add board dropdown to use instead of whichever board theyre viewing

.dev_scripts/diff_images.py

@@ -20,13 +20,13 @@ def calc_images_mean_L1(image1_path, image2_path):
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("image1_path")
-    parser.add_argument("image2_path")
+    parser.add_argument('image1_path')
+    parser.add_argument('image2_path')
     args = parser.parse_args()
     return args
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     args = parse_args()
     mean_L1 = calc_images_mean_L1(args.image1_path, args.image2_path)
     print(mean_L1)

.dockerignore

@@ -1,9 +1,25 @@
+# use this file as a whitelist
 *
 !invokeai
+!ldm
 !pyproject.toml
-!docker/docker-entrypoint.sh
-!LICENSE
 
-**/node_modules
-**/__pycache__
-**/*.egg-info
+# ignore frontend/web but whitelist dist
+invokeai/frontend/web/
+!invokeai/frontend/web/dist/
+
+# ignore invokeai/assets but whitelist invokeai/assets/web
+invokeai/assets/
+!invokeai/assets/web/
+
+# Guard against pulling in any models that might exist in the directory tree
+**/*.pt*
+**/*.ckpt
+
+# Byte-compiled / optimized / DLL files
+**/__pycache__/
+**/*.py[cod]
+
+# Distribution / packaging
+**/*.egg-info/
+**/*.egg

.git-blame-ignore-revs

@@ -1,2 +1 @@
 b3dccfaeb636599c02effc377cdd8a87d658256c
-218b6d0546b990fc449c876fb99f44b50c4daa35

.github/CODEOWNERS

@@ -6,7 +6,7 @@
 /mkdocs.yml @lstein  @blessedcoolant
 
 # nodes
-/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising 
+/invokeai/app/ @Kyle0654 @blessedcoolant
 
 # installation and configuration
 /pyproject.toml  @lstein @blessedcoolant
@@ -22,7 +22,7 @@
 /invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp
 
 # generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising
+/invokeai/backend  @damian0815 @lstein @blessedcoolant @jpphoto @gregghelt2 @StAlKeR7779
 
 # front ends
 /invokeai/frontend/CLI @lstein

.github/workflows/build-container.yml

@@ -3,15 +3,17 @@ on:
   push:
     branches:
       - 'main'
+      - 'update/ci/docker/*'
+      - 'update/docker/*'
+      - 'dev/ci/docker/*'
+      - 'dev/docker/*'
     paths:
       - 'pyproject.toml'
       - '.dockerignore'
       - 'invokeai/**'
       - 'docker/Dockerfile'
-      - 'docker/docker-entrypoint.sh'
-      - 'workflows/build-container.yml'
     tags:
-      - 'v*'
+      - 'v*.*.*'
   workflow_dispatch:
 
 permissions:
@@ -24,27 +26,23 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        gpu-driver:
-        - cuda
-        - cpu
-        - rocm
+        flavor:
+          - rocm
+          - cuda
+          - cpu
+        include:
+          - flavor: rocm
+            pip-extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
+          - flavor: cuda
+            pip-extra-index-url: ''
+          - flavor: cpu
+            pip-extra-index-url: 'https://download.pytorch.org/whl/cpu'
     runs-on: ubuntu-latest
-    name: ${{ matrix.gpu-driver }}
+    name: ${{ matrix.flavor }}
     env:
-      # torch/arm64 does not support GPU currently, so arm64 builds
-      # would not be GPU-accelerated.
-      # re-enable arm64 if there is sufficient demand.
-      # PLATFORMS: 'linux/amd64,linux/arm64'
-      PLATFORMS: 'linux/amd64'
+      PLATFORMS: 'linux/amd64,linux/arm64'
+      DOCKERFILE: 'docker/Dockerfile'
     steps:
-      - name: Free up more disk space on the runner
-        # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
-        run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf "$AGENT_TOOLSDIRECTORY"
-          sudo swapoff /mnt/swapfile
-          sudo rm -rf /mnt/swapfile
-
       - name: Checkout
         uses: actions/checkout@v3
 
@@ -55,7 +53,7 @@ jobs:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           images: |
             ghcr.io/${{ github.repository }}
-            ${{ env.DOCKERHUB_REPOSITORY }}
+            ${{ vars.DOCKERHUB_REPOSITORY }}
           tags: |
             type=ref,event=branch
             type=ref,event=tag
@@ -64,8 +62,8 @@ jobs:
             type=pep440,pattern={{major}}
             type=sha,enable=true,prefix=sha-,format=short
           flavor: |
-            latest=${{ matrix.gpu-driver == 'cuda' && github.ref == 'refs/heads/main' }}
-            suffix=-${{ matrix.gpu-driver }},onlatest=false
+            latest=${{ matrix.flavor == 'cuda' && github.ref == 'refs/heads/main' }}
+            suffix=-${{ matrix.flavor }},onlatest=false
 
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v2
@@ -83,33 +81,34 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      # - name: Login to Docker Hub
-      #   if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
-      #   uses: docker/login-action@v2
-      #   with:
-      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
-      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Login to Docker Hub
+        if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
 
       - name: Build container
         id: docker_build
         uses: docker/build-push-action@v4
         with:
           context: .
-          file: docker/Dockerfile
+          file: ${{ env.DOCKERFILE }}
           platforms: ${{ env.PLATFORMS }}
           push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
+          build-args: PIP_EXTRA_INDEX_URL=${{ matrix.pip-extra-index-url }}
           cache-from: |
-            type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
-            type=gha,scope=main-${{ matrix.gpu-driver }}
-          cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+            type=gha,scope=${{ github.ref_name }}-${{ matrix.flavor }}
+            type=gha,scope=main-${{ matrix.flavor }}
+          cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.flavor }}
 
-      # - name: Docker Hub Description
-      #   if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
-      #   uses: peter-evans/dockerhub-description@v3
-      #   with:
-      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
-      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
-      #     repository: ${{ vars.DOCKERHUB_REPOSITORY }}
-      #     short-description: ${{ github.event.repository.description }}
+      - name: Docker Hub Description
+        if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
+        uses: peter-evans/dockerhub-description@v3
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+          repository: ${{ vars.DOCKERHUB_REPOSITORY }}
+          short-description: ${{ github.event.repository.description }}

.github/workflows/close-inactive-issues.yml

@@ -1,11 +1,11 @@
 name: Close inactive issues
 on:
   schedule:
-    - cron: "00 4 * * *"
+    - cron: "00 6 * * *"
 
 env:
-  DAYS_BEFORE_ISSUE_STALE: 30
-  DAYS_BEFORE_ISSUE_CLOSE: 14
+  DAYS_BEFORE_ISSUE_STALE: 14
+  DAYS_BEFORE_ISSUE_CLOSE: 28
 
 jobs:
   close-issues:
@@ -14,7 +14,7 @@ jobs:
       issues: write
       pull-requests: write
     steps:
-      - uses: actions/stale@v8
+      - uses: actions/stale@v5
         with:
           days-before-issue-stale: ${{ env.DAYS_BEFORE_ISSUE_STALE }}
           days-before-issue-close: ${{ env.DAYS_BEFORE_ISSUE_CLOSE }}
@@ -23,6 +23,5 @@ jobs:
           close-issue-message: "Due to inactivity, this issue was automatically closed. If you are still experiencing the issue, please recreate the issue."
           days-before-pr-stale: -1
           days-before-pr-close: -1
-          exempt-issue-labels: "Active Issue"
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           operations-per-run: 500

.github/workflows/lint-frontend.yml

@@ -2,6 +2,8 @@ name: Lint frontend
 
 on:
   pull_request:
+    paths:
+      - 'invokeai/frontend/web/**'
     types:
       - 'ready_for_review'
       - 'opened'
@@ -9,6 +11,8 @@ on:
   push:
     branches:
       - 'main'
+    paths:
+      - 'invokeai/frontend/web/**'
   merge_group:
   workflow_dispatch:
 

.github/workflows/mkdocs-material.yml

@@ -2,7 +2,7 @@ name: mkdocs-material
 on:
   push:
     branches:
-      - 'refs/heads/main'
+      - 'refs/heads/v2.3'
 
 permissions:
     contents: write
@@ -43,7 +43,7 @@ jobs:
             --verbose
 
       - name: deploy to gh-pages
-        if: ${{ github.ref == 'refs/heads/main' }}
+        if: ${{ github.ref == 'refs/heads/v2.3' }}
         run: |
           python -m \
             mkdocs gh-deploy \

.github/workflows/style-checks.yml

@@ -1,27 +0,0 @@
-name: Black  # TODO: add isort and flake8 later
-
-on:
-  pull_request: {}
-  push:
-    branches: master
-    tags: "*"
-
-jobs:
-  test:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-
-      - name: Setup Python
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.10'
-
-      - name: Install dependencies with pip
-        run: |
-          pip install --upgrade pip wheel
-          pip install .[test]
-
-      # - run: isort --check-only .
-      - run: black --check .
-      # - run: flake8

.gitignore

@@ -38,6 +38,7 @@ develop-eggs/
 downloads/
 eggs/
 .eggs/
+lib/
 lib64/
 parts/
 sdist/

.pre-commit-config.yaml

@@ -1,10 +0,0 @@
-# See https://pre-commit.com/ for usage and config
-repos:
-- repo: local
-  hooks:
-  - id: black
-    name: black
-    stages: [commit]
-    language: system
-    entry: black
-    types: [python]

LICENSE-SDXL.txt

@@ -1,290 +0,0 @@
-Copyright (c) 2023 Stability AI
-CreativeML Open RAIL++-M License dated July 26, 2023
-
-Section I: PREAMBLE
-
-Multimodal generative models are being widely adopted and used, and
-have the potential to transform the way artists, among other
-individuals, conceive and benefit from AI or ML technologies as a tool
-for content creation.
-
-Notwithstanding the current and potential benefits that these
-artifacts can bring to society at large, there are also concerns about
-potential misuses of them, either due to their technical limitations
-or ethical considerations.
-
-In short, this license strives for both the open and responsible
-downstream use of the accompanying model. When it comes to the open
-character, we took inspiration from open source permissive licenses
-regarding the grant of IP rights. Referring to the downstream
-responsible use, we added use-based restrictions not permitting the
-use of the model in very specific scenarios, in order for the licensor
-to be able to enforce the license in case potential misuses of the
-Model may occur. At the same time, we strive to promote open and
-responsible research on generative models for art and content
-generation.
-
-Even though downstream derivative versions of the model could be
-released under different licensing terms, the latter will always have
-to include - at minimum - the same use-based restrictions as the ones
-in the original license (this license). We believe in the intersection
-between open and responsible AI development; thus, this agreement aims
-to strike a balance between both in order to enable responsible
-open-science in the field of AI.
-
-This CreativeML Open RAIL++-M License governs the use of the model
-(and its derivatives) and is informed by the model card associated
-with the model.
-
-NOW THEREFORE, You and Licensor agree as follows:
-
-Definitions
-
-"License" means the terms and conditions for use, reproduction, and
-Distribution as defined in this document.
-
-"Data" means a collection of information and/or content extracted from
-the dataset used with the Model, including to train, pretrain, or
-otherwise evaluate the Model. The Data is not licensed under this
-License.
-
-"Output" means the results of operating a Model as embodied in
-informational content resulting therefrom.
-
-"Model" means any accompanying machine-learning based assemblies
-(including checkpoints), consisting of learnt weights, parameters
-(including optimizer states), corresponding to the model architecture
-as embodied in the Complementary Material, that have been trained or
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-"Derivatives of the Model" means all modifications to the Model, works
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-by transfer of patterns of the weights, parameters, activations or
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-model to perform similarly to the Model, including - but not limited
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-representations or methods based on the generation of synthetic data
-by the Model for training the other model.
-
-"Complementary Material" means the accompanying source code and
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-and used to prepare data for training or evaluation, if any. This
-includes any accompanying documentation, tutorials, examples, etc, if
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-
-"Distribution" means any transmission, reproduction, publication or
-other sharing of the Model or Derivatives of the Model to a third
-party, including providing the Model as a hosted service made
-available by electronic or other remote means - e.g. API-based or web
-access.
-
-"Licensor" means the copyright owner or entity authorized by the
-copyright owner that is granting the License, including the persons or
-entities that may have rights in the Model and/or distributing the
-Model.
-
-"You" (or "Your") means an individual or Legal Entity exercising
-permissions granted by this License and/or making use of the Model for
-whichever purpose and in any field of use, including usage of the
-Model in an end-use application - e.g. chatbot, translator, image
-generator.
-
-"Third Parties" means individuals or legal entities that are not under
-common control with Licensor or You.
-
-"Contribution" means any work of authorship, including the original
-version of the Model and any modifications or additions to that Model
-or Derivatives of the Model thereof, that is intentionally submitted
-to Licensor for inclusion in the Model by the copyright owner or by an
-individual or Legal Entity authorized to submit on behalf of the
-copyright owner. For the purposes of this definition, "submitted"
-means any form of electronic, verbal, or written communication sent to
-the Licensor or its representatives, including but not limited to
-communication on electronic mailing lists, source code control
-systems, and issue tracking systems that are managed by, or on behalf
-of, the Licensor for the purpose of discussing and improving the
-Model, but excluding communication that is conspicuously marked or
-otherwise designated in writing by the copyright owner as "Not a
-Contribution."
-
-"Contributor" means Licensor and any individual or Legal Entity on
-behalf of whom a Contribution has been received by Licensor and
-subsequently incorporated within the Model.
-
-Section II: INTELLECTUAL PROPERTY RIGHTS
-
-Both copyright and patent grants apply to the Model, Derivatives of
-the Model and Complementary Material. The Model and Derivatives of the
-Model are subject to additional terms as described in
-
-Section III.
-
-Grant of Copyright License. Subject to the terms and conditions of
-this License, each Contributor hereby grants to You a perpetual,
-worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-copyright license to reproduce, prepare, publicly display, publicly
-perform, sublicense, and distribute the Complementary Material, the
-Model, and Derivatives of the Model.
-
-Grant of Patent License. Subject to the terms and conditions of this
-License and where and as applicable, each Contributor hereby grants to
-You a perpetual, worldwide, non-exclusive, no-charge, royalty-free,
-irrevocable (except as stated in this paragraph) patent license to
-make, have made, use, offer to sell, sell, import, and otherwise
-transfer the Model and the Complementary Material, where such license
-applies only to those patent claims licensable by such Contributor
-that are necessarily infringed by their Contribution(s) alone or by
-combination of their Contribution(s) with the Model to which such
-Contribution(s) was submitted. If You institute patent litigation
-against any entity (including a cross-claim or counterclaim in a
-lawsuit) alleging that the Model and/or Complementary Material or a
-Contribution incorporated within the Model and/or Complementary
-Material constitutes direct or contributory patent infringement, then
-any patent licenses granted to You under this License for the Model
-and/or Work shall terminate as of the date such litigation is asserted
-or filed.
-
-Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
-
-Distribution and Redistribution. You may host for Third Party remote
-access purposes (e.g. software-as-a-service), reproduce and distribute
-copies of the Model or Derivatives of the Model thereof in any medium,
-with or without modifications, provided that You meet the following
-conditions: Use-based restrictions as referenced in paragraph 5 MUST
-be included as an enforceable provision by You in any type of legal
-agreement (e.g. a license) governing the use and/or distribution of
-the Model or Derivatives of the Model, and You shall give notice to
-subsequent users You Distribute to, that the Model or Derivatives of
-the Model are subject to paragraph 5. This provision does not apply to
-the use of Complementary Material. You must give any Third Party
-recipients of the Model or Derivatives of the Model a copy of this
-License; You must cause any modified files to carry prominent notices
-stating that You changed the files; You must retain all copyright,
-patent, trademark, and attribution notices excluding those notices
-that do not pertain to any part of the Model, Derivatives of the
-Model. You may add Your own copyright statement to Your modifications
-and may provide additional or different license terms and conditions -
-respecting paragraph 4.a. - for use, reproduction, or Distribution of
-Your modifications, or for any such Derivatives of the Model as a
-whole, provided Your use, reproduction, and Distribution of the Model
-otherwise complies with the conditions stated in this License.
-
-Use-based restrictions. The restrictions set forth in Attachment A are
-considered Use-based restrictions. Therefore You cannot use the Model
-and the Derivatives of the Model for the specified restricted
-uses. You may use the Model subject to this License, including only
-for lawful purposes and in accordance with the License. Use may
-include creating any content with, finetuning, updating, running,
-training, evaluating and/or reparametrizing the Model. You shall
-require all of Your users who use the Model or a Derivative of the
-Model to comply with the terms of this paragraph (paragraph 5).
-
-The Output You Generate.  Except as set forth herein, Licensor claims
-no rights in the Output You generate using the Model. You are
-accountable for the Output you generate and its subsequent uses. No
-use of the output can contravene any provision as stated in the
-License.
-
-Section IV: OTHER PROVISIONS
-
-Updates and Runtime Restrictions. To the maximum extent permitted by
-law, Licensor reserves the right to restrict (remotely or otherwise)
-usage of the Model in violation of this License.
-
-Trademarks and related. Nothing in this License permits You to make
-use of Licensors’ trademarks, trade names, logos or to otherwise
-suggest endorsement or misrepresent the relationship between the
-parties; and any rights not expressly granted herein are reserved by
-the Licensors.
-
-Disclaimer of Warranty. Unless required by applicable law or agreed to
-in writing, Licensor provides the Model and the Complementary Material
-(and each Contributor provides its Contributions) on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-implied, including, without limitation, any warranties or conditions
-of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-PARTICULAR PURPOSE. You are solely responsible for determining the
-appropriateness of using or redistributing the Model, Derivatives of
-the Model, and the Complementary Material and assume any risks
-associated with Your exercise of permissions under this License.
-
-Limitation of Liability. In no event and under no legal theory,
-whether in tort (including negligence), contract, or otherwise, unless
-required by applicable law (such as deliberate and grossly negligent
-acts) or agreed to in writing, shall any Contributor be liable to You
-for damages, including any direct, indirect, special, incidental, or
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-Complementary Material (including but not limited to damages for loss
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-
-Accepting Warranty or Additional Liability. While redistributing the
-Model, Derivatives of the Model and the Complementary Material
-thereof, You may choose to offer, and charge a fee for, acceptance of
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-You agree to indemnify, defend, and hold each Contributor harmless for
-any liability incurred by, or claims asserted against, such
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-additional liability.
-
-If any provision of this License is held to be invalid, illegal or
-unenforceable, the remaining provisions shall be unaffected thereby
-and remain valid as if such provision had not been set forth herein.
-
-
-END OF TERMS AND CONDITIONS
-
-Attachment A
-
-Use Restrictions
-
-You agree not to use the Model or Derivatives of the Model:
-
-* In any way that violates any applicable national, federal, state,
-local or international law or regulation;
-
-* For the purpose of exploiting, harming or attempting to exploit or
-harm minors in any way;
-
-* To generate or disseminate verifiably false information and/or
-  content with the purpose of harming others;
-
-* To generate or disseminate personal identifiable information that
-  can be used to harm an individual;
-
-* To defame, disparage or otherwise harass others;
-
-* For fully automated decision making that adversely impacts an
-  individual’s legal rights or otherwise creates or modifies a
-  binding, enforceable obligation;
-
-* For any use intended to or which has the effect of discriminating
-  against or harming individuals or groups based on online or offline
-  social behavior or known or predicted personal or personality
-  characteristics;
-
-* To exploit any of the vulnerabilities of a specific group of persons
-  based on their age, social, physical or mental characteristics, in
-  order to materially distort the behavior of a person pertaining to
-  that group in a manner that causes or is likely to cause that person
-  or another person physical or psychological harm;
-
-* For any use intended to or which has the effect of discriminating
-  against individuals or groups based on legally protected
-  characteristics or categories;
-
-* To provide medical advice and medical results interpretation;
-
-* To generate or disseminate information for the purpose to be used
-  for administration of justice, law enforcement, immigration or
-  asylum processes, such as predicting an individual will commit
-  fraud/crime commitment (e.g. by text profiling, drawing causal
-  relationships between assertions made in documents, indiscriminate
-  and arbitrarily-targeted use).
-

README.md

@@ -36,6 +36,15 @@
 
 </div>
 
+_**Note: This is an alpha release. Bugs are expected and not all
+features are fully implemented. Please use the GitHub [Issues
+pages](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen)
+to report unexpected problems. Also note that InvokeAI root directory
+which contains models, outputs and configuration files, has changed
+between the 2.x and 3.x release. If you wish to use your v2.3 root
+directory with v3.0, please follow the directions in [Migrating a 2.3
+root directory to 3.0](#migrating-to-3).**_
+
 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
@@ -123,10 +132,8 @@ and go to http://localhost:9090.
 
 ### Command-Line Installation (for developers and users familiar with Terminals)
 
-You must have Python 3.9 through 3.11 installed on your machine. Earlier or
-later versions are not supported.
-Node.js also needs to be installed along with yarn (can be installed with
-the command `npm install -g yarn` if needed)
+You must have Python 3.9 or 3.10 installed on your machine. Earlier or later versions are
+not supported.
 
 1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
 2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
@@ -190,18 +197,11 @@ the command `npm install -g yarn` if needed)
 7. Launch the web server (do it every time you run InvokeAI):
 
     ```terminal
-    invokeai-web
+    invokeai --web
     ```
 
-8. Build Node.js assets
-
-  ```terminal
-  cd invokeai/frontend/web/
-  yarn vite build
-  ```
-
-9. Point your browser to http://localhost:9090 to bring up the web interface.
-10. Type `banana sushi` in the box on the top left and click `Invoke`.
+8. Point your browser to http://localhost:9090 to bring up the web interface.
+9. Type `banana sushi` in the box on the top left and click `Invoke`.
 
 Be sure to activate the virtual environment each time before re-launching InvokeAI,
 using `source .venv/bin/activate` or `.venv\Scripts\activate`.
@@ -255,24 +255,19 @@ old models directory (which contains the models selected at install
 time) will be renamed `models.orig` and can be deleted once you have
 confirmed that the migration was successful.
 
- If you wish, you can pass the 2.3 root directory to both `--from` and
-`--to` in order to update in place. Warning: this directory will no
-longer be usable with InvokeAI 2.3.
-
 #### Migrating in place
 
 For the adventurous, you may do an in-place upgrade from 2.3 to 3.0
-without touching the command line. ***This recipe does not work on
-Windows platforms due to a bug in the Windows version of the 2.3
-upgrade script.** See the next section for a Windows recipe.
-
-##### For Mac and Linux Users:
+without touching the command line. The recipe is as follows>
 
 1. Launch the InvokeAI launcher script in your current v2.3 root directory.
 
 2. Select option [9] "Update InvokeAI" to bring up the updater dialog.
 
-3. Select option [1] to upgrade to the latest release.
+3a. During the alpha release phase, select option [3] and manually
+enter the tag name `v3.0.0+a2`.
+
+3b. Once 3.0 is released, select option [1] to upgrade to the latest release.
 
 4. Once the upgrade is finished you will be returned to the launcher
 menu. Select option [7] "Re-run the configure script to fix a broken
@@ -291,33 +286,14 @@ worked, you can safely remove these files. Alternatively you can
 restore a working v2.3 directory by removing the new files and
 restoring the ".orig" files' original names.
 
-##### For Windows Users:
-
-Windows Users can upgrade with the
-
-1. Enter the 2.3 root directory you wish to upgrade
-2. Launch `invoke.sh` or `invoke.bat`
-3. Select the "Developer's console" option [8]
-4. Type the following commands
-
-```
-pip install "invokeai @ https://github.com/invoke-ai/InvokeAI/archive/refs/tags/v3.0.0" --use-pep517 --upgrade
-invokeai-configure --root .
-```
-(Replace `v3.0.0` with the current release number if this document is out of date).
-
-The first command will install and upgrade new software to run
-InvokeAI. The second will prepare the 2.3 directory for use with 3.0.
-You may now launch the WebUI in the usual way, by selecting option [1]
-from the launcher script
-
 #### Migration Caveats
 
 The migration script will migrate your invokeai settings and models,
 including textual inversion models, LoRAs and merges that you may have
 installed previously. However it does **not** migrate the generated
-images stored in your 2.3-format outputs directory. You will need to
-manually import selected images into the 3.0 gallery via drag-and-drop.
+images stored in your 2.3-format outputs directory. The released
+version of 3.0 is expected to have an interface for importing an
+entire directory of image files as a batch.
 
 ## Hardware Requirements
 
@@ -329,12 +305,9 @@ AMD card (using the ROCm driver).
 
 You will need one of the following:
 
-- An NVIDIA-based graphics card with 4 GB or more VRAM memory. 6-8 GB
-  of VRAM is highly recommended for rendering using the Stable
-  Diffusion XL models
+- An NVIDIA-based graphics card with 4 GB or more VRAM memory.
 - An Apple computer with an M1 chip.
-- An AMD-based graphics card with 4GB or more VRAM memory (Linux
-  only), 6-8 GB for XL rendering.
+- An AMD-based graphics card with 4GB or more VRAM memory. (Linux only)
 
 We do not recommend the GTX 1650 or 1660 series video cards. They are
 unable to run in half-precision mode and do not have sufficient VRAM
@@ -367,12 +340,13 @@ Invoke AI provides an organized gallery system for easily storing, accessing, an
 ### Other features
 
 - *Support for both ckpt and diffusers models*
-- *SD 2.0, 2.1, XL support*
+- *SD 2.0, 2.1 support*
 - *Upscaling Tools*
 - *Embedding Manager & Support*
 - *Model Manager & Support*
 - *Node-Based Architecture*
 - *Node-Based Plug-&-Play UI (Beta)*
+- *SDXL Support* (Coming soon)
 
 ### Latest Changes
 

docker/.env.sample

@@ -1,13 +0,0 @@
-## Make a copy of this file named `.env` and fill in the values below.
-## Any environment variables supported by InvokeAI can be specified here.
-
-# INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
-# Outputs will also be stored here by default.
-# This **must** be an absolute path.
-INVOKEAI_ROOT=
-
-HUGGINGFACE_TOKEN=
-
-## optional variables specific to the docker setup
-# GPU_DRIVER=cuda
-# CONTAINER_UID=1000

docker/Dockerfile

@@ -1,129 +1,107 @@
-# syntax=docker/dockerfile:1.4
+# syntax=docker/dockerfile:1
 
-## Builder stage
+ARG PYTHON_VERSION=3.9
+##################
+##  base image  ##
+##################
+FROM --platform=${TARGETPLATFORM} python:${PYTHON_VERSION}-slim AS python-base
 
-FROM library/ubuntu:22.04 AS builder
+LABEL org.opencontainers.image.authors="mauwii@outlook.de"
 
-ARG DEBIAN_FRONTEND=noninteractive
-RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt,sharing=locked \
-    apt update && apt-get install -y \
-        git \
-        python3.10-venv \
-        python3-pip \
-        build-essential
+# Prepare apt for buildkit cache
+RUN rm -f /etc/apt/apt.conf.d/docker-clean \
+  && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' >/etc/apt/apt.conf.d/keep-cache
 
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV VIRTUAL_ENV=/opt/venv/invokeai
+# Install dependencies
+RUN \
+  --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
+  apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    libgl1-mesa-glx=20.3.* \
+    libglib2.0-0=2.66.* \
+    libopencv-dev=4.5.*
 
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.0.1
-ARG TORCHVISION_VERSION=0.15.2
-ARG GPU_DRIVER=cuda
-ARG TARGETPLATFORM="linux/amd64"
-# unused but available
-ARG BUILDPLATFORM
+# Set working directory and env
+ARG APPDIR=/usr/src
+ARG APPNAME=InvokeAI
+WORKDIR ${APPDIR}
+ENV PATH ${APPDIR}/${APPNAME}/bin:$PATH
+# Keeps Python from generating .pyc files in the container
+ENV PYTHONDONTWRITEBYTECODE 1
+# Turns off buffering for easier container logging
+ENV PYTHONUNBUFFERED 1
+# Don't fall back to legacy build system
+ENV PIP_USE_PEP517=1
 
-WORKDIR ${INVOKEAI_SRC}
+#######################
+##  build pyproject  ##
+#######################
+FROM python-base AS pyproject-builder
 
-# Install pytorch before all other pip packages
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is default
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m venv ${VIRTUAL_ENV} &&\
-    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
-    elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
-    else \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \
-    fi &&\
-    pip install $extra_index_url_arg \
-        torch==$TORCH_VERSION \
-        torchvision==$TORCHVISION_VERSION
+# Install build dependencies
+RUN \
+  --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
+  apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    build-essential=12.9 \
+    gcc=4:10.2.* \
+    python3-dev=3.9.*
 
-# Install the local package.
-# Editable mode helps use the same image for development:
-# the local working copy can be bind-mounted into the image
-# at path defined by ${INVOKEAI_SRC}
-COPY invokeai ./invokeai
-COPY pyproject.toml ./
-RUN --mount=type=cache,target=/root/.cache/pip \
-    # xformers + triton fails to install on arm64
-    if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
-        pip install -e ".[xformers]"; \
-    else \
-        pip install -e "."; \
-    fi
+# Prepare pip for buildkit cache
+ARG PIP_CACHE_DIR=/var/cache/buildkit/pip
+ENV PIP_CACHE_DIR ${PIP_CACHE_DIR}
+RUN mkdir -p ${PIP_CACHE_DIR}
 
-# #### Build the Web UI ------------------------------------
+# Create virtual environment
+RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+  python3 -m venv "${APPNAME}" \
+  --upgrade-deps
 
-FROM node:18 AS web-builder
-WORKDIR /build
-COPY invokeai/frontend/web/ ./
-RUN --mount=type=cache,target=/usr/lib/node_modules \
-    npm install --include dev
-RUN --mount=type=cache,target=/usr/lib/node_modules \
-    yarn vite build
+# Install requirements
+COPY --link pyproject.toml .
+COPY --link invokeai/version/invokeai_version.py invokeai/version/__init__.py invokeai/version/
+ARG PIP_EXTRA_INDEX_URL
+ENV PIP_EXTRA_INDEX_URL ${PIP_EXTRA_INDEX_URL}
+RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+  "${APPNAME}"/bin/pip install .
 
+# Install pyproject.toml
+COPY --link . .
+RUN --mount=type=cache,target=${PIP_CACHE_DIR} \
+  "${APPNAME}/bin/pip" install .
 
