Merge branch 'main' into lstein/threaded-download

.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/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 \

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
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-
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-generator.
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-"Third Parties" means individuals or legal entities that are not under
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-"Contribution" means any work of authorship, including the original
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-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
-
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-or filed.
-
-Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
-
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-conditions: Use-based restrictions as referenced in paragraph 5 MUST
-be included as an enforceable provision by You in any type of legal
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-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
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-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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-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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-responsibility, not on behalf of any other Contributor, and only if
-You agree to indemnify, defend, and hold each Contributor harmless for
-any liability incurred by, or claims asserted against, such
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-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
@@ -255,24 +264,19 @@ 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 +295,14 @@ 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 +314,9 @@ 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 +349,13 @@ 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
 

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/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 <******************************link to roadmap>******************************. 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

@@ -65,6 +65,7 @@ InvokeAI:
     esrgan: true
     internet_available: true
     log_tokenization: false
+    nsfw_checker: false
     patchmatch: true
     restore: true
 ...
@@ -135,16 +136,19 @@ 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]
+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]
+                [--nsfw_checker | --no-nsfw_checker] [--patchmatch | --no-patchmatch] [--restore | --no-restore]
+                [--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_cache_size MAX_CACHE_SIZE]
+                [--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--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}]
+...
 ```
 
 ## The Configuration Settings
@@ -174,6 +178,7 @@ These configuration settings allow you to enable and disable various InvokeAI fe
 | `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 |
+| `nsfw_checker` | `true`     | Activate the NSFW checker to blur out risque images |
 | `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) |
 

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

@@ -61,13 +61,11 @@ A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from
 | 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 |
@@ -118,49 +116,49 @@ There are several node grouping concepts that can be examined with a narrow focu
 
 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)
+<img width="654" alt="groupsnoise" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/2e8d297e-ad55-4d27-bc93-c119dad2a2c5">
 
 ### 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)
+<img width="1024" alt="groupsconditioning" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/f8f7ad8a-8d9c-418e-b5ad-1437b774b27e">
 
 ### 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)
+<img width="637" alt="groupsimgvae" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/dd99969c-e0a8-4f78-9b17-3ffe179cef9a">
 
 ### 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)
+<img width="922" alt="groupsrandseed" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/af55bc20-60f6-438e-aba5-3ec871443710">
 
 ### 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)
+<img width="805" alt="groupscontrol" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/cc9c5de7-23a7-46c8-bbad-1f3609d999a6">
 
 ### 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)
+<img width="993" alt="groupslora" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/630962b0-d914-4505-b3ea-ccae9b0269da">
 
 ### 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)
+<img width="644" alt="groupsallscale" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/99314f05-dd9f-4b6d-b378-31de55346a13">
 
 ### 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)
+<img width="788" alt="groupsiterate" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/4af5ca27-82c9-4018-8c5b-024d3ee0a121">
 
 ### Multiple Image Generation + Random Seeds
 
@@ -168,7 +166,7 @@ Multiple image generation in the node editor is done using the RandomRange node.
 
 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)
+<img width="1027" alt="groupsmultigenseeding" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/518d1b2b-fed1-416b-a052-ab06552521b3">
 
 ## Examples
 
@@ -176,7 +174,7 @@ With our knowledge of node grouping and the diffusion process, let’s break dow
 
 ### Basic text-to-image Node Graph
 
-![nodest2i](../assets/nodes/nodest2i.png)
+<img width="875" alt="nodest2i" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/17c67720-c376-4db8-94f0-5e00381a61ee">
 
 - 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.
@@ -186,7 +184,7 @@ With our knowledge of node grouping and the diffusion process, let’s break dow
 
 ### Basic image-to-image Node Graph
 
-![nodesi2i](../assets/nodes/nodesi2i.png)
+<img width="998" alt="nodesi2i" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/3f2c95d5-cee7-4415-9b79-b46ee60a92fe">
 
 - 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.
@@ -197,7 +195,7 @@ With our knowledge of node grouping and the diffusion process, let’s break dow
 
 ### Basic ControlNet Node Graph
 
-![nodescontrol](../assets/nodes/nodescontrol.png)
+<img width="703" alt="nodescontrol" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/b02ded86-ceb4-44a2-9910-e19ad184d471">
 
 - Model Loader
 - Prompt (Compel)

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

@@ -17,12 +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.
@@ -33,28 +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](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/index.md

@@ -24,7 +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]
@@ -54,10 +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
@@ -82,25 +82,6 @@ Q&A</a>]
 
     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-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 is the recommended installation method for first-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)
-
 ## :fontawesome-solid-computer: Hardware Requirements
 
 ### :octicons-cpu-24: System
@@ -126,6 +107,24 @@ images in full-precision mode:
 - 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
 
@@ -146,9 +145,9 @@ images in full-precision mode:
 ### 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)
@@ -223,10 +222,14 @@ 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
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -124,9 +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
@@ -215,6 +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
@@ -246,30 +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.
@@ -353,8 +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

docs/installation/020_INSTALL_MANUAL.md

@@ -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

@@ -15,7 +15,7 @@ See the [troubleshooting
 section](010_INSTALL_AUTOMATED.md#troubleshooting) of the automated
 install guide for frequently-encountered installation issues.
 
-## Installation options
+## Main Application
 
 1. [Automated Installer](010_INSTALL_AUTOMATED.md)
 
@@ -24,9 +24,6 @@ install guide for frequently-encountered installation issues.
     "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

@@ -1,52 +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
-
-### Face Mask
-
-**Description:** This node autodetects a face in the image using MediaPipe and masks it by making it transparent. Via outpainting you can swap faces with other faces, or invert the mask and swap things around the face with other things. Additionally, you can supply X and Y offset values to scale/change the shape of the mask for finer control. The node also outputs an all-white mask in the same dimensions as the input image. This is needed by the inpaint node (and unified canvas) for outpainting.
-
-**Node Link:** https://github.com/ymgenesis/InvokeAI/blob/facemaskmediapipe/invokeai/app/invocations/facemask.py
-
-**Example Node Graph:** https://www.mediafire.com/file/gohn5sb1bfp8use/21-July_2023-FaceMask.json/file
-
-**Output Examples** 
-
-![2e3168cb-af6a-475d-bfac-c7b7fd67b4c2](https://github.com/ymgenesis/InvokeAI/assets/25252829/a5ad7d44-2ada-4b3c-a56e-a21f8244a1ac)
-![2_annotated](https://github.com/ymgenesis/InvokeAI/assets/25252829/53416c8a-a23b-4d76-bb6d-3cfd776e0096)
-![2fe2150c-fd08-4e26-8c36-f0610bf441bb](https://github.com/ymgenesis/InvokeAI/assets/25252829/b0f7ecfe-f093-4147-a904-b9f131b41dc9)
-![831b6b98-4f0f-4360-93c8-69a9c1338cbe](https://github.com/ymgenesis/InvokeAI/assets/25252829/fc7b0622-e361-4155-8a76-082894d084f0)
-
---------------------------------
-### 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** 
-
-![Invoke AI](https://invoke-ai.github.io/InvokeAI/assets/invoke_ai_banner.png)
-
-### 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
-
-## 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**
 

invokeai/app/api/dependencies.py

@@ -58,8 +58,7 @@ class ApiDependencies:
 
     @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)}")
+        logger.debug(f"InvokeAI version {__version__}")
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
         events = FastAPIEventService(event_handler_id)

invokeai/app/api/events.py

@@ -9,7 +9,6 @@ from fastapi_events.dispatcher import dispatch
 
 from ..services.events import EventServiceBase
 
-
 class FastAPIEventService(EventServiceBase):
     event_handler_id: int
     __queue: Queue
@@ -28,6 +27,9 @@ class FastAPIEventService(EventServiceBase):
         self.__queue.put(None)
 
     def dispatch(self, event_name: str, payload: Any) -> None:
+        # TODO: Remove next two debugging lines
+        from .dependencies import ApiDependencies
+        ApiDependencies.invoker.services.logger.debug(f'dispatch {event_name} / {payload}')
         self.__queue.put(dict(event_name=event_name, payload=payload))
 
     async def __dispatch_from_queue(self, stop_event: threading.Event):

invokeai/app/api/routers/app_info.py

@@ -1,32 +1,9 @@
-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 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"])
 
 
@@ -40,9 +17,6 @@ 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(
@@ -59,51 +33,4 @@ 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)
+    return AppConfig(infill_methods=infill_methods)

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",
@@ -46,10 +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

@@ -1,28 +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",
@@ -81,42 +69,25 @@ async def update_board(
         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:
+) -> None:
     """Deletes a board"""
     try:
         if include_images is True:
-            deleted_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_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(
@@ -144,19 +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

@@ -1,7 +1,8 @@
 import io
 from typing import Optional
 
-from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
+from fastapi import (Body, HTTPException, Path, Query, Request, Response,
+                     UploadFile)
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
 from PIL import Image
@@ -10,11 +11,9 @@ from invokeai.app.invocations.metadata import ImageMetadata
 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.models.image_record import (ImageDTO,
+                                                       ImageRecordChanges,
+                                                       ImageUrlsDTO)
 
 from ..dependencies import ApiDependencies
 
@@ -40,15 +39,9 @@ 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"
-    ),
 ) -> ImageDTO:
     """Uploads an image"""
     if not file.content_type.startswith("image"):
@@ -58,9 +51,6 @@ 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")
@@ -71,7 +61,6 @@ async def upload_image(
             image_origin=ResourceOrigin.EXTERNAL,
             image_category=image_category,
             session_id=session_id,
-            board_id=board_id,
             is_intermediate=is_intermediate,
         )
 
@@ -96,18 +85,6 @@ 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",
@@ -142,7 +119,6 @@ async def get_image_dto(
     except Exception as e:
         raise HTTPException(status_code=404)
 
-
 @images_router.get(
     "/{image_name}/metadata",
     operation_id="get_image_metadata",
@@ -258,17 +234,16 @@ async def get_image_urls(
 )
 async def list_image_dtos(
     image_origin: Optional[ResourceOrigin] = Query(
-        default=None, description="The origin of images to list."
+        default=None, description="The origin of images to list"
     ),
     categories: Optional[list[ImageCategory]] = Query(
-        default=None, description="The categories of image to include."
+        default=None, description="The categories of image to include"
     ),
     is_intermediate: Optional[bool] = Query(
-        default=None, description="Whether to list intermediate images."
+        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"),

invokeai/app/api/routers/models.py

@@ -2,6 +2,7 @@
 
 
 import pathlib
+import threading
 from typing import Literal, List, Optional, Union
 
 from fastapi import Body, Path, Query, Response
@@ -127,54 +128,43 @@ async def update_model(
     "/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"},
+        200: {"description" : "The path was queued for import"},
     },
-    status_code=201,
-    response_model=ImportModelResponse
+    status_code=200
 )
 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"),
-) -> ImportModelResponse:
-    """ Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically """
+) -> str:
+    """ 
+    Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically.
+    This call launches a background thread to process the imported model and always succeeds. Results are reported in the background
+    as the following events:
+    - model_import_started(import_path:str)
+    - model_import_completed(import_path:str, import_info:AddModelResults, success:bool, error:str)
+    - download_started(url:str)
+    - download_progress(url:str, downloaded_bytes:int, total_bytes:int)
+    - download_completed(url:str, status_code:int, download_path:str)
+    """
     
     items_to_import = {location}
     prediction_types = { x.value: x for x in SchedulerPredictionType }
     logger = ApiDependencies.invoker.services.logger
+    events = ApiDependencies.invoker.services.events
 
     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)
-        )
-        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}')
-        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
-            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:
+        import_thread = threading.Thread(target = ApiDependencies.invoker.services.model_manager.heuristic_import,
+                                         kwargs = dict(items_to_import = items_to_import,
+                                                       prediction_type_helper = lambda x: prediction_types.get(prediction_type),
+                                                       event_bus = events,
+                                                       )
+                                         )
+        import_thread.start()
+        return 'request queued'
+    except Exception 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))
+        raise HTTPException(status_code=500, detail=str(e))
         
 @models_router.post(
     "/add",
@@ -298,7 +288,7 @@ async def 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)
+    return ApiDependencies.invoker.services.model_manager.search_for_models([search_path])
 
 @models_router.get(
     "/ckpt_confs",
@@ -315,21 +305,20 @@ async def list_ckpt_configs(
     return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()
     
         
-@models_router.post(
+@models_router.get(
     "/sync",
     operation_id="sync_to_config",
     responses={
         201: { "description": "synchronization successful" },
     },
     status_code = 201,
-    response_model = bool
+    response_model = None
 )
 async def sync_to_config(
-)->bool:
+)->None:
     """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
+    return ApiDependencies.invoker.services.model_manager.sync_to_config()
         
 @models_router.put(
     "/merge/{base_model}",
@@ -374,3 +363,50 @@ async def merge_models(
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     return response
+
+# The rename operation is now supported by update_model and no longer needs to be
+# a standalone route.
