install the ESRGAN and/or GFPGAN modules")
+        print(">> You may need to install the ESRGAN and/or GFPGAN modules")
     return gfpgan, codeformer, esrgan
 
 
 def make_step_callback(gen, opt, prefix):
     destination = os.path.join(opt.outdir, "intermediates", prefix)
     os.makedirs(destination, exist_ok=True)
-    logger.info(f"Intermediate images will be written into {destination}")
+    print(f">> Intermediate images will be written into {destination}")
 
     def callback(state: PipelineIntermediateState):
         latents = state.latents
@@ -1180,20 +1196,21 @@ def retrieve_dream_command(opt, command, completer):
     try:
         cmd = dream_cmd_from_png(path)
     except OSError:
-        logger.error(f"{tokens[0]}: file could not be read")
+        print(f"## {tokens[0]}: file could not be read")
     except (KeyError, AttributeError, IndexError):
-        logger.error(f"{tokens[0]}: file has no metadata")
+        print(f"## {tokens[0]}: file has no metadata")
     except:
-        logger.error(f"{tokens[0]}: file could not be processed")
+        print(f"## {tokens[0]}: file could not be processed")
     if len(cmd) > 0:
         completer.set_line(cmd)
 
+
 def write_commands(opt, file_path: str, outfilepath: str):
     dir, basename = os.path.split(file_path)
     try:
         paths = sorted(list(Path(dir).glob(basename)))
     except ValueError:
-        logger.error(f'"{basename}": unacceptable pattern')
+        print(f'## "{basename}": unacceptable pattern')
         return
 
     commands = []
@@ -1202,9 +1219,9 @@ def write_commands(opt, file_path: str, outfilepath: str):
         try:
             cmd = dream_cmd_from_png(path)
         except (KeyError, AttributeError, IndexError):
-            logger.error(f"{path}: file has no metadata")
+            print(f"## {path}: file has no metadata")
         except:
-            logger.error(f"{path}: file could not be processed")
+            print(f"## {path}: file could not be processed")
         if cmd:
             commands.append(f"# {path}")
             commands.append(cmd)
@@ -1214,18 +1231,18 @@ def write_commands(opt, file_path: str, outfilepath: str):
             outfilepath = os.path.join(opt.outdir, basename)
         with open(outfilepath, "w", encoding="utf-8") as f:
             f.write("\n".join(commands))
-        logger.info(f"File {outfilepath} with commands created")
+        print(f">> File {outfilepath} with commands created")
 
 
 def report_model_error(opt: Namespace, e: Exception):
-    logger.warning(f'An error occurred while attempting to initialize the model: "{str(e)}"')
-    logger.warning(
-        "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
+    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
+    print(
+        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
     )
     yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
     if yes_to_all:
-        logger.warning(
-            "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
+        print(
+            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
         )
     else:
         if not click.confirm(
@@ -1234,7 +1251,7 @@ def report_model_error(opt: Namespace, e: Exception):
         ):
             return
 
-    logger.info("invokeai-configure is launching....\n")
+    print("invokeai-configure is launching....\n")
 
     # Match arguments that were set on the CLI
     # only the arguments accepted by the configuration script are parsed
@@ -1251,7 +1268,7 @@ def report_model_error(opt: Namespace, e: Exception):
     from ..install import invokeai_configure
 
     invokeai_configure()
-    logger.warning("InvokeAI will now restart")
+    print("** InvokeAI will now restart")
     sys.argv = previous_args
     main()  # would rather do a os.exec(), but doesn't exist?
     sys.exit(0)

invokeai/frontend/install/invokeai_update.py

@@ -1,9 +1,10 @@
-'''
+"""
 Minimalist updater script. Prompts user for the tag or branch to update to and runs
 pip install <path_to_git_source>.
-'''
+"""
 import os
 import platform
+
 import requests
 from rich import box, print
 from rich.console import Console, Group, group
@@ -15,10 +16,8 @@ from rich.text import Text
 
 from invokeai.version import __version__
 
-INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive"
-INVOKE_AI_TAG="https://github.com/invoke-ai/InvokeAI/archive/refs/tags"
-INVOKE_AI_BRANCH="https://github.com/invoke-ai/InvokeAI/archive/refs/heads"
-INVOKE_AI_REL="https://api.github.com/repos/invoke-ai/InvokeAI/releases"
+INVOKE_AI_SRC = "https://github.com/invoke-ai/InvokeAI/archive"
+INVOKE_AI_REL = "https://api.github.com/repos/invoke-ai/InvokeAI/releases"
 