-#### Runtime stage ---------------------------------------
-
-FROM library/ubuntu:22.04 AS runtime
-
-ARG DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV PYTHONDONTWRITEBYTECODE=1
-
-RUN apt update && apt install -y --no-install-recommends \
-        git \
-        curl \
-        vim \
-        tmux \
-        ncdu \
-        iotop \
-        bzip2 \
-        gosu \
-        libglib2.0-0 \
-        libgl1-mesa-glx \
-        python3-venv \
-        python3-pip \
-        build-essential \
-        libopencv-dev \
-        libstdc++-10-dev &&\
-    apt-get clean && apt-get autoclean
-
-# globally add magic-wormhole
-# for ease of transferring data to and from the container
-# when running in sandboxed cloud environments; e.g. Runpod etc.
-RUN pip install magic-wormhole
-
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV VIRTUAL_ENV=/opt/venv/invokeai
-ENV INVOKEAI_ROOT=/invokeai
-ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
-
-# --link requires buldkit w/ dockerfile syntax 1.4
-COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
-COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
-COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
-
-# Link amdgpu.ids for ROCm builds
-# contributed by https://github.com/Rubonnek
-RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-  ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
-
-WORKDIR ${INVOKEAI_SRC}
-
-# build patchmatch
-RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
+# Build patchmatch
 RUN python3 -c "from patchmatch import patch_match"
 
-# Create unprivileged user and make the local dir
-RUN useradd --create-home --shell /bin/bash -u 1000 --comment "container local user" invoke
-RUN mkdir -p ${INVOKEAI_ROOT} && chown -R invoke:invoke ${INVOKEAI_ROOT}
+#####################
+##  runtime image  ##
+#####################
+FROM python-base AS runtime
 
-COPY docker/docker-entrypoint.sh ./
-ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
-CMD ["invokeai-web", "--host", "0.0.0.0"]
+# Create a new user
+ARG UNAME=appuser
+RUN useradd \
+  --no-log-init \
+  -m \
+  -U \
+  "${UNAME}"
+
+# Create volume directory
+ARG VOLUME_DIR=/data
+RUN mkdir -p "${VOLUME_DIR}" \
+  && chown -hR "${UNAME}:${UNAME}" "${VOLUME_DIR}"
+
+# Setup runtime environment
+USER ${UNAME}:${UNAME}
+COPY --chown=${UNAME}:${UNAME} --from=pyproject-builder ${APPDIR}/${APPNAME} ${APPNAME}
+ENV INVOKEAI_ROOT ${VOLUME_DIR}
+ENV TRANSFORMERS_CACHE ${VOLUME_DIR}/.cache
+ENV INVOKE_MODEL_RECONFIGURE "--yes --default_only"
+EXPOSE 9090
+ENTRYPOINT [ "invokeai" ]
+CMD [ "--web", "--host", "0.0.0.0", "--port", "9090" ]
+VOLUME [ "${VOLUME_DIR}" ]

docker/README.md

@@ -1,77 +0,0 @@
-# InvokeAI Containerized
-
-All commands are to be run from the `docker` directory: `cd docker`
-
-#### Linux
-
-1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
-2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04).
-    - The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
-3. Ensure docker daemon is able to access the GPU.
-    - You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
-
-#### macOS
-
-1. Ensure Docker has at least 16GB RAM
-2. Enable VirtioFS for file sharing
-3. Enable `docker compose` V2 support
-
-This is done via Docker Desktop preferences
-
-## Quickstart
-
-
-1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
-    a. the desired location of the InvokeAI runtime directory, or
-    b. an existing, v3.0.0 compatible runtime directory.
-1. `docker compose up`
-
-The image will be built automatically if needed.
-
-The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. The runtime directory will be populated with the base configs and models necessary to start generating.
-
-### Use a GPU
-
-- Linux is *recommended* for GPU support in Docker.
-- WSL2 is *required* for Windows.
-- only `x86_64` architecture is supported.
-
-The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.
-
-## Customize
-
-Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used.
-
-You can also set these values in `docker compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
-
-Example (most values are optional):
-
-```
-INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
-HUGGINGFACE_TOKEN=the_actual_token
-CONTAINER_UID=1000
-GPU_DRIVER=cuda
-```
-
-## Even Moar Customizing!
-
-See the `docker compose.yaml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
-
-### Reconfigure the runtime directory
-
-Can be used to download additional models from the supported model list
-
-In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
-
-```
-command:
-  - invokeai-configure
-  - --yes
-```
-
-Or install models:
-
-```
-command:
-  - invokeai-model-install
-```

docker/build.sh

@@ -1,11 +1,51 @@
 #!/usr/bin/env bash
 set -e
 
-build_args=""
+# If you want to build a specific flavor, set the CONTAINER_FLAVOR environment variable
+#   e.g. CONTAINER_FLAVOR=cpu ./build.sh
+#   Possible Values are:
+#     - cpu
+#     - cuda
+#     - rocm
+#   Don't forget to also set it when executing run.sh
+#   if it is not set, the script will try to detect the flavor by itself.
+#
+# Doc can be found here:
+#   https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
 
-[[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env)
+SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
+cd "$SCRIPTDIR" || exit 1
 
-echo "docker-compose build args:"
-echo $build_args
+source ./env.sh
 
-docker-compose build $build_args
+DOCKERFILE=${INVOKE_DOCKERFILE:-./Dockerfile}
+
+# print the settings
+echo -e "You are using these values:\n"
+echo -e "Dockerfile:\t\t${DOCKERFILE}"
+echo -e "index-url:\t\t${PIP_EXTRA_INDEX_URL:-none}"
+echo -e "Volumename:\t\t${VOLUMENAME}"
+echo -e "Platform:\t\t${PLATFORM}"
+echo -e "Container Registry:\t${CONTAINER_REGISTRY}"
+echo -e "Container Repository:\t${CONTAINER_REPOSITORY}"
+echo -e "Container Tag:\t\t${CONTAINER_TAG}"
+echo -e "Container Flavor:\t${CONTAINER_FLAVOR}"
+echo -e "Container Image:\t${CONTAINER_IMAGE}\n"
+
+# Create docker volume
+if [[ -n "$(docker volume ls -f name="${VOLUMENAME}" -q)" ]]; then
+    echo -e "Volume already exists\n"
+else
+    echo -n "creating docker volume "
+    docker volume create "${VOLUMENAME}"
+fi
+
+# Build Container
+docker build \
+    --platform="${PLATFORM:-linux/amd64}" \
+    --tag="${CONTAINER_IMAGE:-invokeai}" \
+    ${CONTAINER_FLAVOR:+--build-arg="CONTAINER_FLAVOR=${CONTAINER_FLAVOR}"} \
+    ${PIP_EXTRA_INDEX_URL:+--build-arg="PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}"} \
+    ${PIP_PACKAGE:+--build-arg="PIP_PACKAGE=${PIP_PACKAGE}"} \
+    --file="${DOCKERFILE}" \
+    ..

docker/docker-compose.yml

@@ -1,48 +0,0 @@
-# Copyright (c) 2023 Eugene Brodsky https://github.com/ebr
-
-version: '3.8'
-
-services:
-  invokeai:
-    image: "local/invokeai:latest"
-    # edit below to run on a container runtime other than nvidia-container-runtime.
-    # not yet tested with rocm/AMD GPUs
-    # Comment out the "deploy" section to run on CPU only
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    build:
-      context: ..
-      dockerfile: docker/Dockerfile
-
-    # variables without a default will automatically inherit from the host environment
-    environment:
-      - INVOKEAI_ROOT
-      - HF_HOME
-
-    # Create a .env file in the same directory as this docker-compose.yml file
-    # and populate it with environment variables. See .env.sample
-    env_file:
-      - .env
-
-    ports:
-      - "${INVOKEAI_PORT:-9090}:9090"
-    volumes:
-      - ${INVOKEAI_ROOT:-~/invokeai}:${INVOKEAI_ROOT:-/invokeai}
-      - ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
-      # - ${INVOKEAI_MODELS_DIR:-${INVOKEAI_ROOT:-/invokeai/models}}
-      # - ${INVOKEAI_MODELS_CONFIG_PATH:-${INVOKEAI_ROOT:-/invokeai/configs/models.yaml}}
-    tty: true
-    stdin_open: true
-
-    # # Example of running alternative commands/scripts in the container
-    # command:
-    #   - bash
-    #   - -c
-    #   - |
-    #     invokeai-model-install --yes --default-only --config_file ${INVOKEAI_ROOT}/config_custom.yaml
-    #     invokeai-nodes-web --host 0.0.0.0

docker/docker-entrypoint.sh

@@ -1,65 +0,0 @@
-#!/bin/bash
-set -e -o pipefail
-
-### Container entrypoint
-# Runs the CMD as defined by the Dockerfile or passed to `docker run`
-# Can be used to configure the runtime dir
-# Bypass by using ENTRYPOINT or `--entrypoint`
-
-### Set INVOKEAI_ROOT pointing to a valid runtime directory
-# Otherwise configure the runtime dir first.
-
-### Configure the InvokeAI runtime directory (done by default)):
-# docker run --rm -it <this image> --configure
-# or skip with --no-configure
-
-### Set the CONTAINER_UID envvar to match your user.
-# Ensures files created in the container are owned by you:
-#   docker run --rm -it -v /some/path:/invokeai -e CONTAINER_UID=$(id -u) <this image>
-# Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS.
-
-USER_ID=${CONTAINER_UID:-1000}
-USER=invoke
-usermod -u ${USER_ID} ${USER} 1>/dev/null
-
-configure() {
-    # Configure the runtime directory
-    if [[ -f ${INVOKEAI_ROOT}/invokeai.yaml ]]; then
-        echo "${INVOKEAI_ROOT}/invokeai.yaml exists. InvokeAI is already configured."
-        echo "To reconfigure InvokeAI, delete the above file."
-        echo "======================================================================"
-    else
-        mkdir -p ${INVOKEAI_ROOT}
-        chown --recursive ${USER} ${INVOKEAI_ROOT}
-        gosu ${USER} invokeai-configure --yes --default_only
-    fi
-}
-
-## Skip attempting to configure.
-## Must be passed first, before any other args.
-if [[ $1 != "--no-configure" ]]; then
-    configure
-else
-    shift
-fi
-
-### Set the $PUBLIC_KEY env var to enable SSH access.
-# We do not install openssh-server in the image by default to avoid bloat.
-# but it is useful to have the full SSH server e.g. on Runpod.
-# (use SCP to copy files to/from the image, etc)
-if [[ -v "PUBLIC_KEY" ]] && [[ ! -d "${HOME}/.ssh" ]]; then
-    apt-get update
-    apt-get install -y openssh-server
-    pushd $HOME
-    mkdir -p .ssh
-    echo ${PUBLIC_KEY} > .ssh/authorized_keys
-    chmod -R 700 .ssh
-    popd
-    service ssh start
-fi
-
-
-cd ${INVOKEAI_ROOT}
-
-# Run the CMD as the Container User (not root).
-exec gosu ${USER} "$@"

docker/env.sh

@@ -0,0 +1,54 @@
+#!/usr/bin/env bash
+
+# This file is used to set environment variables for the build.sh and run.sh scripts.
+
+# Try to detect the container flavor if no PIP_EXTRA_INDEX_URL got specified
+if [[ -z "$PIP_EXTRA_INDEX_URL" ]]; then
+
+  # Activate virtual environment if not already activated and exists
+  if [[ -z $VIRTUAL_ENV ]]; then
+    [[ -e "$(dirname "${BASH_SOURCE[0]}")/../.venv/bin/activate" ]] \
+      && source "$(dirname "${BASH_SOURCE[0]}")/../.venv/bin/activate" \
+      && echo "Activated virtual environment: $VIRTUAL_ENV"
+  fi
+
+  # Decide which container flavor to build if not specified
+  if [[ -z "$CONTAINER_FLAVOR" ]] && python -c "import torch" &>/dev/null; then
+    # Check for CUDA and ROCm
+    CUDA_AVAILABLE=$(python -c "import torch;print(torch.cuda.is_available())")
+    ROCM_AVAILABLE=$(python -c "import torch;print(torch.version.hip is not None)")
+    if [[ "${CUDA_AVAILABLE}" == "True" ]]; then
+      CONTAINER_FLAVOR="cuda"
+    elif [[ "${ROCM_AVAILABLE}" == "True" ]]; then
+      CONTAINER_FLAVOR="rocm"
+    else
+      CONTAINER_FLAVOR="cpu"
+    fi
+  fi
+
+  # Set PIP_EXTRA_INDEX_URL based on container flavor
+  if [[ "$CONTAINER_FLAVOR" == "rocm" ]]; then
+    PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/rocm"
+  elif [[ "$CONTAINER_FLAVOR" == "cpu" ]]; then
+    PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
+  # elif [[ -z "$CONTAINER_FLAVOR" || "$CONTAINER_FLAVOR" == "cuda" ]]; then
+  #   PIP_PACKAGE=${PIP_PACKAGE-".[xformers]"}
+  fi
+fi
+
+# Variables shared by build.sh and run.sh
+REPOSITORY_NAME="${REPOSITORY_NAME-$(basename "$(git rev-parse --show-toplevel)")}"
+REPOSITORY_NAME="${REPOSITORY_NAME,,}"
+VOLUMENAME="${VOLUMENAME-"${REPOSITORY_NAME}_data"}"
+ARCH="${ARCH-$(uname -m)}"
+PLATFORM="${PLATFORM-linux/${ARCH}}"
+INVOKEAI_BRANCH="${INVOKEAI_BRANCH-$(git branch --show)}"
+CONTAINER_REGISTRY="${CONTAINER_REGISTRY-"ghcr.io"}"
+CONTAINER_REPOSITORY="${CONTAINER_REPOSITORY-"$(whoami)/${REPOSITORY_NAME}"}"
+CONTAINER_FLAVOR="${CONTAINER_FLAVOR-cuda}"
+CONTAINER_TAG="${CONTAINER_TAG-"${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}"}"
+CONTAINER_IMAGE="${CONTAINER_REGISTRY}/${CONTAINER_REPOSITORY}:${CONTAINER_TAG}"
+CONTAINER_IMAGE="${CONTAINER_IMAGE,,}"
+
+# enable docker buildkit
+export DOCKER_BUILDKIT=1

docker/run.sh

@@ -1,8 +1,41 @@
 #!/usr/bin/env bash
 set -e
 
+# How to use: https://invoke-ai.github.io/InvokeAI/installation/040_INSTALL_DOCKER/
+
 SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
 cd "$SCRIPTDIR" || exit 1
 
-docker-compose up --build -d
-docker-compose logs -f
+source ./env.sh
+
+# Create outputs directory if it does not exist
+[[ -d ./outputs ]] || mkdir ./outputs
+
+echo -e "You are using these values:\n"
+echo -e "Volumename:\t${VOLUMENAME}"
+echo -e "Invokeai_tag:\t${CONTAINER_IMAGE}"
+echo -e "local Models:\t${MODELSPATH:-unset}\n"
+
+docker run \
+  --interactive \
+  --tty \
+  --rm \
+  --platform="${PLATFORM}" \
+  --name="${REPOSITORY_NAME}" \
+  --hostname="${REPOSITORY_NAME}" \
+  --mount type=volume,volume-driver=local,source="${VOLUMENAME}",target=/data \
+  --mount type=bind,source="$(pwd)"/outputs/,target=/data/outputs/ \
+  ${MODELSPATH:+--mount="type=bind,source=${MODELSPATH},target=/data/models"} \
+  ${HUGGING_FACE_HUB_TOKEN:+--env="HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}"} \
+  --publish=9090:9090 \
+  --cap-add=sys_nice \
+  ${GPU_FLAGS:+--gpus="${GPU_FLAGS}"} \
+  "${CONTAINER_IMAGE}" ${@:+$@}
+
+echo -e "\nCleaning trash folder ..."
+for f in outputs/.Trash*; do
+  if [ -e "$f" ]; then
+    rm -Rf "$f"
+    break
+  fi
+done

docker/runpod-readme.md

@@ -1,60 +0,0 @@
-# InvokeAI - A Stable Diffusion Toolkit
-
-Stable Diffusion distribution by InvokeAI: https://github.com/invoke-ai
-
-The Docker image tracks the `main` branch of the InvokeAI project, which means it includes the latest features, but may contain some bugs.
-
-Your working directory is mounted under the `/workspace` path inside the pod. The models are in `/workspace/invokeai/models`, and outputs are in `/workspace/invokeai/outputs`.
-
-> **Only the /workspace directory will persist between pod restarts!**
-
-> **If you _terminate_ (not just _stop_) the pod, the /workspace will be lost.**
-
-## Quickstart
-
-1. Launch a pod from this template. **It will take about 5-10 minutes to run through the initial setup**. Be patient.
-1. Wait for the application to load.
-    - TIP: you know it's ready when the CPU usage goes idle
-    - You can also check the logs for a line that says "_Point your browser at..._"
-1. Open the Invoke AI web UI: click the `Connect` => `connect over  HTTP` button.
-1. Generate some art!
-
-## Other things you can do
-
-At any point you may edit the pod configuration and set an arbitrary Docker command. For example, you could run a command to downloads some models using `curl`, or fetch some images and place them into your outputs to continue a working session.
-
-If you need to run *multiple commands*, define them in the Docker Command field like this:
-
-`bash -c "cd ${INVOKEAI_ROOT}/outputs; wormhole receive 2-foo-bar; invoke.py --web --host 0.0.0.0"`
-
-### Copying your data in and out of the pod
-
-This image includes a couple of handy tools to help you get the data into the pod (such as your custom models or embeddings), and out of the pod (such as downloading your outputs). Here are your options for getting your data in and out of the pod:
-
-- **SSH server**:
-    1. Make sure to create and set your Public Key in the RunPod settings (follow the official instructions)
-    1. Add an exposed port 22 (TCP) in the pod settings!
-    1. When your pod restarts, you will see a new entry in the `Connect` dialog. Use this SSH server to `scp` or `sftp` your files as necessary, or SSH into the pod using the fully fledged SSH server.
-
-- [**Magic Wormhole**](https://magic-wormhole.readthedocs.io/en/latest/welcome.html):
-    1. On your computer, `pip install magic-wormhole` (see above instructions for details)
-    1. Connect to the command line **using the "light" SSH client** or the browser-based console. _Currently there's a bug where `wormhole` isn't available when connected to "full" SSH server, as described above_.
-    1. `wormhole send /workspace/invokeai/outputs` will send the entire `outputs` directory. You can also send individual files.
-    1. Once packaged, you will see a `wormhole receive <123-some-words>` command. Copy it
-    1. Paste this command into the terminal on your local machine to securely download the payload.
-    1. It works the same in reverse: you can `wormhole send` some models from your computer to the pod. Again, save your files somewhere in `/workspace` or they will be lost when the pod is stopped.
-
-- **RunPod's Cloud Sync feature** may be used to sync the persistent volume to cloud storage. You could, for example, copy the entire `/workspace` to S3, add some custom models to it, and copy it back from S3 when launching new pod configurations. Follow the Cloud Sync instructions.
-
-
-### Disable the NSFW checker
-
-The NSFW checker is enabled by default. To disable it, edit the pod configuration and set the following command:
-
-```
-invoke --web --host 0.0.0.0 --no-nsfw_checker
-```
-
----
-
-Template ©2023 Eugene Brodsky [ebr](https://github.com/ebr)

docs/CHANGELOG.md

@@ -617,6 +617,8 @@ sections describe what's new for InvokeAI.
 - `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
   backward compatibility.
 - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+- Support for [inpainting](deprecated/INPAINTING.md) and
+  [outpainting](features/OUTPAINTING.md)
 - img2img runs on all k\* samplers
 - Support for
   [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)

docs/contributing/CONTRIBUTING.md

@@ -1,38 +1,42 @@
-# How to Contribute
-
 ## Welcome to Invoke AI
+
+We're thrilled to have you here and we're excited for you to contribute. 
+
 Invoke AI originated as a project built by the community, and that vision carries forward today as we aim to build the best pro-grade tools available. We work together to incorporate the latest in AI/ML research, making these tools available in over 20 languages to artists and creatives around the world as part of our fully permissive OSS project designed for individual users to self-host and use.
 
+Here are some guidelines to help you get started:
 
-## Contributing to Invoke AI
-Anyone who wishes to contribute to InvokeAI, whether features, bug fixes, code cleanup, testing, code reviews, documentation or translation is very much encouraged to do so.
+### Technical Prerequisites
 
-To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
+Front-end: You'll need a working knowledge of React and TypeScript.
 
-### Areas of contribution: 
+Back-end: Depending on the scope of your contribution, you may need to know SQLite, FastAPI, Python, and Socketio. Also, a good majority of the backend logic involved in processing images is built in a modular way using a concept called "Nodes", which are isolated functions that carry out individual, discrete operations. This design allows for easy contributions of novel pipelines and capabilities.
 
-#### Development
-If you’d like to help with development, please see our [development guide](contribution_guides/development.md). If you’re unfamiliar with contributing to open source projects, there is a tutorial contained within the development guide.
+### How to Submit Contributions
 
-#### Documentation
-If you’d like to help with documentation, please see our [documentation guide](contribution_guides/documenation.md).
+To start contributing, please follow these steps:
 
-#### Translation
-If you'd like to help with translation, please see our [translation guide](docs/contributing/.contribution_guides/translation.md).
+1. Familiarize yourself with our roadmap and open projects to see where your skills and interests align. These documents can serve as a source of inspiration.
+2. Open a Pull Request (PR) with a clear description of the feature you're adding or the problem you're solving. Make sure your contribution aligns with the project's vision.
+3. Adhere to general best practices. This includes assuming interoperability with other nodes, keeping the scope of your functions as small as possible, and organizing your code according to our architecture documents.
 
-#### Tutorials 
-Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.
+### Types of Contributions We're Looking For
 
-We hope you enjoy using our software as much as we enjoy creating it, and we hope that some of those of you who are reading this will elect to become part of our contributor community.
+We welcome all contributions that improve the project. Right now, we're especially looking for:
 
+1. Quality of life (QOL) enhancements on the front-end.
+2. New backend capabilities added through nodes.
+3. Incorporating additional optimizations from the broader open-source software community.
 
-### Contributors
+### Communication and Decision-making Process
 
-This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for their time, hard work and effort.
+Project maintainers and code owners review PRs to ensure they align with the project's goals. They may provide design or architectural guidance, suggestions on user experience, or provide more significant feedback on the contribution itself. Expect to receive feedback on your submissions, and don't hesitate to ask questions or propose changes.
 
-### Code of Conduct
+For more robust discussions, or if you're planning to add capabilities not currently listed on our roadmap, please reach out to us on our Discord server. That way, we can ensure your proposed contribution aligns with the project's direction before you start writing code.
 
-The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
+### Code of Conduct and Contribution Expectations
+
+We want everyone in our community to have a positive experience. To facilitate this, we've established a code of conduct and a statement of values that we expect all contributors to adhere to. Please take a moment to review these documents—they're essential to maintaining a respectful and inclusive environment.
 
 By making a contribution to this project, you certify that:
 
@@ -45,12 +49,6 @@ This disclaimer is not a license and does not grant any rights or permissions. Y
 
 This disclaimer is provided "as is" without warranty of any kind, whether expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the contribution or the use or other dealings in the contribution.
 
-### Support
-
-For support, please use this repository's [GitHub Issues](https://github.com/invoke-ai/InvokeAI/issues), or join the [Discord](https://discord.gg/ZmtBAhwWhy).
-
-Original portions of the software are Copyright (c) 2023 by respective contributors.
-
 ---
 
 Remember, your contributions help make this project great. We're excited to see what you'll bring to our community!

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -81,193 +81,3 @@ pytest --cov; open ./coverage/html/index.html
 <!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
 
 --8<-- "invokeai/frontend/web/README.md"
-
-## Developing InvokeAI in VSCode
-
-VSCode offers some nice tools:
-
-- python debugger
-- automatic `venv` activation
-- remote dev (e.g. run InvokeAI on a beefy linux desktop while you type in
-  comfort on your macbook)
-
-### Setup
-
-You'll need the
-[Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
-and
-[Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
-extensions installed first.
-
-It's also really handy to install the `Jupyter` extensions:
-
-- [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter)
-- [Jupyter Cell Tags](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-cell-tags)
-- [Jupyter Notebook Renderers](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter-renderers)
-- [Jupyter Slide Show](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-slideshow)
-
-#### InvokeAI workspace
-
-Creating a VSCode workspace for working on InvokeAI is highly recommended. It
-can hold InvokeAI-specific settings and configs.
-
-To make a workspace:
-
-- Open the InvokeAI repo dir in VSCode
-- `File` > `Save Workspace As` > save it _outside_ the repo
-
-#### Default python interpreter (i.e. automatic virtual environment activation)
-
-- Use command palette to run command
-  `Preferences: Open Workspace Settings (JSON)`
-- Add `python.defaultInterpreterPath` to `settings`, pointing to your `venv`'s
-  python
-
-Should look something like this:
-
-```jsonc
-{
-  // I like to have all InvokeAI-related folders in my workspace
-  "folders": [
-    {
-      // repo root
-      "path": "InvokeAI"
-    },
-    {
-      // InvokeAI root dir, where `invokeai.yaml` lives
-      "path": "/path/to/invokeai_root"
-    }
-  ],
-  "settings": {
-    // Where your InvokeAI `venv`'s python executable lives
-    "python.defaultInterpreterPath": "/path/to/invokeai_root/.venv/bin/python"
-  }
-}
-```
-
-Now when you open the VSCode integrated terminal, or do anything that needs to
-run python, it will automatically be in your InvokeAI virtual environment.
-
-Bonus: When you create a Jupyter notebook, when you run it, you'll be prompted
-for the python interpreter to run in. This will default to your `venv` python,
-and so you'll have access to the same python environment as the InvokeAI app.
-
-This is _super_ handy.
-
-#### Debugging configs with `launch.json`
-
-Debugging configs are managed in a `launch.json` file. Like most VSCode configs,
-these can be scoped to a workspace or folder.
-
-Follow the [official guide](https://code.visualstudio.com/docs/python/debugging)
-to set up your `launch.json` and try it out.
-
-Now we can create the InvokeAI debugging configs:
-
-```jsonc
-{
-  // Use IntelliSense to learn about possible attributes.
-  // Hover to view descriptions of existing attributes.
-  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-  "version": "0.2.0",
-  "configurations": [
-    {
-      // Run the InvokeAI backend & serve the pre-built UI
-      "name": "InvokeAI Web",
-      "type": "python",
-      "request": "launch",
-      "program": "scripts/invokeai-web.py",
-      "args": [
-        // Your InvokeAI root dir (where `invokeai.yaml` lives)
-        "--root",
-        "/path/to/invokeai_root",
-        // Access the app from anywhere on your local network
-        "--host",
-        "0.0.0.0"
-      ],
-      "justMyCode": true
-    },
-    {
-      // Run the nodes-based CLI
-      "name": "InvokeAI CLI",
-      "type": "python",
-      "request": "launch",
-      "program": "scripts/invokeai-cli.py",
-      "justMyCode": true
-    },
-    {
-      // Run tests
-      "name": "InvokeAI Test",
-      "type": "python",
-      "request": "launch",
-      "module": "pytest",
-      "args": ["--capture=no"],
-      "justMyCode": true
-    },
-    {
-      // Run a single test
-      "name": "InvokeAI Single Test",
-      "type": "python",
-      "request": "launch",
-      "module": "pytest",
-      "args": [
-        // Change this to point to the specific test you are working on
-        "tests/nodes/test_invoker.py"
-      ],
-      "justMyCode": true
-    },
-    {
-      // This is the default, useful to just run a single file
-      "name": "Python: File",
-      "type": "python",
-      "request": "launch",
-      "program": "${file}",
-      "justMyCode": true
-    }
-  ]
-}
-```
-
-You'll see these configs in the debugging configs drop down. Running them will
-start InvokeAI with attached debugger, in the correct environment, and work just
-like the normal app.
-
-Enjoy debugging InvokeAI with ease (not that we have any bugs of course).
-
-#### Remote dev
-
-This is very easy to set up and provides the same very smooth experience as
-local development. Environments and debugging, as set up above, just work,
-though you'd need to recreate the workspace and debugging configs on the remote.
-
-Consult the
-[official guide](https://code.visualstudio.com/docs/remote/remote-overview) to
-get it set up.
-
-Suggest using VSCode's included settings sync so that your remote dev host has
-all the same app settings and extensions automagically.
-
-##### One remote dev gotcha
-
-I've found the automatic port forwarding to be very flakey. You can disable it
-in `Preferences: Open Remote Settings (ssh: hostname)`. Search for
-`remote.autoForwardPorts` and untick the box.
-
-To forward ports very reliably, use SSH on the remote dev client (e.g. your
-macbook). Here's how to forward both backend API port (`9090`) and the frontend
-live dev server port (`5173`):
-
-```bash
-ssh \
-    -L 9090:localhost:9090 \
-    -L 5173:localhost:5173 \
-    user@remote-dev-host
-```
-
-The forwarding stops when you close the terminal window, so suggest to do this
-_outside_ the VSCode integrated terminal in case you need to restart VSCode for
-an extension update or something
-
-Now, on your remote dev client, you can open `localhost:9090` and access the UI,
-now served from the remote dev host, just the same as if it was running on the
-client.

docs/contributing/contribution_guides/development.md

@@ -1,91 +0,0 @@
-# Development
-
-## **What do I need to know to help?**
-
-If you are looking to help to with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
-
-For more information, please review our area specific documentation:
-
-* #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](development_guides/contributingToFrontend.md)
-* #### [Node Documentation](../INVOCATIONS.md)
-* #### [Local Development](../LOCAL_DEVELOPMENT.md)
-
-If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md) or [translation](translation.md).
-
-There are two paths to making a development contribution: 
-
-1. Choosing an open issue to address. Open issues can be found in the [Issues](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen) section of the InvokeAI repository. These are tagged by the issue type (bug, enhancement, etc.) along with the “good first issues” tag denoting if they are suitable for first time contributors.
-    1. Additional items can be found on our [roadmap](https://github.com/orgs/invoke-ai/projects/7). The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help. 
-2. Opening a new issue or feature to add. **Please make sure you have searched through existing issues before creating new ones.**
-
-*Regardless of what you choose, please post in the  [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord before you start development in order to confirm that the issue or feature is aligned with the current direction of the project. We value our contributors time and effort and want to ensure that no one’s time is being misspent.*
-
-## Best Practices: 
-* Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
-* Comments! Commenting your code helps reviwers easily understand your contribution
-* Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
-* Make all communications public. This ensure knowledge is shared with the whole community
-
-## **How do I make a contribution?**
-
-Never made an open source contribution before? Wondering how contributions work in our project? Here's a quick rundown!
-
-Before starting these steps, ensure you have your local environment [configured for development](../LOCAL_DEVELOPMENT.md).
-
-1.  Find a [good first issue](https://github.com/invoke-ai/InvokeAI/contribute) that you are interested in addressing or a feature that you would like to add. Then, reach out to our team in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord to ensure you are  setup for success. 
-2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
-3. Clone the repository to your local machine using:
-
-```bash
-git clone https://github.com/your-GitHub-username/InvokeAI.git
-```
-
-If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 
-
-4. Create a new branch for your fix using:
-
-```bash
-git checkout -b branch-name-here
-```
-
-5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
-6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:
-
-```bash
-git add insert-paths-of-changed-files-here
-```
-
-7. Store the contents of the index with a descriptive message.
-
-```bash
-git commit -m "Insert a short message of the changes made here"
-```
-
-8. Push the changes to the remote repository using
-
-```markdown
-git push origin branch-name-here
-```
-
-9. Submit a pull request to the **main** branch of the InvokeAI repository.
-10. Title the pull request with a short description of the changes made and the issue or bug number associated with your change. For example, you can title an issue like so "Added more log outputting to resolve #1234".
-11. In the description of the pull request, explain the changes that you made, any issues you think exist with the pull request you made, and any questions you have for the maintainer. It's OK if your pull request is not perfect (no pull request is), the reviewer will be able to help you fix any problems and improve it!
-12. Wait for the pull request to be reviewed by other collaborators.
-13. Make changes to the pull request if the reviewer(s) recommend them.
-14. Celebrate your success after your pull request is merged!
-
-If you’d like to learn more about contributing to Open Source projects, here is a [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). 
-
-## **Where can I go for help?**
-
-If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
-
-For frontend related work, **@pyschedelicious** is the best person to reach out to. 
-
-For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@pyschedelicious**.
-
-## **What does the Code of Conduct mean for me?**
-
-Our [Code of Conduct](CODE_OF_CONDUCT.md)  means that you are responsible for treating everyone on the project with respect and courtesy regardless of their identity. If you are the victim of any inappropriate behavior or comments as described in our Code of Conduct, we are here for you and will do the best to ensure that the abuser is reprimanded appropriately, per our code.
-

docs/contributing/contribution_guides/development_guides/contributingToFrontend.md