+# @models_router.post(
+#     "/rename/{base_model}/{model_type}/{model_name}",
+#     operation_id="rename_model",
+#     responses= {
+#         201: {"description" : "The model was renamed successfully"},
+#         404: {"description" : "The model could not be found"},
+#         409: {"description" : "There is already a model corresponding to the new name"},
+#     },
+#     status_code=201,
+#     response_model=ImportModelResponse
+# )
+# async def rename_model(
+#         base_model: BaseModelType = Path(description="Base model"),
+#         model_type: ModelType = Path(description="The type of model"),
+#         model_name: str = Path(description="current model name"),
+#         new_name: Optional[str] = Query(description="new model name", default=None),
+#         new_base: Optional[BaseModelType] = Query(description="new model base", default=None),
+# ) -> ImportModelResponse:
+#     """ Rename a model"""
+    
+#     logger = ApiDependencies.invoker.services.logger
+
+#     try:
+#         result = ApiDependencies.invoker.services.model_manager.rename_model(
+#             base_model = base_model,
+#             model_type = model_type,
+#             model_name = model_name,
+#             new_name = new_name,
+#             new_base = new_base,
+#         )
+#         logger.debug(result)
+#         logger.info(f'Successfully renamed {model_name}=>{new_name}')
+#         model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+#             model_name=new_name or model_name,
+#             base_model=new_base or base_model,
+#             model_type=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 ValueError as e:
+#         logger.error(str(e))
+#         raise HTTPException(status_code=409, detail=str(e))

invokeai/app/api_app.py

@@ -4,7 +4,6 @@ import sys
 from inspect import signature
 
 import uvicorn
-import socket
 
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
@@ -194,25 +193,9 @@ app.mount("/",
          )
 
 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)
+    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)
     loop.run_until_complete(server.serve())

invokeai/app/invocations/compel.py

@@ -95,7 +95,7 @@ class CompelInvocation(BaseInvocation):
         def _lora_loader():
             for lora in self.clip.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context)
+                    **lora.dict(exclude={"weight"}))
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
@@ -171,16 +171,16 @@ class CompelInvocation(BaseInvocation):
 class SDXLPromptInvocationBase:
     def run_clip_raw(self, context, clip_field, prompt, get_pooled):
         tokenizer_info = context.services.model_manager.get_model(
-            **clip_field.tokenizer.dict(), context=context,
+            **clip_field.tokenizer.dict(),
         )
         text_encoder_info = context.services.model_manager.get_model(
-            **clip_field.text_encoder.dict(), context=context,
+            **clip_field.text_encoder.dict(),
         )
 
         def _lora_loader():
             for lora in clip_field.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context)
+                    **lora.dict(exclude={"weight"}))
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
@@ -196,7 +196,6 @@ class SDXLPromptInvocationBase:
                         model_name=name,
                         base_model=clip_field.text_encoder.base_model,
                         model_type=ModelType.TextualInversion,
-                        context=context,
                     ).context.model
                 )
             except ModelNotFoundException:
@@ -241,16 +240,16 @@ class SDXLPromptInvocationBase:
 
     def run_clip_compel(self, context, clip_field, prompt, get_pooled):
         tokenizer_info = context.services.model_manager.get_model(
-            **clip_field.tokenizer.dict(), context=context,
+            **clip_field.tokenizer.dict(),
         )
         text_encoder_info = context.services.model_manager.get_model(
-            **clip_field.text_encoder.dict(), context=context,
+            **clip_field.text_encoder.dict(),
         )
 
         def _lora_loader():
             for lora in clip_field.loras:
                 lora_info = context.services.model_manager.get_model(
-                    **lora.dict(exclude={"weight"}), context=context)
+                    **lora.dict(exclude={"weight"}))
                 yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
@@ -266,7 +265,6 @@ class SDXLPromptInvocationBase:
                         model_name=name,
                         base_model=clip_field.text_encoder.base_model,
                         model_type=ModelType.TextualInversion,
-                        context=context,
                     ).context.model
                 )
             except ModelNotFoundException:
@@ -333,8 +331,8 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     crop_left: int = Field(0, description="")
     target_width: int = Field(1024, description="")
     target_height: int = Field(1024, description="")
-    clip: ClipField = Field(None, description="Clip to use")
-    clip2: ClipField = Field(None, description="Clip2 to use")
+    clip1: ClipField = Field(None, description="Clip to use")
+    clip2: ClipField = Field(None, description="Clip to use")
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -350,7 +348,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False)
+        c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip1, self.prompt, False)
         if self.style.strip() == "":
             c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True)
         else:
@@ -453,8 +451,8 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     crop_left: int = Field(0, description="")
     target_width: int = Field(1024, description="")
     target_height: int = Field(1024, description="")
-    clip: ClipField = Field(None, description="Clip to use")
-    clip2: ClipField = Field(None, description="Clip2 to use")
+    clip1: ClipField = Field(None, description="Clip to use")
+    clip2: ClipField = Field(None, description="Clip to use")
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -470,7 +468,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> CompelOutput:
-        c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip, self.prompt, False)
+        c1, c1_pooled, ec1 = self.run_clip_raw(context, self.clip1, self.prompt, False)
         if self.style.strip() == "":
             c2, c2_pooled, ec2 = self.run_clip_raw(context, self.clip2, self.prompt, True)
         else:

invokeai/app/invocations/controlnet_image_processors.py

@@ -20,7 +20,7 @@ from ...backend.model_management import BaseModelType, ModelType
 from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)
-from ..models.image import ImageOutput, PILInvocationConfig
+from .image import ImageOutput, PILInvocationConfig
 
 CONTROLNET_DEFAULT_MODELS = [
     ###########################################
@@ -85,8 +85,8 @@ CONTROLNET_DEFAULT_MODELS = [
 CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
 CONTROLNET_MODE_VALUES = Literal[tuple(
     ["balanced", "more_prompt", "more_control", "unbalanced"])]
-CONTROLNET_RESIZE_VALUES = Literal[tuple(
-    ["just_resize", "crop_resize", "fill_resize", "just_resize_simple",])]
+# crop and fill options not ready yet
+# CONTROLNET_RESIZE_VALUES = Literal[tuple(["just_resize", "crop_resize", "fill_resize"])]
 
 
 class ControlNetModelField(BaseModel):
@@ -111,8 +111,7 @@ class ControlField(BaseModel):
         description="When the ControlNet is last applied (% of total steps)")
     control_mode: CONTROLNET_MODE_VALUES = Field(
         default="balanced", description="The control mode to use")
-    resize_mode: CONTROLNET_RESIZE_VALUES = Field(
-        default="just_resize", description="The resize mode to use")
+    # resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
 
     @validator("control_weight")
     def validate_control_weight(cls, v):
@@ -162,7 +161,6 @@ class ControlNetInvocation(BaseInvocation):
     end_step_percent: float = Field(default=1, ge=0, le=1,
                                     description="When the ControlNet is last applied (% of total steps)")
     control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode used")
-    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode used")
     # fmt: on
 
     class Config(InvocationConfig):
@@ -189,7 +187,6 @@ class ControlNetInvocation(BaseInvocation):
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,
                 control_mode=self.control_mode,
-                resize_mode=self.resize_mode,
             ),
         )
 

invokeai/app/invocations/image.py

@@ -4,21 +4,61 @@ from typing import Literal, Optional
 
 import numpy
 from PIL import Image, ImageFilter, ImageOps, ImageChops
-from pydantic import Field
-from pathlib import Path
+from pydantic import BaseModel, Field
 from typing import Union
-from invokeai.app.invocations.metadata import CoreMetadata
-from ..models.image import (
-    ImageCategory, ImageField, ResourceOrigin,
-    PILInvocationConfig, ImageOutput, MaskOutput,
-)    
+
+from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from .baseinvocation import (
     BaseInvocation,
+    BaseInvocationOutput,
     InvocationContext,
     InvocationConfig,
 )
-from invokeai.backend.image_util.safety_checker import SafetyChecker
-from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
+
+
+class PILInvocationConfig(BaseModel):
+    """Helper class to provide all PIL invocations with additional config"""
+
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "tags": ["PIL", "image"],
+            },
+        }
+
+
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+
+    # fmt: off
+    type: Literal["image_output"] = "image_output"
+    image:      ImageField = Field(default=None, description="The output image")
+    width:             int = Field(description="The width of the image in pixels")
+    height:            int = Field(description="The height of the image in pixels")
+    # fmt: on
+
+    class Config:
+        schema_extra = {"required": ["type", "image", "width", "height"]}
+
+
+class MaskOutput(BaseInvocationOutput):
+    """Base class for invocations that output a mask"""
+
+    # fmt: off
+    type: Literal["mask"] = "mask"
+    mask:      ImageField = Field(default=None, description="The output mask")
+    width:            int = Field(description="The width of the mask in pixels")
+    height:           int = Field(description="The height of the mask in pixels")
+    # fmt: on
+
+    class Config:
+        schema_extra = {
+            "required": [
+                "type",
+                "mask",
+            ]
+        }
+
 
 class LoadImageInvocation(BaseInvocation):
     """Load an image and provide it as output."""
@@ -357,6 +397,7 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
             height=image_dto.height,
         )
 
+
 class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
     """Blurs an image"""
 
@@ -477,8 +518,8 @@ class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
     type: Literal["img_scale"] = "img_scale"
 
     # Inputs
-    image:          Optional[ImageField] = Field(default=None, description="The image to scale")
-    scale_factor:        Optional[float] = Field(default=2.0, gt=0, description="The factor by which to scale the image")
+    image:       Optional[ImageField] = Field(default=None, description="The image to scale")
+    scale_factor:                  float = Field(gt=0, description="The factor by which to scale the image")
     resample_mode:  PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
     # fmt: on
 
@@ -561,6 +602,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
             height=image_dto.height,
         )
 
+
 class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
     """Inverse linear interpolation of all pixels of an image"""
 
@@ -608,97 +650,3 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
             width=image_dto.width,
             height=image_dto.height,
         )
-
-class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
-    """Add blur to NSFW-flagged images"""
-
-    # fmt: off
-    type: Literal["img_nsfw"] = "img_nsfw"
-
-    # Inputs
-    image: Optional[ImageField]  = Field(default=None, description="The image to check")
-    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")    
-    # fmt: on
-
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "title": "Blur NSFW Images",
-                "tags": ["image", "nsfw", "checker"]
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-        
-        logger = context.services.logger
-        logger.debug("Running NSFW checker")
-        if SafetyChecker.has_nsfw_concept(image):
-            logger.info("A potentially NSFW image has been detected. Image will be blurred.")
-            blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
-            caution = self._get_caution_img()
-            blurry_image.paste(caution,(0,0),caution)
-            image = blurry_image
-
-        image_dto = context.services.images.create(
-            image=image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
-        )
-                
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-    
-    def _get_caution_img(self)->Image:
-        import invokeai.app.assets.images as image_assets
-        caution = Image.open(Path(image_assets.__path__[0]) / 'caution.png')
-        return caution.resize((caution.width // 2, caution.height //2))
-
-class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
-    """ Add an invisible watermark to an image """
-
-    # fmt: off
-    type: Literal["img_watermark"] = "img_watermark"
-
-    # Inputs
-    image: Optional[ImageField]  = Field(default=None, description="The image to check")
-    text: str = Field(default='InvokeAI', description="Watermark text")
-    metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")    
-    # fmt: on
-
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "title": "Add Invisible Watermark",
-                "tags": ["image", "watermark", "invisible"]
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_name)
-        new_image = InvisibleWatermark.add_watermark(image, self.text)
-        image_dto = context.services.images.create(
-            image=new_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-            metadata=self.metadata.dict() if self.metadata else None,
-        )
-
-        return ImageOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-

invokeai/app/invocations/latent.py

@@ -22,7 +22,7 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
     PostprocessingSettings
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision
+from ...backend.util.devices import torch_dtype
 from ..models.image import ImageCategory, ImageField, ResourceOrigin
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)
@@ -30,7 +30,6 @@ from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .image import ImageOutput
 from .model import ModelInfo, UNetField, VaeField
-from invokeai.app.util.controlnet_utils import prepare_control_image
 
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
@@ -40,9 +39,6 @@ from diffusers.models.attention_processor import (
 )
 
 
-DEFAULT_PRECISION = choose_precision(choose_torch_device())
-
-
 class LatentsField(BaseModel):
     """A latents field used for passing latents between invocations"""
 
@@ -289,7 +285,7 @@ class TextToLatentsInvocation(BaseInvocation):
                 #        and add in batch_size, num_images_per_prompt?
                 #        and do real check for classifier_free_guidance?
                 # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
-                control_image = prepare_control_image(
+                control_image = model.prepare_control_image(
                     image=input_image,
                     do_classifier_free_guidance=do_classifier_free_guidance,
                     width=control_width_resize,
@@ -299,18 +295,13 @@ class TextToLatentsInvocation(BaseInvocation):
                     device=control_model.device,
                     dtype=control_model.dtype,
                     control_mode=control_info.control_mode,
-                    resize_mode=control_info.resize_mode,
                 )
                 control_item = ControlNetData(
-                    model=control_model,
-                    image_tensor=control_image,
+                    model=control_model, image_tensor=control_image,
                     weight=control_info.control_weight,
                     begin_step_percent=control_info.begin_step_percent,
                     end_step_percent=control_info.end_step_percent,
                     control_mode=control_info.control_mode,
-                    # any resizing needed should currently be happening in prepare_control_image(),
-                    #    but adding resize_mode to ControlNetData in case needed in the future
-                    resize_mode=control_info.resize_mode,
                 )
                 control_data.append(control_item)
                 # MultiControlNetModel has been refactored out, just need list[ControlNetData]
@@ -501,8 +492,8 @@ class LatentsToImageInvocation(BaseInvocation):
     vae: VaeField = Field(default=None, description="Vae submodel")
     tiled: bool = Field(
         default=False,
-        description="Decode latents by overlapping tiles(less memory consumption)")
-    fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
+        description="Decode latents by overlaping tiles(less memory consumption)")
+    fp32: bool = Field(False, description="Decode in full precision")
     metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
 
     # Schema customisation
@@ -607,7 +598,7 @@ class ResizeLatentsInvocation(BaseInvocation):
     antialias: bool = Field(
         default=False,
         description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
-
+    
     class Config(InvocationConfig):
         schema_extra = {
             "ui": {
@@ -653,7 +644,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     antialias: bool = Field(
         default=False,
         description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
-
+    
     class Config(InvocationConfig):
         schema_extra = {
             "ui": {
@@ -696,7 +687,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     tiled: bool = Field(
         default=False,
         description="Encode latents by overlaping tiles(less memory consumption)")
-    fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
+    fp32: bool = Field(False, description="Decode in full precision")
 
 
     # Schema customisation
@@ -764,7 +755,7 @@ class ImageToLatentsInvocation(BaseInvocation):
                     dtype=vae.dtype
                 )  # FIXME: uses torch.randn. make reproducible!
 
-            latents = vae.config.scaling_factor * latents
+            latents = 0.18215 * latents
             latents = latents.to(dtype=orig_dtype)
 
         name = f"{context.graph_execution_state_id}__{self.id}"

invokeai/app/invocations/metadata.py

@@ -2,18 +2,16 @@ from typing import Literal, Optional, Union
 
 from pydantic import BaseModel, Field
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InvocationConfig,
-    InvocationContext,
-)
+from invokeai.app.invocations.baseinvocation import (BaseInvocation,
+                                                     BaseInvocationOutput, InvocationConfig,
+                                                     InvocationContext)
 from invokeai.app.invocations.controlnet_image_processors import ControlField
-from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
+from invokeai.app.invocations.model import (LoRAModelField, MainModelField,
+                                            VAEModelField)
+
 
 class LoRAMetadataField(BaseModel):
     """LoRA metadata for an image generated in InvokeAI."""
-
     lora: LoRAModelField = Field(description="The LoRA model")
     weight: float = Field(description="The weight of the LoRA model")
 
@@ -21,9 +19,7 @@ class LoRAMetadataField(BaseModel):
 class CoreMetadata(BaseModel):
     """Core generation metadata for an image generated in InvokeAI."""
 
-    generation_mode: str = Field(
-        description="The generation mode that output this image",
-    )
+    generation_mode: str = Field(description="The generation mode that output this image",)
     positive_prompt: str = Field(description="The positive prompt parameter")
     negative_prompt: str = Field(description="The negative prompt parameter")
     width: int = Field(description="The width parameter")
@@ -33,20 +29,10 @@ class CoreMetadata(BaseModel):
     cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
     steps: int = Field(description="The number of steps used for inference")
     scheduler: str = Field(description="The scheduler used for inference")
-    clip_skip: int = Field(
-        description="The number of skipped CLIP layers",
-    )
+    clip_skip: int = Field(description="The number of skipped CLIP layers",)
     model: MainModelField = Field(description="The main model used for inference")
-    controlnets: list[ControlField] = Field(
-        description="The ControlNets used for inference"
-    )
+    controlnets: list[ControlField]= Field(description="The ControlNets used for inference")
     loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
-    vae: Union[VAEModelField, None] = Field(
-        default=None,
-        description="The VAE used for decoding, if the main model's default was not used",
-    )
-
-    # Latents-to-Latents
     strength: Union[float, None] = Field(
         default=None,
         description="The strength used for latents-to-latents",
@@ -54,34 +40,9 @@ class CoreMetadata(BaseModel):
     init_image: Union[str, None] = Field(
         default=None, description="The name of the initial image"
     )
-
-    # SDXL
-    positive_style_prompt: Union[str, None] = Field(
-        default=None, description="The positive style prompt parameter"
-    )
-    negative_style_prompt: Union[str, None] = Field(
-        default=None, description="The negative style prompt parameter"
-    )
-
-    # SDXL Refiner
-    refiner_model: Union[MainModelField, None] = Field(
-        default=None, description="The SDXL Refiner model used"
-    )
-    refiner_cfg_scale: Union[float, None] = Field(
+    vae: Union[VAEModelField, None] = Field(
         default=None,
-        description="The classifier-free guidance scale parameter used for the refiner",
-    )
-    refiner_steps: Union[int, None] = Field(
-        default=None, description="The number of steps used for the refiner"
-    )
-    refiner_scheduler: Union[str, None] = Field(
-        default=None, description="The scheduler used for the refiner"
-    )
-    refiner_aesthetic_store: Union[float, None] = Field(
-        default=None, description="The aesthetic score used for the refiner"
-    )
-    refiner_start: Union[float, None] = Field(
-        default=None, description="The start value used for refiner denoising"
+        description="The VAE used for decoding, if the main model's default was not used",
     )
 
 
@@ -110,9 +71,7 @@ class MetadataAccumulatorInvocation(BaseInvocation):
 
     type: Literal["metadata_accumulator"] = "metadata_accumulator"
 
-    generation_mode: str = Field(
-        description="The generation mode that output this image",
-    )
+    generation_mode: str = Field(description="The generation mode that output this image",)
     positive_prompt: str = Field(description="The positive prompt parameter")
     negative_prompt: str = Field(description="The negative prompt parameter")
     width: int = Field(description="The width parameter")
@@ -122,13 +81,9 @@ class MetadataAccumulatorInvocation(BaseInvocation):
     cfg_scale: float = Field(description="The classifier-free guidance scale parameter")
     steps: int = Field(description="The number of steps used for inference")
     scheduler: str = Field(description="The scheduler used for inference")
-    clip_skip: int = Field(
-        description="The number of skipped CLIP layers",
-    )
+    clip_skip: int = Field(description="The number of skipped CLIP layers",)
     model: MainModelField = Field(description="The main model used for inference")
-    controlnets: list[ControlField] = Field(
-        description="The ControlNets used for inference"
-    )
+    controlnets: list[ControlField]= Field(description="The ControlNets used for inference")
     loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
     strength: Union[float, None] = Field(
         default=None,
@@ -142,44 +97,36 @@ class MetadataAccumulatorInvocation(BaseInvocation):
         description="The VAE used for decoding, if the main model's default was not used",
     )
 
-    # SDXL
-    positive_style_prompt: Union[str, None] = Field(
-        default=None, description="The positive style prompt parameter"
-    )
-    negative_style_prompt: Union[str, None] = Field(
-        default=None, description="The negative style prompt parameter"
-    )
-
-    # SDXL Refiner
-    refiner_model: Union[MainModelField, None] = Field(
-        default=None, description="The SDXL Refiner model used"
-    )
-    refiner_cfg_scale: Union[float, None] = Field(
-        default=None,
-        description="The classifier-free guidance scale parameter used for the refiner",
-    )
-    refiner_steps: Union[int, None] = Field(
-        default=None, description="The number of steps used for the refiner"
-    )
-    refiner_scheduler: Union[str, None] = Field(
-        default=None, description="The scheduler used for the refiner"
-    )
-    refiner_aesthetic_store: Union[float, None] = Field(
-        default=None, description="The aesthetic score used for the refiner"
-    )
-    refiner_start: Union[float, None] = Field(
-        default=None, description="The start value used for refiner denoising"
-    )
-
     class Config(InvocationConfig):
         schema_extra = {
             "ui": {
                 "title": "Metadata Accumulator",
-                "tags": ["image", "metadata", "generation"],
+                "tags": ["image", "metadata", "generation"]
             },
         }
 
+
     def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
         """Collects and outputs a CoreMetadata object"""
 
-        return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.dict()))
+        return MetadataAccumulatorOutput(
+            metadata=CoreMetadata(
+                generation_mode=self.generation_mode,
+                positive_prompt=self.positive_prompt,
+                negative_prompt=self.negative_prompt,
+                width=self.width,
+                height=self.height,
+                seed=self.seed,
+                rand_device=self.rand_device,
+                cfg_scale=self.cfg_scale,
+                steps=self.steps,
+                scheduler=self.scheduler,
+                model=self.model,
+                strength=self.strength,
+                init_image=self.init_image,
+                vae=self.vae,
+                controlnets=self.controlnets,
+                loras=self.loras,
+                clip_skip=self.clip_skip,
+            )
+        )

invokeai/app/invocations/noise.py

@@ -119,8 +119,8 @@ class NoiseInvocation(BaseInvocation):
 
     @validator("seed", pre=True)
     def modulo_seed(cls, v):
-        """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
-        return v % (SEED_MAX + 1)
+        """Returns the seed modulo SEED_MAX to ensure it is within the valid range."""