 OS = platform.uname().system
 ARCH = platform.uname().machine
@@ -29,22 +28,22 @@ if OS == "Windows":
 else:
     console = Console(style=Style(color="grey74", bgcolor="grey19"))
 
-def get_versions()->dict:
+
+def get_versions() -> dict:
     return requests.get(url=INVOKE_AI_REL).json()
 
+
 def welcome(versions: dict):
-    
     @group()
     def text():
-        yield f'InvokeAI Version: [bold yellow]{__version__}'
-        yield ''
-        yield 'This script will update InvokeAI to the latest release, or to a development version of your choice.'
-        yield ''
-        yield '[bold yellow]Options:'
-        yield f'''[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
+        yield f"InvokeAI Version: [bold yellow]{__version__}"
+        yield ""
+        yield "This script will update InvokeAI to the latest release, or to a development version of your choice."
+        yield ""
+        yield "[bold yellow]Options:"
+        yield f"""[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
 [2] Update to the bleeding-edge development version ([italic]main[/italic])
-[3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
-[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to'''        
+[3] Manually enter the tag or branch name you wish to update"""
 
     console.rule()
     print(
@@ -60,41 +59,33 @@ def welcome(versions: dict):
     )
     console.line()
 
+
 def main():
     versions = get_versions()
     welcome(versions)
 
     tag = None
-    branch = None
-    release = None
-    choice = Prompt.ask('Choice:',choices=['1','2','3','4'],default='1')
-    
-    if choice=='1':
-        release = versions[0]['tag_name']
-    elif choice=='2':
-        release = 'main'
-    elif choice=='3':
-        tag = Prompt.ask('Enter an InvokeAI tag name')
-    elif choice=='4':
-        branch = Prompt.ask('Enter an InvokeAI branch name')
+    choice = Prompt.ask("Choice:", choices=["1", "2", "3"], default="1")
 
-    print(f':crossed_fingers: Upgrading to [yellow]{tag if tag else release}[/yellow]')
-    if release:
-        cmd = f'pip install {INVOKE_AI_SRC}/{release}.zip --use-pep517 --upgrade'
-    elif tag:
-        cmd = f'pip install {INVOKE_AI_TAG}/{tag}.zip --use-pep517 --upgrade'
+    if choice == "1":
+        tag = versions[0]["tag_name"]
+    elif choice == "2":
+        tag = "main"
+    elif choice == "3":
+        tag = Prompt.ask("Enter an InvokeAI tag or branch name")
+
+    print(f":crossed_fingers: Upgrading to [yellow]{tag}[/yellow]")
+    cmd = f"pip install {INVOKE_AI_SRC}/{tag}.zip --use-pep517"
+    print("")
+    print("")
+    if os.system(cmd) == 0:
+        print(f":heavy_check_mark: Upgrade successful")
     else:
-        cmd = f'pip install {INVOKE_AI_BRANCH}/{branch}.zip --use-pep517 --upgrade'
-    print('')
-    print('')
-    if os.system(cmd)==0:
-        print(f':heavy_check_mark: Upgrade successful')
-    else:
-        print(f':exclamation: [bold red]Upgrade failed[/red bold]')
-    
+        print(f":exclamation: [bold red]Upgrade failed[/red bold]")
+
+
 if __name__ == "__main__":
     try:
         main()
     except KeyboardInterrupt:
         pass
-

invokeai/frontend/install/model_install.py

@@ -22,7 +22,6 @@ import torch
 from npyscreen import widget
 from omegaconf import OmegaConf
 