@@ -1,75 +0,0 @@
-# Contributing to the Frontend
-
-# InvokeAI Web UI
-
-- [InvokeAI Web UI](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#invokeai-web-ui)
-    - [Stack](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#stack)
-    - [Contributing](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#contributing)
-        - [Dev Environment](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#dev-environment)
-        - [Production builds](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web/docs#production-builds)
-
-The UI is a fairly straightforward Typescript React app, with the Unified Canvas being more complex.
-
-Code is located in `invokeai/frontend/web/` for review.
-
-## Stack
-
-State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). We lean heavily on RTK:
-
-- `createAsyncThunk` for HTTP requests
-- `createEntityAdapter` for fetching images and models
-- `createListenerMiddleware` for workflows
-
-The API client and associated types are generated from the OpenAPI schema. See API_CLIENT.md.
-
-Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a simple socket.io redux middleware to help).
-
-[Chakra-UI](https://github.com/chakra-ui/chakra-ui) & [Mantine](https://github.com/mantinedev/mantine) for components and styling.
-
-[Konva](https://github.com/konvajs/react-konva) for the canvas, but we are pushing the limits of what is feasible with it (and HTML canvas in general). We plan to rebuild it with [PixiJS](https://github.com/pixijs/pixijs) to take advantage of WebGL's improved raster handling.
-
-[Vite](https://vitejs.dev/) for bundling.
-
-Localisation is via [i18next](https://github.com/i18next/react-i18next), but translation happens on our [Weblate](https://hosted.weblate.org/engage/invokeai/) project. Only the English source strings should be changed on this repo.
-
-## Contributing
-
-Thanks for your interest in contributing to the InvokeAI Web UI!
-
-We encourage you to ping @psychedelicious and @blessedcoolant on [Discord](https://discord.gg/ZmtBAhwWhy) if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
-
-### Dev Environment
-
-**Setup** 
-
-1. Install [node](https://nodejs.org/en/download/). You can confirm node is installed with:
-```bash
-node --version
-```
-2. Install [yarn classic](https://classic.yarnpkg.com/lang/en/) and confirm it is installed by running this:
-```bash
-npm install --global yarn
-yarn --version
-```
-
-From `invokeai/frontend/web/` run `yarn install` to get everything set up.
-
-Start everything in dev mode:
-1. Ensure your virtual environment is running
-2. Start the dev server: `yarn dev`
-3. Start the InvokeAI Nodes backend: `python scripts/invokeai-web.py # run from the repo root`
-4. Point your browser to the dev server address e.g. [http://localhost:5173/](http://localhost:5173/)
-
-### VSCode Remote Dev
-
-We've noticed an intermittent issue with the VSCode Remote Dev port forwarding. If you use this feature of VSCode, you may intermittently click the Invoke button and then get nothing until the request times out. Suggest disabling the IDE's port forwarding feature and doing it manually via SSH:
-
-`ssh -L 9090:localhost:9090 -L 5173:localhost:5173 user@host`
-
-### Production builds
-
-For a number of technical and logistical reasons, we need to commit UI build artefacts to the repo.
-
-If you submit a PR, there is a good chance we will ask you to include a separate commit with a build of the app.
-
-To build for production, run `yarn build`.

docs/contributing/contribution_guides/documentation.md

@@ -1,13 +0,0 @@
-# Documentation
-
-Documentation is an important part of any open source project. It provides a clear and concise way to communicate how the software works, how to use it, and how to troubleshoot issues. Without proper documentation, it can be difficult for users to understand the purpose and functionality of the project. 
-
-## Contributing
-
-All documentation is maintained in the InvokeAI GitHub repository. If you come across documentation that is out of date or incorrect, please submit a pull request with the necessary changes. 
-
-When updating or creating documentation, please keep in mind InvokeAI is a tool for everyone, not just those who have familiarity with generative art. 
-
-## Help & Questions
-
-Please ping @imic1 or @hipsterusername in the [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.

docs/contributing/contribution_guides/translation.md

@@ -1,19 +0,0 @@
-# Translation
-
-InvokeAI uses [Weblate](https://weblate.org/) for translation. Weblate is a FOSS project providing a scalable translation service. Weblate automates the tedious parts of managing translation of a growing project, and the service is generously provided at no cost to FOSS projects like InvokeAI.
-
-## Contributing
-
-If you'd like to contribute by adding or updating a translation, please visit our [Weblate project](https://hosted.weblate.org/engage/invokeai/). You'll need to sign in with your GitHub account (a number of other accounts are supported, including Google).
-
-Once signed in, select a language and then the Web UI component. From here you can Browse and Translate strings from English to your chosen language. Zen mode offers a simpler translation experience.
-
-Your changes will be attributed to you in the automated PR process; you don't need to do anything else.
-
-## Help & Questions
-
-Please check Weblate's [documentation](https://docs.weblate.org/en/latest/index.html) or ping @Harvestor on [Discord](https://discord.com/channels/1020123559063990373/1049495067846524939) if you have any questions.
-
-## Thanks
-
-Thanks to the InvokeAI community for their efforts to translate the project!

docs/contributing/contribution_guides/tutorials.md

@@ -1,11 +0,0 @@
-# Tutorials
-
-Tutorials help new & existing users expand their abilty to use InvokeAI to the full extent of our features and services.  
-
-Currently, we have a set of tutorials available on our [YouTube channel](https://www.youtube.com/@invokeai), but as InvokeAI continues to evolve with new updates, we want to ensure that we are giving our users the resources they need to succeed. 
-
-Tutorials can be in the form of videos or article walkthroughs on a subject of your choice. We recommend focusing tutorials on the key image generation methods, or on a specific component within one of the image generation methods.
-
-## Contributing
-
-Please reach out to @imic or @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy) to help create tutorials for InvokeAI.

docs/features/CONCEPTS.md

@@ -1,8 +1,8 @@
 ---
-title: Textual Inversion Embeddings and LoRAs
+title: Concepts 
 ---
 
-# :material-library-shelves: Textual Inversions and LoRAs
+# :material-library-shelves: The Hugging Face Concepts Library and Importing Textual Inversion files
 
 With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
 
@@ -64,25 +64,21 @@ select the embedding you'd like to use. This UI has type-ahead support, so you c
 
 ## Using LoRAs
 
-LoRA files are models that customize the output of Stable Diffusion
-image generation.  Larger than embeddings, but much smaller than full
-models, they augment SD with improved understanding of subjects and
-artistic styles.
+LoRA files are models that customize the output of Stable Diffusion image generation.
+Larger than embeddings, but much smaller than full models, they augment SD with improved
+understanding of subjects and artistic styles.
 
-Unlike TI files, LoRAs do not introduce novel vocabulary into the
-model's known tokens. Instead, LoRAs augment the model's weights that
-are applied to generate imagery. LoRAs may be supplied with a
-"trigger" word that they have been explicitly trained on, or may
-simply apply their effect without being triggered.
+Unlike TI files, LoRAs do not introduce novel vocabulary into the model's known tokens. Instead,
+LoRAs augment the model's weights that are applied to generate imagery. LoRAs may be supplied
+with a "trigger" word that they have been explicitly trained on, or may simply apply their 
+effect without being triggered.
 
-LoRAs are typically stored in .safetensors files, which are the most
-secure way to store and transmit these types of weights. You may
-install any number of `.safetensors` LoRA files simply by copying them
-into the `autoimport/lora` directory of the corresponding InvokeAI models
-directory (usually `invokeai` in your home directory).
+LoRAs are typically stored in .safetensors files, which are the most secure way to store and transmit
+these types of weights. You may install any number of `.safetensors` LoRA files simply by copying them into
+the `lora` directory of the corresponding InvokeAI models directory (usually `invokeai`
+in your home directory). For example, you can simply move a Stable Diffusion 1.5 LoRA file to
+the `sd-1/lora` folder.
 
-To use these when generating, open the LoRA menu item in the options
-panel, select the LoRAs you want to apply and ensure that they have
-the appropriate weight recommended by the model provider. Typically,
-most LoRAs perform best at a weight of .75-1.
+To use these when generating, open the LoRA menu item in the options panel, select the LoRAs you want to apply
+and ensure that they have the appropriate weight recommended by the model provider. Typically, most LoRAs perform best at a weight of .75-1. 
 

docs/features/CONFIGURATION.md

@@ -1,282 +0,0 @@
----
-title: Configuration
----
-
-# :material-tune-variant: InvokeAI Configuration
-
-## Intro
-
-InvokeAI has numerous runtime settings which can be used to adjust
-many aspects of its operations, including the location of files and
-directories, memory usage, and performance. These settings can be
-viewed and customized in several ways:
-
-1. By editing settings in the `invokeai.yaml` file.
-2. By setting environment variables.
-3. On the command-line, when InvokeAI is launched.
-
-In addition, the most commonly changed settings are accessible
-graphically via the `invokeai-configure` script.
-
-### How the Configuration System Works
-
-When InvokeAI is launched, the very first thing it needs to do is to
-find its "root" directory, which contains its configuration files,
-installed models, its database of images, and the folder(s) of
-generated images themselves. In this document, the root directory will
-be referred to as ROOT.
-
-#### Finding the Root Directory
-
-To find its root directory, InvokeAI uses the following recipe:
-
-1. It first looks for the argument `--root <path>` on the command line
-it was launched from, and uses the indicated path if present.
-
-2. Next it looks for the environment variable INVOKEAI_ROOT, and uses
-the directory path found there if present.
-
-3. If neither of these are present, then InvokeAI looks for the
-folder containing the `.venv` Python virtual environment directory for
-the currently active environment. This directory is checked for files
-expected inside the InvokeAI root before it is used.
-
-4. Finally, InvokeAI looks for a directory in the current user's home
-directory named `invokeai`.
-
-#### Reading the InvokeAI Configuration File
-
-Once the root directory has been located, InvokeAI looks for a file
-named `ROOT/invokeai.yaml`, and if present reads configuration values
-from it. The top of this file looks like this:
-
-```
-InvokeAI:
-  Web Server:
-    host: localhost
-    port: 9090
-    allow_origins: []
-    allow_credentials: true
-    allow_methods:
-    - '*'
-    allow_headers:
-    - '*'
-  Features:
-    esrgan: true
-    internet_available: true
-    log_tokenization: false
-    patchmatch: true
-    restore: true
-...
-```
-
-This lines in this file are used to establish default values for
-Invoke's settings. In the above fragment, the Web Server's listening
-port is set to 9090 by the `port` setting.
-
-You can edit this file with a text editor such as "Notepad" (do not
-use Word or any other word processor). When editing, be careful to
-maintain the indentation, and do not add extraneous text, as syntax
-errors will prevent InvokeAI from launching. A basic guide to the
-format of YAML files can be found
-[here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
-
-You can fix a broken `invokeai.yaml` by deleting it and running the
-configuration script again -- option [7] in the launcher, "Re-run the
-configure script".
-
-#### Reading Environment Variables
-
-Next InvokeAI looks for defined environment variables in the format
-`INVOKEAI_<setting_name>`, for example `INVOKEAI_port`. Environment
-variable values take precedence over configuration file variables. On
-a Macintosh system, for example, you could change the port that the
-web server listens on by setting the environment variable this way:
-
-```
-export INVOKEAI_port=8000
-invokeai-web
-```
-
-Please check out these
-[Macintosh](https://phoenixnap.com/kb/set-environment-variable-mac)
-and
-[Windows](https://phoenixnap.com/kb/windows-set-environment-variable)
-guides for setting temporary and permanent environment variables.
-
-#### Reading the Command Line
-
-Lastly, InvokeAI takes settings from the command line, which override
-everything else. The command-line settings have the same name as the
-corresponding configuration file settings, preceded by a `--`, for
-example `--port 8000`.
-
-If you are using the launcher (`invoke.sh` or `invoke.bat`) to launch
-InvokeAI, then just pass the command-line arguments to the launcher:
-
-```
-invoke.bat --port 8000 --host 0.0.0.0
-```
-
-The arguments will be applied when you select the web server option
-(and the other options as well).
-
-If, on the other hand, you prefer to launch InvokeAI directly from the
-command line, you would first activate the virtual environment (known
-as the "developer's console" in the launcher), and run `invokeai-web`:
-
-```
-> C:\Users\Fred\invokeai\.venv\scripts\activate
-(.venv) > invokeai-web --port 8000 --host 0.0.0.0
-```
-
-You can get a listing and brief instructions for each of the
-command-line options by giving the `--help` argument:
-
-```
-(.venv) > invokeai-web --help
-usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials] [--allow_methods [ALLOW_METHODS ...]]
-                [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan] [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
-                [--patchmatch | --no-patchmatch] [--restore | --no-restore]
-                [--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_loaded_models MAX_LOADED_MODELS] [--max_cache_size MAX_CACHE_SIZE]
-                [--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--gpu_mem_reserved GPU_MEM_RESERVED] [--precision {auto,float16,float32,autocast}]
-                [--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled] [--tiled_decode | --no-tiled_decode] [--root ROOT]
-                [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR] [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH]
-                [--models_dir MODELS_DIR] [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
-                [--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]] [--log_format {plain,color,syslog,legacy}]
-                [--log_level {debug,info,warning,error,critical}] [--version | --no-version]
-```
-
-## The Configuration Settings
-
-The configuration settings are divided into several distinct
-groups in `invokeia.yaml`:
-
-### Web Server
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `host`     | `localhost`      | Name or IP address of the network interface that the web server will listen on  |
-| `port`     | `9090`           | Network port number that the web server will listen on  |
-| `allow_origins`  | `[]`       | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
-| `allow_credentials | `true`   | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
-| `allow_methods` | `*`         | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API  |
-| `allow_headers` | `*`         | List of HTTP headers that the web server will accept when accessing the API  |
-
-The documentation for InvokeAI's API can be accessed by browsing to the following URL: [http://localhost:9090/docs].
-
-### Features
-
-These configuration settings allow you to enable and disable various InvokeAI features:
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `esrgan`     | `true`      | Activate the ESRGAN upscaling options|
-| `internet_available` | `true`     | When a resource is not available locally, try to fetch it via the internet |
-| `log_tokenization` | `false`      | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected |
-| `patchmatch` | `true`     | Activate the "patchmatch" algorithm for improved inpainting |
-| `restore`    | `true`     | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) |
-
-### Memory/Performance
-
-These options tune InvokeAI's memory and performance characteristics.
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `always_use_cpu`     | `false`      | Use the CPU to generate images, even if a GPU is available |
-| `free_gpu_mem`       | `false`      | Aggressively free up GPU memory after each operation; this will allow you to run in low-VRAM environments with some performance penalties |
-| `max_cache_size`       | `6`      | Amount of CPU RAM (in GB) to reserve for caching models in memory; more cache allows you to keep models in memory and switch among them quickly |
-| `max_vram_cache_size`  | `2.75`   | Amount of GPU VRAM (in GB) to reserve for caching models in VRAM; more cache speeds up generation but reduces the size of the images that can be generated. This can be set to zero to maximize the amount of memory available for generation. |
-| `precision`       | `auto`      | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
-| `sequential_guidance`     | `false`      | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss |
-| `xformers_enabled`        | `true`      | If the x-formers memory-efficient attention module is installed, activate it for better memory usage and generation speed|
-| `tiled_decode`            | `false`     | If true, then during the VAE decoding phase the image will be decoded a section at a time, reducing memory consumption at the cost of a performance hit |
-
-### Paths
-
-These options set the paths of various directories and files used by
-InvokeAI. Relative paths are interpreted relative to INVOKEAI_ROOT, so
-if INVOKEAI_ROOT is `/home/fred/invokeai` and the path is
-`autoimport/main`, then the corresponding directory will be located at
-`/home/fred/invokeai/autoimport/main`.
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `autoimport_dir` | `autoimport/main`     | At startup time, read and import any main model files found in this directory |
-| `lora_dir` | `autoimport/lora`     | At startup time, read and import any LoRA/LyCORIS models found in this directory |
-| `embedding_dir` | `autoimport/embedding`  | At startup time, read and import any textual inversion (embedding) models found in this directory |
-| `controlnet_dir` | `autoimport/controlnet`  | At startup time, read and import any ControlNet models found in this directory |
-| `conf_path` | `configs/models.yaml`  | Location of the `models.yaml` model configuration file |
-| `models_dir` | `models`  | Location of the directory containing models installed by InvokeAI's model manager |
-| `legacy_conf_dir` | `configs/stable-diffusion`  | Location of the directory containing the .yaml configuration files for legacy checkpoint models |
-| `db_dir` | `databases`  | Location of the directory containing InvokeAI's image, schema and session database |
-| `outdir` | `outputs`  | Location of the directory in which the gallery of generated and uploaded images will be stored |
-| `use_memory_db` | `false`  | Keep database information in memory rather than on disk; this will not preserve image gallery information across restarts |
-
-Note that the autoimport directories will be searched recursively,
-allowing you to organize the models into folders and subfolders in any
-way you wish. In addition, while we have split up autoimport
-directories by the type of model they contain, this isn't
-necessary. You can combine different model types in the same folder
-and InvokeAI will figure out what they are. So you can easily use just
-one autoimport directory by commenting out the unneeded paths:
-
-```
-Paths:
-  autoimport_dir: autoimport
-#  lora_dir: null
-#  embedding_dir: null
-#  controlnet_dir: null
-```
-
-### Logging
-
-These settings control the information, warning, and debugging
-messages printed to the console log while InvokeAI is running:
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `log_handlers` | `console` | This controls where log messages are sent, and can be a list of one or more destinations. Values include `console`, `file`, `syslog` and `http`. These are described in more detail below |
-| `log_format` | `color` | This controls the formatting of the log messages. Values are `plain`, `color`, `legacy` and `syslog` |
-| `log_level`  | `debug` | This filters messages according to the level of severity and can be one of `debug`, `info`, `warning`, `error` and `critical`. For example, setting to `warning` will display all messages at the warning level or higher, but won't display "debug" or "info" messages |
-
-Several different log handler destinations are available, and multiple destinations are supported by providing a list:
-
-```
-  log_handlers:
-     - console
-     - syslog=localhost
-     - file=/var/log/invokeai.log
-```
-
-* `console` is the default. It prints log messages to the command-line window from which InvokeAI was launched.
-
-* `syslog` is only available on Linux and Macintosh systems. It uses
-  the operating system's "syslog" facility to write log file entries
-  locally or to a remote logging machine. `syslog` offers a variety
-  of configuration options:
-
-```
-  syslog=/dev/log`      - log to the /dev/log device
-  syslog=localhost`     - log to the network logger running on the local machine
-  syslog=localhost:512` - same as above, but using a non-standard port
-  syslog=fredserver,facility=LOG_USER,socktype=SOCK_DRAM`
-                        - Log to LAN-connected server "fredserver" using the facility LOG_USER and datagram packets.
-```
-
-* `http` can be used to log to a remote web server. The server must be
-  properly configured to receive and act on log messages. The option
-  accepts the URL to the web server, and a `method` argument
-  indicating whether the message should be submitted using the GET or
-  POST method.
-
-```
- http=http://my.server/path/to/logger,method=POST
-```
-
-The `log_format` option provides several alternative formats:
-
-* `color`    - default format providing time, date and a message, using text colors to distinguish different log severities
-* `plain`    - same as above, but monochrome text only
-* `syslog`   - the log level and error message only, allowing the syslog system to attach the time and date
-* `legacy`   - a format similar to the one used by the legacy 2.3 InvokeAI releases.

docs/features/CONTROLNET.md

@@ -8,64 +8,20 @@ title: ControlNet
 
 ControlNet
 
-ControlNet is a powerful set of features developed by the open-source
-community (notably, Stanford researcher
-[**@ilyasviel**](https://github.com/lllyasviel)) that allows you to
-apply a secondary neural network model to your image generation
-process in Invoke.
+ControlNet is a powerful set of features developed by the open-source community (notably, Stanford researcher [**@ilyasviel**](https://github.com/lllyasviel)) that allows you to apply a secondary neural network model to your image generation process in Invoke.
 
-With ControlNet, you can get more control over the output of your
-image generation, providing you with a way to direct the network
-towards generating images that better fit your desired style or
-outcome.
+With ControlNet, you can get more control over the output of your image generation, providing you with a way to direct the network towards generating images that better fit your desired style or outcome.
 
 
 ### How it works
 
-ControlNet works by analyzing an input image, pre-processing that
-image to identify relevant information that can be interpreted by each
-specific ControlNet model, and then inserting that control information
-into the generation process. This can be used to adjust the style,
-composition, or other aspects of the image to better achieve a
-specific result.
+ControlNet works by analyzing an input image, pre-processing that image to identify relevant information that can be interpreted by each specific ControlNet model, and then inserting that control information into the generation process. This can be used to adjust the style, composition, or other aspects of the image to better achieve a specific result.
 
 
 ### Models
 
-InvokeAI provides access to a series of ControlNet models that provide
-different effects or styles in your generated images.  Currently
-InvokeAI only supports "diffuser" style ControlNet models. These are
-folders that contain the files `config.json` and/or
-`diffusion_pytorch_model.safetensors` and
-`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
-the name of the model.
+As part of the model installation, ControlNet models can be selected including a variety of pre-trained models that have been added to achieve different effects or styles in your generated images. Further ControlNet models may require additional code functionality to also be incorporated into Invoke's Invocations folder. You should expect to follow any installation instructions for ControlNet models loaded  outside the default models provided by Invoke. The default models include:
 
-***InvokeAI does not currently support checkpoint-format
-ControlNets. These come in the form of a single file with the
-extension `.safetensors`.***
-
-Diffuser-style ControlNet models are available at HuggingFace
-(http://huggingface.co) and accessed via their repo IDs (identifiers
-in the format "author/modelname"). The easiest way to install them is
-to use the InvokeAI model installer application. Use the
-`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
-to the CONTROLNETS section. Select the models you wish to install and
-press "APPLY CHANGES". You may also enter additional HuggingFace
-repo_ids in the "Additional models" textbox:
-
-![Model Installer -
-Controlnetl](../assets/installing-models/model-installer-controlnet.png){:width="640px"}
-
-Command-line users can launch the model installer using the command
-`invokeai-model-install`.
-
-_Be aware that some ControlNet models require additional code
-functionality in order to work properly, so just installing a
-third-party ControlNet model may not have the desired effect._ Please
-read and follow the documentation for installing a third party model
-not currently included among InvokeAI's default list.
-
-The models currently supported include:
 
 **Canny**:
 

docs/features/NODES.md

@@ -1,208 +0,0 @@
-# Nodes Editor (Experimental)
-
-🚨
-*The node editor is experimental. We've made it accessible because we use it to develop the application, but we have not addressed the many known rough edges. It's very easy to shoot yourself in the foot, and we cannot offer support for it until it sees full release (ETA v3.1). Everything is subject to change without warning.* 
-🚨
-
-The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. The node processing flow is usually done from left (inputs) to right (outputs), though linearity can become abstracted the more complex the node graph becomes. Nodes inputs and outputs are connected by dragging connectors from node to node.
-
-To better understand how nodes are used, think of how an electric power bar works. It takes in one input (electricity from a wall outlet) and passes it to multiple devices through multiple outputs. Similarly, a node could have multiple inputs and outputs functioning at the same (or different) time, but all node outputs pass information onward like a power bar passes electricity. Not all outputs are compatible with all inputs, however - Each node has different constraints on how it is expecting to input/output information. In general, node outputs are colour-coded to match compatible inputs of other nodes.
-
-## Anatomy of a Node
-
-Individual nodes are made up of the following:
-
-- Inputs: Edge points on the left side of the node window where you connect outputs from other nodes.
-- Outputs: Edge points on the right side of the node window where you connect to inputs on other nodes.
-- Options: Various options which are either manually configured, or overridden by connecting an output from another node to the input.
-
-## Diffusion Overview
-
-Taking the time to understand the diffusion process will help you to understand how to set up your nodes in the nodes editor. 
-
-There are two main spaces Stable Diffusion works in: image space and latent space.
-
-Image space represents images in pixel form that you look at. Latent space represents compressed inputs. It’s in latent space that Stable Diffusion processes images. A VAE (Variational Auto Encoder) is responsible for compressing and encoding inputs into latent space, as well as decoding outputs back into image space.
-
-When you generate an image using text-to-image, multiple steps occur in latent space:
-1. Random noise is generated at the chosen height and width. The noise’s characteristics are dictated by the chosen (or not chosen) seed. This noise tensor is passed into latent space. We’ll call this noise A.
-1. Using a model’s U-Net, a noise predictor examines noise A, and the words tokenized by CLIP from your prompt (conditioning). It generates its own noise tensor to predict what the final image might look like in latent space. We’ll call this noise B.
-1. Noise B is subtracted from noise A in an attempt to create a final latent image indicative of the inputs. This step is repeated for the number of sampler steps chosen.
-1. The VAE decodes the final latent image from latent space into image space.
-
-image-to-image is a similar process, with only step 1 being different:
-1. The input image is decoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how much noise is added, 0 being none, and 1 being all-encompassing. We’ll call this noise A. The process is then the same as steps 2-4 in the text-to-image explanation above. 
-
-Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
-
-A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from the latent image across the sampler steps chosen (step 3 above). Less noise is usually subtracted at higher sampler steps. 
-
-## Node Types (Base Nodes)
-
-| Node <img width=160 align="right"> | Function                                                                              |
-| ---------------------------------- | --------------------------------------------------------------------------------------|
-| Add                                | Adds two numbers |
-| CannyImageProcessor                | Canny edge detection for ControlNet |
-| ClipSkip                           | Skip layers in clip text_encoder model |
-| Collect                            | Collects values into a collection |
-| Prompt (Compel)                    | Parse prompt using compel package to conditioning |
-| ContentShuffleImageProcessor       | Applies content shuffle processing to image |
-| ControlNet                         | Collects ControlNet info to pass to other nodes |
-| CvInpaint                          | Simple inpaint using opencv |
-| Divide                             | Divides two numbers |
-| DynamicPrompt                      | Parses a prompt using adieyal/dynamic prompt's random or combinatorial generator |
-| FloatLinearRange                   | Creates a range |
-| HedImageProcessor                  | Applies HED edge detection to image |
-| ImageBlur                          | Blurs an image |
-| ImageChannel                       | Gets a channel from an image |
-| ImageCollection                    | Load a collection of images and provide it as output |
-| ImageConvert                       | Converts an image to a different mode |
-| ImageCrop                          | Crops an image to a specified box. The box can be outside of the image. |
-| ImageInverseLerp                   | Inverse linear interpolation of all pixels of an image |
-| ImageLerp                          | Linear interpolation of all pixels of an image |
-| ImageMultiply                      | Multiplies two images together using `PIL.ImageChops.Multiply()` |
-| ImageNSFWBlurInvocation            | Detects and blurs images that may contain sexually explicit content |
-| ImagePaste                         | Pastes an image into another image |
-| ImageProcessor                     | Base class for invocations that reprocess images for ControlNet |
-| ImageResize                        | Resizes an image to specific dimensions |
-| ImageScale                         | Scales an image by a factor |
-| ImageToLatents                     | Scales latents by a given factor |
-| ImageWatermarkInvocation           | Adds an invisible watermark to images |
-| InfillColor                        | Infills transparent areas of an image with a solid color |
-| InfillPatchMatch                   | Infills transparent areas of an image using the PatchMatch algorithm |
-| InfillTile                         | Infills transparent areas of an image with tiles of the image |
-| Inpaint                            | Generates an image using inpaint |
-| Iterate                            | Iterates over a list of items |
-| LatentsToImage                     | Generates an image from latents |
-| LatentsToLatents                   | Generates latents using latents as base image |
-| LeresImageProcessor                | Applies leres processing to image |
-| LineartAnimeImageProcessor         | Applies line art anime processing to image |
-| LineartImageProcessor              | Applies line art processing to image |
-| LoadImage                          | Load an image and provide it as output |
-| Lora Loader                        | Apply selected lora to unet and text_encoder |
-| Model Loader                       | Loads a main model, outputting its submodels |
-| MaskFromAlpha                      | Extracts the alpha channel of an image as a mask |
-| MediapipeFaceProcessor             | Applies mediapipe face processing to image |
-| MidasDepthImageProcessor           | Applies Midas depth processing to image |
-| MlsdImageProcessor                 | Applied MLSD processing to image |
-| Multiply                           | Multiplies two numbers |
-| Noise                              | Generates latent noise |
-| NormalbaeImageProcessor            | Applies NormalBAE processing to image |
-| OpenposeImageProcessor             | Applies Openpose processing to image |
-| ParamFloat                         | A float parameter |
-| ParamInt                           | An integer parameter |
-| PidiImageProcessor                 | Applies PIDI processing to an image |
-| Progress Image                     | Displays the progress image in the Node Editor |
-| RandomInit                         | Outputs a single random integer |
-| RandomRange                        | Creates a collection of random numbers |
-| Range                              | Creates a range of numbers from start to stop with step |
-| RangeOfSize                        | Creates a range from start to start + size with step |
-| ResizeLatents                      | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8. |
-| RestoreFace                        | Restores faces in the image |
-| ScaleLatents                       | Scales latents by a given factor |
-| SegmentAnythingProcessor           | Applies segment anything processing to image |
-| ShowImage                          | Displays a provided image, and passes it forward in the pipeline |
-| StepParamEasing                    | Experimental per-step parameter for easing for denoising steps |
-| Subtract                           | Subtracts two numbers |
-| TextToLatents                      | Generates latents from conditionings |
-| TileResampleProcessor              | Bass class for invocations that preprocess images for ControlNet |
-| Upscale                            | Upscales an image |
-| VAE Loader                         | Loads a VAE model, outputting a VaeLoaderOutput |
-| ZoeDepthImageProcessor             | Applies Zoe depth processing to image |
-
-## Node Grouping Concepts
-
-There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
-
-### Noise
-
-As described, an initial noise tensor is necessary for the latent diffusion process. As a result, all non-image *ToLatents nodes require a noise node input.  
-
-![groupsnoise](../assets/nodes/groupsnoise.png)
-
-### Conditioning
-
-As described, conditioning is necessary for the latent diffusion process, whether empty or not. As a result, all non-image *ToLatents nodes require positive and negative conditioning inputs. Conditioning is reliant on a CLIP tokenizer provided by the Model Loader node.
-
-![groupsconditioning](../assets/nodes/groupsconditioning.png)
-
-### Image Space & VAE
-
-The ImageToLatents node doesn't require a noise node input, but requires a VAE input to convert the image from image space into latent space. In reverse, the LatentsToImage node requires a VAE input to convert from latent space back into image space.
-
-![groupsimgvae](../assets/nodes/groupsimgvae.png)
-
-### Defined & Random Seeds
-
-It is common to want to use both the same seed (for continuity) and random seeds (for variance). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
-
-![groupsrandseed](../assets/nodes/groupsrandseed.png)
-
-### Control
-
-Control means to guide the diffusion process to adhere to a defined input or structure. Control can be provided as input to non-image *ToLatents nodes from ControlNet nodes. ControlNet nodes usually require an image processor which converts an input image for use with ControlNet.
-
-![groupscontrol](../assets/nodes/groupscontrol.png)
-
-### LoRA
-
-The Lora Loader node lets you load a LoRA (say that ten times fast) and pass it as output to both the Prompt (Compel) and non-image *ToLatents nodes. A model's CLIP tokenizer is passed through the LoRA into Prompt (Compel), where it affects conditioning. A model's U-Net is also passed through the LoRA into a non-image *ToLatents node, where it affects noise prediction.
-
-![groupslora](../assets/nodes/groupslora.png)
-
-### Scaling
-
-Use the ImageScale, ScaleLatents, and Upscale nodes to upscale images and/or latent images. The chosen method differs across contexts. However, be aware that latents are already noisy and compressed at their original resolution; scaling an image could produce more detailed results.
-
-![groupsallscale](../assets/nodes/groupsallscale.png)
-
-### Iteration + Multiple Images as Input
-
-Iteration is a common concept in any processing, and means to repeat a process with given input. In nodes, you're able to use the Iterate node to iterate through collections usually gathered by the Collect node. The Iterate node has many potential uses, from processing a collection of images one after another, to varying seeds across multiple image generations and more. This screenshot demonstrates how to collect several images and pass them out one at a time.
-
-![groupsiterate](../assets/nodes/groupsiterate.png)
-
-### Multiple Image Generation + Random Seeds
-
-Multiple image generation in the node editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. 
-
-To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
-
-![groupsmultigenseeding](../assets/nodes/groupsmultigenseeding.png)
-
-## Examples
-
-With our knowledge of node grouping and the diffusion process, let’s break down some basic graphs in the nodes editor. Note that a node's options can be overridden by inputs from other nodes. These examples aren't strict rules to follow and only demonstrate some basic configurations.
-
-### Basic text-to-image Node Graph
-
-![nodest2i](../assets/nodes/nodest2i.png)
-
-- Model Loader: A necessity to generating images (as we’ve read above). We choose our model from the dropdown. It outputs a U-Net, CLIP tokenizer, and VAE.
-- Prompt (Compel): Another necessity. Two prompt nodes are created. One will output positive conditioning (what you want, ‘dog’), one will output negative (what you don’t want, ‘cat’). They both input the CLIP tokenizer that the Model Loader node outputs.
-- Noise: Consider this noise A from step one of the text-to-image explanation above. Choose a seed number, width, and height.
-- TextToLatents: This node takes many inputs for converting and processing text & noise from image space into latent space, hence the name TextTo**Latents**. In this setup, it inputs positive and negative conditioning from the prompt nodes for processing (step 2 above). It inputs noise from the noise node for processing (steps 2 & 3 above). Lastly, it inputs a U-Net from the Model Loader node for processing (step 2 above). It outputs latents for use in the next LatentsToImage node. Choose number of sampler steps, CFG scale, and scheduler.
-- LatentsToImage: This node takes in processed latents from the TextToLatents node, and the model’s VAE from the Model Loader node which is responsible for decoding latents back into the image space, hence the name LatentsTo**Image**. This node is the last stop, and once the image is decoded, it is saved to the gallery.
-
-### Basic image-to-image Node Graph
-
-![nodesi2i](../assets/nodes/nodesi2i.png)
-
-- Model Loader: Choose a model from the dropdown.
-- Prompt (Compel): Two prompt nodes. One positive (dog), one negative (dog). Same CLIP inputs from the Model Loader node as before.
-- ImageToLatents: Upload a source image directly in the node window, via drag'n'drop from the gallery, or passed in as input. The ImageToLatents node inputs the VAE from the Model Loader node to decode the chosen image from image space into latent space, hence the name ImageTo**Latents**. It outputs latents for use in the next LatentsToLatents node. It also outputs the source image's width and height for use in the next Noise node if the final image is to be the same dimensions as the source image.
-- Noise: A noise tensor is created with the width and height of the source image, and connected to the next LatentsToLatents node. Notice the width and height fields are overridden by the input from the ImageToLatents width and height outputs.
-- LatentsToLatents: The inputs and options are nearly identical to TextToLatents, except that LatentsToLatents also takes latents as an input. Considering our source image is already converted to latents in the last ImageToLatents node, and text + noise are no longer the only inputs to process, we use the LatentsToLatents node. 
-- LatentsToImage: Like previously, the LatentsToImage node will use the VAE from the Model Loader as input to decode the latents from LatentsToLatents into image space, and save it to the gallery.
-
-### Basic ControlNet Node Graph
-
-![nodescontrol](../assets/nodes/nodescontrol.png)
-
-- Model Loader
-- Prompt (Compel)
-- Noise: Width and height of the CannyImageProcessor ControlNet image is passed in to set the dimensions of the noise passed to TextToLatents.
-- CannyImageProcessor: The CannyImageProcessor node is used to process the source image being used as a ControlNet. Each ControlNet processor node applies control in different ways, and has some different options to configure. Width and height are passed to noise, as mentioned. The processed ControlNet image is output to the ControlNet node.
-- ControlNet: Select the type of control model. In this case, canny is chosen as the CannyImageProcessor was used to generate the ControlNet image. Configure the control node options, and pass the control output to TextToLatents.
-- TextToLatents: Similar to the basic text-to-image example, except ControlNet is passed to the control input edge point.
-- LatentsToImage

docs/features/NSFW.md

@@ -1,40 +1,12 @@
 ---
-title: Watermarking, NSFW Image Checking
+title: The NSFW Checker
 ---
 
-# :material-image-off: Invisible Watermark and the NSFW Checker
-
-## Watermarking
-
-InvokeAI does not apply watermarking to images by default. However,
-many computer scientists working in the field of generative AI worry
-that a flood of computer-generated imagery will contaminate the image
-data sets needed to train future generations of generative models.
-
-InvokeAI offers an optional watermarking mode that writes a small bit
-of text, **InvokeAI**, into each image that it generates using an
-"invisible" watermarking library that spreads the information
-throughout the image in a way that is not perceptible to the human
-eye. If you are planning to share your generated images on
-internet-accessible services, we encourage you to activate the
-invisible watermark mode in order to help preserve the digital image
-environment.
-
-The downside of watermarking is that it increases the size of the
-image moderately, and has been reported by some individuals to degrade
-image quality. Your mileage may vary.
-
-To read the watermark in an image, activate the InvokeAI virtual
-environment (called the "developer's console" in the launcher) and run
-the command:
-
-```
-invisible-watermark -a decode -t bytes -m dwtDct -l 64 /path/to/image.png
-```
+# :material-image-off: NSFW Checker
 
 ## The NSFW ("Safety") Checker
 
-Stable Diffusion 1.5-based image generation models will produce sexual
+The Stable Diffusion image generation models will produce sexual
 imagery if deliberately prompted, and will occasionally produce such
 images when this is not intended. Such images are colloquially known
 as "Not Safe for Work" (NSFW). This behavior is due to the nature of
@@ -46,17 +18,35 @@ jurisdictions it may be illegal to publicly distribute such imagery,
 including mounting a publicly-available server that provides
 unfiltered images to the public. Furthermore, the [Stable Diffusion
 weights
-License](https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE-SD1+SD2.txt),
-and the [Stable Diffusion XL
-License][https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE-SDXL.txt]
-both forbid the models from being used to "exploit any of the
+License](https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE-ModelWeights.txt)
+forbids the model from being used to "exploit any of the
 vulnerabilities of a specific group of persons."
 