+        return v % SEED_MAX
 
     def invoke(self, context: InvocationContext) -> NoiseOutput:
         noise = get_noise(

invokeai/app/invocations/sdxl.py

@@ -6,7 +6,6 @@ from typing import List, Literal, Optional, Union
 from pydantic import Field, validator
 
 from ...backend.model_management import ModelType, SubModelType
-from invokeai.app.util.step_callback import stable_diffusion_xl_step_callback
 from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                              InvocationConfig, InvocationContext)
 
@@ -138,7 +137,7 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
             "ui": {
                 "title": "SDXL Refiner Model Loader",
                 "tags": ["model", "loader", "sdxl_refiner"],
-                "type_hints": {"model": "refiner_model"},
+                "type_hints": {"model": "model"},
             },
         }
 
@@ -244,31 +243,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
             },
         }
 
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        sample,
-        step,
-        total_steps,
-    ) -> None:
-        stable_diffusion_xl_step_callback(
-            context=context,
-            node=self.dict(),
-            source_node_id=source_node_id,
-            sample=sample,
-            step=step,
-            total_steps=total_steps,
-        )
-
     # based on
     # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
         latents = context.services.latents.get(self.noise.latents_name)
 
         positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
@@ -295,7 +273,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
 
 
         unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict(), context=context
+            **self.unet.unet.dict()
         )
         do_classifier_free_guidance = True
         cross_attention_kwargs = None
@@ -363,7 +341,6 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
                         # call the callback, if provided
                         if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
                             progress_bar.update()
-                            self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
                             #if callback is not None and i % callback_steps == 0:
                             #    callback(i, t, latents)
             else:
@@ -432,7 +409,6 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
                         # call the callback, if provided
                         if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
                             progress_bar.update()
-                            self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
                             #if callback is not None and i % callback_steps == 0:
                             #    callback(i, t, latents)
 
@@ -463,8 +439,8 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
     unet: UNetField = Field(default=None, description="UNet submodel")
     latents: Optional[LatentsField] = Field(description="Initial latents")
 
-    denoising_start: float = Field(default=0.0, ge=0, le=1, description="")
-    denoising_end: float = Field(default=1.0, ge=0, le=1, description="")
+    denoising_start: float = Field(default=0.0, ge=0, lt=1, description="")
+    denoising_end: float = Field(default=1.0, gt=0, le=1, description="")
 
     #control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
     #seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
@@ -497,31 +473,10 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
             },
         }
 
-    def dispatch_progress(
-        self,
-        context: InvocationContext,
-        source_node_id: str,
-        sample,
-        step,
-        total_steps,
-    ) -> None:
-        stable_diffusion_xl_step_callback(
-            context=context,
-            node=self.dict(),
-            source_node_id=source_node_id,
-            sample=sample,
-            step=step,
-            total_steps=total_steps,
-        )
-
     # based on
     # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        graph_execution_state = context.services.graph_execution_manager.get(
-            context.graph_execution_state_id
-        )
-        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
         latents = context.services.latents.get(self.latents.latents_name)
 
         positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
@@ -549,13 +504,13 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
         num_inference_steps = num_inference_steps - t_start
 
         # apply noise(if provided)
-        if self.noise is not None and timesteps.shape[0] > 0:
+        if self.noise is not None:
             noise = context.services.latents.get(self.noise.latents_name)
             latents = scheduler.add_noise(latents, noise, timesteps[:1])
             del noise
 
         unet_info = context.services.model_manager.get_model(
-            **self.unet.unet.dict(), context=context,
+            **self.unet.unet.dict()
         )
         do_classifier_free_guidance = True
         cross_attention_kwargs = None
@@ -624,7 +579,6 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
                         # call the callback, if provided
                         if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
                             progress_bar.update()
-                            self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
                             #if callback is not None and i % callback_steps == 0:
                             #    callback(i, t, latents)
             else:
@@ -693,7 +647,6 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
                         # call the callback, if provided
                         if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
                             progress_bar.update()
-                            self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
                             #if callback is not None and i % callback_steps == 0:
                             #    callback(i, t, latents)
 

invokeai/app/invocations/upscale.py

@@ -1,6 +1,6 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
-from pathlib import Path
-from typing import Literal, Union
+from pathlib import Path, PosixPath
+from typing import Literal, Union, cast
 
 import cv2 as cv
 import numpy as np
@@ -16,20 +16,19 @@ from .image import ImageOutput
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
-ESRGAN_MODELS = Literal[
+REALESRGAN_MODELS = Literal[
     "RealESRGAN_x4plus.pth",
     "RealESRGAN_x4plus_anime_6B.pth",
     "ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
-    "RealESRGAN_x2plus.pth",
 ]
 
 
-class ESRGANInvocation(BaseInvocation):
+class RealESRGANInvocation(BaseInvocation):
     """Upscales an image using RealESRGAN."""
 
-    type: Literal["esrgan"] = "esrgan"
+    type: Literal["realesrgan"] = "realesrgan"
     image: Union[ImageField, None] = Field(default=None, description="The input image")
-    model_name: ESRGAN_MODELS = Field(
+    model_name: REALESRGAN_MODELS = Field(
         default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use"
     )
 
@@ -74,17 +73,19 @@ class ESRGANInvocation(BaseInvocation):
                 scale=4,
             )
             netscale = 4
-        elif self.model_name in ["RealESRGAN_x2plus.pth"]:
-            # x2 RRDBNet model
-            rrdbnet_model = RRDBNet(
-                num_in_ch=3,
-                num_out_ch=3,
-                num_feat=64,
-                num_block=23,
-                num_grow_ch=32,
-                scale=2,
-            )
-            netscale = 2
+        # TODO: add x2 models handling?
+        # elif self.model_name in ["RealESRGAN_x2plus"]:
+        #     # x2 RRDBNet model
+        #     model = RRDBNet(
+        #         num_in_ch=3,
+        #         num_out_ch=3,
+        #         num_feat=64,
+        #         num_block=23,
+        #         num_grow_ch=32,
+        #         scale=2,
+        #     )
+        #     model_path = Path()
+        #     netscale = 2
         else:
             msg = f"Invalid RealESRGAN model: {self.model_name}"
             context.services.logger.error(msg)

invokeai/app/models/image.py

@@ -1,80 +1,9 @@
 from enum import Enum
-from typing import Optional, Tuple, Literal
+from typing import Optional, Tuple
 from pydantic import BaseModel, Field
 
 from invokeai.app.util.metaenum import MetaEnum
-from ..invocations.baseinvocation import (
-    BaseInvocationOutput,
-    InvocationConfig,
-)
 
-class ImageField(BaseModel):
-    """An image field used for passing image objects between invocations"""
-
-    image_name: Optional[str] = Field(default=None, description="The name of the image")
-
-    class Config:
-        schema_extra = {"required": ["image_name"]}
-
-
-class ColorField(BaseModel):
-    r: int = Field(ge=0, le=255, description="The red component")
-    g: int = Field(ge=0, le=255, description="The green component")
-    b: int = Field(ge=0, le=255, description="The blue component")
-    a: int = Field(ge=0, le=255, description="The alpha component")
-
-    def tuple(self) -> Tuple[int, int, int, int]:
-        return (self.r, self.g, self.b, self.a)
-
-
-class ProgressImage(BaseModel):
-    """The progress image sent intermittently during processing"""
-
-    width: int = Field(description="The effective width of the image in pixels")
-    height: int = Field(description="The effective height of the image in pixels")
-    dataURL: str = Field(description="The image data as a b64 data URL")
-
-class PILInvocationConfig(BaseModel):
-    """Helper class to provide all PIL invocations with additional config"""
-
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["PIL", "image"],
-            },
-        }
-
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
-    image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {"required": ["type", "image", "width", "height"]}
-
-
-class MaskOutput(BaseInvocationOutput):
-    """Base class for invocations that output a mask"""
-
-    # fmt: off
-    type: Literal["mask"] = "mask"
-    mask:      ImageField = Field(default=None, description="The output mask")
-    width:            int = Field(description="The width of the mask in pixels")
-    height:           int = Field(description="The height of the mask in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {
-            "required": [
-                "type",
-                "mask",
-            ]
-        }
 
 class ResourceOrigin(str, Enum, metaclass=MetaEnum):
     """The origin of a resource (eg image).
@@ -134,3 +63,28 @@ class InvalidImageCategoryException(ValueError):
         super().__init__(message)
 
 
+class ImageField(BaseModel):
+    """An image field used for passing image objects between invocations"""
+
+    image_name: Optional[str] = Field(default=None, description="The name of the image")
+
+    class Config:
+        schema_extra = {"required": ["image_name"]}
+
+
+class ColorField(BaseModel):
+    r: int = Field(ge=0, le=255, description="The red component")
+    g: int = Field(ge=0, le=255, description="The green component")
+    b: int = Field(ge=0, le=255, description="The blue component")
+    a: int = Field(ge=0, le=255, description="The alpha component")
+
+    def tuple(self) -> Tuple[int, int, int, int]:
+        return (self.r, self.g, self.b, self.a)
+
+
+class ProgressImage(BaseModel):
+    """The progress image sent intermittently during processing"""
+
+    width: int = Field(description="The effective width of the image in pixels")
+    height: int = Field(description="The effective height of the image in pixels")
+    dataURL: str = Field(description="The image data as a b64 data URL")

invokeai/app/services/board_image_record_storage.py

@@ -32,11 +32,11 @@ class BoardImageRecordStorageBase(ABC):
         pass
 
     @abstractmethod
-    def get_all_board_image_names_for_board(
+    def get_images_for_board(
         self,
         board_id: str,
-    ) -> list[str]:
-        """Gets all board images for a board, as a list of the image names."""
+    ) -> OffsetPaginatedResults[ImageRecord]:
+        """Gets images for a board."""
         pass
 
     @abstractmethod
@@ -211,26 +211,6 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
             items=images, offset=offset, limit=limit, total=count
         )
 
-    def get_all_board_image_names_for_board(self, board_id: str) -> list[str]:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                SELECT image_name
-                FROM board_images
-                WHERE board_id = ?;
-                """,
-                (board_id,),
-            )
-            result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            image_names = list(map(lambda r: r[0], result))
-            return image_names
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise e
-        finally:
-            self._lock.release()
-
     def get_board_for_image(
         self,
         image_name: str,

invokeai/app/services/board_images.py

@@ -38,11 +38,11 @@ class BoardImagesServiceABC(ABC):
         pass
 
     @abstractmethod
-    def get_all_board_image_names_for_board(
+    def get_images_for_board(
         self,
         board_id: str,
-    ) -> list[str]:
-        """Gets all board images for a board, as a list of the image names."""
+    ) -> OffsetPaginatedResults[ImageDTO]:
+        """Gets images for a board."""
         pass
 
     @abstractmethod
@@ -98,13 +98,30 @@ class BoardImagesService(BoardImagesServiceABC):
     ) -> None:
         self._services.board_image_records.remove_image_from_board(board_id, image_name)
 
-    def get_all_board_image_names_for_board(
+    def get_images_for_board(
         self,
         board_id: str,
-    ) -> list[str]:
-        return self._services.board_image_records.get_all_board_image_names_for_board(
+    ) -> OffsetPaginatedResults[ImageDTO]:
+        image_records = self._services.board_image_records.get_images_for_board(
             board_id
         )
+        image_dtos = list(
+            map(
+                lambda r: image_record_to_dto(
+                    r,
+                    self._services.urls.get_image_url(r.image_name),
+                    self._services.urls.get_image_url(r.image_name, True),
+                    board_id,
+                ),
+                image_records.items,
+            )
+        )
+        return OffsetPaginatedResults[ImageDTO](
+            items=image_dtos,
+            offset=image_records.offset,
+            limit=image_records.limit,
+            total=image_records.total,
+        )
 
     def get_board_for_image(
         self,
@@ -119,7 +136,7 @@ def board_record_to_dto(
 ) -> BoardDTO:
     """Converts a board record to a board DTO."""
     return BoardDTO(
-        **board_record.dict(exclude={"cover_image_name"}),
+        **board_record.dict(exclude={'cover_image_name'}),
         cover_image_name=cover_image_name,
         image_count=image_count,
     )

invokeai/app/services/config.py

@@ -28,6 +28,7 @@ InvokeAI:
     always_use_cpu: false
     free_gpu_mem: false
   Features:
+    nsfw_checker: true
     restore: true
     esrgan: true
     patchmatch: true
@@ -91,18 +92,18 @@ Typical usage at the top level file:
 
  from invokeai.app.services.config import InvokeAIAppConfig
 
- # get global configuration and print its cache size
+ # get global configuration and print its nsfw_checker value
  conf = InvokeAIAppConfig.get_config()
  conf.parse_args()
- print(conf.max_cache_size)
+ print(conf.nsfw_checker)
 
 Typical usage in a backend module:
 
  from invokeai.app.services.config import InvokeAIAppConfig
 
- # get global configuration and print its cache size value
+ # get global configuration and print its nsfw_checker value
  conf = InvokeAIAppConfig.get_config()
- print(conf.max_cache_size)
+ print(conf.nsfw_checker)
 
 
 Computed properties:
@@ -276,7 +277,7 @@ class InvokeAISettings(BaseSettings):
     @classmethod
     def _excluded_from_yaml(self)->List[str]:
         # combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
-        return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore', 'root', 'nsfw_checker']
+        return ['type','initconf', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore']
 
     class Config:
         env_file_encoding = 'utf-8'
@@ -363,6 +364,7 @@ setting environment variables INVOKEAI_<setting>.
     esrgan              : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
     internet_available  : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
     log_tokenization    : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
+    nsfw_checker        : bool = Field(default=True, description="Enable/disable the NSFW checker", category='Features')
     patchmatch          : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
     restore             : bool = Field(default=True, description="Enable/disable face restoration code (DEPRECATED)", category='DEPRECATED')
 
@@ -372,17 +374,16 @@ setting environment variables INVOKEAI_<setting>.
     max_cache_size      : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
     max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
     gpu_mem_reserved    : float = Field(default=2.75, ge=0, description="DEPRECATED: use max_vram_cache_size. Amount of VRAM reserved for model storage", category='DEPRECATED')
-    nsfw_checker        : bool = Field(default=True, description="DEPRECATED: use Web settings to enable/disable", category='DEPRECATED')
-    precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='auto',description='Floating point precision', category='Memory/Performance')
+    precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance')
     sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
     xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
     tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
 
     root                : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths')
-    autoimport_dir      : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
-    lora_dir            : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
-    embedding_dir       : Path = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='Paths')
-    controlnet_dir      : Path = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', category='Paths')
+    autoimport_dir      : Path = Field(default='autoimport/main', description='Path to a directory of models files to be imported on startup.', category='Paths')
+    lora_dir            : Path = Field(default='autoimport/lora', description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
+    embedding_dir       : Path = Field(default='autoimport/embedding', description='Path to a directory of Textual Inversion embeddings to be imported on startup.', category='Paths')
+    controlnet_dir      : Path = Field(default='autoimport/controlnet', description='Path to a directory of ControlNet embeddings to be imported on startup.', category='Paths')
     conf_path           : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
     models_dir          : Path = Field(default='models', description='Path to the models directory', category='Paths')
     legacy_conf_dir     : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
@@ -396,7 +397,7 @@ setting environment variables INVOKEAI_<setting>.
     log_handlers        : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
     # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
     log_format          : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
-    log_level           : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or  higher", category="Logging")
+    log_level           : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="debug", description="Emit logging messages at this level or  higher", category="Logging")
 
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
     #fmt: on
@@ -445,7 +446,7 @@ setting environment variables INVOKEAI_<setting>.