-import invokeai.backend.util.logging as logger
 from invokeai.backend.globals import Globals, global_config_dir
 
 from ...backend.config.model_install_backend import (
@@ -200,6 +199,17 @@ class addModelsForm(npyscreen.FormMultiPage):
             relx=4,
             scroll_exit=True,
         )
+        self.nextrely += 1
+        self.convert_models = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name="== CONVERT IMPORTED MODELS INTO DIFFUSERS==",
+            values=["Keep original format", "Convert to diffusers"],
+            value=0,
+            begin_entry_at=4,
+            max_height=4,
+            hidden=True,  # will appear when imported models box is edited
+            scroll_exit=True,
+        )
         self.cancel = self.add_widget_intelligent(
             npyscreen.ButtonPress,
             name="CANCEL",
@@ -234,6 +244,8 @@ class addModelsForm(npyscreen.FormMultiPage):
             self.show_directory_fields.addVisibleWhenSelected(i)
 
         self.show_directory_fields.when_value_edited = self._clear_scan_directory
+        self.import_model_paths.when_value_edited = self._show_hide_convert
+        self.autoload_directory.when_value_edited = self._show_hide_convert
 
     def resize(self):
         super().resize()
@@ -244,6 +256,13 @@ class addModelsForm(npyscreen.FormMultiPage):
         if not self.show_directory_fields.value:
             self.autoload_directory.value = ""
 
+    def _show_hide_convert(self):
+        model_paths = self.import_model_paths.value or ""
+        autoload_directory = self.autoload_directory.value or ""
+        self.convert_models.hidden = (
+            len(model_paths) == 0 and len(autoload_directory) == 0
+        )
+
     def _get_starter_model_labels(self) -> List[str]:
         window_width, window_height = get_terminal_size()
         label_width = 25
@@ -303,6 +322,7 @@ class addModelsForm(npyscreen.FormMultiPage):
         .scan_directory: Path to a directory of models to scan and import
         .autoscan_on_startup:  True if invokeai should scan and import at startup time
         .import_model_paths:   list of URLs, repo_ids and file paths to import
+        .convert_to_diffusers: if True, convert legacy checkpoints into diffusers
         """
         # we're using a global here rather than storing the result in the parentapp
         # due to some bug in npyscreen that is causing attributes to be lost
@@ -339,6 +359,7 @@ class addModelsForm(npyscreen.FormMultiPage):
 
         # URLs and the like
         selections.import_model_paths = self.import_model_paths.value.split()
+        selections.convert_to_diffusers = self.convert_models.value[0] == 1
 
 
 class AddModelApplication(npyscreen.NPSAppManaged):
@@ -351,6 +372,7 @@ class AddModelApplication(npyscreen.NPSAppManaged):
             scan_directory=None,
             autoscan_on_startup=None,
             import_model_paths=None,
+            convert_to_diffusers=None,
         )
 
     def onStart(self):
@@ -371,6 +393,7 @@ def process_and_execute(opt: Namespace, selections: Namespace):
     directory_to_scan = selections.scan_directory
     scan_at_startup = selections.autoscan_on_startup
     potential_models_to_install = selections.import_model_paths
+    convert_to_diffusers = selections.convert_to_diffusers
 
     install_requested_models(
         install_initial_models=models_to_install,
@@ -378,6 +401,7 @@ def process_and_execute(opt: Namespace, selections: Namespace):
         scan_directory=Path(directory_to_scan) if directory_to_scan else None,
         external_models=potential_models_to_install,
         scan_at_startup=scan_at_startup,
+        convert_to_diffusers=convert_to_diffusers,
         precision="float32"
         if opt.full_precision
         else choose_precision(torch.device(choose_torch_device())),
@@ -456,8 +480,8 @@ def main():
     Globals.root = os.path.expanduser(get_root(opt.root) or "")
 
     if not global_config_dir().exists():
-        logger.info(
-            "Your InvokeAI root directory is not set up. Calling invokeai-configure."
+        print(
+            ">> Your InvokeAI root directory is not set up. Calling invokeai-configure."
         )
         from invokeai.frontend.install import invokeai_configure
 
@@ -467,18 +491,18 @@ def main():
     try:
         select_and_download_models(opt)
     except AssertionError as e:
-        logger.error(e)
+        print(str(e))
         sys.exit(-1)
     except KeyboardInterrupt:
-        logger.info("Goodbye! Come back soon.")
+        print("\nGoodbye! Come back soon.")
     except widget.NotEnoughSpaceForWidget as e:
         if str(e).startswith("Height of 1 allocated"):
-            logger.error(
-                "Insufficient vertical space for the interface. Please make your window taller and try again"
+            print(
+                "** Insufficient vertical space for the interface. Please make your window taller and try again"
             )
         elif str(e).startswith("addwstr"):
-            logger.error(
-                "Insufficient horizontal space for the interface. Please make your window wider and try again."
+            print(
+                "** Insufficient horizontal space for the interface. Please make your window wider and try again."
             )