 For these reasons Stable Diffusion offers a "safety checker," a
 machine learning model trained to recognize potentially disturbing
 imagery. When a potentially NSFW image is detected, the checker will
 blur the image and paste a warning icon on top. The checker can be
-turned on and off in the Web interface under Settings.
+turned on and off on the command line using `--nsfw_checker` and
+`--no-nsfw_checker`.
+
+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
+(`invokeai.yaml` in the InvokeAI root directory).  You can change the
+default at any time by opening this file in a text editor and
+changing the line `nsfw_checker:` from true to false or vice-versa:
+
+
+```
+...
+  Features:
+    esrgan: true
+    internet_available: true
+    log_tokenization: false
+    nsfw_checker: true
+    patchmatch: true
+    restore: true
+```
 
 ## Caveats
 
@@ -94,3 +84,10 @@ are encouraged to turn **off** intermediate image rendering when you
 are using the checker. Future versions of InvokeAI will apply
 additional blurring to intermediate images when the checker is active.
 
+### Watermarking
+
+InvokeAI does not apply any sort of watermark to images it
+generates. However, it does write metadata into the PNG data area,
+including the prompt used to generate the image and relevant parameter
+settings. These fields can be examined using the `sd-metadata.py`
+script that comes with the InvokeAI package.

docs/features/OTHER.md

@@ -16,24 +16,21 @@ Output Example:
 
 ---
 
-## **Invisible Watermark**
+## **Seamless Tiling**
 
-In keeping with the principles for responsible AI generation, and to
-help AI researchers avoid synthetic images contaminating their
-training sets, InvokeAI adds an invisible watermark to each of the
-final images it generates. The watermark consists of the text
-"InvokeAI" and can be viewed using the
-[invisible-watermarks](https://github.com/ShieldMnt/invisible-watermark)
-tool.
+The seamless tiling mode causes generated images to seamlessly tile
+with itself creating repetitive wallpaper-like patterns. To use it,
+activate the Seamless Tiling option in the Web GUI and then select
+whether to tile on the X (horizontal) and/or Y (vertical) axes. Tiling
+will then be active for the next set of generations.
 
-Watermarking is controlled using the `invisible-watermark` setting in
-`invokeai.yaml`. To turn it off, add the following line under the `Features`
-category.
+A nice prompt to test seamless tiling with is:
 
 ```
-invisible_watermark: false
+pond garden with lotus by claude monet"
 ```
 
+---
 
 ## **Weighted Prompts**
 
@@ -42,10 +39,34 @@ priority to them, by adding `:<percent>` to the end of the section you wish to u
 example consider this prompt:
 
 ```bash
-(tabby cat):0.25 (white duck):0.75 hybrid
+tabby cat:0.25 white duck:0.75 hybrid
 ```
 
 This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75%
 on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
 combination of integers and floating point numbers, and they do not need to add up to 1.
 
+## **Thresholding and Perlin Noise Initialization Options**
+
+Under the Noise section of the Web UI, you will find two options named
+Perlin Noise and Noise Threshold. [Perlin
+noise](https://en.wikipedia.org/wiki/Perlin_noise) is a type of
+structured noise used to simulate terrain and other natural
+textures. The slider controls the percentage of perlin noise that will
+be mixed into the image at the beginning of generation. Adding a little
+perlin noise to a generation will alter the image substantially.
+
+The noise threshold limits the range of the latent values during
+sampling and helps combat the oversharpening seem with higher CFG
+scale values.
+
+For better intuition into what these options do in practice:
+
+![here is a graphic demonstrating them both](../assets/truncation_comparison.jpg)
+
+In generating this graphic, perlin noise at initialization was
+programmatically varied going across on the diagram by values 0.0,
+0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied
+going down from 0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are
+fixed using the prompt "a portrait of a beautiful young lady" a CFG of
+20, 100 steps, and a seed of 1950357039.

docs/features/WEB.md

@@ -4,19 +4,15 @@ title: InvokeAI Web Server
 
 # :material-web: InvokeAI Web Server
 
-## Quick guided walkthrough of the WebUI's features
+As of version 2.0.0, this distribution comes with a full-featured web server
+(see screenshot).
 
-While most of the WebUI's features are intuitive, here is a guided walkthrough
-through its various components.
-
-### Launching the WebUI
-
-To run the InvokeAI web server, start the `invoke.sh`/`invoke.bat`
-script and select option (1). Alternatively, with the InvokeAI
-environment active, run `invokeai-web`:
+To use it, launch the `invoke.sh`/`invoke.bat` script and select
+option (2). Alternatively, with the InvokeAI environment active, run
+the `invokeai` script by adding the `--web` option:
 
 ```bash
-invokeai-web
+invokeai --web
 ```
 
 You can then connect to the server by pointing your web browser at
@@ -32,32 +28,33 @@ invoke.sh --host 0.0.0.0
 or
 
 ```bash
-invokeai-web --host 0.0.0.0
+invokeai --web --host 0.0.0.0
 ```
 
-### The InvokeAI Web Interface
+## Quick guided walkthrough of the WebUI's features
+
+While most of the WebUI's features are intuitive, here is a guided walkthrough
+through its various components.
 
 ![Invoke Web Server - Major Components](../assets/invoke-web-server-1.png){:width="640px"}
 
 The screenshot above shows the Text to Image tab of the WebUI. There are three
 main sections:
 
-1. A **control panel** on the left, which contains various settings
-   for text to image generation. The most important part is the text
-   field (currently showing `fantasy painting, horned demon`) for
-   entering the positive text prompt, another text field right below it for an
-   optional negative text prompt (concepts to exclude), and a _Invoke_ button 
-   to begin the image rendering process.
+1. A **control panel** on the left, which contains various settings for text to
+   image generation. The most important part is the text field (currently
+   showing `strawberry sushi`) for entering the text prompt, and the camera icon
+   directly underneath that will render the image. We'll call this the _Invoke_
+   button from now on.
 
-2. The **current image** section in the middle, which shows a large
-   format version of the image you are currently working on. A series
-   of buttons at the top lets you modify and manipulate the image in
-   various ways.
+2. The **current image** section in the middle, which shows a large format
+   version of the image you are currently working on. A series of buttons at the
+   top ("image to image", "Use All", "Use Seed", etc) lets you modify the image
+   in various ways.
 
-3. A **gallery** section on the left that contains a history of the images you
+3. A \*_gallery_ section on the left that contains a history of the images you
    have generated. These images are read and written to the directory specified
-   in the `INVOKEAIROOT/invokeai.yaml` initialization file, usually a directory
-   named `outputs` in `INVOKEAIROOT`.
+   at launch time in `--outdir`.
 
 In addition to these three elements, there are a series of icons for changing
 global settings, reporting bugs, and changing the theme on the upper right.
@@ -79,10 +76,14 @@ From top to bottom, these are:
    with outpainting,and modify interior portions of the image with
    inpainting, erase portions of a starting image and have the AI fill in
    the erased region from a text prompt.
-4. Node Editor - (experimental) this panel allows you to create
+4. Workflow Management (not yet implemented) - this panel will allow you to create
    pipelines of common operations and combine them into workflows.
-5. Model Manager - this panel allows you to import and configure new
-   models using URLs, local paths, or HuggingFace diffusers repo_ids.
+5. Training (not yet implemented) - this panel will provide an interface to [textual
+   inversion training](TEXTUAL_INVERSION.md) and fine tuning.
+
+The inpainting, outpainting and postprocessing tabs are currently in
+development. However, limited versions of their features can already be accessed
+through the Text to Image and Image to Image tabs.
 
 ## Walkthrough
 
@@ -91,54 +92,43 @@ feature set.
 
 ### Text to Image
 
-1. Launch the WebUI using launcher option [1] and connect to it with
-   your browser by accessing `http://localhost:9090`. If the browser
-   and server are running on different machines on your LAN, add the
-   option `--host 0.0.0.0` to the `invoke.sh` launch command line and connect to
-   the machine hosting the web server using its IP address or domain
-   name.
+1. Launch the WebUI using `python scripts/invoke.py --web` and connect to it
+   with your browser by accessing `http://localhost:9090`. If the browser and
+   server are running on different machines on your LAN, add the option
+   `--host 0.0.0.0` to the launch command line and connect to the machine
+   hosting the web server using its IP address or domain name.
 
-2. If all goes well, the WebUI should come up and you'll see a green dot
-   meaning `connected`  on the upper right.
-
-![Invoke Web Server - Control Panel](../assets/invoke-control-panel-1.png){ align=right width=300px }
+2. If all goes well, the WebUI should come up and you'll see a green
+   `connected` message on the upper right.
 
 #### Basics
 
-1.  Generate an image by typing _bluebird_ into the large prompt field
-    on the upper left and then clicking on the Invoke button or pressing
-	the return button.
-	After a short wait, you'll see a large image of a bluebird in the
+1.  Generate an image by typing _strawberry sushi_ into the large prompt field
+    on the upper left and then clicking on the Invoke button (the one with the
+    Camera icon). After a short wait, you'll see a large image of sushi in the
     image panel, and a new thumbnail in the gallery on the right.
 
-    If you need more room on the screen, you can turn the gallery off
-    by typing the **g** hotkey. You can turn it back on later by clicking the
-    image icon that appears in the gallery's place. The list of hotkeys can
-	be found by clicking on the keyboard icon above the image gallery.
+    If you need more room on the screen, you can turn the gallery off by
+    clicking on the **x** to the right of "Your Invocations". You can turn it
+    back on later by clicking the image icon that appears in the gallery's
+    place.
 
-2.  Generate a bunch of bluebird images by increasing the number of
-    requested images by adjusting the Images counter just below the Invoke
+    The images are written into the directory indicated by the `--outdir` option
+    provided at script launch time. By default, this is `outputs/img-samples`
+    under the InvokeAI directory.
+
+2.  Generate a bunch of strawberry sushi images by increasing the number of
+    requested images by adjusting the Images counter just below the Camera
     button. As each is generated, it will be added to the gallery. You can
     switch the active image by clicking on the gallery thumbnails.
-	
-	If you'd like to watch the image generation progress, click the hourglass
-	icon above the main image area. As generation progresses, you'll see
-	increasingly detailed versions of the ultimate image.
 
-3.  Try playing with different settings, including changing the main
-    model, the image width and height, the Scheduler, the Steps and
-    the CFG scale.
-	
-	The _Model_ changes the main model. Thousands of custom models are
-	now available, which generate a variety of image styles and
-	subjects. While InvokeAI comes with a few starter models, it is
-	easy to import new models into the application. See [Installing
-	Models](../installation/050_INSTALLING_MODELS.md) for more details.
+3.  Try playing with different settings, including image width and height, the
+    Sampler, the Steps and the CFG scale.
 
     Image _Width_ and _Height_ do what you'd expect. However, be aware that
     larger images consume more VRAM memory and take longer to generate.
 
-    The _Scheduler_ controls how the AI selects the image to display. Some
+    The _Sampler_ controls how the AI selects the image to display. Some
     samplers are more "creative" than others and will produce a wider range of
     variations (see next section). Some samplers run faster than others.
 
@@ -152,27 +142,17 @@ feature set.
     to the input prompt. You can go as high or low as you like, but generally
     values greater than 20 won't improve things much, and values lower than 5
     will produce unexpected images. There are complex interactions between
-    _Steps_, _CFG Scale_ and the _Scheduler_, so experiment to find out what works
+    _Steps_, _CFG Scale_ and the _Sampler_, so experiment to find out what works
     for you.
-	
-	The _Seed_ controls the series of values returned by InvokeAI's
-    random number generator. Each unique seed value will generate a different
-	image. To regenerate a previous image, simply use the original image's
-	seed value. A slider to the right of the _Seed_ field will change the
-	seed each time an image is generated.
 
-![Invoke Web Server - Control Panel 2](../assets/control-panel-2.png){ align=right width=400px }
+4.  To regenerate a previously-generated image, select the image you want and
+    click _Use All_. This loads the text prompt and other original settings into
+    the control panel. If you then press _Invoke_ it will regenerate the image
+    exactly. You can also selectively modify the prompt or other settings to
+    tweak the image.
 
-4.  To regenerate a previously-generated image, select the image you
-    want and click the asterisk ("*") button at the top of the
-    image. This loads the text prompt and other original settings into
-    the control panel. If you then press _Invoke_ it will regenerate
-    the image exactly. You can also selectively modify the prompt or
-    other settings to tweak the image.
-
-    Alternatively, you may click on the "sprouting plant icon" to load
-    just the image's seed, and leave other settings unchanged or the
-    quote icon to load just the positive and negative prompts.
+    Alternatively, you may click on _Use Seed_ to load just the image's seed,
+    and leave other settings unchanged.
 
 5.  To regenerate a Stable Diffusion image that was generated by another SD
     package, you need to know its text prompt and its _Seed_. Copy-paste the
@@ -181,22 +161,62 @@ feature set.
     you Invoke, you will get something similar to the original image. It will
     not be exact unless you also set the correct values for the original
     sampler, CFG, steps and dimensions, but it will (usually) be close.
-	
-6.  To save an image, right click on it to bring up a menu that will
-	let you download the image, save it to a named image gallery, and
-	copy it to the clipboard, among other things.
 
-#### Upscaling
+#### Variations on a theme
 
-![Invoke Web Server - Upscaling](../assets/upscaling.png){ align=right width=400px }
+1.  Let's try generating some variations. Select your favorite sushi image from
+    the gallery to load it. Then select "Use All" from the list of buttons
+    above. This will load up all the settings used to generate this image,
+    including its unique seed.
 
-"Upscaling" is the process of increasing the size of an image while
-    retaining the sharpness. InvokeAI uses an external library called
-    "ESRGAN" to do this. To invoke upscaling, simply select an image
-    and press the "expanding arrows" button above it. You can select
-    between 2X and 4X upscaling, and adjust the upscaling strength,
-    which has much the same meaning as in facial reconstruction. Try
-    running this on one of your previously-generated images.
+    Go down to the Variations section of the Control Panel and set the button to
+    On. Set Variation Amount to 0.2 to generate a modest number of variations on
+    the image, and also set the Image counter to `4`. Press the `invoke` button.
+    This will generate a series of related images. To obtain smaller variations,
+    just lower the Variation Amount. You may also experiment with changing the
+    Sampler. Some samplers generate more variability than others. _k_euler_a_ is
+    particularly creative, while _ddim_ is pretty conservative.
+
+2.  For even more variations, experiment with increasing the setting for
+    _Perlin_. This adds a bit of noise to the image generation process. Note
+    that values of Perlin noise greater than 0.15 produce poor images for
+    several of the samplers.
+
+#### Facial reconstruction and upscaling
+
+Stable Diffusion frequently produces mangled faces, particularly when there are
+multiple figures in the same scene. Stable Diffusion has particular issues with
+generating reallistic eyes. InvokeAI provides the ability to reconstruct faces
+using either the GFPGAN or CodeFormer libraries. For more information see
+[POSTPROCESS](POSTPROCESS.md).
+
+1.  Invoke a prompt that generates a mangled face. A prompt that often gives
+    this is "portrait of a lawyer, 3/4 shot" (this is not intended as a slur
+    against lawyers!) Once you have an image that needs some touching up, load
+    it into the Image panel, and press the button with the face icon
+    (highlighted in the first screenshot below). A dialog box will appear. Leave
+    _Strength_ at 0.8 and press \*Restore Faces". If all goes well, the eyes and
+    other aspects of the face will be improved (see the second screenshot)
+
+    ![Invoke Web Server - Original Image](../assets/invoke-web-server-3.png)
+
+    ![Invoke Web Server - Retouched Image](../assets/invoke-web-server-4.png)
+
+    The facial reconstruction _Strength_ field adjusts how aggressively the face
+    library will try to alter the face. It can be as high as 1.0, but be aware
+    that this often softens the face airbrush style, losing some details. The
+    default 0.8 is usually sufficient.
+
+2.  "Upscaling" is the process of increasing the size of an image while
+    retaining the sharpness. InvokeAI uses an external library called "ESRGAN"
+    to do this. To invoke upscaling, simply select an image and press the _HD_
+    button above it. You can select between 2X and 4X upscaling, and adjust the
+    upscaling strength, which has much the same meaning as in facial
+    reconstruction. Try running this on one of your previously-generated images.
+
+3.  Finally, you can run facial reconstruction and/or upscaling automatically
+    after each Invocation. Go to the Advanced Options section of the Control
+    Panel and turn on _Restore Face_ and/or _Upscale_.
 
 ### Image to Image
 
@@ -204,14 +224,24 @@ InvokeAI lets you take an existing image and use it as the basis for a new
 creation. You can use any sort of image, including a photograph, a scanned
 sketch, or a digital drawing, as long as it is in PNG or JPEG format.
 
-For this tutorial, we'll use the file named
-[Lincoln-and-Parrot-512.png](../assets/Lincoln-and-Parrot-512.png).
+For this tutorial, we'll use files named
+[Lincoln-and-Parrot-512.png](../assets/Lincoln-and-Parrot-512.png), and
+[Lincoln-and-Parrot-512-transparent.png](../assets/Lincoln-and-Parrot-512-transparent.png).
+Download these images to your local machine now to continue with the
+walkthrough.
 
-1.  Click on the _Image to Image_ tab icon, which is the second icon
-    from the top on the left-hand side of the screen. This will bring
-    you to a screen similar to the one shown here:
+1.  Click on the _Image to Image_ tab icon, which is the second icon from the
+    top on the left-hand side of the screen:
 
-    ![Invoke Web Server - Image to Image Tab](../assets/invoke-web-server-6.png){ width="640px" }
+    <figure markdown>
+    ![Invoke Web Server - Image to Image Icon](../assets/invoke-web-server-5.png)
+    </figure>
+
+    This will bring you to a screen similar to the one shown here:
+
+    <figure markdown>
+    ![Invoke Web Server - Image to Image Tab](../assets/invoke-web-server-6.png){:width="640px"}
+    </figure>
 
 2.  Drag-and-drop the Lincoln-and-Parrot image into the Image panel, or click
     the blank area to get an upload dialog. The image will load into an area
@@ -225,99 +255,120 @@ For this tutorial, we'll use the file named
     ![Invoke Web Server - Image to Image example](../assets/invoke-web-server-7.png){:width="640px"}
 
 4.  Experiment with the different settings. The most influential one in Image to
-    Image is _Denoising Strength_ located about midway down the control
+    Image is _Image to Image Strength_ located about midway down the control
     panel. By default it is set to 0.75, but can range from 0.0 to 0.99. The
     higher the value, the more of the original image the AI will replace. A
     value of 0 will leave the initial image completely unchanged, while 0.99
-    will replace it completely. However, the _Scheduler_ and _CFG Scale_ also
+    will replace it completely. However, the Sampler and CFG Scale also
     influence the final result. You can also generate variations in the same way
     as described in Text to Image.
 
-5.  What if we only want to change certain part(s) of the image and
-    leave the rest intact? This is called Inpainting, and you can do
-    it in the [Unified Canvas](UNIFIED_CANVAS.md). The Unified Canvas
-    also allows you to extend borders of the image and fill in the
-    blank areas, a process called outpainting.
+5.  What if we only want to change certain part(s) of the image and leave the
+    rest intact? This is called Inpainting, and a future version of the InvokeAI
+    web server will provide an interactive painting canvas on which you can
+    directly draw the areas you wish to Inpaint into. For now, you can achieve
+    this effect by using an external photoeditor tool to make one or more
+    regions of the image transparent as described in [INPAINTING.md] and
+    uploading that.
+
+    The file
+    [Lincoln-and-Parrot-512-transparent.png](../assets/Lincoln-and-Parrot-512-transparent.png)
+    is a version of the earlier image in which the area around the parrot has
+    been replaced with transparency. Click on the "x" in the upper right of the
+    Initial Image and upload the transparent version. Using the same prompt "old
+    sea captain with raven on shoulder" try Invoking an image. This time, only
+    the parrot will be replaced, leaving the rest of the original image intact:
+
+    <figure markdown>
+    ![Invoke Web Server - Inpainting](../assets/invoke-web-server-8.png){:width="640px"}
+    </figure>
 
 6.  Would you like to modify a previously-generated image using the Image to
-    Image facility? Easy! While in the Image to Image panel, drag and drop any
-	image in the gallery into the Initial Image area, and it will be ready for
-	use. You can do the same thing with the main image display. Click on the
-	_Send to_ icon to get a menu of
-	commands and choose "Send to Image to Image".
-	
-	![Send To Icon](../assets/send-to-icon.png) 
+    Image facility? Easy! While in the Image to Image panel, hover over any of
+    the gallery images to see a little menu of icons pop up. Click the picture
+    icon to instantly send the selected image to Image to Image as the initial
+    image.
 
-### Textual Inversion, LoRA and ControlNet
+You can do the same from the Text to Image tab by clicking on the picture icon
+above the central image panel. The screenshot below shows where the "use as
+initial image" icons are located.
 
-InvokeAI supports several different types of model files that
-extending the capabilities of the main model by adding artistic
-styles, special effects, or subjects. By mixing and matching textual
-inversion, LoRA and ControlNet models, you can achieve many
-interesting and beautiful effects.
+![Invoke Web Server - Use as Image Links](../assets/invoke-web-server-9.png){:width="640px"}
 
-We will give an example using a LoRA model named "Ink Scenery". This
-LoRA, which can be downloaded from Civitai (civitai.com), is
-specialized to paint landscapes that look like they were made with
-dripping india ink. To install this LoRA, we first download it and 
-put it into the `autoimport/lora` folder located inside the
-`invokeai` root directory. After restarting the web server, the
-LoRA will now become available for use.
+### Unified Canvas
 
-To see this LoRA at work, we'll first generate an image without it
-using the standard `stable-diffusion-v1-5` model. Choose this
-model and enter the prompt "mountains, ink". Here is a typical
-generated image, a mountain range rendered in ink and watercolor
-wash:
+See the [Unified Canvas Guide](UNIFIED_CANVAS.md)
 
-![Ink Scenery without LoRA](../assets/lora-example-0.png){ width=512px }
+## Reference
 
-Now let's install and activate the Ink Scenery LoRA. Go to
-https://civitai.com/models/78605/ink-scenery-or and download the LoRA
-model file to `invokeai/autoimport/lora` and restart the web
-server. (Alternatively, you can use [InvokeAI's Web Model
-Manager](../installation/050_INSTALLING_MODELS.md) to download and
-install the LoRA directly by typing its URL into the _Import
-Models_->_Location_ field).
+### Additional Options
 
-Scroll down the control panel until you get to the LoRA accordion
-section, and open it:
+| parameter <img width=160 align="right"> | effect                                                                                                                                     |
+| --------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| `--web_develop`                         | Starts the web server in development mode.                                                                                                 |
+| `--web_verbose`                         | Enables verbose logging                                                                                                                    |
+| `--cors [CORS ...]`                     | Additional allowed origins, comma-separated                                                                                                |
+| `--host HOST`                           | Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.                                  |
+| `--port PORT`                           | Web server: Port to listen on                                                                                                              |
+| `--certfile CERTFILE`                   | Web server: Path to certificate file to use for SSL. Use together with --keyfile                                                           |
+| `--keyfile KEYFILE`                     | Web server: Path to private key file to use for SSL. Use together with --certfile'                                                         |
+| `--gui`                                 | Start InvokeAI GUI - This is the "desktop mode" version of the web app. It uses Flask to create a desktop app experience of the webserver. |
 
-![LoRA Section](../assets/lora-example-1.png){ width=512px }
+### Web Specific Features
 
-Click the popup menu and select "Ink scenery". (If it isn't there, then
-the model wasn't installed to the right place, or perhaps you forgot
-to restart the web server.) The LoRA section will change to look like this:
+The web experience offers an incredibly easy-to-use experience for interacting
+with the InvokeAI toolkit. For detailed guidance on individual features, see the
+Feature-specific help documents available in this directory. Note that the
+latest functionality available in the CLI may not always be available in the Web
+interface.
 
-![LoRA Section Loaded](../assets/lora-example-2.png){ width=512px }
+#### Dark Mode & Light Mode
 
-Note that there is now a slider control for _Ink scenery_. The slider
-controls how much influence the LoRA model will have on the generated
-image.
+The InvokeAI interface is available in a nano-carbon black & purple Dark Mode,
+and a "burn your eyes out Nosferatu" Light Mode. These can be toggled by
+clicking the Sun/Moon icons at the top right of the interface.
 
-Run the "mountains, ink" prompt again and observe the change in style:
+![InvokeAI Web Server - Dark Mode](../assets/invoke_web_dark.png)
 
-![Ink Scenery](../assets/lora-example-3.png){ width=512px }
+![InvokeAI Web Server - Light Mode](../assets/invoke_web_light.png)
 
-Try adjusting the weight slider for larger and smaller weights and
-generate the image after each adjustment. The higher the weight, the
-more influence the LoRA will have.
+#### Invocation Toolbar
 
-To remove the LoRA completely, just click on its trash can icon.
+The left side of the InvokeAI interface is available for customizing the prompt
+and the settings used for invoking your new image. Typing your prompt into the
+open text field and clicking the Invoke button will produce the image based on
+the settings configured in the toolbar.
 
-Multiple LoRAs can be added simultaneously and combined with textual
-inversions and ControlNet models. Please see [Textual Inversions and
-LoRAs](CONCEPTS.md) and [Using ControlNet](CONTROLNET.md) for details.
+See below for additional documentation related to each feature:
 
-## Summary
+- [Variations](./VARIATIONS.md)
+- [Upscaling](./POSTPROCESS.md#upscaling)
+- [Image to Image](./IMG2IMG.md)
+- [Other](./OTHER.md)
 
-This walkthrough just skims the surface of the many things InvokeAI
-can do. Please see [Features](index.md) for more detailed reference
-guides.
+#### Invocation Gallery
+
+The currently selected --outdir (or the default outputs folder) will display all
+previously generated files on load. As new invocations are generated, these will
+be dynamically added to the gallery, and can be previewed by selecting them.
+Each image also has a simple set of actions (e.g., Delete, Use Seed, Use All
+Parameters, etc.) that can be accessed by hovering over the image.
+
+#### Image Workspace
+
+When an image from the Invocation Gallery is selected, or is generated, the
+image will be displayed within the center of the interface. A quickbar of common
+image interactions are displayed along the top of the image, including:
+
+- Use image in the `Image to Image` workflow
+- Initialize Face Restoration on the selected file
+- Initialize Upscaling on the selected file
+- View File metadata and details
+- Delete the file
 
 ## Acknowledgements
 
-A huge shout-out to the core team working to make the Web GUI a reality,
+A huge shout-out to the core team working to make this vision a reality,
 including [psychedelicious](https://github.com/psychedelicious),
 [Kyle0654](https://github.com/Kyle0654) and
 [blessedcoolant](https://github.com/blessedcoolant).

docs/features/index.md

@@ -4,9 +4,6 @@ title: Overview
 
 Here you can find the documentation for InvokeAI's various features.
 
-## The [Getting Started Guide](../help/gettingStartedWithAI) 
-A getting started guide for those new to AI image generation. 
-
 ## The Basics
 ### * The [Web User Interface](WEB.md)
 Guide to the Web interface. Also see the [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
@@ -20,12 +17,8 @@ a single convenient digital artist-optimized user interface.
 ### * [Prompt Engineering](PROMPTS.md)
 Get the images you want with the InvokeAI  prompt engineering language.
 