         Path to the runtime root directory
         '''
         if self.root:
-            return Path(self.root).expanduser().absolute()
+            return Path(self.root).expanduser()
         else:
             return self.find_root()
 
@@ -524,16 +525,6 @@ setting environment variables INVOKEAI_<setting>.
         """Return true if patchmatch true"""
         return self.patchmatch
 
-    @property
-    def nsfw_checker(self)->bool:
-        """ NSFW node is always active and disabled from Web UIe"""
-        return True
-
-    @property
-    def invisible_watermark(self)->bool:
-        """ invisible watermark node is always active and disabled from Web UIe"""
-        return True
-    
     @staticmethod
     def find_root()->Path:
         '''

invokeai/app/services/default_graphs.py

@@ -1,5 +1,4 @@
 from ..invocations.latent import LatentsToImageInvocation, TextToLatentsInvocation
-from ..invocations.image import ImageNSFWBlurInvocation
 from ..invocations.noise import NoiseInvocation
 from ..invocations.compel import CompelInvocation
 from ..invocations.params import ParamIntInvocation
@@ -25,7 +24,6 @@ def create_text_to_image() -> LibraryGraph:
                 '5': CompelInvocation(id='5'),
                 '6': TextToLatentsInvocation(id='6'),
                 '7': LatentsToImageInvocation(id='7'),
-                '8': ImageNSFWBlurInvocation(id='8'),
             },
             edges=[
                 Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
@@ -35,7 +33,6 @@ def create_text_to_image() -> LibraryGraph:
                 Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
                 Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
                 Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
-                Edge(source=EdgeConnection(node_id='7', field='image'), destination=EdgeConnection(node_id='8', field='image')),
             ]
         ),
         exposed_inputs=[
@@ -46,7 +43,7 @@ def create_text_to_image() -> LibraryGraph:
             ExposedNodeInput(node_path='seed', field='a', alias='seed'),
         ],
         exposed_outputs=[
-            ExposedNodeOutput(node_path='8', field='image', alias='image')
+            ExposedNodeOutput(node_path='7', field='image', alias='image')
         ])
 
 

invokeai/app/services/events.py

@@ -1,15 +1,11 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from typing import Any, Optional
+from pathlib import Path
+
 from invokeai.app.models.image import ProgressImage
 from invokeai.app.util.misc import get_timestamp
-from invokeai.app.services.model_manager_service import (
-    BaseModelType,
-    ModelType,
-    SubModelType,
-    ModelInfo,
-)
-
+from invokeai.app.services.model_manager_service import BaseModelType, ModelType, SubModelType, ModelInfo, AddModelResult
 
 class EventServiceBase:
     session_event: str = "session_event"
@@ -44,9 +40,7 @@ class EventServiceBase:
                 graph_execution_state_id=graph_execution_state_id,
                 node=node,
                 source_node_id=source_node_id,
-                progress_image=progress_image.dict()
-                if progress_image is not None
-                else None,
+                progress_image=progress_image.dict() if progress_image is not None else None,
                 step=step,
                 total_steps=total_steps,
             ),
@@ -75,7 +69,6 @@ class EventServiceBase:
         graph_execution_state_id: str,
         node: dict,
         source_node_id: str,
-        error_type: str,
         error: str,
     ) -> None:
         """Emitted when an invocation has completed"""
@@ -85,7 +78,6 @@ class EventServiceBase:
                 graph_execution_state_id=graph_execution_state_id,
                 node=node,
                 source_node_id=source_node_id,
-                error_type=error_type,
                 error=error,
             ),
         )
@@ -112,16 +104,16 @@ class EventServiceBase:
             ),
         )
 
-    def emit_model_load_started(
-        self,
-        graph_execution_state_id: str,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        submodel: SubModelType,
+    def emit_model_load_started (
+            self,
+            graph_execution_state_id: str,
+            model_name: str,
+            base_model: BaseModelType,
+            model_type: ModelType,
+            submodel: SubModelType,
     ) -> None:
         """Emitted when a model is requested"""
-        self.__emit_session_event(
+        self.dispatch(
             event_name="model_load_started",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
@@ -133,16 +125,16 @@ class EventServiceBase:
         )
 
     def emit_model_load_completed(
-        self,
-        graph_execution_state_id: str,
-        model_name: str,
-        base_model: BaseModelType,
-        model_type: ModelType,
-        submodel: SubModelType,
-        model_info: ModelInfo,
+            self,
+            graph_execution_state_id: str,
+            model_name: str,
+            base_model: BaseModelType,
+            model_type: ModelType,
+            submodel: SubModelType,
+            model_info: ModelInfo,
     ) -> None:
         """Emitted when a model is correctly loaded (returns model info)"""
-        self.__emit_session_event(
+        self.dispatch(
             event_name="model_load_completed",
             payload=dict(
                 graph_execution_state_id=graph_execution_state_id,
@@ -151,41 +143,96 @@ class EventServiceBase:
                 model_type=model_type,
                 submodel=submodel,
                 hash=model_info.hash,
-                location=str(model_info.location),
+                location=model_info.location,
                 precision=str(model_info.precision),
             ),
         )
 
-    def emit_session_retrieval_error(
-        self,
-        graph_execution_state_id: str,
-        error_type: str,
-        error: str,
-    ) -> None:
-        """Emitted when session retrieval fails"""
-        self.__emit_session_event(
-            event_name="session_retrieval_error",
+    def emit_model_import_started (
+            self,
+            import_path: str,  # can be a local path, URL or repo_id
+    )->None:
+        """Emitted when a model import commences"""
+        self.dispatch(
+            event_name="model_import_started",
             payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-                error_type=error_type,
-                error=error,
-            ),
+                import_path = import_path,
+            )
+        )
+        
+    def emit_model_import_completed (
+            self,
+            import_path: str,  # can be a local path, URL or repo_id
+            import_info: AddModelResult,
+            success: bool= True,
+            error: str = None,
+            
+    )->None:
+        """Emitted when a model import completes"""
+        self.dispatch(
+            event_name="model_import_completed",
+            payload=dict(
+                import_path = import_path,
+                import_info = import_info,
+                success = success,
+                error = error,
+            )
+        )
+    
+    def emit_download_started (
+            self,
+            url: str,
+            
+    )->None:
+        """Emitted when a download thread starts"""
+        self.dispatch(
+            event_name="download_started",
+            payload=dict(
+                url = url,
+            )
         )
 
-    def emit_invocation_retrieval_error(
-        self,
-        graph_execution_state_id: str,
-        node_id: str,
-        error_type: str,
-        error: str,
-    ) -> None:
-        """Emitted when invocation retrieval fails"""
-        self.__emit_session_event(
-            event_name="invocation_retrieval_error",
+    def emit_download_progress (
+            self,
+            url: str,
+            downloaded_size: int,
+            total_size: int,
+    )->None:
+        """
+        Emitted at intervals during a download process
+        :param url: Requested URL
+        :param downloaded_size: Bytes downloaded so far
+        :param total_size: Total bytes to download
+        """
+        self.dispatch(
+            event_name="download_progress",
             payload=dict(
-                graph_execution_state_id=graph_execution_state_id,
-                node_id=node_id,
-                error_type=error_type,
-                error=error,
-            ),
+                url = url,
+                downloaded_size = downloaded_size,
+                total_size = total_size,
+            )
         )
+
+    def emit_download_completed (
+            self,
+            url: str,
+            status_code: int,
+            download_path: Path,
+            
+    )->None:
+        """
+        Emitted when a download thread completes. 
+        :param url: Requested URL
+        :param status_code: HTTP status code from request
+        :param download_path: Path to downloaded file
+        """
+        self.dispatch(
+            event_name="download_completed",
+            payload=dict(
+                url = url,
+                status_code = status_code,
+                download_path = download_path,
+            )
+        )
+
+    

invokeai/app/services/image_record_storage.py

@@ -10,10 +10,7 @@ from pydantic.generics import GenericModel
 
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from invokeai.app.services.models.image_record import (
-    ImageRecord,
-    ImageRecordChanges,
-    deserialize_image_record,
-)
+    ImageRecord, ImageRecordChanges, deserialize_image_record)
 
 T = TypeVar("T", bound=BaseModel)
 
@@ -100,8 +97,8 @@ class ImageRecordStorageBase(ABC):
     @abstractmethod
     def get_many(
         self,
-        offset: Optional[int] = None,
-        limit: Optional[int] = None,
+        offset: int = 0,
+        limit: int = 10,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
@@ -122,11 +119,6 @@ class ImageRecordStorageBase(ABC):
         """Deletes many image records."""
         pass
 
-    @abstractmethod
-    def delete_intermediates(self) -> list[str]:
-        """Deletes all intermediate image records, returning a list of deleted image names."""
-        pass
-
     @abstractmethod
     def save(
         self,
@@ -330,8 +322,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
     def get_many(
         self,
-        offset: Optional[int] = None,
-        limit: Optional[int] = None,
+        offset: int = 0,
+        limit: int = 10,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
@@ -385,15 +377,11 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
                 query_params.append(is_intermediate)
 
-            # board_id of "none" is reserved for images without a board
-            if board_id == "none":
-                query_conditions += """--sql
-                AND board_images.board_id IS NULL
-                """
-            elif board_id is not None:
+            if board_id is not None:
                 query_conditions += """--sql
                 AND board_images.board_id = ?
                 """
+
                 query_params.append(board_id)
 
             query_pagination = """--sql
@@ -404,12 +392,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             images_query += query_conditions + query_pagination + ";"
             # Add all the parameters
             images_params = query_params.copy()
-
-            if limit is not None:
-                images_params.append(limit)
-            if offset is not None:
-                images_params.append(offset)
-
+            images_params.append(limit)
+            images_params.append(offset)
             # Build the list of images, deserializing each row
             self._cursor.execute(images_query, images_params)
             result = cast(list[sqlite3.Row], self._cursor.fetchall())
@@ -466,32 +450,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         finally:
             self._lock.release()
 
-
-    def delete_intermediates(self) -> list[str]:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                SELECT image_name FROM images
-                WHERE is_intermediate = TRUE;
-                """
-            )
-            result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            image_names = list(map(lambda r: r[0], result))
-            self._cursor.execute(
-                """--sql
-                DELETE FROM images
-                WHERE is_intermediate = TRUE;
-                """
-            )
-            self._conn.commit()
-            return image_names
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise ImageRecordDeleteException from e
-        finally:
-            self._lock.release()
-
     def save(
         self,
         image_name: str,

invokeai/app/services/images.py

@@ -6,33 +6,22 @@ from typing import TYPE_CHECKING, Optional
 from PIL.Image import Image as PILImageType
 
 from invokeai.app.invocations.metadata import ImageMetadata
-from invokeai.app.models.image import (
-    ImageCategory,
-    InvalidImageCategoryException,
-    InvalidOriginException,
-    ResourceOrigin,
-)
-from invokeai.app.services.board_image_record_storage import BoardImageRecordStorageBase
+from invokeai.app.models.image import (ImageCategory,
+                                       InvalidImageCategoryException,
+                                       InvalidOriginException, ResourceOrigin)
+from invokeai.app.services.board_image_record_storage import \
+    BoardImageRecordStorageBase
+from invokeai.app.services.graph import Graph
 from invokeai.app.services.image_file_storage import (
-    ImageFileDeleteException,
-    ImageFileNotFoundException,
-    ImageFileSaveException,
-    ImageFileStorageBase,
-)
+    ImageFileDeleteException, ImageFileNotFoundException,
+    ImageFileSaveException, ImageFileStorageBase)
 from invokeai.app.services.image_record_storage import (
-    ImageRecordDeleteException,
-    ImageRecordNotFoundException,
-    ImageRecordSaveException,
-    ImageRecordStorageBase,
-    OffsetPaginatedResults,
-)
+    ImageRecordDeleteException, ImageRecordNotFoundException,
+    ImageRecordSaveException, ImageRecordStorageBase, OffsetPaginatedResults)
 from invokeai.app.services.item_storage import ItemStorageABC
-from invokeai.app.services.models.image_record import (
-    ImageDTO,
-    ImageRecord,
-    ImageRecordChanges,
-    image_record_to_dto,
-)
+from invokeai.app.services.models.image_record import (ImageDTO, ImageRecord,
+                                                       ImageRecordChanges,
+                                                       image_record_to_dto)
 from invokeai.app.services.resource_name import NameServiceBase
 from invokeai.app.services.urls import UrlServiceBase
 from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
@@ -52,7 +41,6 @@ class ImageServiceABC(ABC):
         image_category: ImageCategory,
         node_id: Optional[str] = None,
         session_id: Optional[str] = None,
-        board_id: Optional[str] = None,
         is_intermediate: bool = False,
         metadata: Optional[dict] = None,
     ) -> ImageDTO:
@@ -121,11 +109,6 @@ class ImageServiceABC(ABC):
         """Deletes an image."""
         pass
 
-    @abstractmethod
-    def delete_intermediates(self) -> int:
-        """Deletes all intermediate images."""
-        pass
-
     @abstractmethod
     def delete_images_on_board(self, board_id: str):
         """Deletes all images on a board."""
@@ -175,7 +158,6 @@ class ImageService(ImageServiceABC):
         image_category: ImageCategory,
         node_id: Optional[str] = None,
         session_id: Optional[str] = None,
-        board_id: Optional[str] = None,
         is_intermediate: bool = False,
         metadata: Optional[dict] = None,
     ) -> ImageDTO:
@@ -216,13 +198,11 @@ class ImageService(ImageServiceABC):
                 metadata=metadata,
                 session_id=session_id,
             )
-            if board_id is not None:
-                self._services.board_image_records.add_image_to_board(
-                    board_id=board_id, image_name=image_name
-                )
+
             self._services.image_files.save(
                 image_name=image_name, image=image, metadata=metadata, graph=graph
             )
+
             image_dto = self.get_dto(image_name)
 
             return image_dto
@@ -233,7 +213,7 @@ class ImageService(ImageServiceABC):
             self._services.logger.error("Failed to save image file")
             raise
         except Exception as e:
-            self._services.logger.error(f"Problem saving image record and file: {str(e)}")
+            self._services.logger.error("Problem saving image record and file")
             raise e
 
     def update(
@@ -398,31 +378,16 @@ class ImageService(ImageServiceABC):
 
     def delete_images_on_board(self, board_id: str):
         try:
-            image_names = (
-                self._services.board_image_records.get_all_board_image_names_for_board(
-                    board_id
+            images = self._services.board_image_records.get_images_for_board(board_id)
+            image_name_list = list(
+                map(
+                    lambda r: r.image_name,
+                    images.items,
                 )
             )
-            for image_name in image_names:
+            for image_name in image_name_list:
                 self._services.image_files.delete(image_name)
-            self._services.image_records.delete_many(image_names)
-        except ImageRecordDeleteException:
-            self._services.logger.error(f"Failed to delete image records")
-            raise
-        except ImageFileDeleteException:
-            self._services.logger.error(f"Failed to delete image files")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem deleting image records and files")
-            raise e
-
-    def delete_intermediates(self) -> int:
-        try:
-            image_names = self._services.image_records.delete_intermediates()
-            count = len(image_names)
-            for image_name in image_names:
-                self._services.image_files.delete(image_name)
-            return count
+            self._services.image_records.delete_many(image_name_list)
         except ImageRecordDeleteException:
             self._services.logger.error(f"Failed to delete image records")
             raise

invokeai/app/services/model_manager_service.py

@@ -26,6 +26,7 @@ import torch
 from invokeai.app.models.exceptions import CanceledException
 from ...backend.util import choose_precision, choose_torch_device
 from .config import InvokeAIAppConfig
+from .events import EventServiceBase
 
 if TYPE_CHECKING:
     from ..invocations.baseinvocation import BaseInvocation, InvocationContext
@@ -299,11 +300,10 @@ class ModelManagerService(ModelManagerServiceBase):
         else:
             config_file = config.root_dir / "configs/models.yaml"
             
-        logger.debug(f'Config file={config_file}')
+        logger.debug(f'config file={config_file}')
 
         device = torch.device(choose_torch_device())
-        device_name =  torch.cuda.get_device_name() if device==torch.device('cuda') else ''
-        logger.info(f'GPU device = {device} {device_name}')
+        logger.debug(f'GPU device = {device}')
 
         precision = config.precision
         if precision == "auto":
@@ -543,6 +543,7 @@ class ModelManagerService(ModelManagerServiceBase):
     def heuristic_import(self,
                          items_to_import: set[str],
                          prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]]=None,
+                         event_bus: Optional[EventServiceBase]=None,
                          )->dict[str, AddModelResult]:
         '''Import a list of paths, repo_ids or URLs. Returns the set of
         successfully imported items.
@@ -560,7 +561,7 @@ class ModelManagerService(ModelManagerServiceBase):
         of the set is a dict corresponding to the newly-created OmegaConf stanza for
         that model.
         '''
-        return self.mgr.heuristic_import(items_to_import, prediction_type_helper)        
+        return self.mgr.heuristic_import(items_to_import, prediction_type_helper, event_bus=event_bus)
 
     def merge_models(
             self,
@@ -600,7 +601,7 @@ class ModelManagerService(ModelManagerServiceBase):
         """
         Return list of all models found in the designated directory.
         """
-        search = FindModels([directory], self.logger)
+        search = FindModels(directory,self.logger)
         return search.list_models()
 
     def sync_to_config(self):

invokeai/app/services/processor.py

@@ -39,41 +39,21 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 try:
                     queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
                 except Exception as e:
-                    self.__invoker.services.logger.error("Exception while getting from queue:\n%s" % e)
+                    logger.debug("Exception while getting from queue: %s" % e)
 
                 if not queue_item:  # Probably stopping
                     # do not hammer the queue
                     time.sleep(0.5)
                     continue
 
-                try:
-                    graph_execution_state = (
-                        self.__invoker.services.graph_execution_manager.get(
-                            queue_item.graph_execution_state_id
-                        )
+                graph_execution_state = (
+                    self.__invoker.services.graph_execution_manager.get(
+                        queue_item.graph_execution_state_id
                     )
-                except Exception as e:
-                    self.__invoker.services.logger.error("Exception while retrieving session:\n%s" % e)
-                    self.__invoker.services.events.emit_session_retrieval_error(
-                        graph_execution_state_id=queue_item.graph_execution_state_id,
-                        error_type=e.__class__.__name__,
-                        error=traceback.format_exc(),
-                    )
-                    continue
-                
-                try:
-                    invocation = graph_execution_state.execution_graph.get_node(
-                        queue_item.invocation_id
-                    )
-                except Exception as e:
-                    self.__invoker.services.logger.error("Exception while retrieving invocation:\n%s" % e)
-                    self.__invoker.services.events.emit_invocation_retrieval_error(
-                        graph_execution_state_id=queue_item.graph_execution_state_id,
-                        node_id=queue_item.invocation_id,
-                        error_type=e.__class__.__name__,
-                        error=traceback.format_exc(),
-                    )
-                    continue
+                )
+                invocation = graph_execution_state.execution_graph.get_node(
+                    queue_item.invocation_id
+                )
 
                 # get the source node id to provide to clients (the prepared node id is not as useful)
                 source_node_id = graph_execution_state.prepared_source_mapping[invocation.id]
@@ -134,13 +114,11 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         graph_execution_state
                     )
 
-                    self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
                     # Send error event
                     self.__invoker.services.events.emit_invocation_error(
                         graph_execution_state_id=graph_execution_state.id,
                         node=invocation.dict(),
                         source_node_id=source_node_id,
-                        error_type=e.__class__.__name__,
                         error=error,
                     )
 
@@ -158,12 +136,11 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     try:
                         self.__invoker.invoke(graph_execution_state, invoke_all=True)
                     except Exception as e:
-                        self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
+                        logger.error("Error while invoking: %s" % e)
                         self.__invoker.services.events.emit_invocation_error(
                             graph_execution_state_id=graph_execution_state.id,
                             node=invocation.dict(),
                             source_node_id=source_node_id,
-                            error_type=e.__class__.__name__,
                             error=traceback.format_exc()
                         )
                 elif is_complete:

invokeai/app/util/controlnet_utils.py

@@ -1,342 +0,0 @@
-import torch
-import numpy as np
-import cv2
-from PIL import Image
-from diffusers.utils import PIL_INTERPOLATION
-
-from einops import rearrange
-from controlnet_aux.util import HWC3, resize_image
-
-###################################################################
-# Copy of scripts/lvminthin.py from Mikubill/sd-webui-controlnet
-###################################################################
-# High Quality Edge Thinning using Pure Python
-# Written by Lvmin Zhangu
-# 2023 April
-# Stanford University
-# If you use this, please Cite "High Quality Edge Thinning using Pure Python", Lvmin Zhang, In Mikubill/sd-webui-controlnet.
-
-lvmin_kernels_raw = [
-    np.array([
-        [-1, -1, -1],
-        [0, 1, 0],
-        [1, 1, 1]
-    ], dtype=np.int32),
-    np.array([
-        [0, -1, -1],
-        [1, 1, -1],
-        [0, 1, 0]
-    ], dtype=np.int32)
-]
-
-lvmin_kernels = []
-lvmin_kernels += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_kernels_raw]
-lvmin_kernels += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_kernels_raw]
-lvmin_kernels += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_kernels_raw]
-lvmin_kernels += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_kernels_raw]
-
-lvmin_prunings_raw = [
-    np.array([
-        [-1, -1, -1],
-        [-1, 1, -1],
-        [0, 0, -1]
-    ], dtype=np.int32),
-    np.array([
-        [-1, -1, -1],
-        [-1, 1, -1],
-        [-1, 0, 0]
-    ], dtype=np.int32)
-]
-
-lvmin_prunings = []
-lvmin_prunings += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_prunings_raw]
-lvmin_prunings += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_prunings_raw]
-lvmin_prunings += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_prunings_raw]
-lvmin_prunings += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_prunings_raw]
-
-
-def remove_pattern(x, kernel):
-    objects = cv2.morphologyEx(x, cv2.MORPH_HITMISS, kernel)
-    objects = np.where(objects > 127)
-    x[objects] = 0
-    return x, objects[0].shape[0] > 0
-
-
-def thin_one_time(x, kernels):
-    y = x
-    is_done = True
-    for k in kernels:
-        y, has_update = remove_pattern(y, k)
-        if has_update:
-            is_done = False
-    return y, is_done
-
-
-def lvmin_thin(x, prunings=True):
-    y = x
-    for i in range(32):
-        y, is_done = thin_one_time(y, lvmin_kernels)
-        if is_done:
-            break
-    if prunings:
-        y, _ = thin_one_time(y, lvmin_prunings)
-    return y
-
-
-def nake_nms(x):
-    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
-    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
-    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
-    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
-    y = np.zeros_like(x)
-    for f in [f1, f2, f3, f4]:
-        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
-    return y
-
-
-################################################################################
-# copied from Mikubill/sd-webui-controlnet external_code.py and modified for InvokeAI
-################################################################################
-# FIXME: not using yet, if used in the future will most likely require modification of preprocessors
-def pixel_perfect_resolution(
-        image: np.ndarray,
-        target_H: int,
-        target_W: int,
-        resize_mode: str,
-) -> int:
-    """
-    Calculate the estimated resolution for resizing an image while preserving aspect ratio.
-
-    The function first calculates scaling factors for height and width of the image based on the target
-    height and width. Then, based on the chosen resize mode, it either takes the smaller or the larger
-    scaling factor to estimate the new resolution.
-
-    If the resize mode is OUTER_FIT, the function uses the smaller scaling factor, ensuring the whole image
-    fits within the target dimensions, potentially leaving some empty space.
-
-    If the resize mode is not OUTER_FIT, the function uses the larger scaling factor, ensuring the target
-    dimensions are fully filled, potentially cropping the image.
-
-    After calculating the estimated resolution, the function prints some debugging information.
-
-    Args:
-        image (np.ndarray): A 3D numpy array representing an image. The dimensions represent [height, width, channels].
-        target_H (int): The target height for the image.
-        target_W (int): The target width for the image.
-        resize_mode (ResizeMode): The mode for resizing.
-
-    Returns:
-        int: The estimated resolution after resizing.
-    """
-    raw_H, raw_W, _ = image.shape
-
-    k0 = float(target_H) / float(raw_H)
-    k1 = float(target_W) / float(raw_W)
-
-    if resize_mode == "fill_resize":
-        estimation = min(k0, k1) * float(min(raw_H, raw_W))
-    else: # "crop_resize" or "just_resize" (or possibly "just_resize_simple"?)
-        estimation = max(k0, k1) * float(min(raw_H, raw_W))
-
-    # print(f"Pixel Perfect Computation:")
-    # print(f"resize_mode = {resize_mode}")
-    # print(f"raw_H = {raw_H}")
-    # print(f"raw_W = {raw_W}")
-    # print(f"target_H = {target_H}")
-    # print(f"target_W = {target_W}")
-    # print(f"estimation = {estimation}")
-
-    return int(np.round(estimation))
-
-
-###########################################################################
-# Copied from detectmap_proc method in scripts/detectmap_proc.py in Mikubill/sd-webui-controlnet
-#    modified for InvokeAI
-###########################################################################
-# def detectmap_proc(detected_map, module, resize_mode, h, w):
-def np_img_resize(
-    np_img: np.ndarray,
-    resize_mode: str,
-    h: int,
-    w: int,
-    device: torch.device = torch.device('cpu')
-):
-    # if 'inpaint' in module:
-    #     np_img = np_img.astype(np.float32)
-    # else:
-    #     np_img = HWC3(np_img)
-    np_img = HWC3(np_img)
-
-    def safe_numpy(x):
-        # A very safe method to make sure that Apple/Mac works
-        y = x
-
-        # below is very boring but do not change these. If you change these Apple or Mac may fail.
-        y = y.copy()
-        y = np.ascontiguousarray(y)
-        y = y.copy()
-        return y
-
-    def get_pytorch_control(x):
-        # A very safe method to make sure that Apple/Mac works
-        y = x
-
-        # below is very boring but do not change these. If you change these Apple or Mac may fail.
-        y = torch.from_numpy(y)
-        y = y.float() / 255.0
-        y = rearrange(y, 'h w c -> 1 c h w')
-        y = y.clone()
-        # y = y.to(devices.get_device_for("controlnet"))
-        y = y.to(device)
-        y = y.clone()
-        return y
-
-    def high_quality_resize(x: np.ndarray,
-                            size):
-        # Written by lvmin
-        # Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
-        inpaint_mask = None
-        if x.ndim == 3 and x.shape[2] == 4:
-            inpaint_mask = x[:, :, 3]
-            x = x[:, :, 0:3]
-
-        new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1])
-        new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1])
-        unique_color_count = np.unique(x.reshape(-1, x.shape[2]), axis=0).shape[0]
-        is_one_pixel_edge = False
-        is_binary = False
-        if unique_color_count == 2:
-            is_binary = np.min(x) < 16 and np.max(x) > 240
-            if is_binary:
-                xc = x
-                xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
-                xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
-                one_pixel_edge_count = np.where(xc < x)[0].shape[0]
-                all_edge_count = np.where(x > 127)[0].shape[0]
-                is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
-
-        if 2 < unique_color_count < 200:
-            interpolation = cv2.INTER_NEAREST
-        elif new_size_is_smaller:
-            interpolation = cv2.INTER_AREA
-        else:
-            interpolation = cv2.INTER_CUBIC  # Must be CUBIC because we now use nms. NEVER CHANGE THIS
-
-        y = cv2.resize(x, size, interpolation=interpolation)
-        if inpaint_mask is not None:
-            inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
-
-        if is_binary:
-            y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
-            if is_one_pixel_edge:
-                y = nake_nms(y)
-                _, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-                y = lvmin_thin(y, prunings=new_size_is_bigger)
-            else:
-                _, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-            y = np.stack([y] * 3, axis=2)
-
-        if inpaint_mask is not None:
-            inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
-            inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
-            y = np.concatenate([y, inpaint_mask], axis=2)
-
-        return y
-
-    # if resize_mode == external_code.ResizeMode.RESIZE:
-    if resize_mode == "just_resize": # RESIZE
-        np_img = high_quality_resize(np_img, (w, h))
-        np_img = safe_numpy(np_img)
-        return get_pytorch_control(np_img), np_img
-
-    old_h, old_w, _ = np_img.shape
-    old_w = float(old_w)
-    old_h = float(old_h)
-    k0 = float(h) / old_h
-    k1 = float(w) / old_w
-
-    safeint = lambda x: int(np.round(x))
-
-    # if resize_mode == external_code.ResizeMode.OUTER_FIT:
-    if resize_mode == "fill_resize":  # OUTER_FIT
-        k = min(k0, k1)
-        borders = np.concatenate([np_img[0, :, :], np_img[-1, :, :], np_img[:, 0, :], np_img[:, -1, :]], axis=0)
-        high_quality_border_color = np.median(borders, axis=0).astype(np_img.dtype)
-        if len(high_quality_border_color) == 4:
-            # Inpaint hijack
-            high_quality_border_color[3] = 255
-        high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
-        np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
-        new_h, new_w, _ = np_img.shape
-        pad_h = max(0, (h - new_h) // 2)
-        pad_w = max(0, (w - new_w) // 2)
-        high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = np_img
-        np_img = high_quality_background
-        np_img = safe_numpy(np_img)
-        return get_pytorch_control(np_img), np_img
-    else: # resize_mode == "crop_resize"  (INNER_FIT)
-        k = max(k0, k1)
-        np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
-        new_h, new_w, _ = np_img.shape
-        pad_h = max(0, (new_h - h) // 2)
-        pad_w = max(0, (new_w - w) // 2)
-        np_img = np_img[pad_h:pad_h + h, pad_w:pad_w + w]
-        np_img = safe_numpy(np_img)
-        return get_pytorch_control(np_img), np_img
-
-def prepare_control_image(
-    # image used to be Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor, List[torch.Tensor]]
-    # but now should be able to assume that image is a single PIL.Image, which simplifies things
-    image: Image,
-    # FIXME: need to fix hardwiring of width and height, change to basing on latents dimensions?