-### * The [LoRA, LyCORIS and Textual Inversion Models](CONCEPTS.md)
-Add custom subjects and styles using a variety of fine-tuned models.
-
-### * [ControlNet](CONTROLNET.md)
-Learn how to install and use ControlNet models for fine control over
-image output.
+## * The [Concepts Library](CONCEPTS.md)
+Add custom subjects and styles using HuggingFace's repository of embeddings.
 
 ### * [Image-to-Image Guide](IMG2IMG.md)
 Use a seed image to build new creations in the CLI.
@@ -36,28 +29,26 @@ are the ticket.
 
 ## Model Management
 
-### * [Model Installation](../installation/050_INSTALLING_MODELS.md)
+## * [Model Installation](../installation/050_INSTALLING_MODELS.md)
 Learn how to import third-party models and switch among them. This
 guide also covers optimizing models to load quickly.
 
-### * [Merging Models](MODEL_MERGING.md)
+## * [Merging Models](MODEL_MERGING.md)
 Teach an old model new tricks. Merge 2-3 models together to create a
 new model that combines characteristics of the originals.
 
-### * [Textual Inversion](TRAINING.md)
+## * [Textual Inversion](TEXTUAL_INVERSION.md)
 Personalize models by adding your own style or subjects.
 
-## Other Features
+# Other Features
 
-### * [The NSFW Checker](WATERMARK+NSFW.md)
+## * [The NSFW Checker](NSFW.md)
 Prevent InvokeAI from displaying unwanted racy images.
 
-### * [Controlling Logging](LOGGING.md)
+## * [Controlling Logging](LOGGING.md)
 Control how InvokeAI logs status messages.
 
-<!-- OUT OF DATE
-### * [Miscellaneous](OTHER.md)
+## * [Miscellaneous](OTHER.md)
 Run InvokeAI on Google Colab, generate images with repeating patterns,
 batch process a file of prompts, increase the "creativity" of image
 generation by adding initial noise, and more!
--->

docs/help/gettingStartedWithAI.md

@@ -1,95 +0,0 @@
-# Getting Started with AI Image Generation
-
-New to image generation with AI? You’re in the right place! 
-
-This is a high level walkthrough of some of the concepts and terms you’ll see as you start using InvokeAI. Please note, this is not an exhaustive guide and may be out of date due to the rapidly changing nature of the space. 
-
-## Using InvokeAI
-
-### **Prompt Crafting**
-
-- Prompts are the basis of using InvokeAI, providing the models directions on what to generate. As a general rule of thumb, the more detailed your prompt is, the better your result will be.
-
- *To get started, here’s an easy template to use for structuring your prompts:*
-
-- Subject, Style, Quality, Aesthetic
-    - **Subject:** What your image will be about. E.g. “a futuristic city with trains”,  “penguins floating on icebergs”, “friends sharing beers”
-    - **Style:** The style or medium in which your image will be in. E.g. “photograph”, “pencil sketch”, “oil paints”, or “pop art”, “cubism”, “abstract”
-    - **Quality:** A particular aspect or trait that you would like to see emphasized in your image. E.g. "award-winning", "featured in {relevant set of high quality works}", "professionally acclaimed". Many people often use "masterpiece".
-    - **Aesthetics:** The visual impact and design of the artwork. This can be colors, mood, lighting, setting, etc.
-- There are two prompt boxes: *Positive Prompt* & *Negative Prompt*.
-    - A **Positive** Prompt includes words you want the model to reference when creating an image.
-    - Negative Prompt is for anything you want the model to eliminate when creating an image. It doesn’t always interpret things exactly the way you would, but helps control the generation process. Always try to include a few terms - you can typically use lower quality image terms like “blurry” or “distorted” with good success.
-- Some examples prompts you can try on your own:
-    - A detailed oil painting of a tranquil forest at sunset with vibrant+ colors and soft, golden light filtering through the trees
-    - friends sharing beers in a busy city, realistic colored pencil sketch, twilight, masterpiece, bright, lively
-
-### Generation Workflows
-
-- Invoke offers a number of different workflows for interacting with models to produce images. Each is extremely powerful on its own, but together provide you an unparalleled way of producing high quality creative outputs that align with your vision.
-    - **Text to Image:** The text to image tab focuses on the key workflow of using a prompt to generate a new image. It includes other features that help control the generation process as well.
-    - **Image to Image:** With image to image, you provide an image as a reference (called the “initial image”), which provides more guidance around color and structure to the AI as it generates a new image. This is provided alongside the same features as Text to Image.
-    - **Unified Canvas:** The Unified Canvas is an advanced AI-first image editing tool that is easy to use, but hard to master. Drag an image onto the canvas from your gallery in order to regenerate certain elements, edit content or colors (known as inpainting), or extend the image with an exceptional degree of consistency and clarity (called outpainting).
-
-### Improving Image Quality
-
-- Fine tuning your prompt - the more specific you are, the closer the image will turn out to what is in your head!  Adding more details in the Positive Prompt or Negative Prompt can help add / remove pieces of your image to improve it - You can also use advanced techniques like upweighting and downweighting to control the influence of certain words. [Learn more here](https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#prompt-syntax-features).
-    - **Tip: If you’re seeing poor results, try adding the things you don’t like about the image to your negative prompt may help. E.g. distorted, low quality, unrealistic, etc.**
-- Explore different models - Other models can produce different results due to the data they’ve been trained on. Each model has specific language and settings it works best with; a model’s documentation is your friend here.  Play around with some and see what works best for you!
-- Increasing Steps - The number of steps used controls how much time the model is given to produce an image, and depends on the “Scheduler” used. The schedule controls how each step is processed by the model. More steps tends to mean better results, but will take longer - We recommend at least 30 steps for most
-- Tweak and Iterate - Remember, it’s best to change one thing at a time so you know what is working and what isn't. Sometimes you just need to try a new image, and other times using a new prompt might be the ticket. For testing, consider turning off the “random” Seed - Using the same seed with the same settings will produce the same image, which makes it the perfect way to learn exactly what your changes are doing.
-- Explore Advanced Settings - InvokeAI has a full suite of tools available to allow you complete control over your image creation process - Check out our [docs if you want to learn more](https://invoke-ai.github.io/InvokeAI/features/).
-
-
-## Terms & Concepts
-
-If you're interested in learning more, check out [this presentation](https://docs.google.com/presentation/d/1IO78i8oEXFTZ5peuHHYkVF-Y3e2M6iM5tCnc-YBfcCM/edit?usp=sharing) from one of our maintainers (@lstein). 
-
-### Stable Diffusion
-
-Stable Diffusion is deep learning, text-to-image model that is the foundation of the capabilities found in InvokeAI. Since the release of Stable Diffusion, there have been many subsequent models created based on Stable Diffusion that are designed to generate specific types of images. 
-
-### Prompts
-
-Prompts provide the models directions on what to generate. As a general rule of thumb, the more detailed your prompt is, the better your result will be.
-
-### Models
-
-Models are the magic that power InvokeAI. These files represent the output of training a machine on understanding massive amounts of images - providing them with the capability to generate new images using just a text description of what you’d like to see. (Like Stable Diffusion!)
-
-Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at ****. Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision!
-
-- *Models that contain “inpainting” in the name are designed for use with the inpainting feature of the Unified Canvas*
-
-### Scheduler
-
-Schedulers guide the process of removing noise (de-noising) from data. They determine:
-
-1. The number of steps to take to remove the noise.
-2. Whether the steps are random (stochastic) or predictable (deterministic).
-3. The specific method (algorithm) used for de-noising.
-
-Experimenting with different schedulers is recommended as each will produce different outputs!
-
-### Steps
-
-The number of de-noising steps each generation through. 
-
-Schedulers can be intricate and there's often a balance to strike between how quickly they can de-noise data and how well they can do it. It's typically advised to experiment with different schedulers to see which one gives the best results. There has been a lot written on the internet about different schedulers, as well as exploring what the right level of "steps" are for each. You can save generation time by reducing the number of steps used, but you'll want to make sure that you are satisfied with the quality of images produced!
-
-### Low-Rank Adaptations / LoRAs
-
-Low-Rank Adaptations (LoRAs) are like a smaller, more focused version of models, intended to focus on training a better understanding of how a specific character, style, or concept looks.
-
-### Textual Inversion Embeddings
-
-Textual Inversion Embeddings, like LoRAs, assist with more easily prompting for certain characters, styles, or concepts. However, embeddings are trained to update the relationship between a specific word (known as the “trigger”) and the intended output. 
-
-### ControlNet
-
-ControlNets are neural network models that are able to extract key features from an existing image and use these features to guide the output of the image generation model. 
-
-### VAE
-
-Variational auto-encoder (VAE) is a encode/decode model that translates the "latents" image produced during the image generation procees to the large pixel images that we see. 
-

docs/index.md

@@ -11,33 +11,6 @@ title: Home
   ```
 -->
 
-<!-- CSS styling -->
-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.1/css/fontawesome.min.css">
-<style>
-    .button {
-      width: 300px;
-      height: 50px;
-      background-color: #448AFF;
-      color: #fff;
-      font-size: 16px;
-      border: none;
-      cursor: pointer;
-      border-radius: 0.2rem;
-    }
-
-    .button-container {
-      display: grid;
-      grid-template-columns: repeat(3, 300px);
-      gap: 20px;
-    }
-
-    .button:hover {
-      background-color: #526CFE;
-    }
-</style>
-
-
-
 <div align="center" markdown>
 
 
@@ -51,7 +24,7 @@ title: Home
 
 [![CI checks on main badge]][ci checks on main link]
 [![CI checks on dev badge]][ci checks on dev link]
-<!-- [![latest commit to dev badge]][latest commit to dev link] -->
+[![latest commit to dev badge]][latest commit to dev link]
 
 [![github open issues badge]][github open issues link]
 [![github open prs badge]][github open prs link]
@@ -81,10 +54,10 @@ title: Home
 [github stars badge]:
   https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
 [github stars link]: https://github.com/invoke-ai/InvokeAI/stargazers
-<!-- [latest commit to dev badge]:
+[latest commit to dev badge]:
   https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
 [latest commit to dev link]:
-  https://github.com/invoke-ai/InvokeAI/commits/main -->
+  https://github.com/invoke-ai/InvokeAI/commits/development
 [latest release badge]:
   https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
 [latest release link]: https://github.com/invoke-ai/InvokeAI/releases
@@ -97,24 +70,61 @@ image-to-image generator. It provides a streamlined process with various new
 features and options to aid the image generation process. It runs on Windows,
 Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 
+**Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</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>]
+
 <div align="center"><img src="assets/invoke-web-server-1.png" width=640></div>
 
-!!! Note
+!!! note
 
-    This project is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates as it will help aid response time.
+    This fork is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
 
-## :octicons-link-24: Quick Links
+## :fontawesome-solid-computer: Hardware Requirements
 
-<div class="button-container">
-    <a href="installation/INSTALLATION"> <button class="button">Installation</button> </a>
-    <a href="features/"> <button class="button">Features</button> </a>
-    <a href="help/gettingStartedWithAI/"> <button class="button">Getting Started</button> </a>
-    <a href="contributing/CONTRIBUTING/"> <button class="button">Contributing</button> </a>
-    <a href="https://github.com/invoke-ai/InvokeAI/"> <button class="button">Code and Downloads</button> </a>
-    <a href="https://github.com/invoke-ai/InvokeAI/issues"> <button class="button">Bug Reports </button> </a>
-    <a href="https://discord.gg/ZmtBAhwWhy"> <button class="button"> Join the Discord Server!</button> </a>
-</div>
+### :octicons-cpu-24: System
 
+You wil need one of the following:
+
+- :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
+- :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux
+  only)
+- :fontawesome-brands-apple: An Apple computer with an M1 chip.
+
+We do **not recommend** the following video cards due to issues with their
+running in half-precision mode and having insufficient VRAM to render 512x512
+images in full-precision mode:
+
+- NVIDIA 10xx series cards such as the 1080ti
+- GTX 1650 series cards
+- GTX 1660 series cards
+
+### :fontawesome-solid-memory: Memory and Disk
+
+- At least 12 GB Main Memory RAM.
+- At least 18 GB of free disk space for the machine learning model, Python, and
+  all its dependencies.
+
+## :octicons-package-dependencies-24: Installation
+
+This fork is supported across Linux, Windows and Macintosh. Linux users can use
+either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
+driver).
+
+### [Installation Getting Started Guide](installation)
+#### [Automated Installer](installation/010_INSTALL_AUTOMATED.md)
+This method is recommended for 1st time users
+#### [Manual Installation](installation/020_INSTALL_MANUAL.md)
+This method is recommended for experienced users and developers
+#### [Docker Installation](installation/040_INSTALL_DOCKER.md)
+This method is recommended for those familiar with running Docker containers
+### Other Installation Guides
+  - [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
+  - [XFormers](installation/070_INSTALL_XFORMERS.md)
+  - [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
+  - [Installing New Models](installation/050_INSTALLING_MODELS.md)
 
 ## :octicons-gift-24: InvokeAI Features
 
@@ -135,17 +145,14 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
 ### Model Management
 - [Installing](installation/050_INSTALLING_MODELS.md)
 - [Model Merging](features/MODEL_MERGING.md)
-- [ControlNet Models](features/CONTROLNET.md)
 - [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
-- [Watermarking and the Not Safe for Work (NSFW) Checker](features/WATERMARK+NSFW.md)
+- [Textual Inversion](features/TEXTUAL_INVERSION.md)
+- [Not Safe for Work (NSFW) Checker](features/NSFW.md)
 <!-- seperator -->
 ### Prompt Engineering
 - [Prompt Syntax](features/PROMPTS.md)
 - [Generating Variations](features/VARIATIONS.md)
 
-### InvokeAI Configuration
-- [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
-
 ## :octicons-log-16: Important Changes Since Version 2.3
 
 ### Nodes
@@ -212,14 +219,18 @@ get solutions for common installation problems and other issues.
 
 Anyone who wishes to contribute to this project, whether documentation,
 features, bug fixes, code cleanup, testing, or code reviews, is very much
-encouraged to do so. 
+encouraged to do so. If you are unfamiliar with how to contribute to GitHub
+projects, here is a
+[Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github).
 
-[Please take a look at our Contribution documentation to learn more about contributing to InvokeAI. 
-](contributing/CONTRIBUTING.md)
+A full set of contribution guidelines, along with templates, are in progress,
+but for now the most important thing is to **make your pull request against the
+"development" branch**, and not against "main". This will help keep public
+breakage to a minimum and will allow you to propose more radical changes.
 
 ## :octicons-person-24: Contributors
 
-This software is a combined effort of various people from across the world.
+This fork is a combined effort of various people from across the world.
 [Check out the list of all these amazing people](other/CONTRIBUTORS.md). We
 thank them for their time, hard work and effort.
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -40,8 +40,10 @@ experimental versions later.
     this, open up a command-line window ("Terminal" on Linux and
     Macintosh, "Command" or "Powershell" on Windows) and type `python
     --version`. If Python is installed, it will print out the version
-    number. If it is version `3.9.*`, `3.10.*` or `3.11.*` you meet
-    requirements.
+    number. If it is version `3.9.*` or `3.10.*`, you meet
+    requirements. We do not recommend using Python 3.11 or higher,
+    as not all the libraries that InvokeAI depends on work properly
+    with this version.
 
     !!! warning "What to do if you have an unsupported version"
 
@@ -122,9 +124,9 @@ experimental versions later.
     [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest),
     and look for a file named:
 
-    - InvokeAI-installer-v3.X.X.zip
+    - InvokeAI-installer-v2.X.X.zip
 
-    where "3.X.X" is the latest released version. The file is located
+    where "2.X.X" is the latest released version. The file is located
     at the very bottom of the release page, under **Assets**.
 
 4.  **Unpack the installer**: Unpack the zip file into a convenient directory. This will create a new
@@ -213,6 +215,17 @@ experimental versions later.
     Generally the defaults are fine, and you can come back to this screen at
     any time to tweak your system. Here are the options you can adjust:
 
+    - ***Output directory for images***
+      This is the path to a directory in which InvokeAI will store all its
+      generated images.
+
+    - ***NSFW checker***
+      If checked, InvokeAI will test images for potential sexual content
+      and blur them out if found. Note that the NSFW checker consumes
+      an additional 0.6 GB of VRAM on top of the 2-3 GB of VRAM used
+      by most image models. If you have a low VRAM GPU (4-6 GB), you
+      can reduce out of memory errors by disabling the checker.
+
     - ***HuggingFace Access Token***
       InvokeAI has the ability to download embedded styles and subjects
       from the HuggingFace Concept Library on-demand. However, some of
@@ -244,30 +257,20 @@ experimental versions later.
 	and graphics cards. The "autocast" option is deprecated and
 	shouldn't be used unless you are asked to by a member of the team.
 
-    - **Size of the RAM cache used for fast model switching***
+    - ***Number of models to cache in CPU memory***
         This allows you to keep models in memory and switch rapidly among
 	them rather than having them load from disk each time. This slider
-	controls how many models to keep loaded at once. A typical SD-1 or SD-2 model
-	uses 2-3 GB of memory. A typical SDXL model uses 6-7 GB. Providing more
-	RAM will allow more models to be co-resident.
+	controls how many models to keep loaded at once. Each
+	model will use 2-4 GB of RAM, so use this cautiously
 
-    - ***Output directory for images***
-      This is the path to a directory in which InvokeAI will store all its
-      generated images.
-
-    - ***Autoimport Folder***
-      This is the directory in which you can place models you have
-	  downloaded and wish to load into InvokeAI. You can place a variety
-	  of models in this directory, including diffusers folders, .ckpt files,
-	  .safetensors files, as well as LoRAs, ControlNet and Textual Inversion
-	  files (both folder and file versions). To help organize this folder,
-	  you can create several levels of subfolders and drop your models into
-      whichever ones you want.
-	
-    - ***Autoimport FolderLICENSE***     
+    - ***Directory containing embedding/textual inversion files***
+        This is the directory in which you can place custom embedding
+	files (.pt or .bin). During startup, this directory will be
+	scanned and InvokeAI will print out the text terms that
+	are available to trigger the embeddings.
 
     At the bottom of the screen you will see a checkbox for accepting
-    the CreativeML Responsible AI Licenses. You need to accept the license
+    the CreativeML Responsible AI License. You need to accept the license
     in order to download Stable Diffusion models from the next screen.
 
     _You can come back to the startup options form_ as many times as you like.
@@ -351,8 +354,8 @@ experimental versions later.
 
 12. **InvokeAI Options**: You can launch InvokeAI with several different command-line arguments that
     customize its behavior. For example, you can change the location of the
-    image output directory or balance memory usage vs performance. See
-    [Configuration](../features/CONFIGURATION.md) for a full list of the options.
+    image output directory, or select your favorite sampler. See the
+    [Command-Line Interface](../features/CLI.md) for a full list of the options.
 
     - To set defaults that will take effect every time you launch InvokeAI,
       use a text editor (e.g. Notepad) to exit the file
@@ -372,71 +375,8 @@ experimental versions later.
         Once InvokeAI is installed, do not move or remove this directory."
 
 
-<a name="troubleshooting"></a>
 ## Troubleshooting
 
-### _OSErrors on Windows while installing dependencies_
-
-During a zip file installation or an online update, installation stops
-with an error like this:
-
-![broken-dependency-screenshot](../assets/troubleshooting/broken-dependency.png){:width="800px"}
-
-This seems to happen particularly often with the `pydantic` and
-`numpy` packages. The most reliable solution requires several manual
-steps to complete installation.
-
-Open up a Powershell window and navigate to the `invokeai` directory
-created by the installer. Then give the following series of commands:
-
-```cmd
-rm .\.venv -r -force
-python -mvenv .venv
-.\.venv\Scripts\activate
-pip install invokeai
-invokeai-configure --yes --root .
-```
-
-If you see anything marked as an error during this process please stop
-and seek help on the Discord [installation support
-channel](https://discord.com/channels/1020123559063990373/1041391462190956654). A
-few warning messages are OK.
-
-If you are updating from a previous version, this should restore your
-system to a working state. If you are installing from scratch, there
-is one additional command to give:
-
-```cmd
-wget -O invoke.bat https://raw.githubusercontent.com/invoke-ai/InvokeAI/main/installer/templates/invoke.bat.in
-```
-
-This will create the `invoke.bat` script needed to launch InvokeAI and
-its related programs.
-
-
-### _Stable Diffusion XL Generation Fails after Trying to Load unet_
-
-InvokeAI is working in other respects, but when trying to generate
-images with Stable Diffusion XL you get a "Server Error". The text log
-in the launch window contains this log line above several more lines of
-error messages:
-
-```INFO --> Loading model:D:\LONG\PATH\TO\MODEL, type sdxl:main:unet```
-
-This failure mode occurs when there is a network glitch during
-downloading the very large SDXL model.
-
-To address this, first go to the Web Model Manager and delete the
-Stable-Diffusion-XL-base-1.X model. Then navigate to HuggingFace and
-manually download the .safetensors version of the model. The 1.0
-version is located at
-https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main
-and the file is named `sd_xl_base_1.0.safetensors`.
-
-Save this file to disk and then reenter the Model Manager. Navigate to
-Import Models->Add Model, then type (or drag-and-drop) the path to the
-.safetensors file. Press "Add Model".
-
 ### _Package dependency conflicts_
 
 If you have previously installed InvokeAI or another Stable Diffusion

docs/installation/020_INSTALL_MANUAL.md

@@ -32,7 +32,7 @@ gaming):
 
 * **Python**
 
-    version 3.9 through 3.11
+    version 3.9 or 3.10 (3.11 is not recommended).
 
 * **CUDA Tools**
 
@@ -65,7 +65,7 @@ gaming):
 To install InvokeAI with virtual environments and the PIP package
 manager, please follow these steps:
 
-1.  Please make sure you are using Python 3.9 through 3.11. The rest of the install
+1.  Please make sure you are using Python 3.9 or 3.10. The rest of the install
     procedure depends on this and will not work with other versions:
 
     ```bash
@@ -256,7 +256,7 @@ manager, please follow these steps:
 
 10.  Render away!
 
-    Browse the [features](../features/index.md) section to learn about all the
+    Browse the [features](../features/CLI.md) section to learn about all the
     things you can do with InvokeAI.
 
 
@@ -270,7 +270,7 @@ manager, please follow these steps:
 
 12. Other scripts
 
-    The [Textual Inversion](../features/TRAINING.md) script can be launched with the command:
+    The [Textual Inversion](../features/TEXTUAL_INVERSION.md) script can be launched with the command:
 
     ```bash
     invokeai-ti --gui

docs/installation/050_INSTALLING_MODELS.md

@@ -43,7 +43,24 @@ InvokeAI comes with support for a good set of starter models. You'll
 find them listed in the master models file
 `configs/INITIAL_MODELS.yaml` in the InvokeAI root directory. The
 subset that are currently installed are found in
-`configs/models.yaml`.
+`configs/models.yaml`. As of v2.3.1, the list of starter models is:
+
+|Model Name | HuggingFace Repo ID | Description | URL |
+|---------- | ---------- | ----------- | --- |
+|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 |
+|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 |
+|dreamlike-photoreal-2.0|dreamlike-art/dreamlike-photoreal-2.0|A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)|https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0 |
+|inkpunk-1.0|Envvi/Inkpunk-Diffusion|Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)|https://huggingface.co/Envvi/Inkpunk-Diffusion |
+|openjourney-4.0|prompthero/openjourney|An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)|https://huggingface.co/prompthero/openjourney |
+|portrait-plus-1.0|wavymulder/portraitplus|An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)|https://huggingface.co/wavymulder/portraitplus |
+|seek-art-mega-1.0|coreco/seek.art_MEGA|A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)|https://huggingface.co/coreco/seek.art_MEGA |
+|trinart-2.0|naclbit/trinart_stable_diffusion_v2|An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)|https://huggingface.co/naclbit/trinart_stable_diffusion_v2 |
+|waifu-diffusion-1.4|hakurei/waifu-diffusion|An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)|https://huggingface.co/hakurei/waifu-diffusion |
 
 Note that these files are covered by an "Ethical AI" license which
 forbids certain uses. When you initially download them, you are asked
@@ -54,7 +71,8 @@ with the model terms by visiting the URLs in the table above.
 
 ## Community-Contributed Models
 
-[HuggingFace](https://huggingface.co/models?library=diffusers)
+There are too many to list here and more are being contributed every
+day.  [HuggingFace](https://huggingface.co/models?library=diffusers)
 is a great resource for diffusers models, and is also the home of a
 [fast-growing repository](https://huggingface.co/sd-concepts-library)
 of embedding (".bin") models that add subjects and/or styles to your
@@ -68,106 +86,310 @@ only `.safetensors` and `.ckpt` models, but they can be easily loaded
 into InvokeAI and/or converted into optimized `diffusers` models. Be
 aware that CIVITAI hosts many models that generate NSFW content.
 
+!!! note
+
+    InvokeAI 2.3.x does not support directly importing and
+    running Stable Diffusion version 2 checkpoint models. You may instead
+    convert them into `diffusers` models using the conversion methods
+    described below.
+
 ## Installation
 
-There are two ways to install and manage models:
+There are multiple ways to install and manage models:
 
-1. The `invokeai-model-install` script which will download and install
-them for you.  In addition to supporting main models, you can install
-ControlNet, LoRA and Textual Inversion models.
+1. The `invokeai-configure` script which will download and install them for you.
 
-2. The web interface (WebUI) has a GUI for importing and managing
+2. The command-line tool (CLI) has commands that allows you to import, configure and modify
+   models files.
+
+3. The web interface (WebUI) has a GUI for importing and managing
    models.
 
-3. By placing models (or symbolic links to models) inside one of the
-InvokeAI root directory's `autoimport` folder.
+### Installation via `invokeai-configure`
 
-### Installation via `invokeai-model-install`
+From the `invoke` launcher, choose option (6) "re-run the configure
+script to download new models." This will launch the same script that
+prompted you to select models at install time. You can use this to add
+models that you skipped the first time around. It is all right to
+specify a model that was previously downloaded; the script will just
+confirm that the files are complete.
 
-From the `invoke` launcher, choose option [5] "Download and install
-models." This will launch the same script that prompted you to select
-models at install time. You can use this to add models that you
-skipped the first time around. It is all right to specify a model that
-was previously downloaded; the script will just confirm that the files
-are complete.
+### Installation via the CLI
 
-The installer has different panels for installing main models from
-HuggingFace, models from Civitai and other arbitrary web sites,
-ControlNet models, LoRA/LyCORIS models, and Textual Inversion
-embeddings. Each section has a text box in which you can enter a new
-model to install. You can refer to a model using its:
+You can install a new model, including any of the community-supported ones, via
+the command-line client's `!import_model` command.
 
-1. Local path to the .ckpt, .safetensors or diffusers folder on your local machine
-2. A directory on your machine that contains multiple models
-3. A URL that points to a downloadable model
-4. A HuggingFace repo id
+#### Installing individual `.ckpt` and `.safetensors` models
 
-Previously-installed models are shown with checkboxes. Uncheck a box
-to unregister the model from InvokeAI. Models that are physically
-installed inside the InvokeAI root directory will be deleted and
-purged (after a confirmation warning). Models that are located outside
-the InvokeAI root directory will be unregistered but not deleted.
+If the model is already downloaded to your local disk, use
+`!import_model /path/to/file.ckpt` to load it. For example:
 
-Note: The installer script uses a console-based text interface that requires
-significant amounts of horizontal and vertical space. If the display
-looks messed up, just enlarge the terminal window and/or relaunch the
-script.
-
-If you wish you can script model addition and deletion, as well as
-listing installed models. Start the "developer's console" and give the
-command `invokeai-model-install --help`. This will give you a series
-of command-line parameters that will let you control model
-installation. Examples:
-
-```
-# (list all controlnet models)
-invokeai-model-install --list controlnet
-
-# (install the model at the indicated URL)
-invokeai-model-install --add http://civitai.com/2860
-
-# (delete the named model)
-invokeai-model-install --delete sd-1/main/analog-diffusion
+```bash
+invoke> !import_model C:/Users/fred/Downloads/martians.safetensors
 ```
 
-### Installation via the Web GUI
+!!! tip "Forward Slashes"
+    On Windows systems, use forward slashes rather than backslashes
+    in your file paths.
+    If you do use backslashes,
+    you must double them like this:
+    `C:\\Users\\fred\\Downloads\\martians.safetensors`
 
-To install a new model using the Web GUI, do the following:
+Alternatively you can directly import the file using its URL:
 
-1. Open the InvokeAI Model Manager (cube at the bottom of the
-left-hand panel) and navigate to *Import Models*
+```bash
+invoke> !import_model https://example.org/sd_models/martians.safetensors
+```
 
-2. In the field labeled *Location* type in the path to the model you
-wish to install. You may use a URL, HuggingFace repo id, or a path on
-your local disk.
+For this to work, the URL must not be password-protected. Otherwise
+you will receive a 404 error.
 
-3. Alternatively, the *Scan for Models* button allows you to paste in
-the path to a folder somewhere on your machine. It will be scanned for
-importable models and prompt you to add the ones of your choice.
+When you import a legacy model, the CLI will first ask you what type
+of model this is. You can indicate whether it is a model based on
+Stable Diffusion 1.x (1.4 or 1.5), one based on Stable Diffusion 2.x,
+or a 1.x inpainting model. Be careful to indicate the correct model
+type, or it will not load correctly. You can correct the model type
+after the fact using the `!edit_model` command.
 
-4. Press *Add Model* and wait for confirmation that the model
-was added.
+The system will then ask you a few other questions about the model,
+including what size image it was trained on (usually 512x512), what
+name and description you wish to use for it, and whether you would
+like to install a custom VAE (variable autoencoder) file for the
+model. For recent models, the answer to the VAE question is usually
+"no," but it won't hurt to answer "yes".
 
-To delete a model, Select *Model Manager* to list all the currently
-installed models. Press the trash can icons to delete any models you
-wish to get rid of. Models whose weights are located inside the
-InvokeAI `models` directory will be purged from disk, while those
-located outside will be unregistered from InvokeAI, but not deleted.
+After importing, the model will load. If this is successful, you will
+be asked if you want to keep the model loaded in memory to start
+generating immediately. You'll also be asked if you wish to make this
+the default model on startup. You can change this later using
+`!edit_model`.
 
-You can see where model weights are located by clicking on the model name.
-This will bring up an editable info panel showing the model's characteristics,
-including the `Model Location` of its files.
+#### Importing a batch of `.ckpt` and `.safetensors` models from a directory
 
-### Installation via the `autoimport` function
+You may also point `!import_model` to a directory containing a set of
+`.ckpt` or `.safetensors` files. They will be imported _en masse_.
 
-In the InvokeAI root directory you will find a series of folders under
-`autoimport`, one each for main models, controlnets, embeddings and
-Loras.  Any models that you add to these directories will be scanned
-at startup time and registered automatically.
+!!! example
 
-You may create symbolic links from these folders to models located
-elsewhere on disk and they will be autoimported. You can also create
-subfolders and organize them as you wish.
+    ```console
+    invoke> !import_model C:/Users/fred/Downloads/civitai_models/
+    ```
 
-The location of the autoimport directories are controlled by settings
-in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).
+You will be given the option to import all models found in the
+directory, or select which ones to import. If there are subfolders
+within the directory, they will be searched for models to import.
+
+#### Installing `diffusers` models
+
+You can install a `diffusers` model from the HuggingFace site using
+`!import_model` and the HuggingFace repo_id for the model:
+
+```bash
+invoke> !import_model andite/anything-v4.0
+```
+
+Alternatively, you can download the model to disk and import it from
+there. The model may be distributed as a ZIP file, or as a Git
+repository:
+
+```bash
+invoke> !import_model C:/Users/fred/Downloads/andite--anything-v4.0
+```
+
+!!! tip "The CLI supports file path autocompletion"
+         Type a bit of the path name and hit ++tab++ in order to get a choice of
+         possible completions.
+
+!!! tip "On Windows, you can drag model files onto the command-line"
+         Once you have typed in `!import_model `, you can drag the
+         model  file or directory onto the command-line to insert the model path. This way, you don't need to
+         type it or copy/paste. However, you will need to reverse or
+         double backslashes as noted above.
+
+Before installing, the CLI will ask you for a short name and
+description for the model, whether to make this the default model that
+is loaded at InvokeAI startup time, and whether to replace its
+VAE. Generally the answer to the latter question is "no".
+
+### Converting legacy models into `diffusers`
+
+The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
+models file into `diffusers` and install it.This will enable the model
+to load and run faster without loss of image quality.
+
+The usage is identical to `!import_model`. You may point the command
+to either a downloaded model file on disk, or to a (non-password
+protected) URL:
+
+```bash
+invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
+```
+
+After a successful conversion, the CLI will offer you the option of
+deleting the original `.ckpt` or `.safetensors` file.
+
+### Optimizing a previously-installed model
+
+Lastly, if you have previously installed a `.ckpt` or `.safetensors`
+file and wish to convert it into a `diffusers` model, you can do this
+without re-downloading and converting the original file using the
+`!optimize_model` command. Simply pass the short name of an existing
+installed model:
+
+```bash
+invoke> !optimize_model martians-v1.0
+```
+
+The model will be converted into `diffusers` format and replace the
+previously installed version. You will again be offered the
+opportunity to delete the original `.ckpt` or `.safetensors` file.
+
+### Related CLI Commands
+
+There are a whole series of additional model management commands in
+the CLI that you can read about in [Command-Line
+Interface](../features/CLI.md). These include:
+
+* `!models` - List all installed models
+* `!switch <model name>` - Switch to the indicated model
+* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
+* `!del_model <model name>` - Delete the indicated model
+
+### Manually editing `configs/models.yaml`
+
+
+If you are comfortable with a text editor then you may simply edit `models.yaml`
+directly.
+
+You will need to download the desired `.ckpt/.safetensors` file and
+place it somewhere on your machine's filesystem. Alternatively, for a
+`diffusers` model, record the repo_id or download the whole model
+directory. Then using a **text** editor (e.g. the Windows Notepad
+application), open the file `configs/models.yaml`, and add a new
+stanza that follows this model:
+
+#### A legacy model
+
+A legacy `.ckpt` or `.safetensors` entry will look like this:
+
+```yaml
+arabian-nights-1.0:
+  description: A great fine-tune in Arabian Nights style
+  weights: ./path/to/arabian-nights-1.0.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  format: ckpt
+  width: 512
+  height: 512
+  default: false
+```
+
+Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
+
+#### A diffusers model
+
+A stanza for a `diffusers` model will look like this for a HuggingFace
+model with a repository ID:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  repo_id: captahab/arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+And for a downloaded directory:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  path: /path/to/captahab-arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+There is additional syntax for indicating an external VAE to use with
+this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
+
+After you save the modified `models.yaml` file relaunch
+`invokeai`. The new model will now be available for your use.
+
+### Installation via the WebUI
+
+To access the WebUI Model Manager, click on the button that looks like
+a cube in the upper right side of the browser screen. This will bring
+up a dialogue that lists the models you have already installed, and
+allows you to load, delete or edit them:
+
+<figure markdown>
+
+![model-manager](../assets/installing-models/webui-models-1.png)
+
+</figure>
+
+To add a new model, click on **+ Add New** and select to either a
+checkpoint/safetensors model, or a diffusers model:
+
+<figure markdown>
+
+![model-manager-add-new](../assets/installing-models/webui-models-2.png)
+
+</figure>
+
+In this example, we chose **Add Diffusers**. As shown in the figure
+below, a new dialogue prompts you to enter the name to use for the
+model, its description, and either the location of the `diffusers`
+model on disk, or its Repo ID on the HuggingFace web site. If you
+choose to enter a path to disk, the system will autocomplete for you
+as you type:
+
+<figure markdown>
+
+![model-manager-add-diffusers](../assets/installing-models/webui-models-3.png)
+
+</figure>
+
+Press **Add Model** at the bottom of the dialogue (scrolled out of
+site in the figure), and the model will be downloaded, imported, and
+registered in `models.yaml`.
+
+The **Add Checkpoint/Safetensor Model** option is similar, except that
+in this case you can choose to scan an entire folder for
+checkpoint/safetensors files to import. Simply type in the path of the
+directory and press the "Search" icon. This will display the
+`.ckpt` and `.safetensors` found inside the directory and its
+subfolders, and allow you to choose which ones to import:
+
+<figure markdown>
+
+![model-manager-add-checkpoint](../assets/installing-models/webui-models-4.png)
+
+</figure>
+
+## Model Management Startup Options
+
+The `invoke` launcher and the `invokeai` script accept a series of
+command-line arguments that modify InvokeAI's behavior when loading
+models. These can be provided on the command line, or added to the
+InvokeAI root directory's `invokeai.init` initialization file.
+
+The arguments are:
+
+* `--model <model name>` -- Start up with the indicated model loaded
+* `--ckpt_convert` -- When a checkpoint/safetensors model is loaded, convert it into a `diffusers` model in memory. This does not permanently save the converted model to disk.
+* `--autoconvert <path/to/directory>` -- Scan the indicated directory path for new checkpoint/safetensors files, convert them into `diffusers` models, and import them into InvokeAI.
+
+Here is an example of providing an argument on the command line using
+the `invoke.sh` launch script:
+
+```bash
+invoke.sh --autoconvert /home/fred/stable-diffusion-checkpoints
+```
+
+And here is what the same argument looks like in `invokeai.init`:
+
+```bash
+--outdir="/home/fred/invokeai/outputs
+--no-nsfw_checker
+--autoconvert /home/fred/stable-diffusion-checkpoints
+```

docs/installation/index.md

@@ -1,4 +1,6 @@
-# Overview
+---
+title: Overview
+---
 
 We offer several ways to install InvokeAI, each one suited to your
 experience and preferences. We suggest that everyone start by
@@ -13,57 +15,7 @@ See the [troubleshooting
 section](010_INSTALL_AUTOMATED.md#troubleshooting) of the automated
 install guide for frequently-encountered installation issues.
 
-This fork is supported across Linux, Windows and Macintosh. Linux users can use
-either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
-driver).
-
-### [Installation Getting Started Guide](installation)
-#### **[Automated Installer](010_INSTALL_AUTOMATED.md)**
-✅ This is the recommended installation method for first-time users. 
-#### [Manual Installation](020_INSTALL_MANUAL.md)
-This method is recommended for experienced users and developers
-#### [Docker Installation](040_INSTALL_DOCKER.md)
-This method is recommended for those familiar with running Docker containers
-### Other Installation Guides
-  - [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
-  - [XFormers](installation/070_INSTALL_XFORMERS.md)
-  - [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
-  - [Installing New Models](installation/050_INSTALLING_MODELS.md)
-
-## :fontawesome-solid-computer: Hardware Requirements
-
-### :octicons-cpu-24: System
-
-You wil need one of the following:
-
-- :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
-- :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux
-  only)
-- :fontawesome-brands-apple: An Apple computer with an M1 chip.
-
-** SDXL 1.0 Requirements*
-To use SDXL, user must have one of the following: 
-- :simple-nvidia: An NVIDIA-based graphics card with 8 GB or more VRAM memory.
-- :simple-amd: An AMD-based graphics card with 16 GB or more VRAM memory (Linux
-  only)
-- :fontawesome-brands-apple: An Apple computer with an M1 chip.
-
-
-### :fontawesome-solid-memory: Memory and Disk
-
-- At least 12 GB Main Memory RAM.
-- At least 18 GB of free disk space for the machine learning model, Python, and
-  all its dependencies.
-
-We do **not recommend** the following video cards due to issues with their
-running in half-precision mode and having insufficient VRAM to render 512x512
-images in full-precision mode:
-
-- NVIDIA 10xx series cards such as the 1080ti
-- GTX 1650 series cards
-- GTX 1660 series cards
-
-## Installation options
+## Main Application
 
 1. [Automated Installer](010_INSTALL_AUTOMATED.md)
 
@@ -72,9 +24,6 @@ images in full-precision mode:
     "developer console" which will help us debug problems with you and
     give you to access experimental features.
 
-
-    ✅ This is the recommended option for first time users. 
-
 2. [Manual Installation](020_INSTALL_MANUAL.md)
 
     In this method you will manually run the commands needed to install

docs/nodes/communityNodes.md

@@ -1,53 +0,0 @@
-# Community Nodes
-
-These are nodes that have been developed by the community, for the community. If you're not sure what a node is, you can learn more about nodes [here](overview.md).
-
-If you'd like to submit a node for the community, please refer to the [node creation overview](./overview.md#contributing-nodes).
-
-To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations/` folder in your Invoke AI install location. Along with the node, an example node graph should be provided to help you get started with the node. 
-
-To use a community node graph, download the the `.json` node graph file and load it into Invoke AI via the **Load Nodes** button on the Node Editor. 
-
-## Disclaimer
-
-The nodes linked below have been developed and contributed by members of the Invoke AI community. While we strive to ensure the quality and safety of these contributions, we do not guarantee the reliability or security of the nodes. If you have issues or concerns with any of the nodes below, please raise it on GitHub or in the Discord.
-
-## List of Nodes
-
-### FaceTools
-
-**Description:** FaceTools is a collection of nodes created to manipulate faces as you would in Unified Canvas. It includes FaceMask, FaceOff, and FacePlace. FaceMask autodetects a face in the image using MediaPipe and creates a mask from it. FaceOff similarly detects a face, then takes the face off of the image by adding a square bounding box around it and cropping/scaling it. FacePlace puts the bounded face image from FaceOff back onto the original image. Using these nodes with other inpainting node(s), you can put new faces on existing things, put new things around existing faces, and work closer with a face as a bounded image. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control on FaceMask and FaceOff. See GitHub repository below for usage examples.
-
-**Node Link:** https://github.com/ymgenesis/FaceTools/
-
-**FaceMask Output Examples** 
-
-![5cc8abce-53b0-487a-b891-3bf94dcc8960](https://github.com/invoke-ai/InvokeAI/assets/25252829/43f36d24-1429-4ab1-bd06-a4bedfe0955e)
-![b920b710-1882-49a0-8d02-82dff2cca907](https://github.com/invoke-ai/InvokeAI/assets/25252829/7660c1ed-bf7d-4d0a-947f-1fc1679557ba)
-![71a91805-fda5-481c-b380-264665703133](https://github.com/invoke-ai/InvokeAI/assets/25252829/f8f6a2ee-2b68-4482-87da-b90221d5c3e2)
-
-<hr>
-
-### Ideal Size
-
-**Description:** This node calculates an ideal image size for a first pass of a multi-pass upscaling. The aim is to avoid duplication that results from choosing a size larger than the model is capable of.
-
-**Node Link:** https://github.com/JPPhoto/ideal-size-node
-
---------------------------------
-### Example Node Template
-
-**Description:** This node allows you to do super cool things with InvokeAI.
-
-**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
-
-**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
-
-**Output Examples** 
-
-![Example Image](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png){: style="height:115px;width:240px"}
-
-## Help
-If you run into any issues with a node, please post in the [InvokeAI Discord](https://discord.gg/ZmtBAhwWhy). 
-
-

docs/nodes/overview.md

@@ -1,42 +0,0 @@
-# Nodes
-
-## What are Nodes?
-An Node is simply a single operation that takes in some inputs and gives
-out some outputs. We can then chain multiple nodes together to create more
-complex functionality. All InvokeAI features are added through nodes.
-
-This means nodes can be used to easily extend the image generation capabilities of InvokeAI, and allow you build workflows to suit your needs. 
-
-You can read more about nodes and the node editor [here](../features/NODES.md). 
-
-
-## Downloading Nodes
-To download a new node, visit our list of [Community Nodes](communityNodes.md). These are nodes that have been created by the community, for the community. 
-
-
-## Contributing Nodes
-
-To learn about creating a new node, please visit our [Node creation documenation](../contributing/INVOCATIONS.md). 
-
-Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps: 
-* Make sure the node is contained in a new Python (.py) file 
-* Submit a pull request with a link to your node in GitHub against the `nodes` branch to add the node to the [Community Nodes](Community Nodes) list
-    * Make sure you are following the template below and have provided all relevant details about the node and what it does.
-* A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you might be asked for permission to include it in the core project.
-
-### Community Node Template
-
-```markdown
---------------------------------
-### Super Cool Node Template
-
-**Description:** This node allows you to do super cool things with InvokeAI.
-
-**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
-
-**Example Node Graph:**  https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
-
-**Output Examples** 
-
-![InvokeAI](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png)
-```

docs/other/CONTRIBUTORS.md

@@ -17,267 +17,67 @@ We thank them for all of their time and hard work.
 
 * @lstein (Lincoln Stein) - Co-maintainer
 * @blessedcoolant - Co-maintainer
-* @hipsterusername (Kent Keirsey) - Co-maintainer, CEO, Positive Vibes
-* @psychedelicious (Spencer Mabrito) - Web Team Leader
+* @hipsterusername (Kent Keirsey) - Product Manager
+* @psychedelicious - Web Team Leader
 * @Kyle0654 (Kyle Schouviller) - Node Architect and General Backend Wizard
-* @damian0815 - Attention Systems and Compel Maintainer
+* @damian0815 - Attention Systems and Gameplay Engineer
+* @mauwii (Matthias Wild) - Continuous integration and product maintenance engineer
+* @Netsvetaev (Artur Netsvetaev) - UI/UX Developer
+* @tildebyte - General gadfly and resident (self-appointed) know-it-all
+* @keturn - Lead for Diffusers port
 * @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
-* @genomancer (Gregg Helt) - Controlnet support
-* @StAlKeR7779 (Sergey Borisov) - Torch stack, ONNX, model management, optimization
-* @cheerio (Mary Rogers) - Lead Engineer & Web App Development
-* @brandon (Brandon Rising) - Platform, Infrastructure, Backend Systems
-* @ryanjdick (Ryan Dick) - Machine Learning & Training
-* @millu (Millun Atluri) - Community Manager, Documentation, Node-wrangler
-* @chainchompa (Jennifer Player) - Web Development & Chain-Chomping
-* @keturn (Kevin Turner) - Diffusers
-* @gogurt enjoyer - Discord moderator and end user support
-* @whosawhatsis - Discord moderator and end user support
-* @dwinrger - Discord moderator and end user support
-* @526christian - Discord moderator and end user support
+* @jpphoto (Jonathan Pollack) - Inference and rendering engine optimization
+* @genomancer (Gregg Helt) - Model training and merging
 
-## **Full List of Contributors by Commit Name**
+## **Contributions by**
 
-- AbdBarho
-- ablattmann
-- AdamOStark
-- Adam Rice
-- Airton Silva
-- Alexander Eichhorn
-- Alexandre D. Roberge
-- Andreas Rozek
-- Andre LaBranche
-- Andy Bearman
-- Andy Luhrs
-- Andy Pilate
-- Any-Winter-4079
-- apolinario
-- ArDiouscuros
-- Armando C. Santisbon
-- Arthur Holstvoogd
-- artmen1516
-- Artur
-- Arturo Mendivil
-- Ben Alkov
-- Benjamin Warner
-- Bernard Maltais
-- blessedcoolant
-- blhook
-- BlueAmulet
-- Bouncyknighter
-- Brandon Rising
-- Brent Ozar
-- Brian Racer
-- bsilvereagle
-- c67e708d
-- CapableWeb
-- Carson Katri
-- Chloe
-- Chris Dawson
-- Chris Hayes
-- Chris Jones
-- chromaticist
-- Claus F. Strasburger
-- cmdr2
-- cody
-- Conor Reid
-- Cora Johnson-Roberson
-- coreco
-- cosmii02
-- cpacker
-- Cragin Godley
-- creachec
-- Damian Stewart
-- Daniel Manzke
-- Danny Beer
-- Dan Sully
-- David Burnett
-- David Ford
-- David Regla
-- David Wager
-- Daya Adianto
-- db3000
-- Denis Olshin
-- Dennis
-- Dominic Letz
-- DrGunnarMallon
-- Edward Johan
-- elliotsayes
-- Elrik
-- ElrikUnderlake
-- Eric Khun
-- Eric Wolf
-- Eugene Brodsky
-- ExperimentalCyborg
-- Fabian Bahl
-- Fabio 'MrWHO' Torchetti
-- fattire
-- Felipe Nogueira
-- Félix Sanz
-- figgefigge
-- Gabriel Mackievicz Telles
-- gabrielrotbart
-- gallegonovato
-- Gérald LONLAS
-- GitHub Actions Bot
-- gogurtenjoyer
-- greentext2
-- Gregg Helt
-- H4rk
-- Håvard Gulldahl
-- henry
-- Henry van Megen
-- hipsterusername
-- hj
-- Hosted Weblate
-- Iman Karim
-- ismail ihsan bülbül
-- Ivan Efimov
-- jakehl
-- Jakub Kolčář
-- JamDon2
-- James Reynolds
-- Jan Skurovec
-- Jari Vetoniemi
-- Jason Toffaletti
-- Jaulustus
-- Jeff Mahoney
-- jeremy
-- Jeremy Clark
-- JigenD
-- Jim Hays
-- Johan Roxendal
-- Johnathon Selstad
-- Jonathan
-- Joseph Dries III
-- JPPhoto
-- jspraul
-- Justin Wong
-- Juuso V
-- Kaspar Emanuel
-- Katsuyuki-Karasawa
-- Kent Keirsey
-- Kevin Coakley
-- Kevin Gibbons
-- Kevin Schaul
-- Kevin Turner
-- krummrey
-- Kyle Lacy
-- Kyle Schouviller
-- Lawrence Norton
-- LemonDouble
-- Leo Pasanen
-- Lincoln Stein
-- LoganPederson
-- Lynne Whitehorn
-- majick
-- Marco Labarile
-- Martin Kristiansen
-- Mary Hipp Rogers
-- mastercaster9000
-- Matthias Wild
-- michaelk71
-- mickr777
-- Mihai
-- Mihail Dumitrescu
-- Mikhail Tishin
-- Millun Atluri
-- Minjune Song
-- mitien
-- mofuzz
-- Muhammad Usama
-- Name
-- _nderscore
-- Netzer R
-- Nicholas Koh
-- Nicholas Körfer
-- nicolai256
-- Niek van der Maas
-- noodlebox
-- Nuno Coração
-- ofirkris
-- Olivier Louvignes
-- owenvincent
-- Patrick Esser
-- Patrick Tien
-- Patrick von Platen
-- Paul Sajna
-- pejotr
-- Peter Baylies
-- Peter Lin
-- plucked
-- prixt
-- psychedelicious
-- Rainer Bernhardt
-- Riccardo Giovanetti
-- Rich Jones
-- rmagur1203
-- Rob Baines
-- Robert Bolender
-- Robin Rombach
-- Rohan Barar
-- rpagliuca
-- rromb
-- Rupesh Sreeraman
-- Ryan Cao
-- Saifeddine
-- Saifeddine ALOUI
-- SammCheese
-- Sammy
-- sammyf
-- Samuel Husso
-- Scott Lahteine
-- Sean McLellan
-- Sebastian Aigner
-- Sergey Borisov
-- Sergey Krashevich
-- Shapor Naghibzadeh
-- Shawn Zhong
-- Simon Vans-Colina
-- skunkworxdark
-- slashtechno
-- spezialspezial
-- ssantos
-- StAlKeR7779
-- Stephan Koglin-Fischer
-- SteveCaruso
-- Steve Martinelli
-- Steven Frank
-- System X - Files
-- Taylor Kems
-- techicode
-- techybrain-dev
-- tesseractcat
-- thealanle
-- Thomas
-- tildebyte
-- Tim Cabbage
-- Tom
-- Tom Elovi Spruce
-- Tom Gouville
-- tomosuto
-- Travco
-- Travis Palmer
-- tyler
-- unknown
-- user1
-- Vedant Madane
-- veprogames
-- wa.code
-- wfng92
-- whosawhatsis
-- Will
-- William Becher
-- William Chong
-- xra
-- Yeung Yiu Hung
-- ymgenesis
-- Yorzaren
-- Yosuke Shinya
-- yun saki
-- Zadagu
-- zeptofine
-- 冯不游
-- 唐澤 克幸
+- [Sean McLellan](https://github.com/Oceanswave)
+- [Kevin Gibbons](https://github.com/bakkot)
+- [Tesseract Cat](https://github.com/TesseractCat)
+- [blessedcoolant](https://github.com/blessedcoolant)
+- [David Ford](https://github.com/david-ford)
+- [yunsaki](https://github.com/yunsaki)
+- [James Reynolds](https://github.com/magnusviri)
+- [David Wager](https://github.com/maddavid123)
+- [Jason Toffaletti](https://github.com/toffaletti)
+- [tildebyte](https://github.com/tildebyte)
+- [Cragin Godley](https://github.com/cgodley)
+- [BlueAmulet](https://github.com/BlueAmulet)
+- [Benjamin Warner](https://github.com/warner-benjamin)
+- [Cora Johnson-Roberson](https://github.com/corajr)
+- [veprogames](https://github.com/veprogames)
+- [JigenD](https://github.com/JigenD)
+- [Niek van der Maas](https://github.com/Niek)
+- [Henry van Megen](https://github.com/hvanmegen)
+- [Håvard Gulldahl](https://github.com/havardgulldahl)
+- [greentext2](https://github.com/greentext2)
+- [Simon Vans-Colina](https://github.com/simonvc)
+- [Gabriel Rotbart](https://github.com/gabrielrotbart)
+- [Eric Khun](https://github.com/erickhun)
+- [Brent Ozar](https://github.com/BrentOzar)
+- [nderscore](https://github.com/nderscore)
+- [Mikhail Tishin](https://github.com/tishin)
+- [Tom Elovi Spruce](https://github.com/ilovecomputers)
+- [spezialspezial](https://github.com/spezialspezial)
+- [Yosuke Shinya](https://github.com/shinya7y)
+- [Andy Pilate](https://github.com/Cubox)
+- [Muhammad Usama](https://github.com/SMUsamaShah)
+- [Arturo Mendivil](https://github.com/artmen1516)
+- [Paul Sajna](https://github.com/sajattack)
+- [Samuel Husso](https://github.com/shusso)
+- [nicolai256](https://github.com/nicolai256)
+- [Mihai](https://github.com/mh-dm)
+- [Any Winter](https://github.com/any-winter-4079)
+- [Doggettx](https://github.com/doggettx)
+- [Matthias Wild](https://github.com/mauwii)
+- [Kyle Schouviller](https://github.com/kyle0654)
+- [rabidcopy](https://github.com/rabidcopy)
+- [Dominic Letz](https://github.com/dominicletz)
+- [Dmitry T.](https://github.com/ArDiouscuros)
+- [Kent Keirsey](https://github.com/hipsterusername)
+- [psychedelicious](https://github.com/psychedelicious)
+- [damian0815](https://github.com/damian0815)
+- [Eugene Brodsky](https://github.com/ebr)
 
 ## **Original CompVis Authors**
 

flake.lock

@@ -1,25 +0,0 @@
-{
-  "nodes": {
-    "nixpkgs": {
-      "locked": {
-        "lastModified": 1690630721,
-        "narHash": "sha256-Y04onHyBQT4Erfr2fc82dbJTfXGYrf4V0ysLUYnPOP8=",
-        "owner": "NixOS",
-        "repo": "nixpkgs",
-        "rev": "d2b52322f35597c62abf56de91b0236746b2a03d",
-        "type": "github"
-      },
-      "original": {
-        "id": "nixpkgs",
-        "type": "indirect"
-      }
-    },
-    "root": {
-      "inputs": {
-        "nixpkgs": "nixpkgs"
-      }
-    }
-  },
-  "root": "root",
-  "version": 7
-}

flake.nix

@@ -1,81 +0,0 @@
-# Important note: this flake does not attempt to create a fully isolated, 'pure'
-# Python environment for InvokeAI. Instead, it depends on local invocations of
-# virtualenv/pip to install the required (binary) packages, most importantly the
-# prebuilt binary pytorch packages with CUDA support.
-# ML Python packages with CUDA support, like pytorch, are notoriously expensive
-# to compile so it's purposefuly not what this flake does.
-
-{
-  description = "An (impure) flake to develop on InvokeAI.";
-
-  outputs = { self, nixpkgs }:
-    let
-      system = "x86_64-linux";
-      pkgs = import nixpkgs {
-        inherit system;
-        config.allowUnfree = true;
-      };
-
-      python = pkgs.python310;
-
-      mkShell = { dir, install }:
-        let
-          setupScript = pkgs.writeScript "setup-invokai" ''
-            # This must be sourced using 'source', not executed.
-            ${python}/bin/python -m venv ${dir}
-            ${dir}/bin/python -m pip install ${install}
-            # ${dir}/bin/python -c 'import torch; assert(torch.cuda.is_available())'
-            source ${dir}/bin/activate
-          '';
-        in
-        pkgs.mkShell rec {
-          buildInputs = with pkgs; [
-            # Backend: graphics, CUDA.
-            cudaPackages.cudnn
-            cudaPackages.cuda_nvrtc
-            cudatoolkit
-            freeglut
-            glib
-            gperf
-            procps
-            libGL
-            libGLU
-            linuxPackages.nvidia_x11
-            python
-            stdenv.cc
-            stdenv.cc.cc.lib
-            xorg.libX11
-            xorg.libXext
-            xorg.libXi
-            xorg.libXmu
-            xorg.libXrandr
-            xorg.libXv
-            zlib
-
-            # Pre-commit hooks.
-            black
-
-            # Frontend.
-            yarn
-            nodejs
-          ];
-          LD_LIBRARY_PATH = pkgs.lib.makeLibraryPath buildInputs;
-          CUDA_PATH = pkgs.cudatoolkit;
-          EXTRA_LDFLAGS = "-L${pkgs.linuxPackages.nvidia_x11}/lib";
-          shellHook = ''
-            if [[ -f "${dir}/bin/activate" ]]; then
-              source "${dir}/bin/activate"
-              echo "Using Python: $(which python)"
-            else
-              echo "Use 'source ${setupScript}' to set up the environment."
-            fi
-          '';
-        };
-    in
-    {
-      devShells.${system} = rec {
-        develop = mkShell { dir = "venv"; install = "-e '.[xformers]' --extra-index-url https://download.pytorch.org/whl/cu118"; };
-        default = develop;
-      };
-    };
-}

installer/create_installer.sh

@@ -24,8 +24,7 @@ read -e -p "Tag this repo with '${VERSION}' and '${LATEST_TAG}'? [n]: " input
 RESPONSE=${input:='n'}
 if [ "$RESPONSE" == 'y' ]; then
 
-    git push origin :refs/tags/$VERSION
-    if ! git tag -fa $VERSION ; then
+    if ! git tag $VERSION ; then
 	    echo "Existing/invalid tag"
 	    exit -1
     fi

installer/install.bat.in

@@ -38,7 +38,7 @@ echo    https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist
 echo.
 echo See %INSTRUCTIONS% for more details.
 echo.
-echo FOR THE BEST USER EXPERIENCE WE SUGGEST MAXIMIZING THIS WINDOW NOW.
+echo "For the best user experience we suggest enlarging or maximizing this window now."
 pause
 
 @rem ---------------------------- check Python version ---------------

installer/install.sh.in

@@ -9,20 +9,16 @@ cd $scriptdir
 function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
 
 MINIMUM_PYTHON_VERSION=3.9.0
-MAXIMUM_PYTHON_VERSION=3.11.100
+MAXIMUM_PYTHON_VERSION=3.11.0
 PYTHON=""
-for candidate in python3.11 python3.10 python3.9 python3 python ; do
+for candidate in python3.10 python3.9 python3 python ; do
     if ppath=`which $candidate`; then
-        # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
-        # we check that this found executable can actually run
-        if [ $($candidate --version &>/dev/null; echo ${PIPESTATUS}) -gt 0 ]; then continue; fi
-
         python_version=$($ppath -V | awk '{ print $2 }')
         if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then
-            if [ $(version $python_version) -le $(version "$MAXIMUM_PYTHON_VERSION") ]; then
-                PYTHON=$ppath
-                break
-            fi
+	    if [ $(version $python_version) -lt $(version "$MAXIMUM_PYTHON_VERSION") ]; then
+		PYTHON=$ppath
+		break
+	    fi
         fi
     fi
 done

installer/lib/installer.py

@@ -13,7 +13,7 @@ from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Union
 
-SUPPORTED_PYTHON = ">=3.9.0,<=3.11.100"
+SUPPORTED_PYTHON = ">=3.9.0,<3.11"
 INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
 BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp"
 
@@ -141,16 +141,15 @@ class Installer:
 
         # upgrade pip in Python 3.9 environments
         if int(platform.python_version_tuple()[1]) == 9:
+
             from plumbum import FG, local
 
             pip = local[get_pip_from_venv(venv_dir)]
-            pip["install", "--upgrade", "pip"] & FG
+            pip[ "install", "--upgrade", "pip"] & FG
 
         return venv_dir
 
-    def install(
-        self, root: str = "~/invokeai", version: str = "latest", yes_to_all=False, find_links: Path = None
-    ) -> None:
+    def install(self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None) -> None:
         """
         Install the InvokeAI application into the given runtime path
 
@@ -168,8 +167,7 @@ class Installer:
 
         messages.welcome()
 
-        default_path = os.environ.get("INVOKEAI_ROOT") or Path(root).expanduser().resolve()
-        self.dest = default_path if yes_to_all else messages.dest_path(root)
+        self.dest = Path(root).expanduser().resolve() if yes_to_all else messages.dest_path(root)
 
         # create the venv for the app
         self.venv = self.app_venv()
@@ -177,7 +175,7 @@ class Installer:
         self.instance = InvokeAiInstance(runtime=self.dest, venv=self.venv, version=version)
 
         # install dependencies and the InvokeAI application
-        (extra_index_url, optional_modules) = get_torch_source() if not yes_to_all else (None, None)
+        (extra_index_url,optional_modules) = get_torch_source() if not yes_to_all else (None,None)
         self.instance.install(
             extra_index_url,
             optional_modules,
@@ -190,7 +188,6 @@ class Installer:
         # run through the configuration flow
         self.instance.configure()
 
-
 class InvokeAiInstance:
     """
     Manages an installed instance of InvokeAI, comprising a virtual environment and a runtime directory.
@@ -199,6 +196,7 @@ class InvokeAiInstance:
     """
 
     def __init__(self, runtime: Path, venv: Path, version: str) -> None:
+
         self.runtime = runtime
         self.venv = venv
         self.pip = get_pip_from_venv(venv)
@@ -249,9 +247,6 @@ class InvokeAiInstance:
             pip[
                 "install",
                 "--require-virtualenv",
-                "numpy~=1.24.0",  # choose versions that won't be uninstalled during phase 2
-                "urllib3~=1.26.0",
-                "requests~=2.28.0",
                 "torch~=2.0.0",
                 "torchmetrics==0.11.4",
                 "torchvision>=0.14.1",
@@ -317,7 +312,7 @@ class InvokeAiInstance:
                 "install",
                 "--require-virtualenv",
                 "--use-pep517",
-                str(src) + (optional_modules if optional_modules else ""),
+                str(src)+(optional_modules if optional_modules else ''),
                 "--find-links" if find_links is not None else None,
                 find_links,
                 "--extra-index-url" if extra_index_url is not None else None,
@@ -334,15 +329,15 @@ class InvokeAiInstance:
 
         # set sys.argv to a consistent state
         new_argv = [sys.argv[0]]
-        for i in range(1, len(sys.argv)):
+        for i in range(1,len(sys.argv)):
             el = sys.argv[i]
-            if el in ["-r", "--root"]:
+            if el in ['-r','--root']:
                 new_argv.append(el)
-                new_argv.append(sys.argv[i + 1])
-            elif el in ["-y", "--yes", "--yes-to-all"]:
+                new_argv.append(sys.argv[i+1])
+            elif el in ['-y','--yes','--yes-to-all']:
                 new_argv.append(el)
         sys.argv = new_argv
-
+        
         import requests  # to catch download exceptions
         from messages import introduction
 