-    # latents_to_match_resolution, # TorchTensor of shape (batch_size, 3, height, width)
-    width=512,  # should be 8 * latent.shape[3]
-    height=512,  # should be 8 * latent height[2]
-    # batch_size=1, # currently no batching
-    # num_images_per_prompt=1, # currently only single image
-    device="cuda",
-    dtype=torch.float16,
-    do_classifier_free_guidance=True,
-    control_mode="balanced",
-    resize_mode="just_resize_simple",
-):
-    # FIXME: implement "crop_resize_simple" and "fill_resize_simple", or pull them out
-    if (resize_mode == "just_resize_simple" or
-        resize_mode == "crop_resize_simple" or
-        resize_mode == "fill_resize_simple"):
-        image = image.convert("RGB")
-        if (resize_mode == "just_resize_simple"):
-            image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
-        elif (resize_mode == "crop_resize_simple"): # not yet implemented
-            pass
-        elif (resize_mode == "fill_resize_simple"): # not yet implemented
-            pass
-        nimage = np.array(image)
-        nimage = nimage[None, :]
-        nimage = np.concatenate([nimage], axis=0)
-        # normalizing RGB values to [0,1] range (in PIL.Image they are [0-255])
-        nimage = np.array(nimage).astype(np.float32) / 255.0
-        nimage = nimage.transpose(0, 3, 1, 2)
-        timage = torch.from_numpy(nimage)
-
-    # use fancy lvmin controlnet resizing
-    elif (resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize"):
-        nimage = np.array(image)
-        timage, nimage = np_img_resize(
-            np_img=nimage,
-            resize_mode=resize_mode,
-            h=height,
-            w=width,
-            # device=torch.device('cpu')
-            device=device,
-        )
-    else:
-        pass
-        print("ERROR: invalid resize_mode ==> ", resize_mode)
-        exit(1)
-
-    timage = timage.to(device=device, dtype=dtype)
-    cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
-    if do_classifier_free_guidance and not cfg_injection:
-        timage = torch.cat([timage] * 2)
-    return timage

invokeai/app/util/misc.py

@@ -14,9 +14,8 @@ def get_datetime_from_iso_timestamp(iso_timestamp: str) -> datetime.datetime:
     return datetime.datetime.fromisoformat(iso_timestamp)
 
 
-SEED_MAX = np.iinfo(np.uint32).max
+SEED_MAX = np.iinfo(np.int32).max
 
 
 def get_random_seed():
-    rng = np.random.default_rng(seed=0)
-    return int(rng.integers(0, SEED_MAX))
+    return np.random.randint(0, SEED_MAX)

invokeai/app/util/step_callback.py

@@ -1,30 +1,9 @@
-import torch
-from PIL import Image
 from invokeai.app.models.exceptions import CanceledException
 from invokeai.app.models.image import ProgressImage
 from ..invocations.baseinvocation import InvocationContext
 from ...backend.util.util import image_to_dataURL
 from ...backend.generator.base import Generator
 from ...backend.stable_diffusion import PipelineIntermediateState
-from invokeai.app.services.config import InvokeAIAppConfig
-
-
-def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix = None):
-    latent_image = samples[0].permute(1, 2, 0) @ latent_rgb_factors
-
-    if smooth_matrix is not None:
-        latent_image = latent_image.unsqueeze(0).permute(3, 0, 1, 2)
-        latent_image = torch.nn.functional.conv2d(latent_image, smooth_matrix.reshape((1,1,3,3)), padding=1)
-        latent_image = latent_image.permute(1, 2, 3, 0).squeeze(0)
-
-    latents_ubyte = (
-        ((latent_image + 1) / 2)
-        .clamp(0, 1)  # change scale from -1..1 to 0..1
-        .mul(0xFF)  # to 0..255
-        .byte()
-    ).cpu()
-
-    return Image.fromarray(latents_ubyte.numpy())
 
 
 def stable_diffusion_step_callback(
@@ -58,24 +37,7 @@ def stable_diffusion_step_callback(
     #     step = intermediate_state.step
 
     # TODO: only output a preview image when requested
-
-    # origingally adapted from code by @erucipe and @keturn here:
-    # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
-
-    # these updated numbers for v1.5 are from @torridgristle
-    v1_5_latent_rgb_factors = torch.tensor(
-        [
-            #    R        G        B
-            [0.3444, 0.1385, 0.0670],  # L1
-            [0.1247, 0.4027, 0.1494],  # L2
-            [-0.3192, 0.2513, 0.2103],  # L3
-            [-0.1307, -0.1874, -0.7445],  # L4
-        ],
-        dtype=sample.dtype,
-        device=sample.device,
-    )
-
-    image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)
+    image = Generator.sample_to_lowres_estimated_image(sample)
 
     (width, height) = image.size
     width *= 8
@@ -91,56 +53,3 @@ def stable_diffusion_step_callback(
         step=intermediate_state.step,
         total_steps=node["steps"],
     )
-
-def stable_diffusion_xl_step_callback(
-    context: InvocationContext,
-    node: dict,
-    source_node_id: str,
-    sample,
-    step,
-    total_steps,
-):
-    if context.services.queue.is_canceled(context.graph_execution_state_id):
-        raise CanceledException
-
-    sdxl_latent_rgb_factors = torch.tensor(
-        [
-            #   R        G        B
-            [ 0.3816,  0.4930,  0.5320],
-            [-0.3753,  0.1631,  0.1739],
-            [ 0.1770,  0.3588, -0.2048],
-            [-0.4350, -0.2644, -0.4289],
-        ],
-        dtype=sample.dtype,
-        device=sample.device,
-    )
-
-    sdxl_smooth_matrix = torch.tensor(
-        [
-            #[ 0.0478,  0.1285,  0.0478],
-            #[ 0.1285,  0.2948,  0.1285],
-            #[ 0.0478,  0.1285,  0.0478],
-            [0.0358, 0.0964, 0.0358],
-            [0.0964, 0.4711, 0.0964],
-            [0.0358, 0.0964, 0.0358],
-        ],
-        dtype=sample.dtype,
-        device=sample.device,
-    )
-
-    image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
-
-    (width, height) = image.size
-    width *= 8
-    height *= 8
-
-    dataURL = image_to_dataURL(image, image_format="JPEG")
-
-    context.services.events.emit_generator_progress(
-        graph_execution_state_id=context.graph_execution_state_id,
-        node=node,
-        source_node_id=source_node_id,
-        progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
-        step=step,
-        total_steps=total_steps,
-    )

invokeai/backend/__init__.py

@@ -12,4 +12,4 @@ from .model_management import (
     ModelManager, ModelCache, BaseModelType,
     ModelType, SubModelType, ModelInfo
     )
-from .model_management.models import SilenceWarnings
+from .safety_checker import SafetyChecker

invokeai/backend/generator/base.py

@@ -28,6 +28,7 @@ from diffusers.schedulers import SchedulerMixin as Scheduler
 import invokeai.backend.util.logging as logger
 from ..image_util import configure_model_padding
 from ..util.util import rand_perlin_2d
+from ..safety_checker import SafetyChecker
 from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
 from ..stable_diffusion.schedulers import SCHEDULER_MAP
 
@@ -51,6 +52,7 @@ class InvokeAIGeneratorBasicParams:
     v_symmetry_time_pct: Optional[float]=None
     variation_amount: float = 0.0
     with_variations: list=field(default_factory=list)
+    safety_checker: Optional[SafetyChecker]=None
 
 @dataclass
 class InvokeAIGeneratorOutput:
@@ -238,6 +240,7 @@ class Generator:
         self.seed = None
         self.latent_channels = model.unet.config.in_channels
         self.downsampling_factor = downsampling  # BUG: should come from model or config
+        self.safety_checker = None
         self.perlin = 0.0
         self.threshold = 0
         self.variation_amount = 0
@@ -274,10 +277,12 @@ class Generator:
         perlin=0.0,
         h_symmetry_time_pct=None,
         v_symmetry_time_pct=None,
+        safety_checker: SafetyChecker=None,
         free_gpu_mem: bool = False,
         **kwargs,
     ):
         scope = nullcontext
+        self.safety_checker = safety_checker
         self.free_gpu_mem = free_gpu_mem
         attention_maps_images = []
         attention_maps_callback = lambda saver: attention_maps_images.append(
@@ -324,6 +329,9 @@ class Generator:
                 # Pass on the seed in case a layer beneath us needs to generate noise on its own.
                 image = make_image(x_T, seed)
 
+                if self.safety_checker is not None:
+                    image = self.safety_checker.check(image)
+
                 results.append([image, seed, attention_maps_images])
 
                 if image_callback is not None:

invokeai/backend/image_util/invisible_watermark.py

@@ -1,34 +0,0 @@
-"""
-This module defines a singleton object, "invisible_watermark" that
-wraps the invisible watermark model. It respects the global "invisible_watermark"
-configuration variable, that allows the watermarking to be supressed.
-"""
-import numpy as np
-import cv2
-from PIL import Image
-from imwatermark import WatermarkEncoder
-from invokeai.app.services.config import InvokeAIAppConfig
-import invokeai.backend.util.logging as logger
-config = InvokeAIAppConfig.get_config()
-
-class InvisibleWatermark:
-    """
-    Wrapper around InvisibleWatermark module.
-    """
-    
-    @classmethod
-    def invisible_watermark_available(self) -> bool:
-        return config.invisible_watermark
-
-    @classmethod
-    def add_watermark(self, image: Image, watermark_text:str) -> Image:
-        if not self.invisible_watermark_available():
-            return image
-        logger.debug(f'Applying invisible watermark "{watermark_text}"')
-        bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
-        encoder = WatermarkEncoder()
-        encoder.set_watermark('bytes', watermark_text.encode('utf-8'))
-        bgr_encoded = encoder.encode(bgr, 'dwtDct')
-        return Image.fromarray(
-            cv2.cvtColor(bgr_encoded, cv2.COLOR_BGR2RGB)
-        ).convert("RGBA")

invokeai/backend/image_util/safety_checker.py

@@ -1,63 +0,0 @@
-"""
-This module defines a singleton object, "safety_checker" that
-wraps the safety_checker model. It respects the global "nsfw_checker"
-configuration variable, that allows the checker to be supressed.
-"""
-import numpy as np
-from PIL import Image
-from invokeai.backend import SilenceWarnings
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.util.devices import choose_torch_device
-import invokeai.backend.util.logging as logger
-config = InvokeAIAppConfig.get_config()
-
-CHECKER_PATH = 'core/convert/stable-diffusion-safety-checker'
-
-class SafetyChecker:
-    """
-    Wrapper around SafetyChecker model.
-    """
-    safety_checker = None
-    feature_extractor = None
-    tried_load: bool = False
-
-    @classmethod
-    def _load_safety_checker(self):
-        if self.tried_load:
-            return
-        
-        if config.nsfw_checker:
-            try:
-                from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-                from transformers import AutoFeatureExtractor
-                self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
-                    config.models_path / CHECKER_PATH
-                )
-                self.feature_extractor = AutoFeatureExtractor.from_pretrained(
-                    config.models_path / CHECKER_PATH)
-                logger.info('NSFW checker initialized')
-            except Exception as e:
-                logger.warning(f'Could not load NSFW checker: {str(e)}')
-        else:
-            logger.info('NSFW checker loading disabled')
-        self.tried_load = True
-
-    @classmethod
-    def safety_checker_available(self) -> bool:
-        self._load_safety_checker()
-        return self.safety_checker is not None
-
-    @classmethod
-    def has_nsfw_concept(self, image: Image) -> bool:
-        if not self.safety_checker_available():
-            return False
-        
-        device = choose_torch_device()
-        features = self.feature_extractor([image], return_tensors="pt")
-        features.to(device)
-        self.safety_checker.to(device)
-        x_image = np.array(image).astype(np.float32) / 255.0
-        x_image = x_image[None].transpose(0, 3, 1, 2)
-        with SilenceWarnings():
-            checked_image, has_nsfw_concept = self.safety_checker(images=x_image, clip_input=features.pixel_values)
-        return has_nsfw_concept[0]

invokeai/backend/install/check_root.py

@@ -1,33 +0,0 @@
-"""
-Check that the invokeai_root is correctly configured and exit if not.
-"""
-import sys
-from invokeai.app.services.config import (
-    InvokeAIAppConfig,
-)
-
-def check_invokeai_root(config: InvokeAIAppConfig):
-    try:
-        assert config.model_conf_path.exists(), f'{config.model_conf_path} not found'
-        assert config.db_path.parent.exists(), f'{config.db_path.parent} not found'
-        assert config.models_path.exists(), f'{config.models_path} not found'
-        for model in [
-                'CLIP-ViT-bigG-14-laion2B-39B-b160k',
-                'bert-base-uncased',
-                'clip-vit-large-patch14',
-                'sd-vae-ft-mse',
-                'stable-diffusion-2-clip',
-                'stable-diffusion-safety-checker']:
-            path = config.models_path / f'core/convert/{model}'
-            assert path.exists(), f'{path} is missing'
-    except Exception as e:
-        print()
-        print(f'An exception has occurred: {str(e)}')
-        print('== STARTUP ABORTED ==')
-        print('** One or more necessary files is missing from your InvokeAI root directory **')
-        print('** Please rerun the configuration script to fix this problem. **')
-        print('** From the launcher, selection option [7]. **')
-        print('** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **')
-        input('Press any key to continue...')
-        sys.exit(0)
-

invokeai/backend/install/invokeai_configure.py

@@ -13,8 +13,8 @@ import os
 import shutil
 import textwrap
 import traceback
-import yaml
 import warnings
+import yaml
 from argparse import Namespace
 from pathlib import Path
 from shutil import get_terminal_size
@@ -23,7 +23,6 @@ from urllib import request
 
 import npyscreen
 import transformers
-import omegaconf
 from diffusers import AutoencoderKL
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from huggingface_hub import HfFolder
@@ -32,7 +31,6 @@ from omegaconf import OmegaConf
 from tqdm import tqdm
 from transformers import (
     CLIPTextModel,
-    CLIPTextConfig,
     CLIPTokenizer,
     AutoFeatureExtractor,
     BertTokenizerFast,
@@ -45,9 +43,7 @@ from invokeai.app.services.config import (
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
 from invokeai.frontend.install.widgets import (
-    SingleSelectColumns,
     CenteredButtonPress,
-    FileBox,
     IntTitleSlider,
     set_min_terminal_size,
     CyclingForm,
@@ -206,15 +202,6 @@ def download_conversion_models():
         pipeline = CLIPTextModel.from_pretrained(repo_id, subfolder="text_encoder", **kwargs)
         pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'text_encoder', safe_serialization=True)
 
-        # sd-xl - tokenizer_2
-        repo_id = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
-        _, model_name = repo_id.split('/')
-        pipeline = CLIPTokenizer.from_pretrained(repo_id, **kwargs)
-        pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
-        
-        pipeline = CLIPTextConfig.from_pretrained(repo_id, **kwargs)
-        pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
-        
         # VAE
         logger.info('Downloading stable diffusion VAE')
         vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse', **kwargs)
@@ -235,7 +222,7 @@ def download_conversion_models():
 
 # ---------------------------------------------
 def download_realesrgan():
-    logger.info("Installing ESRGAN Upscaling models...")
+    logger.info("Installing RealESRGAN models...")
     URLs = [
         dict(
             url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
@@ -252,11 +239,6 @@ def download_realesrgan():
             dest= "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
             description = "ESRGAN_SRx4_DF2KOST_official.pth",
         ),
-        dict(
-            url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-            dest= "core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
-            description = "RealESRGAN_x2plus.pth",
-        ),
     ]
     for model in URLs:
         download_with_progress_bar(model['url'], config.models_path / model['dest'], model['description'])
@@ -298,6 +280,47 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
                 color="CONTROL",
             )
 
+        self.nextrely += 1
+        self.add_widget_intelligent(
+            npyscreen.TitleFixedText,
+            name="== BASIC OPTIONS ==",
+            begin_entry_at=0,
+            editable=False,
+            color="CONTROL",
+            scroll_exit=True,
+        )
+        self.nextrely -= 1
+        self.add_widget_intelligent(
+            npyscreen.FixedText,
+            value="Select an output directory for images:",
+            editable=False,
+            color="CONTROL",
+        )
+        self.outdir = self.add_widget_intelligent(
+            npyscreen.TitleFilename,
+            name="(<tab> autocompletes, ctrl-N advances):",
+            value=str(default_output_dir()),
+            select_dir=True,
+            must_exist=False,
+            use_two_lines=False,
+            labelColor="GOOD",
+            begin_entry_at=40,
+            scroll_exit=True,
+        )
+        self.nextrely += 1
+        self.add_widget_intelligent(
+            npyscreen.FixedText,
+            value="Activate the NSFW checker to blur images showing potential sexual imagery:",
+            editable=False,
+            color="CONTROL",
+        )
+        self.nsfw_checker = self.add_widget_intelligent(
+            npyscreen.Checkbox,
+            name="NSFW checker",
+            value=old_opts.nsfw_checker,
+            relx=5,
+            scroll_exit=True,
+        )
         self.nextrely += 1
         label = """HuggingFace access token (OPTIONAL) for automatic model downloads. See https://huggingface.co/settings/tokens."""
         for line in textwrap.wrap(label,width=window_width-6):
@@ -317,6 +340,15 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             scroll_exit=True,
         )
         self.nextrely += 1
+        self.add_widget_intelligent(
+            npyscreen.TitleFixedText,
+            name="== ADVANCED OPTIONS ==",
+            begin_entry_at=0,
+            editable=False,
+            color="CONTROL",
+            scroll_exit=True,
+        )
+        self.nextrely -= 1
         self.add_widget_intelligent(
             npyscreen.TitleFixedText,
             name="GPU Management",
@@ -330,49 +362,34 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             npyscreen.Checkbox,
             name="Free GPU memory after each generation",
             value=old_opts.free_gpu_mem,
-            max_width=45,
             relx=5,
             scroll_exit=True,
         )
-        self.nextrely -= 1
         self.xformers_enabled = self.add_widget_intelligent(
             npyscreen.Checkbox,
-            name="Enable xformers support",
+            name="Enable xformers support if available",
             value=old_opts.xformers_enabled,
-            max_width=30,
-            relx=50,
+            relx=5,
             scroll_exit=True,
         )
-        self.nextrely -=1
         self.always_use_cpu = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Force CPU to be used on GPU systems",
             value=old_opts.always_use_cpu,
-            relx=80,
+            relx=5,
             scroll_exit=True,
         )
         precision = old_opts.precision or (
             "float32" if program_opts.full_precision else "auto"
         )
-        self.nextrely +=1
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="Floating Point Precision",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -=1
         self.precision = self.add_widget_intelligent(
-            SingleSelectColumns,
-            columns = 3,
+            npyscreen.TitleSelectOne,
+            columns = 2,
             name="Precision",
             values=PRECISION_CHOICES,
             value=PRECISION_CHOICES.index(precision),
             begin_entry_at=3,
-            max_height=2,
-            max_width=80,
+            max_height=len(PRECISION_CHOICES) + 1,
             scroll_exit=True,
         )
         self.max_cache_size = self.add_widget_intelligent(
@@ -385,36 +402,38 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             scroll_exit=True,
         )
         self.nextrely += 1
-        self.outdir = self.add_widget_intelligent(
-            FileBox,
-            name="Output directory for images (<tab> autocompletes, ctrl-N advances):",
-            value=str(default_output_dir()),
-            select_dir=True,
-            must_exist=False,
-            use_two_lines=False,
-            labelColor="GOOD",
-            begin_entry_at=40,
-            max_height=3,
-            scroll_exit=True,
+        self.add_widget_intelligent(
+            npyscreen.FixedText,
+            value="Directories containing textual inversion, controlnet and LoRA models (<tab> autocompletes, ctrl-N advances):",
+            editable=False,
+            color="CONTROL",
         )
         self.autoimport_dirs = {}
-        self.autoimport_dirs['autoimport_dir'] = self.add_widget_intelligent(
-                FileBox,
-                name=f'Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models',
-                value=str(config.root_path / config.autoimport_dir),
+        for description, config_name, path in autoimport_paths(old_opts):
+            self.autoimport_dirs[config_name] = self.add_widget_intelligent(
+                npyscreen.TitleFilename,
+                name=description+':',
+                value=str(path),
                 select_dir=True,
                 must_exist=False,
                 use_two_lines=False,
                 labelColor="GOOD",
                 begin_entry_at=32,
-                max_height = 3,
                 scroll_exit=True
             )
         self.nextrely += 1
+        self.add_widget_intelligent(
+            npyscreen.TitleFixedText,
+            name="== LICENSE ==",
+            begin_entry_at=0,
+            editable=False,
+            color="CONTROL",
+            scroll_exit=True,
+        )
+        self.nextrely -= 1
         label = """BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ
-AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSES LOCATED AT
-https://huggingface.co/spaces/CompVis/stable-diffusion-license and
-https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md
+AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT
+https://huggingface.co/spaces/CompVis/stable-diffusion-license
 """
         for i in textwrap.wrap(label,width=window_width-6):
             self.add_widget_intelligent(
@@ -425,7 +444,7 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
             )
         self.license_acceptance = self.add_widget_intelligent(
             npyscreen.Checkbox,
-            name="I accept the CreativeML Responsible AI Licenses",
+            name="I accept the CreativeML Responsible AI License",
             value=not first_time,
             relx=2,
             scroll_exit=True,
@@ -440,6 +459,7 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
             CenteredButtonPress,
             name=label,
             relx=(window_width - len(label)) // 2,
+            rely=-3,
             when_pressed_function=self.on_ok,
         )
 
@@ -479,6 +499,7 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
 
         for attr in [
                 "outdir",
+                "nsfw_checker",
                 "free_gpu_mem",
                 "max_cache_size",
                 "xformers_enabled",
@@ -514,7 +535,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
             "MAIN",
             editOptsForm,
             name="InvokeAI Startup Options",
-            cycle_widgets=False,
+            cycle_widgets=True,
         )
         if not (self.program_opts.skip_sd_weights or self.program_opts.default_only):
             self.model_select = self.addForm(
@@ -522,7 +543,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
                 addModelsForm,
                 name="Install Stable Diffusion Models",
                 multipage=True,
-                cycle_widgets=False,
+                cycle_widgets=True,
             )
 
     def new_opts(self):
@@ -534,19 +555,15 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
     editApp.run()
     return editApp.new_opts()
 
+
 def default_startup_options(init_file: Path) -> Namespace:
     opts = InvokeAIAppConfig.get_config()
+    if not init_file.exists():
+        opts.nsfw_checker = True
     return opts
 
 def default_user_selections(program_opts: Namespace) -> InstallSelections:
-    
-    try:
-        installer = ModelInstall(config)
-    except omegaconf.errors.ConfigKeyError:
-        logger.warning('Your models.yaml file is corrupt or out of date. Reinitializing')
-        initialize_rootdir(config.root_path, True)
-        installer = ModelInstall(config)
-        
+    installer = ModelInstall(config)
     models = installer.all_models()
     return InstallSelections(
         install_models=[models[installer.default_model()].path or models[installer.default_model()].repo_id]
@@ -554,11 +571,22 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections:
         else [models[x].path or models[x].repo_id for x in installer.recommended_models()]
         if program_opts.yes_to_all
         else list(),
+#        scan_directory=None,
+#        autoscan_on_startup=None,
     )
 
+# -------------------------------------
+def autoimport_paths(config: InvokeAIAppConfig):
+    return [
+        ('Checkpoints & diffusers models', 'autoimport_dir', config.root_path / config.autoimport_dir),
+        ('LoRA/LyCORIS models',            'lora_dir',       config.root_path / config.lora_dir),
+        ('Controlnet models',              'controlnet_dir', config.root_path / config.controlnet_dir),
+        ('Textual Inversion Embeddings',   'embedding_dir',  config.root_path / config.embedding_dir),
+    ]
+    
 # -------------------------------------
 def initialize_rootdir(root: Path, yes_to_all: bool = False):
-    logger.info("Initializing InvokeAI runtime directory")
+    logger.info("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **")
     for name in (
             "models",
             "databases",
@@ -583,18 +611,7 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
     path = dest / 'core'
     path.mkdir(parents=True, exist_ok=True)
 
-    maybe_create_models_yaml(root)
-
-def maybe_create_models_yaml(root: Path):
-    models_yaml = root / 'configs' / 'models.yaml'
-    if models_yaml.exists():
-        if OmegaConf.load(models_yaml).get('__metadata__'):  # up to date
-            return
-        else:
-            logger.info('Creating new models.yaml, original saved as models.yaml.orig')
-            models_yaml.rename(models_yaml.parent / 'models.yaml.orig')
-    
-    with open(models_yaml,'w') as yaml_file:
+    with open(root / 'configs' / 'models.yaml','w') as yaml_file:
         yaml_file.write(yaml.dump({'__metadata__':
                                    {'version':'3.0.0'}
                                    }
@@ -642,9 +659,6 @@ def write_opts(opts: Namespace, init_file: Path):
     with open(init_file,'w', encoding='utf-8') as file:
         file.write(new_config.to_yaml())
 
-    if hasattr(opts,'hf_token') and opts.hf_token:
-        HfLogin(opts.hf_token)
-
 # -------------------------------------
 def default_output_dir() -> Path:
     return config.root_path / "outputs"
@@ -670,6 +684,7 @@ def migrate_init_file(legacy_format:Path):
 
     # a few places where the field names have changed and we have to
     # manually add in the new names/values
+    new.nsfw_checker = old.safety_checker
     new.xformers_enabled = old.xformers
     new.conf_path = old.conf
     new.root = legacy_format.parent.resolve()
@@ -778,8 +793,8 @@ def main():
         if migrate_if_needed(opt, config.root_path):
             sys.exit(0)
 
-        # run this unconditionally in case new directories need to be added
-        initialize_rootdir(config.root_path, opt.yes_to_all)
+        if not config.model_conf_path.exists():
+            initialize_rootdir(config.root_path, opt.yes_to_all)
 
         models_to_download = default_user_selections(opt)
         new_init_file = config.root_path / 'invokeai.yaml'
@@ -799,14 +814,15 @@ def main():
                 sys.exit(0)
                 
         if opt.skip_support_models:
-            logger.info("Skipping support models at user's request")
+            logger.info("SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST")
         else:
-            logger.info("Installing support models")
+            logger.info("CHECKING/UPDATING SUPPORT MODELS")
             download_support_models()
 
         if opt.skip_sd_weights:
-            logger.warning("Skipping diffusion weights download per user request")
+            logger.warning("SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST")
         elif models_to_download:
+            logger.info("DOWNLOADING DIFFUSION WEIGHTS")
             process_and_execute(opt, models_to_download)
 
         postscript(errors=errors)

invokeai/backend/install/model_install_backend.py

@@ -10,7 +10,7 @@ from tempfile import TemporaryDirectory
 from typing import List, Dict, Callable, Union, Set
 
 import requests
-from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline
 from diffusers import logging as dlogging
 from huggingface_hub import hf_hub_url, HfFolder, HfApi
 from omegaconf import OmegaConf
@@ -58,15 +58,7 @@ LEGACY_CONFIGS = {
             SchedulerPredictionType.Epsilon: 'v2-inpainting-inference.yaml',
             SchedulerPredictionType.VPrediction: 'v2-inpainting-inference-v.yaml',
         }
-    },
-    
-    BaseModelType.StableDiffusionXL: {
-        ModelVariantType.Normal: 'sd_xl_base.yaml',
-    },
-    
-    BaseModelType.StableDiffusionXLRefiner: {
-        ModelVariantType.Normal: 'sd_xl_refiner.yaml',
-    },
+    }
 }
 
 @dataclass
@@ -97,13 +89,16 @@ class ModelInstall(object):
                  config:InvokeAIAppConfig,
                  prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
                  model_manager: ModelManager = None,
-                 access_token:str = None):
+                 access_token:str = None,
+                 event_bus = None, # EventServicesBase - getting circular import errors
+                 ):
         self.config = config
         self.mgr = model_manager or ModelManager(config.model_conf_path)
         self.datasets = OmegaConf.load(Dataset_path)
         self.prediction_helper = prediction_type_helper
         self.access_token = access_token or HfFolder.get_token()
         self.reverse_paths = self._reverse_paths(self.datasets)
+        self.event_bus = event_bus
 
     def all_models(self)->Dict[str,ModelLoadInfo]:
         '''
@@ -149,17 +144,16 @@ class ModelInstall(object):
         for i in installed:
             print(f"{i['model_name']}\t{i['base_model']}\t{i['path']}")
 
-    # logic here a little reversed to maintain backward compatibility
-    def starter_models(self, all_models: bool=False)->Set[str]:
+    def starter_models(self)->Set[str]:
         models = set()
         for key, value in self.datasets.items():
             name,base,model_type = ModelManager.parse_key(key)
-            if all_models or model_type in [ModelType.Main, ModelType.Vae]:
+            if model_type==ModelType.Main:
                 models.add(key)
         return models
 
     def recommended_models(self)->Set[str]:
-        starters = self.starter_models(all_models=True)
+        starters = self.starter_models()
         return set([x for x in starters if self.datasets[x].get('recommended',False)])
     
     def default_model(self)->str:
@@ -206,39 +200,63 @@ class ModelInstall(object):
         Returns a set of dict objects corresponding to newly-created stanzas in models.yaml.
         '''
 
+        if self.event_bus:
+            self.event_bus.emit_model_import_started(str(model_path_id_or_url))
+
         if not models_installed:
             models_installed = dict()
             
         # A little hack to allow nested routines to retrieve info on the requested ID
         self.current_id = model_path_id_or_url
         path = Path(model_path_id_or_url)
-        # checkpoint file, or similar
-        if path.is_file():
-            models_installed.update({str(path):self._install_path(path)})
 
-        # folders style or similar
-        elif path.is_dir() and any([(path/x).exists() for x in \
-                                    {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
-                                    ]
-                                   ):
-            models_installed.update({str(model_path_id_or_url): self._install_path(path)})
+        try:
+            # checkpoint file, or similar
+            if path.is_file():
+                models_installed.update({str(path):self._install_path(path)})
 
-        # recursive scan
-        elif path.is_dir():
-            for child in path.iterdir():
-                self.heuristic_import(child, models_installed=models_installed)
+            # folders style or similar
+            elif path.is_dir() and any([(path/x).exists() for x in \
+                                        {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
+                                        ]
+                                       ):
+                models_installed.update({str(model_path_id_or_url): self._install_path(path)})
 
-        # huggingface repo
-        elif len(str(model_path_id_or_url).split('/')) == 2:
-            models_installed.update({str(model_path_id_or_url): self._install_repo(str(model_path_id_or_url))})
+            # recursive scan
+            elif path.is_dir():
+                for child in path.iterdir():
+                    self.heuristic_import(child, models_installed=models_installed)
 
-        # a URL
-        elif str(model_path_id_or_url).startswith(("http:", "https:", "ftp:")):
-            models_installed.update({str(model_path_id_or_url): self._install_url(model_path_id_or_url)})
+            # huggingface repo
+            elif len(str(model_path_id_or_url).split('/')) == 2:
+                models_installed.update({str(model_path_id_or_url): self._install_repo(str(model_path_id_or_url))})
 
-        else:
-            raise KeyError(f'{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping')
+            # a URL
+            elif str(model_path_id_or_url).startswith(("http:", "https:", "ftp:")):
+                models_installed.update({str(model_path_id_or_url): self._install_url(model_path_id_or_url)})
 
+            else:
+                errmsg = f'{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping'
+                raise KeyError(errmsg)
+            
+            if self.event_bus:
+                for path, add_model_result in models_installed.items():
+                    self.event_bus.emit_model_import_completed(
+                        str(path),
+                        import_info = add_model_result,
+                    )
+        except Exception as e:
+            if self.event_bus:
+                self.event_bus.emit_model_import_completed(
+                    str(path),
+                    import_info = None,
+                    success = False,
+                    error = str(e),
+                )
+                return models_installed
+            else:
+                raise
+                
         return models_installed
 
     # install a model from a local path. The optional info parameter is there to prevent
@@ -247,10 +265,14 @@ class ModelInstall(object):
         info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
         if not info:
             logger.warning(f'Unable to parse format of {path}')
-            return None
+            raise ValueError(f'Unable to parse format of {path}')
+        
         model_name = path.stem if path.is_file() else path.name
+        
         if self.mgr.model_exists(model_name, info.base_type, info.model_type):
-            raise ValueError(f'A model named "{model_name}" is already installed.')
+            errmsg = f'A model named "{model_name}" is already installed.'
+            raise ValueError(errmsg)
+            
         attributes = self._make_attributes(path,info)
         return self.mgr.add_model(model_name = model_name,
                                   base_model = info.base_type,
@@ -260,7 +282,7 @@ class ModelInstall(object):
 
     def _install_url(self, url: str)->AddModelResult:
         with TemporaryDirectory(dir=self.config.models_path) as staging:
-            location = download_with_resume(url,Path(staging))
+            location = download_with_resume(url,Path(staging),event_bus=self.event_bus)
             if not location:
                 logger.error(f'Unable to download {url}. Skipping.')
             info = ModelProbe().heuristic_probe(location)
@@ -319,8 +341,6 @@ class ModelInstall(object):
         if key := self.reverse_paths.get(path_name):
             (name, base, mtype) = ModelManager.parse_key(key)
             return name
-        elif location.is_dir():
-            return location.name
         else:
             return location.stem
 
@@ -338,7 +358,6 @@ class ModelInstall(object):
             description = str(description),
             model_format = info.format,
             )
-        legacy_conf = None
         if info.model_type == ModelType.Main:
             attributes.update(dict(variant = info.variant_type,))
             if info.format=="checkpoint":
@@ -353,17 +372,11 @@ class ModelInstall(object):
                 except KeyError:
                     legacy_conf = Path(self.config.legacy_conf_dir, 'v1-inference.yaml')  # best guess
                     
-        if info.model_type == ModelType.ControlNet and info.format=="checkpoint":
-            possible_conf = path.with_suffix('.yaml')
-            if possible_conf.exists():
-                legacy_conf = str(self.relative_to_root(possible_conf))
-
-        if legacy_conf:
-            attributes.update(
-                dict(
-                    config = str(legacy_conf)
+                attributes.update(
+                    dict(
+                        config = str(legacy_conf)
+                    )
                 )
-            )
         return attributes
 
     def relative_to_root(self, path: Path)->Path:
@@ -383,7 +396,7 @@ class ModelInstall(object):
         model = None
         for revision in revisions:
             try:
-                model = DiffusionPipeline.from_pretrained(repo_id,revision=revision,safety_checker=None)
+                model = StableDiffusionPipeline.from_pretrained(repo_id,revision=revision,safety_checker=None)
             except:  # most errors are due to fp16 not being present. Fix this to catch other errors
                 pass
             if model:
@@ -402,7 +415,8 @@ class ModelInstall(object):
             p = hf_download_with_resume(repo_id,
                                         model_dir=location,
                                         model_name=filename,
-                                        access_token = self.access_token
+                                        access_token = self.access_token,
+                                        event_bus = self.event_bus,
                                         )
             if p:
                 paths.append(p)
@@ -443,12 +457,15 @@ def hf_download_from_pretrained(
     return destination
 
 # ---------------------------------------------
+# TODO: This function is almost identical to invokeai.backend.util.download_with_resume
+# and should be merged
 def hf_download_with_resume(
         repo_id: str,
         model_dir: str,
         model_name: str,
         model_dest: Path = None,
         access_token: str = None,
+        event_bus = None,
 ) -> Path:
     model_dest = model_dest or Path(os.path.join(model_dir, model_name))
     os.makedirs(model_dir, exist_ok=True)
@@ -465,15 +482,22 @@ def hf_download_with_resume(
         open_mode = "ab"
 
     resp = requests.get(url, headers=header, stream=True)
-    total = int(resp.headers.get("content-length", 0))
+    content_length = int(resp.headers.get("content-length", 0))
+
+    if event_bus:
+        event_bus.emit_download_started(url)
 
     if (
         resp.status_code == 416
     ):  # "range not satisfiable", which means nothing to return
         logger.info(f"{model_name}: complete file found. Skipping.")