@@ -358,16 +353,16 @@ class InvokeAiInstance:
             invokeai_configure()
             succeeded = True
         except requests.exceptions.ConnectionError as e:
-            print(f"\nA network error was encountered during configuration and download: {str(e)}")
+            print(f'\nA network error was encountered during configuration and download: {str(e)}')
         except OSError as e:
-            print(f"\nAn OS error was encountered during configuration and download: {str(e)}")
+            print(f'\nAn OS error was encountered during configuration and download: {str(e)}')
         except Exception as e:
-            print(f"\nA problem was encountered during the configuration and download steps: {str(e)}")
+            print(f'\nA problem was encountered during the configuration and download steps: {str(e)}')
         finally:
             if not succeeded:
                 print('To try again, find the "invokeai" directory, run the script "invoke.sh" or "invoke.bat"')
-                print("and choose option 7 to fix a broken install, optionally followed by option 5 to install models.")
-                print("Alternatively you can relaunch the installer.")
+                print('and choose option 7 to fix a broken install, optionally followed by option 5 to install models.')
+                print('Alternatively you can relaunch the installer.')
 
     def install_user_scripts(self):
         """
@@ -376,11 +371,11 @@ class InvokeAiInstance:
 
         ext = "bat" if OS == "Windows" else "sh"
 
-        # scripts = ['invoke', 'update']
-        scripts = ["invoke"]
-
+        #scripts = ['invoke', 'update']
+        scripts = ['invoke']
+        
         for script in scripts:
-            src = Path(__file__).parent / ".." / "templates" / f"{script}.{ext}.in"
+            src = Path(__file__).parent / '..' / "templates" / f"{script}.{ext}.in"
             dest = self.runtime / f"{script}.{ext}"
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
@@ -425,7 +420,11 @@ def set_sys_path(venv_path: Path) -> None:
     # filter out any paths in sys.path that may be system- or user-wide
     # but leave the temporary bootstrap virtualenv as it contains packages we
     # temporarily need at install time
-    sys.path = list(filter(lambda p: not p.endswith("-packages") or p.find(BOOTSTRAP_VENV_PREFIX) != -1, sys.path))
+    sys.path = list(filter(
+        lambda p: not p.endswith("-packages")
+        or p.find(BOOTSTRAP_VENV_PREFIX) != -1,
+        sys.path
+    ))
 
     # determine site-packages/lib directory location for the venv
     lib = "Lib" if OS == "Windows" else f"lib/python{sys.version_info.major}.{sys.version_info.minor}"
@@ -434,7 +433,7 @@ def set_sys_path(venv_path: Path) -> None:
     sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
 
 
-def get_torch_source() -> (Union[str, None], str):
+def get_torch_source() -> (Union[str, None],str):
     """
     Determine the extra index URL for pip to use for torch installation.
     This depends on the OS and the graphics accelerator in use.
@@ -455,19 +454,16 @@ def get_torch_source() -> (Union[str, None], str):
     device = graphical_accelerator()
 
     url = None
-    optional_modules = "[onnx]"
+    optional_modules = None
     if OS == "Linux":
         if device == "rocm":
             url = "https://download.pytorch.org/whl/rocm5.4.2"
         elif device == "cpu":
             url = "https://download.pytorch.org/whl/cpu"
 
-    if device == "cuda":
-        url = "https://download.pytorch.org/whl/cu117"
-        optional_modules = "[xformers,onnx-cuda]"
-    if device == "cuda_and_dml":
-        url = "https://download.pytorch.org/whl/cu117"
-        optional_modules = "[xformers,onnx-directml]"
+    if device == 'cuda':
+        url = 'https://download.pytorch.org/whl/cu117'
+        optional_modules = '[xformers]'
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13
 

installer/lib/main.py

@@ -3,7 +3,6 @@ InvokeAI Installer
 """
 
 import argparse
-import os
 from pathlib import Path
 from installer import Installer
 
@@ -16,7 +15,7 @@ if __name__ == "__main__":
         dest="root",
         type=str,
         help="Destination path for installation",
-        default=os.environ.get("INVOKEAI_ROOT") or "~/invokeai",
+        default="~/invokeai",
     )
     parser.add_argument(
         "-y",
@@ -42,7 +41,7 @@ if __name__ == "__main__":
         type=Path,
         default=None,
     )
-
+    
     args = parser.parse_args()
 
     inst = Installer()

installer/lib/messages.py

@@ -36,15 +36,13 @@ else:
 
 
 def welcome():
+
     @group()
     def text():
         if (platform_specific := _platform_specific_help()) != "":
             yield platform_specific
             yield ""
-        yield Text.from_markup(
-            "Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.",
-            justify="center",
-        )
+        yield Text.from_markup("Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.", justify="center")
 
     console.rule()
     print(
@@ -60,7 +58,6 @@ def welcome():
     )
     console.line()
 
-
 def confirm_install(dest: Path) -> bool:
     if dest.exists():
         print(f":exclamation: Directory {dest} already exists :exclamation:")
@@ -95,6 +92,7 @@ def dest_path(dest=None) -> Path:
     dest_confirmed = confirm_install(dest)
 
     while not dest_confirmed:
+
         # if the given destination already exists, the starting point for browsing is its parent directory.
         # the user may have made a typo, or otherwise wants to place the root dir next to an existing one.
         # if the destination dir does NOT exist, then the user must have changed their mind about the selection.
@@ -167,10 +165,6 @@ def graphical_accelerator():
         "an [gold1 b]NVIDIA[/] GPU (using CUDA™)",
         "cuda",
     )
-    nvidia_with_dml = (
-        "an [gold1 b]NVIDIA[/] GPU (using CUDA™, and DirectML™ for ONNX) -- ALPHA",
-        "cuda_and_dml",
-    )
     amd = (
         "an [gold1 b]AMD[/] GPU (using ROCm™)",
         "rocm",
@@ -185,7 +179,7 @@ def graphical_accelerator():
     )
 
     if OS == "Windows":
-        options = [nvidia, nvidia_with_dml, cpu]
+        options = [nvidia, cpu]
     if OS == "Linux":
         options = [nvidia, amd, cpu]
     elif OS == "Darwin":
@@ -306,20 +300,15 @@ def introduction() -> None:
     )
     console.line(2)
 
-
-def _platform_specific_help() -> str:
+def _platform_specific_help()->str:
     if OS == "Darwin":
-        text = Text.from_markup(
-            """[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/]."""
-        )
+        text = Text.from_markup("""[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/].""")
     elif OS == "Windows":
-        text = Text.from_markup(
-            """[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
+        text = Text.from_markup("""[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
   1. Double-click on the file [b wheat1]WinLongPathsEnabled.reg[/] in order to
      enable long path support on your system.
   2. Make sure you have the [b wheat1]Visual C++ core libraries[/] installed. If not, install from
-     [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
-        )
+     [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]""")
     else:
         text = ""
     return text

installer/templates/invoke.bat.in

@@ -19,7 +19,7 @@ echo 8. Open the developer console
 echo 9. Update InvokeAI
 echo 10. Command-line help
 echo Q - Quit
-set /P choice="Please enter 1-10, Q: [1] "
+set /P choice="Please enter 1-10, Q: [2] "
 if not defined choice set choice=1
 IF /I "%choice%" == "1" (
     echo Starting the InvokeAI browser-based UI..
@@ -41,7 +41,7 @@ IF /I "%choice%" == "1" (
     python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
 ) ELSE IF /I "%choice%" == "7" (
     echo Running invokeai-configure...
-    python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight
+    python .venv\Scripts\invokeai-configure.exe --yes --default_only
 ) ELSE IF /I "%choice%" == "8" (
     echo Developer Console
     echo Python command is:

installer/templates/invoke.sh.in

@@ -82,7 +82,7 @@ do_choice() {
     7)
         clear
         printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n"
-        invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights
+        invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
         ;;
     8)
         clear

invokeai/app/api/dependencies.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Optional
 from logging import Logger
 import os
 from invokeai.app.services.board_image_record_storage import (
@@ -12,9 +11,9 @@ from invokeai.app.services.board_images import (
 )
 from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
 from invokeai.app.services.boards import BoardService, BoardServiceDependencies
-from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
 from invokeai.app.services.images import ImageService, ImageServiceDependencies
+from invokeai.app.services.metadata import CoreMetadataService
 from invokeai.app.services.resource_name import SimpleNameService
 from invokeai.app.services.urls import LocalUrlService
 from invokeai.backend.util.logging import InvokeAILogger
@@ -22,6 +21,7 @@ from invokeai.version.invokeai_version import __version__
 
 from ..services.default_graphs import create_system_graphs
 from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
+from ..services.restoration_services import RestorationServices
 from ..services.graph import GraphExecutionState, LibraryGraph
 from ..services.image_file_storage import DiskImageFileStorage
 from ..services.invocation_queue import MemoryInvocationQueue
@@ -55,12 +55,11 @@ logger = InvokeAILogger.getLogger()
 class ApiDependencies:
     """Contains and initializes all dependencies for the API"""
 
-    invoker: Optional[Invoker] = None
+    invoker: Invoker = None
 
     @staticmethod
-    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
-        logger.info(f"InvokeAI version {__version__}")
-        logger.info(f"Root directory = {str(config.root_path)}")
+    def initialize(config, event_handler_id: int, logger: Logger = logger):
+        logger.debug(f'InvokeAI version {__version__}')
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
         events = FastAPIEventService(event_handler_id)
@@ -76,10 +75,13 @@ class ApiDependencies:
         )
 
         urls = LocalUrlService()
+        metadata = CoreMetadataService()
         image_record_storage = SqliteImageRecordStorage(db_location)
         image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
         names = SimpleNameService()
-        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
+        latents = ForwardCacheLatentsStorage(
+            DiskLatentsStorage(f"{output_folder}/latents")
+        )
 
         board_record_storage = SqliteBoardRecordStorage(db_location)
         board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
@@ -109,6 +111,7 @@ class ApiDependencies:
                 board_image_record_storage=board_image_record_storage,
                 image_record_storage=image_record_storage,
                 image_file_storage=image_file_storage,
+                metadata=metadata,
                 url=urls,
                 logger=logger,
                 names=names,
@@ -117,16 +120,19 @@ class ApiDependencies:
         )
 
         services = InvocationServices(
-            model_manager=ModelManagerService(config, logger),
+            model_manager=ModelManagerService(config,logger),
             events=events,
             latents=latents,
             images=images,
             boards=boards,
             board_images=board_images,
             queue=MemoryInvocationQueue(),
-            graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
+            graph_library=SqliteItemStorage[LibraryGraph](
+                filename=db_location, table_name="graphs"
+            ),
             graph_execution_manager=graph_execution_manager,
             processor=DefaultInvocationProcessor(),
+            restoration=RestorationServices(config, logger),
             configuration=config,
             logger=logger,
         )

invokeai/app/api/routers/app_info.py

@@ -1,105 +1,18 @@
-import typing
-from enum import Enum
-from fastapi import Body
 from fastapi.routing import APIRouter
-from pathlib import Path
-from pydantic import BaseModel, Field
-
-from invokeai.backend.image_util.patchmatch import PatchMatch
-from invokeai.backend.image_util.safety_checker import SafetyChecker
-from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
-from invokeai.app.invocations.upscale import ESRGAN_MODELS
+from pydantic import BaseModel
 
 from invokeai.version import __version__
 
-from ..dependencies import ApiDependencies
-from invokeai.backend.util.logging import logging
-
-
-class LogLevel(int, Enum):
-    NotSet = logging.NOTSET
-    Debug = logging.DEBUG
-    Info = logging.INFO
-    Warning = logging.WARNING
-    Error = logging.ERROR
-    Critical = logging.CRITICAL
-
-
-class Upscaler(BaseModel):
-    upscaling_method: str = Field(description="Name of upscaling method")
-    upscaling_models: list[str] = Field(description="List of upscaling models for this method")
-
-
-app_router = APIRouter(prefix="/v1/app", tags=["app"])
+app_router = APIRouter(prefix="/v1/app", tags=['app'])
 
 
 class AppVersion(BaseModel):
     """App Version Response"""
-
-    version: str = Field(description="App version")
+    version: str
 
 
-class AppConfig(BaseModel):
-    """App Config Response"""
-
-    infill_methods: list[str] = Field(description="List of available infill methods")
-    upscaling_methods: list[Upscaler] = Field(description="List of upscaling methods")
-    nsfw_methods: list[str] = Field(description="List of NSFW checking methods")
-    watermarking_methods: list[str] = Field(description="List of invisible watermark methods")
-
-
-@app_router.get("/version", operation_id="app_version", status_code=200, response_model=AppVersion)
+@app_router.get('/version', operation_id="app_version",
+                status_code=200,
+                response_model=AppVersion)
 async def get_version() -> AppVersion:
     return AppVersion(version=__version__)
-
-
-@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
-async def get_config() -> AppConfig:
-    infill_methods = ["tile"]
-    if PatchMatch.patchmatch_available():
-        infill_methods.append("patchmatch")
-
-    upscaling_models = []
-    for model in typing.get_args(ESRGAN_MODELS):
-        upscaling_models.append(str(Path(model).stem))
-    upscaler = Upscaler(upscaling_method="esrgan", upscaling_models=upscaling_models)
-
-    nsfw_methods = []
-    if SafetyChecker.safety_checker_available():
-        nsfw_methods.append("nsfw_checker")
-
-    watermarking_methods = []
-    if InvisibleWatermark.invisible_watermark_available():
-        watermarking_methods.append("invisible_watermark")
-
-    return AppConfig(
-        infill_methods=infill_methods,
-        upscaling_methods=[upscaler],
-        nsfw_methods=nsfw_methods,
-        watermarking_methods=watermarking_methods,
-    )
-
-
-@app_router.get(
-    "/logging",
-    operation_id="get_log_level",
-    responses={200: {"description": "The operation was successful"}},
-    response_model=LogLevel,
-)
-async def get_log_level() -> LogLevel:
-    """Returns the log level"""
-    return LogLevel(ApiDependencies.invoker.services.logger.level)
-
-
-@app_router.post(
-    "/logging",
-    operation_id="set_log_level",
-    responses={200: {"description": "The operation was successful"}},
-    response_model=LogLevel,
-)
-async def set_log_level(
-    level: LogLevel = Body(description="New log verbosity level"),
-) -> LogLevel:
-    """Sets the log verbosity level"""
-    ApiDependencies.invoker.services.logger.setLevel(level)
-    return LogLevel(ApiDependencies.invoker.services.logger.level)

invokeai/app/api/routers/board_images.py

@@ -24,14 +24,11 @@ async def create_board_image(
 ):
     """Creates a board_image"""
     try:
-        result = ApiDependencies.invoker.services.board_images.add_image_to_board(
-            board_id=board_id, image_name=image_name
-        )
+        result = ApiDependencies.invoker.services.board_images.add_image_to_board(board_id=board_id, image_name=image_name)
         return result
     except Exception as e:
         raise HTTPException(status_code=500, detail="Failed to add to board")
-
-
+    
 @board_images_router.delete(
     "/",
     operation_id="remove_board_image",
@@ -46,9 +43,27 @@ async def remove_board_image(
 ):
     """Deletes a board_image"""
     try:
-        result = ApiDependencies.invoker.services.board_images.remove_image_from_board(
-            board_id=board_id, image_name=image_name
-        )
+        result = ApiDependencies.invoker.services.board_images.remove_image_from_board(board_id=board_id, image_name=image_name)
         return result
     except Exception as e:
         raise HTTPException(status_code=500, detail="Failed to update board")
+
+
+
+@board_images_router.get(
+    "/{board_id}",
+    operation_id="list_board_images",
+    response_model=OffsetPaginatedResults[ImageDTO],
+)
+async def list_board_images(
+    board_id: str = Path(description="The id of the board"),
+    offset: int = Query(default=0, description="The page offset"),
+    limit: int = Query(default=10, description="The number of boards per page"),
+) -> OffsetPaginatedResults[ImageDTO]:
+    """Gets a list of images for a board"""
+
+    results = ApiDependencies.invoker.services.board_images.get_images_for_board(
+        board_id,
+    )
+    return results
+

invokeai/app/api/routers/boards.py

@@ -1,26 +1,16 @@
 from typing import Optional, Union
-
 from fastapi import Body, HTTPException, Path, Query
 from fastapi.routing import APIRouter
-from pydantic import BaseModel, Field
-
 from invokeai.app.services.board_record_storage import BoardChanges
 from invokeai.app.services.image_record_storage import OffsetPaginatedResults
 from invokeai.app.services.models.board_record import BoardDTO
 
+
 from ..dependencies import ApiDependencies
 
 boards_router = APIRouter(prefix="/v1/boards", tags=["boards"])
 
 
-class DeleteBoardResult(BaseModel):
-    board_id: str = Field(description="The id of the board that was deleted.")
-    deleted_board_images: list[str] = Field(
-        description="The image names of the board-images relationships that were deleted."
-    )
-    deleted_images: list[str] = Field(description="The names of the images that were deleted.")
-
-
 @boards_router.post(
     "/",
     operation_id="create_board",
@@ -71,42 +61,33 @@ async def update_board(
 ) -> BoardDTO:
     """Updates a board"""
     try:
-        result = ApiDependencies.invoker.services.boards.update(board_id=board_id, changes=changes)
+        result = ApiDependencies.invoker.services.boards.update(
+            board_id=board_id, changes=changes
+        )
         return result
     except Exception as e:
         raise HTTPException(status_code=500, detail="Failed to update board")
 
 
-@boards_router.delete("/{board_id}", operation_id="delete_board", response_model=DeleteBoardResult)
+@boards_router.delete("/{board_id}", operation_id="delete_board")
 async def delete_board(
     board_id: str = Path(description="The id of board to delete"),
-    include_images: Optional[bool] = Query(description="Permanently delete all images on the board", default=False),
-) -> DeleteBoardResult:
+    include_images: Optional[bool] = Query(
+        description="Permanently delete all images on the board", default=False
+    ),
+) -> None:
     """Deletes a board"""
     try:
         if include_images is True:
-            deleted_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
+            ApiDependencies.invoker.services.images.delete_images_on_board(
                 board_id=board_id
             )
-            ApiDependencies.invoker.services.images.delete_images_on_board(board_id=board_id)
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
-            return DeleteBoardResult(
-                board_id=board_id,
-                deleted_board_images=[],
-                deleted_images=deleted_images,
-            )
         else:
-            deleted_board_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
-                board_id=board_id
-            )
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
-            return DeleteBoardResult(
-                board_id=board_id,
-                deleted_board_images=deleted_board_images,
-                deleted_images=[],
-            )
     except Exception as e:
-        raise HTTPException(status_code=500, detail="Failed to delete board")
+        # TODO: Does this need any exception handling at all?
+        pass
 
 
 @boards_router.get(
@@ -117,7 +98,9 @@ async def delete_board(
 async def list_boards(
     all: Optional[bool] = Query(default=None, description="Whether to list all boards"),
     offset: Optional[int] = Query(default=None, description="The page offset"),
-    limit: Optional[int] = Query(default=None, description="The number of boards per page"),
+    limit: Optional[int] = Query(
+        default=None, description="The number of boards per page"
+    ),
 ) -> Union[OffsetPaginatedResults[BoardDTO], list[BoardDTO]]:
     """Gets a list of boards"""
     if all:
@@ -132,19 +115,3 @@ async def list_boards(
             status_code=400,
             detail="Invalid request: Must provide either 'all' or both 'offset' and 'limit'",
         )
-
-
-@boards_router.get(
-    "/{board_id}/image_names",
-    operation_id="list_all_board_image_names",
-    response_model=list[str],
-)
-async def list_all_board_image_names(
-    board_id: str = Path(description="The id of the board"),
-) -> list[str]:
-    """Gets a list of images for a board"""
-
-    image_names = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
-        board_id,
-    )
-    return image_names

invokeai/app/api/routers/images.py

@@ -1,28 +1,25 @@
 import io
 from typing import Optional
-
 from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
-from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
+from fastapi.responses import FileResponse
 from PIL import Image
-
-from invokeai.app.invocations.metadata import ImageMetadata
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
+from invokeai.app.models.image import (
+    ImageCategory,
+    ResourceOrigin,
+)
 from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.item_storage import PaginatedResults
 from invokeai.app.services.models.image_record import (
     ImageDTO,
     ImageRecordChanges,
     ImageUrlsDTO,
 )
+from invokeai.app.services.item_storage import PaginatedResults
 
 from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
 
-# images are immutable; set a high max-age
-IMAGE_MAX_AGE = 31536000
-
 
 @images_router.post(
     "/",
@@ -40,9 +37,9 @@ async def upload_image(
     response: Response,
     image_category: ImageCategory = Query(description="The category of the image"),
     is_intermediate: bool = Query(description="Whether this is an intermediate image"),
-    board_id: Optional[str] = Query(default=None, description="The board to add this image to, if any"),
-    session_id: Optional[str] = Query(default=None, description="The session ID associated with this upload, if any"),
-    crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
+    session_id: Optional[str] = Query(
+        default=None, description="The session ID associated with this upload, if any"
+    ),
 ) -> ImageDTO:
     """Uploads an image"""
     if not file.content_type.startswith("image"):
@@ -52,9 +49,6 @@ async def upload_image(
 
     try:
         pil_image = Image.open(io.BytesIO(contents))
-        if crop_visible:
-            bbox = pil_image.getbbox()
-            pil_image = pil_image.crop(bbox)
     except:
         # Error opening the image
         raise HTTPException(status_code=415, detail="Failed to read image")
@@ -65,7 +59,6 @@ async def upload_image(
             image_origin=ResourceOrigin.EXTERNAL,
             image_category=image_category,
             session_id=session_id,
-            board_id=board_id,
             is_intermediate=is_intermediate,
         )
 
@@ -90,18 +83,6 @@ async def delete_image(
         pass
 
 
-@images_router.post("/clear-intermediates", operation_id="clear_intermediates")
-async def clear_intermediates() -> int:
-    """Clears all intermediates"""
-
-    try:
-        count_deleted = ApiDependencies.invoker.services.images.delete_intermediates()
-        return count_deleted
-    except Exception as e:
-        raise HTTPException(status_code=500, detail="Failed to clear intermediates")
-        pass
-
-
 @images_router.patch(
     "/{image_name}",
     operation_id="update_image",
@@ -109,7 +90,9 @@ async def clear_intermediates() -> int:
 )
 async def update_image(
     image_name: str = Path(description="The name of the image to update"),
-    image_changes: ImageRecordChanges = Body(description="The changes to apply to the image"),
+    image_changes: ImageRecordChanges = Body(
+        description="The changes to apply to the image"
+    ),
 ) -> ImageDTO:
     """Updates an image"""
 
@@ -120,14 +103,14 @@ async def update_image(
 
 
 @images_router.get(
-    "/{image_name}",
-    operation_id="get_image_dto",
+    "/{image_name}/metadata",
+    operation_id="get_image_metadata",
     response_model=ImageDTO,
 )
-async def get_image_dto(
+async def get_image_metadata(
     image_name: str = Path(description="The name of image to get"),
 ) -> ImageDTO:
-    """Gets an image's DTO"""
+    """Gets an image's metadata"""
 
     try:
         return ApiDependencies.invoker.services.images.get_dto(image_name)
@@ -136,23 +119,7 @@ async def get_image_dto(
 
 
 @images_router.get(
-    "/{image_name}/metadata",
-    operation_id="get_image_metadata",
-    response_model=ImageMetadata,
-)
-async def get_image_metadata(
-    image_name: str = Path(description="The name of image to get"),
-) -> ImageMetadata:
-    """Gets an image's metadata"""
-
-    try:
-        return ApiDependencies.invoker.services.images.get_metadata(image_name)
-    except Exception as e:
-        raise HTTPException(status_code=404)
-
-
-@images_router.get(
-    "/{image_name}/full",
+    "/{image_name}",
     operation_id="get_image_full",
     response_class=Response,
     responses={
@@ -174,14 +141,12 @@ async def get_image_full(
         if not ApiDependencies.invoker.services.images.validate_path(path):
             raise HTTPException(status_code=404)
 
-        response = FileResponse(
+        return FileResponse(
             path,
             media_type="image/png",
             filename=image_name,
             content_disposition_type="inline",
         )
-        response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
-        return response
     except Exception as e:
         raise HTTPException(status_code=404)
 
@@ -204,13 +169,15 @@ async def get_image_thumbnail(
     """Gets a thumbnail image file"""
 
     try:
-        path = ApiDependencies.invoker.services.images.get_path(image_name, thumbnail=True)
+        path = ApiDependencies.invoker.services.images.get_path(
+            image_name, thumbnail=True
+        )
         if not ApiDependencies.invoker.services.images.validate_path(path):
             raise HTTPException(status_code=404)
 
-        response = FileResponse(path, media_type="image/webp", content_disposition_type="inline")
-        response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
-        return response
+        return FileResponse(
+            path, media_type="image/webp", content_disposition_type="inline"
+        )
     except Exception as e:
         raise HTTPException(status_code=404)
 
@@ -227,7 +194,9 @@ async def get_image_urls(
 
     try:
         image_url = ApiDependencies.invoker.services.images.get_url(image_name)
-        thumbnail_url = ApiDependencies.invoker.services.images.get_url(image_name, thumbnail=True)
+        thumbnail_url = ApiDependencies.invoker.services.images.get_url(
+            image_name, thumbnail=True
+        )
         return ImageUrlsDTO(
             image_name=image_name,
             image_url=image_url,
@@ -239,21 +208,26 @@ async def get_image_urls(
 
 @images_router.get(
     "/",
-    operation_id="list_image_dtos",
+    operation_id="list_images_with_metadata",
     response_model=OffsetPaginatedResults[ImageDTO],
 )
-async def list_image_dtos(
-    image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to list."),
-    categories: Optional[list[ImageCategory]] = Query(default=None, description="The categories of image to include."),
-    is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate images."),
+async def list_images_with_metadata(
+    image_origin: Optional[ResourceOrigin] = Query(
+        default=None, description="The origin of images to list"
+    ),
+    categories: Optional[list[ImageCategory]] = Query(
+        default=None, description="The categories of image to include"
+    ),
+    is_intermediate: Optional[bool] = Query(
+        default=None, description="Whether to list intermediate images"
+    ),
     board_id: Optional[str] = Query(
-        default=None,
-        description="The board id to filter by. Use 'none' to find images without a board.",
+        default=None, description="The board id to filter by"
     ),
     offset: int = Query(default=0, description="The page offset"),
     limit: int = Query(default=10, description="The number of images per page"),
 ) -> OffsetPaginatedResults[ImageDTO]:
-    """Gets a list of image DTOs"""
+    """Gets a list of images"""
 
     image_dtos = ApiDependencies.invoker.services.images.get_many(
         offset,

invokeai/app/api/routers/models.py

@@ -1,7 +1,6 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2023 Lincoln D. Stein
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2024 Lincoln Stein
 
 
-import pathlib
 from typing import Literal, List, Optional, Union
 
 from fastapi import Body, Path, Query, Response
@@ -13,11 +12,8 @@ from invokeai.backend import BaseModelType, ModelType
 from invokeai.backend.model_management.models import (
     OPENAPI_MODEL_CONFIGS,
     SchedulerPredictionType,
-    ModelNotFoundException,
-    InvalidModelException,
 )
 from invokeai.backend.model_management import MergeInterpolationMethod
-
 from ..dependencies import ApiDependencies
 
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
@@ -26,357 +22,211 @@ UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
 MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
-ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
-
 
 class ModelsList(BaseModel):
     models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
 
-
 @models_router.get(
     "/",
     operation_id="list_models",
-    responses={200: {"model": ModelsList}},
+    responses={200: {"model": ModelsList }},
 )
 async def list_models(
-    base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
+    base_model: Optional[BaseModelType] = Query(default=None, description="Base model"),
     model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
 ) -> ModelsList:
     """Gets a list of models"""
-    if base_models and len(base_models) > 0:
-        models_raw = list()
-        for base_model in base_models:
-            models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
-    else:
-        models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
-    models = parse_obj_as(ModelsList, {"models": models_raw})
+    models_raw = ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type)
+    models = parse_obj_as(ModelsList, { "models": models_raw })
     return models
 
-
 @models_router.patch(
     "/{base_model}/{model_type}/{model_name}",
     operation_id="update_model",
-    responses={
-        200: {"description": "The model was updated successfully"},
-        400: {"description": "Bad request"},
-        404: {"description": "The model could not be found"},
-        409: {"description": "There is already a model corresponding to the new name"},
-    },
-    status_code=200,
-    response_model=UpdateModelResponse,
+    responses={200: {"description" : "The model was updated successfully"},
+               404: {"description" : "The model could not be found"},
+               400: {"description" : "Bad request"}
+               },
+    status_code = 200,
+    response_model = UpdateModelResponse,
 )
 async def update_model(
-    base_model: BaseModelType = Path(description="Base model"),
-    model_type: ModelType = Path(description="The type of model"),
-    model_name: str = Path(description="model name"),
-    info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
+        base_model: BaseModelType = Path(description="Base model"),
+        model_type: ModelType = Path(description="The type of model"),
+        model_name: str = Path(description="model name"),
+        info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
 ) -> UpdateModelResponse:
-    """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
-    logger = ApiDependencies.invoker.services.logger
-
+    """ Add Model """
     try:
-        previous_info = ApiDependencies.invoker.services.model_manager.list_model(
+        ApiDependencies.invoker.services.model_manager.update_model(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
+            model_attributes=info.dict()
         )
-
-        # rename operation requested
-        if info.model_name != model_name or info.base_model != base_model:
-            ApiDependencies.invoker.services.model_manager.rename_model(
-                base_model=base_model,
-                model_type=model_type,
-                model_name=model_name,
-                new_name=info.model_name,
-                new_base=info.base_model,
-            )
-            logger.info(f"Successfully renamed {base_model.value}/{model_name}=>{info.base_model}/{info.model_name}")
-            # update information to support an update of attributes
-            model_name = info.model_name
-            base_model = info.base_model
-            new_info = ApiDependencies.invoker.services.model_manager.list_model(
-                model_name=model_name,
-                base_model=base_model,
-                model_type=model_type,
-            )
-            if new_info.get("path") != previous_info.get(
-                "path"
-            ):  # model manager moved model path during rename - don't overwrite it
-                info.path = new_info.get("path")
-
-        ApiDependencies.invoker.services.model_manager.update_model(
-            model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info.dict()
-        )
-
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
             model_name=model_name,
             base_model=base_model,
             model_type=model_type,
         )
         model_response = parse_obj_as(UpdateModelResponse, model_raw)
-    except ModelNotFoundException as e:
+    except KeyError as e:
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    except Exception as e:
-        logger.error(str(e))
         raise HTTPException(status_code=400, detail=str(e))
 
     return model_response
 
-
 @models_router.post(
-    "/import",
+    "/",
     operation_id="import_model",
-    responses={
-        201: {"description": "The model imported successfully"},
-        404: {"description": "The model could not be found"},
-        415: {"description": "Unrecognized file/folder format"},
-        424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
-        409: {"description": "There is already a model corresponding to this path or repo_id"},
+    responses= {
+        201: {"description" : "The model imported successfully"},
+        404: {"description" : "The model could not be found"},
+        424: {"description" : "The model appeared to import successfully, but could not be found in the model manager"},
+        409: {"description" : "There is already a model corresponding to this path or repo_id"},
     },
     status_code=201,
-    response_model=ImportModelResponse,
+    response_model=ImportModelResponse
 )
 async def import_model(
-    location: str = Body(description="A model path, repo_id or URL to import"),
-    prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
-        description="Prediction type for SDv2 checkpoint files", default="v_prediction"
-    ),
+        location: str = Body(description="A model path, repo_id or URL to import"),
+        prediction_type: Optional[Literal['v_prediction','epsilon','sample']] = \
+                Body(description='Prediction type for SDv2 checkpoint files', default="v_prediction"),
 ) -> ImportModelResponse:
-    """Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
-
+    """ Add a model using its local path, repo_id, or remote URL """
+    
     items_to_import = {location}
-    prediction_types = {x.value: x for x in SchedulerPredictionType}
+    prediction_types = { x.value: x for x in SchedulerPredictionType }
     logger = ApiDependencies.invoker.services.logger
 
     try:
         installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
-            items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type)
+            items_to_import = items_to_import,
+            prediction_type_helper = lambda x: prediction_types.get(prediction_type)
         )
         info = installed_models.get(location)
 
         if not info:
             logger.error("Import failed")
-            raise HTTPException(status_code=415)
-
-        logger.info(f"Successfully imported {location}, got {info}")
+            raise HTTPException(status_code=424)
+        
+        logger.info(f'Successfully imported {location}, got {info}')
         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            model_name=info.name, base_model=info.base_model, model_type=info.model_type
+            model_name=info.name,
+            base_model=info.base_model,
+            model_type=info.model_type
         )
         return parse_obj_as(ImportModelResponse, model_raw)
-
-    except ModelNotFoundException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=404, detail=str(e))
-    except InvalidModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=415)
-    except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-
-
-@models_router.post(
-    "/add",
-    operation_id="add_model",
-    responses={
-        201: {"description": "The model added successfully"},
-        404: {"description": "The model could not be found"},
-        424: {"description": "The model appeared to add successfully, but could not be found in the model manager"},
-        409: {"description": "There is already a model corresponding to this path or repo_id"},
-    },
-    status_code=201,
-    response_model=ImportModelResponse,
-)
-async def add_model(
-    info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
-) -> ImportModelResponse:
-    """Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
-
-    logger = ApiDependencies.invoker.services.logger
-
-    try:
-        ApiDependencies.invoker.services.model_manager.add_model(
-            info.model_name, info.base_model, info.model_type, model_attributes=info.dict()
-        )
-        logger.info(f"Successfully added {info.model_name}")
-        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            model_name=info.model_name, base_model=info.base_model, model_type=info.model_type
-        )
-        return parse_obj_as(ImportModelResponse, model_raw)
-    except ModelNotFoundException as e:
+    
+    except KeyError as e:
         logger.error(str(e))
         raise HTTPException(status_code=404, detail=str(e))
     except ValueError as e:
         logger.error(str(e))
         raise HTTPException(status_code=409, detail=str(e))
-
+        
 
 @models_router.delete(
     "/{base_model}/{model_type}/{model_name}",
     operation_id="del_model",
-    responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}},
-    status_code=204,
-    response_model=None,
+    responses={
+        204: {
+        "description": "Model deleted successfully"
+        }, 
+        404: {
+        "description": "Model not found"
+        }
+    },
 )
 async def delete_model(
-    base_model: BaseModelType = Path(description="Base model"),
-    model_type: ModelType = Path(description="The type of model"),
-    model_name: str = Path(description="model name"),
+        base_model: BaseModelType = Path(description="Base model"),
+        model_type: ModelType = Path(description="The type of model"),
+        model_name: str = Path(description="model name"),
 ) -> Response:
     """Delete Model"""
     logger = ApiDependencies.invoker.services.logger
-
+    
     try:
-        ApiDependencies.invoker.services.model_manager.del_model(
-            model_name, base_model=base_model, model_type=model_type
-        )
+        ApiDependencies.invoker.services.model_manager.del_model(model_name,
+                                                                 base_model = base_model,
+                                                                 model_type = model_type
+                                                                 )
         logger.info(f"Deleted model: {model_name}")
         return Response(status_code=204)
-    except ModelNotFoundException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=404, detail=str(e))
-
+    except KeyError:
+        logger.error(f"Model not found: {model_name}")
+        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
 
 @models_router.put(
     "/convert/{base_model}/{model_type}/{model_name}",
     operation_id="convert_model",
     responses={
-        200: {"description": "Model converted successfully"},
-        400: {"description": "Bad request"},
-        404: {"description": "Model not found"},
+        200: { "description": "Model converted successfully" },
+        400: {"description" : "Bad request"  },
+        404: { "description": "Model not found"  },
     },
-    status_code=200,
-    response_model=ConvertModelResponse,
+    status_code = 200,
+    response_model = ConvertModelResponse,
 )
 async def convert_model(
-    base_model: BaseModelType = Path(description="Base model"),
-    model_type: ModelType = Path(description="The type of model"),
-    model_name: str = Path(description="model name"),
-    convert_dest_directory: Optional[str] = Query(
-        default=None, description="Save the converted model to the designated directory"
-    ),
+        base_model: BaseModelType = Path(description="Base model"),
+        model_type: ModelType = Path(description="The type of model"),
+        model_name: str = Path(description="model name"),
 ) -> ConvertModelResponse:
-    """Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none."""
+    """Convert a checkpoint model into a diffusers model"""
     logger = ApiDependencies.invoker.services.logger
     try:
         logger.info(f"Converting model: {model_name}")
-        dest = pathlib.Path(convert_dest_directory) if convert_dest_directory else None
-        ApiDependencies.invoker.services.model_manager.convert_model(
-            model_name,
-            base_model=base_model,
-            model_type=model_type,
-            convert_dest_directory=dest,
-        )
-        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            model_name, base_model=base_model, model_type=model_type
-        )
+        ApiDependencies.invoker.services.model_manager.convert_model(model_name,
+                                                                     base_model = base_model,
+                                                                     model_type = model_type
+                                                                     )
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(model_name,
+                                                                              base_model = base_model,
+                                                                              model_type = model_type)
         response = parse_obj_as(ConvertModelResponse, model_raw)
-    except ModelNotFoundException as e:
-        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
+    except KeyError:
+        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     return response
-
-
-@models_router.get(
-    "/search",
-    operation_id="search_for_models",
-    responses={
-        200: {"description": "Directory searched successfully"},
-        404: {"description": "Invalid directory path"},
-    },
-    status_code=200,
-    response_model=List[pathlib.Path],
-)
-async def search_for_models(
-    search_path: pathlib.Path = Query(description="Directory path to search for models"),
-) -> List[pathlib.Path]:
-    if not search_path.is_dir():
-        raise HTTPException(
-            status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory"
-        )
-    return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
-
-
-@models_router.get(
-    "/ckpt_confs",
-    operation_id="list_ckpt_configs",
-    responses={
-        200: {"description": "paths retrieved successfully"},
-    },
-    status_code=200,
-    response_model=List[pathlib.Path],
-)
-async def list_ckpt_configs() -> List[pathlib.Path]:
-    """Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT."""
-    return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()
-
-
-@models_router.post(
-    "/sync",
-    operation_id="sync_to_config",
-    responses={
-        201: {"description": "synchronization successful"},
-    },
-    status_code=201,
-    response_model=bool,
-)
-async def sync_to_config() -> bool:
-    """Call after making changes to models.yaml, autoimport directories or models directory to synchronize
-    in-memory data structures with disk data structures."""
-    ApiDependencies.invoker.services.model_manager.sync_to_config()
-    return True
-
-
+        
 @models_router.put(
     "/merge/{base_model}",
     operation_id="merge_models",
     responses={
-        200: {"description": "Model converted successfully"},
-        400: {"description": "Incompatible models"},
-        404: {"description": "One or more models not found"},
+        200: { "description": "Model converted successfully" },
+        400: { "description": "Incompatible models"  },
+        404: { "description": "One or more models not found"  },
     },
-    status_code=200,
-    response_model=MergeModelResponse,
+    status_code = 200,
+    response_model = MergeModelResponse,
 )
 async def merge_models(
-    base_model: BaseModelType = Path(description="Base model"),
-    model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
-    merged_model_name: Optional[str] = Body(description="Name of destination model"),
-    alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
-    interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
-    force: Optional[bool] = Body(
-        description="Force merging of models created with different versions of diffusers", default=False
-    ),
-    merge_dest_directory: Optional[str] = Body(
-        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
-        default=None,
-    ),
+        base_model: BaseModelType                  = Path(description="Base model"),
+        model_names: List[str]                     = Body(description="model name", min_items=2, max_items=3),
+        merged_model_name: Optional[str]           = Body(description="Name of destination model"),
+        alpha: Optional[float]                     = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
+        interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
+        force: Optional[bool]                      = Body(description="Force merging of models created with different versions of diffusers", default=False),
 ) -> MergeModelResponse:
     """Convert a checkpoint model into a diffusers model"""
     logger = ApiDependencies.invoker.services.logger
     try:
-        logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
-        dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
-        result = ApiDependencies.invoker.services.model_manager.merge_models(
-            model_names,
-            base_model,
-            merged_model_name=merged_model_name or "+".join(model_names),
-            alpha=alpha,
-            interp=interp,
-            force=force,
-            merge_dest_directory=dest,
-        )
-        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            result.name,
-            base_model=base_model,
-            model_type=ModelType.Main,
-        )
+        logger.info(f"Merging models: {model_names}")
+        result = ApiDependencies.invoker.services.model_manager.merge_models(model_names,
+                                                                             base_model,
+                                                                             merged_model_name or "+".join(model_names),
+                                                                             alpha,
+                                                                             interp,
+                                                                             force)
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(result.name,
+                                                                              base_model = base_model,
+                                                                              model_type = ModelType.Main,
+                                                                              )
         response = parse_obj_as(ConvertModelResponse, model_raw)
-    except ModelNotFoundException:
+    except KeyError:
         raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))

invokeai/app/api/routers/sessions.py

@@ -30,7 +30,9 @@ session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
     },
 )
 async def create_session(
-    graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with")
+    graph: Optional[Graph] = Body(
+        default=None, description="The graph to initialize the session with"
+    )
 ) -> GraphExecutionState:
     """Creates a new session, optionally initializing it with an invocation graph"""
     session = ApiDependencies.invoker.create_execution_state(graph)
@@ -49,9 +51,13 @@ async def list_sessions(
 ) -> PaginatedResults[GraphExecutionState]:
     """Gets a list of sessions, optionally searching"""
     if query == "":
-        result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
+        result = ApiDependencies.invoker.services.graph_execution_manager.list(
+            page, per_page
+        )
     else:
-        result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
+        result = ApiDependencies.invoker.services.graph_execution_manager.search(
+            query, page, per_page
+        )
     return result
 
 
@@ -85,9 +91,9 @@ async def get_session(
 )
 async def add_node(
     session_id: str = Path(description="The id of the session"),
-    node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(  # type: ignore
-        description="The node to add"
-    ),
+    node: Annotated[
+        Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
+    ] = Body(description="The node to add"),
 ) -> str:
     """Adds a node to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@@ -118,9 +124,9 @@ async def add_node(
 async def update_node(
     session_id: str = Path(description="The id of the session"),
     node_path: str = Path(description="The path to the node in the graph"),
-    node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(  # type: ignore
-        description="The new node"
-    ),
+    node: Annotated[
+        Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
+    ] = Body(description="The new node"),
 ) -> GraphExecutionState:
     """Updates a node in the graph and removes all linked edges"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@@ -224,7 +230,7 @@ async def delete_edge(
     try:
         edge = Edge(
             source=EdgeConnection(node_id=from_node_id, field=from_field),
-            destination=EdgeConnection(node_id=to_node_id, field=to_field),
+            destination=EdgeConnection(node_id=to_node_id, field=to_field)
         )
         session.delete_edge(edge)
         ApiDependencies.invoker.services.graph_execution_manager.set(
@@ -249,7 +255,9 @@ async def delete_edge(
 )
 async def invoke_session(
     session_id: str = Path(description="The id of the session to invoke"),
-    all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
+    all: bool = Query(
+        default=False, description="Whether or not to invoke all remaining invocations"
+    ),
 ) -> Response:
     """Invokes a session"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@@ -266,7 +274,9 @@ async def invoke_session(
 @session_router.delete(
     "/{session_id}/invoke",
     operation_id="cancel_session_invoke",
-    responses={202: {"description": "The invocation is canceled"}},
+    responses={
+        202: {"description": "The invocation is canceled"}
+    },
 )
 async def cancel_session_invoke(
     session_id: str = Path(description="The id of the session to cancel"),

invokeai/app/api/sockets.py

@@ -16,7 +16,9 @@ class SocketIO:
         self.__sio.on("subscribe", handler=self._handle_sub)
         self.__sio.on("unsubscribe", handler=self._handle_unsub)
 
-        local_handler.register(event_name=EventServiceBase.session_event, _func=self._handle_session_event)
+        local_handler.register(
+            event_name=EventServiceBase.session_event, _func=self._handle_session_event
+        )
 
     async def _handle_session_event(self, event: Event):
         await self.__sio.emit(

invokeai/app/api_app.py

@@ -3,9 +3,7 @@ import asyncio
 import sys
 from inspect import signature
 
-import logging
 import uvicorn
-import socket
 
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
@@ -17,10 +15,9 @@ from fastapi_events.middleware import EventHandlerASGIMiddleware
 from pathlib import Path
 from pydantic.schema import schema
 
-# This should come early so that modules can log their initialization properly
+#This should come early so that modules can log their initialization properly
 from .services.config import InvokeAIAppConfig
 from ..backend.util.logging import InvokeAILogger
-
 app_config = InvokeAIAppConfig.get_config()
 app_config.parse_args()
 logger = InvokeAILogger.getLogger(config=app_config)
@@ -29,7 +26,7 @@ from invokeai.version.invokeai_version import __version__
 # we call this early so that the message appears before
 # other invokeai initialization messages
 if app_config.version:
-    print(f"InvokeAI version {__version__}")
+    print(f'InvokeAI version {__version__}')
     sys.exit(0)
 
 import invokeai.frontend.web as web_dir
@@ -39,18 +36,16 @@ from .api.dependencies import ApiDependencies
 from .api.routers import sessions, models, images, boards, board_images, app_info
 from .api.sockets import SocketIO
 from .invocations.baseinvocation import BaseInvocation
-
+    
 
 import torch
-import invokeai.backend.util.hotfixes
-
 if torch.backends.mps.is_available():
     import invokeai.backend.util.mps_fixes
 
 # fix for windows mimetypes registry entries being borked
 # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
-mimetypes.add_type("application/javascript", ".js")
-mimetypes.add_type("text/css", ".css")
+mimetypes.add_type('application/javascript', '.js')
+mimetypes.add_type('text/css', '.css')
 
 # Create the app
 # TODO: create this all in a method so configuration/etc. can be passed in?
@@ -60,13 +55,14 @@ app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
 event_handler_id: int = id(app)
 app.add_middleware(
     EventHandlerASGIMiddleware,
-    handlers=[local_handler],  # TODO: consider doing this in services to support different configurations
+    handlers=[
+        local_handler
+    ],  # TODO: consider doing this in services to support different configurations
     middleware_id=event_handler_id,
 )
 
 socket_io = SocketIO(app)
 
-
 # Add startup event to load dependencies
 @app.on_event("startup")
 async def startup_event():
@@ -78,7 +74,9 @@ async def startup_event():
         allow_headers=app_config.allow_headers,
     )
 
-    ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
+    ApiDependencies.initialize(
+        config=app_config, event_handler_id=event_handler_id, logger=logger
+    )
 
 
 # Shut down threads
@@ -103,8 +101,7 @@ app.include_router(boards.boards_router, prefix="/api")
 
 app.include_router(board_images.board_images_router, prefix="/api")
 
-app.include_router(app_info.app_router, prefix="/api")
-
+app.include_router(app_info.app_router, prefix='/api')
 
 # Build a custom OpenAPI to include all outputs
 # TODO: can outputs be included on metadata of invocation schemas somehow?
@@ -145,7 +142,6 @@ def custom_openapi():
         invoker_schema["output"] = outputs_ref
 
     from invokeai.backend.model_management.models import get_model_config_enums
-
     for model_config_format_enum in set(get_model_config_enums()):
         name = model_config_format_enum.__qualname__
 
@@ -168,8 +164,7 @@ def custom_openapi():
 app.openapi = custom_openapi
 
 # Override API doc favicons
-app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
-
+app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], 'static/dream_web')), name="static")
 
 @app.get("/docs", include_in_schema=False)
 def overridden_swagger():
@@ -190,48 +185,19 @@ def overridden_redoc():
 
 
 # Must mount *after* the other routes else it borks em
-app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui")
-
+app.mount("/", 
+          StaticFiles(directory=Path(web_dir.__path__[0],"dist"), 
+                      html=True
+                     ), name="ui"
+         )
 
 def invoke_api():
-    def find_port(port: int):
-        """Find a port not in use starting at given port"""
-        # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
-        # https://github.com/WaylonWalker
-        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-            if s.connect_ex(("localhost", port)) == 0:
-                return find_port(port=port + 1)
-            else:
-                return port
-
-    from invokeai.backend.install.check_root import check_invokeai_root
-
-    check_invokeai_root(app_config)  # note, may exit with an exception if root not set up
-
-    port = find_port(app_config.port)
-    if port != app_config.port:
-        logger.warn(f"Port {app_config.port} in use, using port {port}")
-
     # Start our own event loop for eventing usage
     loop = asyncio.new_event_loop()
-    config = uvicorn.Config(
-        app=app,
-        host=app_config.host,
-        port=port,
-        loop=loop,
-        log_level=app_config.log_level,
-    )
+    config = uvicorn.Config(app=app, host=app_config.host, port=app_config.port, loop=loop)
+    # Use access_log to turn off logging
     server = uvicorn.Server(config)
-
-    # replace uvicorn's loggers with InvokeAI's for consistent appearance
-    for logname in ["uvicorn.access", "uvicorn"]:
-        l = logging.getLogger(logname)
-        l.handlers.clear()
-        for ch in logger.handlers:
-            l.addHandler(ch)
-
     loop.run_until_complete(server.serve())
 
-
 if __name__ == "__main__":
     invoke_api()

invokeai/app/cli/commands.py

@@ -14,14 +14,8 @@ from ..services.graph import GraphExecutionState, LibraryGraph, Edge
 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()
-    )
+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()
@@ -53,8 +47,8 @@ def add_parsers(
     commands: list[type],
     command_field: str = "type",
     exclude_fields: list[str] = ["id", "type"],
-    add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None,
-):
+    add_arguments: Union[Callable[[argparse.ArgumentParser], None],None] = None
+    ):
     """Adds parsers for each command to the subparsers"""
 
     # Create subparsers for each command
@@ -67,7 +61,7 @@ def add_parsers(
             add_arguments(command_parser)
 
         # Convert all fields to arguments
-        fields = command.__fields__  # type: ignore
+        fields = command.__fields__ # type: ignore
         for name, field in fields.items():
             if name in exclude_fields:
                 continue
@@ -76,11 +70,13 @@ def add_parsers(
 
 
 def add_graph_parsers(
-    subparsers, graphs: list[LibraryGraph], add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
+    subparsers,
+    graphs: list[LibraryGraph],
+    add_arguments: Union[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)
 
@@ -132,7 +128,6 @@ class CliContext:
 
 class ExitCli(Exception):
     """Exception to exit the CLI"""
-
     pass
 
 
@@ -160,7 +155,7 @@ class BaseCommand(ABC, BaseModel):
     @classmethod
     def get_commands_map(cls):
         # Get the type strings out of the literals and into a dictionary
-        return dict(map(lambda t: (get_args(get_type_hints(t)["type"])[0], t), BaseCommand.get_all_subclasses()))
+        return dict(map(lambda t: (get_args(get_type_hints(t)['type'])[0], t),BaseCommand.get_all_subclasses()))
 
     @abstractmethod
     def run(self, context: CliContext) -> None:
@@ -170,8 +165,7 @@ class BaseCommand(ABC, BaseModel):
 
 class ExitCommand(BaseCommand):
     """Exits the CLI"""
-
-    type: Literal["exit"] = "exit"
+    type: Literal['exit'] = 'exit'
 
     def run(self, context: CliContext) -> None:
         raise ExitCli()
@@ -179,8 +173,7 @@ class ExitCommand(BaseCommand):
 
 class HelpCommand(BaseCommand):
     """Shows help"""
-
-    type: Literal["help"] = "help"
+    type: Literal['help'] = 'help'
 
     def run(self, context: CliContext) -> None:
         context.parser.print_help()
@@ -190,7 +183,11 @@ def get_graph_execution_history(
     graph_execution_state: GraphExecutionState,
 ) -> Iterable[str]:
     """Gets the history of fully-executed invocations for a graph execution"""
-    return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes)
+    return (
+        n
+        for n in reversed(graph_execution_state.executed_history)
+        if n in graph_execution_state.graph.nodes
+    )
 
 
 def get_invocation_command(invocation) -> str:
@@ -221,8 +218,7 @@ def get_invocation_command(invocation) -> str:
 
 class HistoryCommand(BaseCommand):
     """Shows the invocation history"""
-
-    type: Literal["history"] = "history"
+    type: Literal['history'] = 'history'
 
     # Inputs
     # fmt: off
@@ -239,8 +235,7 @@ class HistoryCommand(BaseCommand):
 
 class SetDefaultCommand(BaseCommand):
     """Sets a default value for a field"""
-
-    type: Literal["default"] = "default"
+    type: Literal['default'] = 'default'
 
     # Inputs
     # fmt: off
@@ -258,8 +253,7 @@ class SetDefaultCommand(BaseCommand):
 
 class DrawGraphCommand(BaseCommand):
     """Debugs a graph"""
-
-    type: Literal["draw_graph"] = "draw_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)
@@ -277,8 +271,7 @@ class DrawGraphCommand(BaseCommand):
 
 class DrawExecutionGraphCommand(BaseCommand):
     """Debugs an execution graph"""
-
-    type: Literal["draw_xgraph"] = "draw_xgraph"
+    type: Literal['draw_xgraph'] = 'draw_xgraph'
 
     def run(self, context: CliContext) -> None:
         session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
@@ -293,7 +286,6 @@ class DrawExecutionGraphCommand(BaseCommand):
         plt.axis("off")
         plt.show()
 
-
 class SortedHelpFormatter(argparse.HelpFormatter):
     def _iter_indented_subactions(self, action):
         try:

invokeai/app/cli/completer.py

@@ -19,8 +19,8 @@ from ..services.invocation_services import InvocationServices
 # singleton object, class variable
 completer = None
 
-
 class Completer(object):
+    
     def __init__(self, model_manager: ModelManager):
         self.commands = self.get_commands()
         self.matches = None
@@ -43,7 +43,7 @@ class Completer(object):
             except IndexError:
                 pass
             options = options or list(self.parse_commands().keys())
-
+            
             if not text:  # first time
                 self.matches = options
             else:
@@ -56,17 +56,17 @@ class Completer(object):
         return match
 
     @classmethod
-    def get_commands(self) -> List[object]:
+    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]:
+    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:
+        if len(buffer)==0:
             return None, None
         tokens = shlex.split(buffer)
         command = None
@@ -78,11 +78,11 @@ class Completer(object):
             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 ""
+        if switch and buffer.endswith(' '):
+            switch=None
+        return command or '', switch or ''
 
-    def parse_commands(self) -> Dict[str, List[str]]:
+    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.
@@ -90,11 +90,11 @@ class Completer(object):
         result = dict()
         for command in self.commands:
             hints = get_type_hints(command)
-            name = get_args(hints["type"])[0]
-            result.update({name: hints})
+            name = get_args(hints['type'])[0]
+            result.update({name:hints})
         return result
 
-    def get_command_options(self, command: str, switch: str) -> List[str]:
+    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.
@@ -102,46 +102,42 @@ class Completer(object):
         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 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])
-                ]
+                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]:
+    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":
+        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(services: InvocationServices) -> Completer:
     global completer
-
+    
     if completer:
         return completer
-
+    
     completer = Completer(services.model_manager)
 
     readline.set_completer(completer.complete)
@@ -166,6 +162,8 @@ def set_autocompleter(services: InvocationServices) -> Completer:
         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}")
+        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

@@ -13,7 +13,6 @@ from pydantic.fields import Field
 # This should come early so that the logger can pick up its configuration options
 from .services.config import InvokeAIAppConfig
 from invokeai.backend.util.logging import InvokeAILogger
-
 config = InvokeAIAppConfig.get_config()
 config.parse_args()
 logger = InvokeAILogger().getLogger(config=config)
@@ -21,7 +20,7 @@ from invokeai.version.invokeai_version import __version__
 
 # we call this early so that the message appears before other invokeai initialization messages
 if config.version:
-    print(f"InvokeAI version {__version__}")
+    print(f'InvokeAI version {__version__}')
     sys.exit(0)
 
 from invokeai.app.services.board_image_record_storage import (
@@ -35,34 +34,31 @@ from invokeai.app.services.board_record_storage import SqliteBoardRecordStorage
 from invokeai.app.services.boards import BoardService, BoardServiceDependencies
 from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
 from invokeai.app.services.images import ImageService, ImageServiceDependencies
+from invokeai.app.services.metadata import CoreMetadataService
 from invokeai.app.services.resource_name import SimpleNameService
 from invokeai.app.services.urls import LocalUrlService
-from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
+from .services.default_graphs import (default_text_to_image_graph_id,
+                                      create_system_graphs)
 from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
 
-from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
+from .cli.commands import (BaseCommand, CliContext, ExitCli,
+                           SortedHelpFormatter, add_graph_parsers, add_parsers)
 from .cli.completer import set_autocompleter
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
-from .services.graph import (
-    Edge,
-    EdgeConnection,
-    GraphExecutionState,
-    GraphInvocation,
-    LibraryGraph,
-    are_connection_types_compatible,
-)
+from .services.graph import (Edge, EdgeConnection, GraphExecutionState,
+                             GraphInvocation, LibraryGraph,
+                             are_connection_types_compatible)
 from .services.image_file_storage import DiskImageFileStorage
 from .services.invocation_queue import MemoryInvocationQueue
 from .services.invocation_services import InvocationServices
 from .services.invoker import Invoker
 from .services.model_manager_service import ModelManagerService
 from .services.processor import DefaultInvocationProcessor
+from .services.restoration_services import RestorationServices
 from .services.sqlite import SqliteItemStorage
 
 import torch
-import invokeai.backend.util.hotfixes
-
 if torch.backends.mps.is_available():
     import invokeai.backend.util.mps_fixes
 
@@ -74,7 +70,6 @@ class CliCommand(BaseModel):
 class InvalidArgs(Exception):
     pass
 
-
 def add_invocation_args(command_parser):
     # Add linking capability
     command_parser.add_argument(
@@ -119,7 +114,7 @@ def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
     return parser
 
 
-class NodeField:
+class NodeField():
     alias: str
     node_path: str
     field: str
@@ -132,20 +127,15 @@ class NodeField:
         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 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],
-    )
+    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:
@@ -153,12 +143,7 @@ def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -
     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],
-    )
+    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]:
@@ -181,7 +166,9 @@ def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[st
         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) -> list[Edge]:
+def generate_matching_edges(
+    a: BaseInvocation, b: BaseInvocation, context: CliContext
+) -> list[Edge]:
     """Generates all possible edges between two invocations"""
     afields = get_node_outputs(a, context)
     bfields = get_node_inputs(b, context)
@@ -193,14 +180,12 @@ def generate_matching_edges(a: BaseInvocation, b: BaseInvocation, context: CliCo
     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)
-    ]
+    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),
+            destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field)
         )
         for alias in matching_fields
     ]
@@ -209,7 +194,6 @@ def generate_matching_edges(a: BaseInvocation, b: BaseInvocation, context: CliCo
 
 class SessionError(Exception):
     """Raised when a session error has occurred"""
-
     pass
 
 
@@ -226,23 +210,22 @@ def invoke_all(context: CliContext):
             context.invoker.services.logger.error(
                 f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
             )
-
+        
         raise SessionError()
 
-
 def invoke_cli():
-    logger.info(f"InvokeAI version {__version__}")
+    logger.info(f'InvokeAI version {__version__}')
     # get the optional list of invocations to execute on the command line
     parser = config.get_parser()
-    parser.add_argument("commands", nargs="*")
+    parser.add_argument('commands',nargs='*')
     invocation_commands = parser.parse_args().commands
 
     # get the optional file to read commands from.
     # Simplest is to use it for STDIN
     if infile := config.from_file:
-        sys.stdin = open(infile, "r")
-
-    model_manager = ModelManagerService(config, logger)
+        sys.stdin = open(infile,"r")
+    
+    model_manager = ModelManagerService(config,logger)
 
     events = EventServiceBase()
     output_folder = config.output_path
@@ -252,15 +235,16 @@ def invoke_cli():
         db_location = ":memory:"
     else:
         db_location = config.db_path
-        db_location.parent.mkdir(parents=True, exist_ok=True)
+        db_location.parent.mkdir(parents=True,exist_ok=True)
 
     logger.info(f'InvokeAI database location is "{db_location}"')
 
     graph_execution_manager = SqliteItemStorage[GraphExecutionState](
-        filename=db_location, table_name="graph_executions"
-    )
+            filename=db_location, table_name="graph_executions"
+        )
 
     urls = LocalUrlService()
+    metadata = CoreMetadataService()
     image_record_storage = SqliteImageRecordStorage(db_location)
     image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
     names = SimpleNameService()
@@ -293,27 +277,32 @@ def invoke_cli():
             board_image_record_storage=board_image_record_storage,
             image_record_storage=image_record_storage,
             image_file_storage=image_file_storage,
+            metadata=metadata,
             url=urls,
             logger=logger,
             names=names,
             graph_execution_manager=graph_execution_manager,
         )
     )
-
+        
     services = InvocationServices(
         model_manager=model_manager,
         events=events,
-        latents=ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents")),
+        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
         images=images,
         boards=boards,
         board_images=board_images,
         queue=MemoryInvocationQueue(),
-        graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
+        graph_library=SqliteItemStorage[LibraryGraph](
+            filename=db_location, table_name="graphs"
+        ),
         graph_execution_manager=graph_execution_manager,
         processor=DefaultInvocationProcessor(),
+        restoration=RestorationServices(config,logger=logger),
         logger=logger,
         configuration=config,
     )
+    
 
     system_graphs = create_system_graphs(services.graph_library)
     system_graph_names = set([g.name for g in system_graphs])
@@ -323,7 +312,7 @@ def invoke_cli():
     session: GraphExecutionState = invoker.create_execution_state()
     parser = get_command_parser(services)
 
-    re_negid = re.compile("^-[0-9]+$")
+    re_negid = re.compile('^-[0-9]+$')
 
     # Uncomment to print out previous sessions at startup
     # print(services.session_manager.list())
@@ -333,7 +322,7 @@ def invoke_cli():
 
     command_line_args_exist = len(invocation_commands) > 0
     done = False
-
+    
     while not done:
         try:
             if command_line_args_exist:
@@ -347,7 +336,7 @@ def invoke_cli():
 
         try:
             # Refresh the state of the session
-            # history = list(get_graph_execution_history(context.session))
+            #history = list(get_graph_execution_history(context.session))
             history = list(reversed(context.nodes_added))
 
             # Split the command for piping
@@ -368,17 +357,17 @@ def invoke_cli():
                         args[field_name] = field_default
 
                 # Parse invocation
-                command: CliCommand = None  # type:ignore
+                command: CliCommand = None # type:ignore
                 system_graph: Optional[LibraryGraph] = None
-                if args["type"] in system_graph_names:
-                    system_graph = next(filter(lambda g: g.name == args["type"], system_graphs))
+                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)
+                    command = CliCommand(command = invocation)
                     context.graph_nodes[invocation.id] = system_graph.id
                 else:
                     args["id"] = current_id
@@ -400,13 +389,17 @@ def invoke_cli():
                 # Pipe previous command output (if there was a previous command)
                 edges: list[Edge] = list()
                 if len(history) > 0 or current_id != start_id:
-                    from_id = history[0] if current_id == start_id else str(current_id - 1)
+                    from_id = (
+                        history[0] if current_id == start_id else str(current_id - 1)
+                    )
                     from_node = (
                         next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
                         if current_id != start_id
                         else context.session.graph.get_node(from_id)
                     )
-                    matching_edges = generate_matching_edges(from_node, command.command, context)
+                    matching_edges = generate_matching_edges(
+                        from_node, command.command, context
+                    )
                     edges.extend(matching_edges)
 
                 # Parse provided links
@@ -417,18 +410,16 @@ def invoke_cli():
                             node_id = str(current_id + int(node_id))
 
                         link_node = context.session.graph.get_node(node_id)
-                        matching_edges = generate_matching_edges(link_node, command.command, context)
+                        matching_edges = generate_matching_edges(
+                            link_node, command.command, context
+                        )
                         matching_destinations = [e.destination for e in matching_edges]
                         edges = [e for e in edges if e.destination not in matching_destinations]
                         edges.extend(matching_edges)
 
                 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]
-                        ]
+                        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):
@@ -441,7 +432,7 @@ def invoke_cli():
                         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),
+                                destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field)
                             )
                         )
 

invokeai/app/invocations/__init__.py

@@ -4,5 +4,9 @@ __all__ = []
 
 dirname = os.path.dirname(os.path.abspath(__file__))
 for f in os.listdir(dirname):
-    if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
+    if (
+        f != "__init__.py"
+        and os.path.isfile("%s/%s" % (dirname, f))
+        and f[-3:] == ".py"
+    ):
         __all__.append(f[:-3])