+        if event_bus:
+            event_bus.emit_download_completed(url,resp.status_code,model_dest)
         return model_dest
     elif resp.status_code == 404:
         logger.warning("File not found")
+        if event_bus:
+            event_bus.emit_download_completed(url,resp.status_code,None)
         return None
     elif resp.status_code != 200:
         logger.warning(f"{model_name}: {resp.reason}")
@@ -482,11 +506,15 @@ def hf_download_with_resume(
     else:
         logger.info(f"{model_name}: Downloading...")
 
+    MB10 = 10 * 1048576
+    downloaded = exist_size
+    previous_interval = 0   
+    
     try:
         with open(model_dest, open_mode) as file, tqdm(
             desc=model_name,
             initial=exist_size,
-            total=total + exist_size,
+            total=content_length + exist_size,
             unit="iB",
             unit_scale=True,
             unit_divisor=1000,
@@ -494,9 +522,20 @@ def hf_download_with_resume(
             for data in resp.iter_content(chunk_size=1024):
                 size = file.write(data)
                 bar.update(size)
+                downloaded += size
+                if event_bus and downloaded // MB10 > previous_interval:
+                    previous_interval = downloaded // MB10
+                    event_bus.emit_download_progress(url, downloaded, content_length)
+                    
     except Exception as e:
         logger.error(f"An error occurred while downloading {model_name}: {str(e)}")
+        if event_bus:
+            event_bus.emit_download_completed(url,500,None)
         return None
+    
+    if event_bus:
+        event_bus.emit_download_completed(url,resp.status_code,model_dest)
+
     return model_dest
 
 

invokeai/backend/model_management/__init__.py

@@ -3,6 +3,6 @@ Initialization file for invokeai.backend.model_management
 """
 from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
 from .model_cache import ModelCache
-from .models import BaseModelType, ModelType, SubModelType, ModelVariantType, ModelNotFoundException, DuplicateModelException
+from .models import BaseModelType, ModelType, SubModelType, ModelVariantType, ModelNotFoundException
 from .model_merge import ModelMerger, MergeInterpolationMethod
 

invokeai/backend/model_management/lora.py

@@ -474,7 +474,7 @@ class ModelPatcher:
 
     @staticmethod
     def _lora_forward_hook(
-        applied_loras: List[Tuple[LoRAModel, float]],
+        applied_loras: List[Tuple[LoraModel, float]],
         layer_name: str,
     ):
 
@@ -519,7 +519,7 @@ class ModelPatcher:
     def apply_lora(
         cls,
         model: torch.nn.Module,
-        loras: List[Tuple[LoRAModel, float]],
+        loras: List[Tuple[LoraModel, float]],
         prefix: str,
     ):
         original_weights = dict()

invokeai/backend/model_management/model_cache.py

@@ -328,25 +328,6 @@ class ModelCache(object):
 
             refs = sys.getrefcount(cache_entry.model)
 
-            # manualy clear local variable references of just finished function calls
-            # for some reason python don't want to collect it even by gc.collect() immidiately
-            if refs > 2:
-                while True:
-                    cleared = False
-                    for referrer in gc.get_referrers(cache_entry.model):
-                        if type(referrer).__name__ == "frame":
-                            # RuntimeError: cannot clear an executing frame
-                            with suppress(RuntimeError):
-                                referrer.clear()
-                                cleared = True
-                                #break
-
-                    # repeat if referrers changes(due to frame clear), else exit loop
-                    if cleared:
-                        gc.collect()
-                    else:
-                        break
-
             device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
             self.logger.debug(f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}, refs: {refs}")
 
@@ -382,9 +363,6 @@ class ModelCache(object):
                 self.logger.debug(f'GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB')
                 vram_in_use += mem.vram_used  # note vram_used is negative
                 self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
-
-        gc.collect()
-        torch.cuda.empty_cache()
         
     def _local_model_hash(self, model_path: Union[str, Path]) -> str:
         sha = hashlib.sha256()

invokeai/backend/model_management/model_manager.py

@@ -106,16 +106,16 @@ providing information about a model defined in models.yaml. For example:
 
    >>> models = mgr.list_models()
    >>> json.dumps(models[0])
-  {"path": "/home/lstein/invokeai-main/models/sd-1/controlnet/canny",
-    "model_format": "diffusers",
-    "name": "canny",
-    "base_model": "sd-1",
+  {"path": "/home/lstein/invokeai-main/models/sd-1/controlnet/canny", 
+    "model_format": "diffusers", 
+    "name": "canny", 
+    "base_model": "sd-1", 
     "type": "controlnet"
    }
 
 You can filter by model type and base model as shown here:
 
-
+  
   controlnets = mgr.list_models(model_type=ModelType.ControlNet,
                                 base_model=BaseModelType.StableDiffusion1)
   for c in controlnets:
@@ -140,14 +140,14 @@ Layout of the `models` directory:
 
  models
  ├── sd-1
- │   ├── controlnet
- │   ├── lora
- │   ├── main
- │   └── embedding
+ │   ├── controlnet
+ │   ├── lora
+ │   ├── main
+ │   └── embedding
  ├── sd-2
- │   ├── controlnet
- │   ├── lora
- │   ├── main
+ │   ├── controlnet
+ │   ├── lora
+ │   ├── main
  │   └── embedding
  └── core
      ├── face_reconstruction
@@ -195,7 +195,7 @@ name, base model, type and a dict of model attributes. See
 `invokeai/backend/model_management/models` for the attributes required
 by each model type.
 
-A model can be deleted using `del_model()`, providing the same
+A model can be deleted using `del_model()`, providing the same 
 identifying information as `get_model()`
 
 The `heuristic_import()` method will take a set of strings
@@ -251,9 +251,7 @@ from .model_search import ModelSearch
 from .models import (
     BaseModelType, ModelType, SubModelType,
     ModelError, SchedulerPredictionType, MODEL_CLASSES,
-    ModelConfigBase,
-    ModelNotFoundException, InvalidModelException,
-    DuplicateModelException,
+    ModelConfigBase, ModelNotFoundException, InvalidModelException,
 )
 
 # We are only starting to number the config file with release 3.
@@ -306,7 +304,7 @@ class ModelManager(object):
         logger: types.ModuleType = logger,
     ):
         """
-        Initialize with the path to the models.yaml config file.
+        Initialize with the path to the models.yaml config file. 
         Optional parameters are the torch device type, precision, max_models,
         and sequential_offload boolean. Note that the default device
         type and precision are set up for a CUDA system running at half precision.
@@ -325,7 +323,7 @@ class ModelManager(object):
         self.config_meta = ConfigMeta(**config.pop("__metadata__"))
         # TODO: metadata not found
         # TODO: version check
-
+        
         self.app_config = InvokeAIAppConfig.get_config()
         self.logger = logger
         self.cache = ModelCache(
@@ -433,7 +431,7 @@ class ModelManager(object):
         :param model_name: symbolic name of the model in models.yaml
         :param model_type: ModelType enum indicating the type of model to return
         :param base_model: BaseModelType enum indicating the base model used by this model
-        :param submode_typel: an ModelType enum indicating the portion of
+        :param submode_typel: an ModelType enum indicating the portion of 
                the model to retrieve (e.g. ModelType.Vae)
         """
         model_class = MODEL_CLASSES[base_model][model_type]
@@ -458,7 +456,7 @@ class ModelManager(object):
                 raise ModelNotFoundException(f"Model not found - {model_key}")
 
         # vae/movq override
-        # TODO:
+        # TODO: 
         if submodel_type is not None and hasattr(model_config, submodel_type):
             override_path = getattr(model_config, submodel_type)
             if override_path:
@@ -491,7 +489,7 @@ class ModelManager(object):
         self.cache_keys[model_key].add(model_context.key)
 
         model_hash = "<NO_HASH>" # TODO:
-
+            
         return ModelInfo(
             context = model_context,
             name = model_name,
@@ -520,7 +518,7 @@ class ModelManager(object):
 
     def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
         """
-        Return a list of (str, BaseModelType, ModelType) corresponding to all models
+        Return a list of (str, BaseModelType, ModelType) corresponding to all models 
         known to the configuration.
         """
         return [(self.parse_key(x)) for x in self.models.keys()]
@@ -673,7 +671,6 @@ class ModelManager(object):
 
         self.models[model_key] = model_config
         self.commit()
-
         return AddModelResult(
             name = model_name,
             model_type = model_type,
@@ -695,12 +692,12 @@ class ModelManager(object):
         if new_name is None and new_base is None:
             self.logger.error("rename_model() called with neither a new_name nor a new_base. {model_name} unchanged.")
             return
-
+        
         model_key = self.create_key(model_name, base_model, model_type)
         model_cfg = self.models.get(model_key, None)
         if not model_cfg:
             raise ModelNotFoundException(f"Unknown model: {model_key}")
-
+        
         old_path = self.app_config.root_path / model_cfg.path
         new_name = new_name or model_name
         new_base = new_base or base_model
@@ -729,7 +726,7 @@ class ModelManager(object):
         self.models.pop(model_key, None) # delete
         self.models[new_key] = model_cfg
         self.commit()
-
+        
     def convert_model (
             self,
             model_name: str,
@@ -754,7 +751,7 @@ class ModelManager(object):
         # We are taking advantage of a side effect of get_model() that converts check points
         # into cached diffusers directories stored at `location`. It doesn't matter
         # what submodeltype we request here, so we get the smallest.
-        submodel = {"submodel_type": SubModelType.Scheduler} if model_type==ModelType.Main else {}
+        submodel = {"submodel_type": SubModelType.Tokenizer} if model_type==ModelType.Main else {}
         model = self.get_model(model_name,
                                base_model,
                                model_type,
@@ -779,12 +776,12 @@ class ModelManager(object):
             # something went wrong, so don't leave dangling diffusers model in directory or it will cause a duplicate model error!
             rmtree(new_diffusers_path)
             raise
-
+        
         if checkpoint_path.exists() and checkpoint_path.is_relative_to(self.app_config.models_path):
             checkpoint_path.unlink()
-
+        
         return result
-
+    
     def search_models(self, search_folder):
         self.logger.info(f"Finding Models In: {search_folder}")
         models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
@@ -827,21 +824,17 @@ class ModelManager(object):
         assert config_file_path is not None,'no config file path to write to'
         config_file_path = self.app_config.root_path / config_file_path
         tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
-        try:
-            with open(tmpfile, "w", encoding="utf-8") as outfile:
-                outfile.write(self.preamble())
-                outfile.write(yaml_str)
-            os.replace(tmpfile, config_file_path)
-        except OSError as err:
-            self.logger.warning(f"Could not modify the config file at {config_file_path}")
-            self.logger.warning(err)
+        with open(tmpfile, "w", encoding="utf-8") as outfile:
+            outfile.write(self.preamble())
+            outfile.write(yaml_str)
+        os.replace(tmpfile, config_file_path)
 
     def preamble(self) -> str:
         """
         Returns the preamble for the config file.
         """
         return textwrap.dedent(
-            """
+            """\
             # This file describes the alternative machine learning models
             # available to InvokeAI script.
             #
@@ -861,7 +854,7 @@ class ModelManager(object):
         loaded_files = set()
         new_models_found = False
 
-        self.logger.info(f'Scanning {self.app_config.models_path} for new models')
+        self.logger.info(f'scanning {self.app_config.models_path} for new models')
         with Chdir(self.app_config.root_path):
             for model_key, model_config in list(self.models.items()):
                 model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
@@ -894,18 +887,15 @@ class ModelManager(object):
                             model_name = model_path.name if model_path.is_dir() else model_path.stem
                             model_key = self.create_key(model_name, cur_base_model, cur_model_type)
 
-                            try:
-                                if model_key in self.models:
-                                    raise DuplicateModelException(f"Model with key {model_key} added twice")
+                            if model_key in self.models:
+                                raise Exception(f"Model with key {model_key} added twice")
 
-                                if model_path.is_relative_to(self.app_config.root_path):
-                                    model_path = model_path.relative_to(self.app_config.root_path)
-                                    
+                            if model_path.is_relative_to(self.app_config.root_path):
+                                model_path = model_path.relative_to(self.app_config.root_path)
+                            try:
                                 model_config: ModelConfigBase = model_class.probe_config(str(model_path))
                                 self.models[model_key] = model_config
                                 new_models_found = True
-                            except DuplicateModelException as e:
-                                self.logger.warning(e)
                             except InvalidModelException:
                                 self.logger.warning(f"Not a valid model: {model_path}")
                             except NotImplementedError as e:
@@ -944,34 +934,26 @@ class ModelManager(object):
             def models_found(self):
                 return self.new_models_found
 
-        config = self.app_config
-
-        # LS: hacky
-        # Patch in the SD VAE from core so that it is available for use by the UI
-        try:
-            self.heuristic_import({config.root_path / 'models/core/convert/sd-vae-ft-mse'})
-        except:
-            pass
 
         installer = ModelInstall(config = self.app_config,
                                  model_manager = self,
                                  prediction_type_helper = ask_user_for_prediction_type,
                                  )
+        config = self.app_config
         known_paths = {config.root_path / x['path'] for x in self.list_models()}
         directories = {config.root_path / x for x in [config.autoimport_dir,
                                                       config.lora_dir,
                                                       config.embedding_dir,
-                                                      config.controlnet_dir,
-                                                      ] if x
+                                                      config.controlnet_dir]
                        }
         scanner = ScanAndImport(directories, self.logger, ignore=known_paths, installer=installer)
         scanner.search()
-        
         return scanner.models_found()
 
     def heuristic_import(self,
                          items_to_import: Set[str],
                          prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
+                         event_bus = None,   # EventServiceBase, with circular dependency issues
                          )->Dict[str, AddModelResult]:
         '''Import a list of paths, repo_ids or URLs. Returns the set of
         successfully imported items.
@@ -996,12 +978,16 @@ class ModelManager(object):
         # avoid circular import here
         from invokeai.backend.install.model_install_backend import ModelInstall
         successfully_installed = dict()
-
+        
         installer = ModelInstall(config = self.app_config,
                                  prediction_type_helper = prediction_type_helper,
-                                 model_manager = self)
+                                 model_manager = self,
+                                 event_bus = event_bus,
+                                 )
         for thing in items_to_import:
             installed = installer.heuristic_import(thing)
             successfully_installed.update(installed)
-        self.commit()
+                
+        self.commit()                
         return successfully_installed
+

invokeai/backend/model_management/model_probe.py

@@ -39,7 +39,6 @@ class ModelProbe(object):
 
     CLASS2TYPE = {
         'StableDiffusionPipeline' : ModelType.Main,
-        'StableDiffusionInpaintPipeline' : ModelType.Main,
         'StableDiffusionXLPipeline' : ModelType.Main,
         'StableDiffusionXLImg2ImgPipeline' : ModelType.Main,
         'AutoencoderKL' : ModelType.Vae,
@@ -253,13 +252,10 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
             return BaseModelType.StableDiffusion1
         if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
             return BaseModelType.StableDiffusion2
-        key_name = 'model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight'
+        # TODO: Verify that this is correct! Need an XL checkpoint file for this.
         if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
             return BaseModelType.StableDiffusionXL
-        elif key_name in state_dict and state_dict[key_name].shape[-1] == 1280:
-            return BaseModelType.StableDiffusionXLRefiner
-        else:
-            raise InvalidModelException("Cannot determine base type")
+        raise InvalidModelException("Cannot determine base type")
 
     def get_scheduler_prediction_type(self)->SchedulerPredictionType:
         type = self.get_base_type()
@@ -405,7 +401,7 @@ class PipelineFolderProbe(FolderProbeBase):
                 
             in_channels = conf['in_channels']
             if in_channels == 9:
-                return ModelVariantType.Inpaint
+                return ModelVariantType.Inpainting
             elif in_channels == 5:
                 return ModelVariantType.Depth
             elif in_channels == 4:
@@ -416,14 +412,7 @@ class PipelineFolderProbe(FolderProbeBase):
 
 class VaeFolderProbe(FolderProbeBase):
     def get_base_type(self)->BaseModelType:
-        config_file = self.folder_path / 'config.json'
-        if not config_file.exists():
-            raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
-        with open(config_file,'r') as file:
-            config = json.load(file)
-        return BaseModelType.StableDiffusionXL \
-            if config.get('scaling_factor',0)==0.13025 and config.get('sample_size') in [512, 1024] \
-            else BaseModelType.StableDiffusion1
+        return BaseModelType.StableDiffusion1
 
 class TextualInversionFolderProbe(FolderProbeBase):
     def get_format(self)->str:

invokeai/backend/model_management/model_search.py

@@ -98,6 +98,6 @@ class FindModels(ModelSearch):
 
     def list_models(self) -> List[Path]:
         self.search()
-        return list(self.models_found)
+        return self.models_found