9432336e2b
## Summary
This three two model manager-related methods to the InvocationContext
uniform API. They are accessible via `context.models.*`:
1. **`load_local_model(model_path: Path, loader:
Optional[Callable[[Path], AnyModel]] = None) ->
LoadedModelWithoutConfig`**
*Load the model located at the indicated path.*
This will load a local model (.safetensors, .ckpt or diffusers
directory) into the model manager RAM cache and return its
`LoadedModelWithoutConfig`. If the optional loader argument is provided,
the loader will be invoked to load the model into memory. Otherwise the
method will call `safetensors.torch.load_file()` `torch.load()` (with a
pickle scan), or `from_pretrained()` as appropriate to the path type.
Be aware that the `LoadedModelWithoutConfig` object differs from
`LoadedModel` by having no `config` attribute.
Here is an example of usage:
```
def invoke(self, context: InvocatinContext) -> ImageOutput:
model_path = Path('/opt/models/RealESRGAN_x4plus.pth')
loadnet = context.models.load_local_model(model_path)
with loadnet as loadnet_model:
upscaler = RealESRGAN(loadnet=loadnet_model,...)
```
---
2. **`load_remote_model(source: str | AnyHttpUrl, loader:
Optional[Callable[[Path], AnyModel]] = None) ->
LoadedModelWithoutConfig`**
*Load the model located at the indicated URL or repo_id.*
This is similar to `load_local_model()` but it accepts either a
HugginFace repo_id (as a string), or a URL. The model's file(s) will be
downloaded to `models/.download_cache` and then loaded, returning a
```
def invoke(self, context: InvocatinContext) -> ImageOutput:
model_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
loadnet = context.models.load_remote_model(model_url)
with loadnet as loadnet_model:
upscaler = RealESRGAN(loadnet=loadnet_model,...)
```
---
3. **`download_and_cache_model( source: str | AnyHttpUrl, access_token:
Optional[str] = None, timeout: Optional[int] = 0) -> Path`**
Download the model file located at source to the models cache and return
its Path. This will check `models/.download_cache` for the desired model
file and download it from the indicated source if not already present.
The local Path to the downloaded file is then returned.
---
## Other Changes
This PR performs a migration, in which it renames `models/.cache` to
`models/.convert_cache`, and migrates previously-downloaded ESRGAN,
openpose, DepthAnything and Lama inpaint models from the `models/core`
directory into `models/.download_cache`.
There are a number of legacy model files in `models/core`, such as
GFPGAN, which are no longer used. This PR deletes them and tidies up the
`models/core` directory.
## Related Issues / Discussions
I have systematically replaced all the calls to
`download_with_progress_bar()`. This function is no longer used
elsewhere and has been removed.
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
I have added unit tests for the three new calls. You may test that the
`load_and_cache_model()` call is working by running the upscaler within
the web app. On first try, you will see the model file being downloaded
into the models `.cache` directory. On subsequent tries, the model will
either load from RAM (if it hasn't been displaced) or will be loaded
from the filesystem.
<!--WHEN APPLICABLE: Describe how we can test the changes in this PR.-->
## Merge Plan
Squash merge when approved.
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
DB schemas, may need some care when merging. For example, a careful
rebase by the change author, timing to not interfere with a pending
release, or a message to contributors on discord after merging.-->
## Checklist
- [X] _The PR has a short but descriptive title, suitable for a
changelog_
- [X] _Tests added / updated (if applicable)_
- [X] _Documentation added / updated (if applicable)_
|
||
|---|---|---|
| .dev_scripts | ||
| .github | ||
| coverage | ||
| docker | ||
| docs | ||
| installer | ||
| invokeai | ||
| scripts | ||
| tests | ||
| .dockerignore | ||
| .editorconfig | ||
| .git-blame-ignore-revs | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .pre-commit-config.yaml | ||
| .prettierrc.yaml | ||
| flake.lock | ||
| flake.nix | ||
| InvokeAI_Statement_of_Values.md | ||
| LICENSE | ||
| LICENSE-SD1+SD2.txt | ||
| LICENSE-SDXL.txt | ||
| Makefile | ||
| mkdocs.yml | ||
| pyproject.toml | ||
| README.md | ||
| Stable_Diffusion_v1_Model_Card.md | ||
Invoke - Professional Creative AI Tools for Visual Media
To learn more about Invoke, or implement our Business solutions, visit invoke.com
Invoke is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. Invoke offers an industry leading web-based UI, and serves as the foundation for multiple commercial products.
Installation and Updates - Documentation and Tutorials - Bug Reports - Contributing
Quick Start
-
Download and unzip the installer from the bottom of the latest release.
-
Run the installer script.
- Windows: Double-click on the
install.batscript. - macOS: Open a Terminal window, drag the file
install.shfrom Finder into the Terminal, and press enter. - Linux: Run
install.sh.
- Windows: Double-click on the
-
When prompted, enter a location for the install and select your GPU type.
-
Once the install finishes, find the directory you selected during install. The default location is
C:\Users\Username\invokeaifor Windows or~/invokeaifor Linux/macOS. -
Run the launcher script (
invoke.batfor Windows,invoke.shfor macOS and Linux) the same way you ran the installer script in step 2. -
Select option 1 to start the application. Once it starts up, open your browser and go to http://localhost:9090.
-
Open the model manager tab to install a starter model and then you'll be ready to generate.
More detail, including hardware requirements and manual install instructions, are available in the installation documentation.
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Please review our FAQ for solutions to common installation problems and other issues.
For more help, please join our Discord.
Features
Full details on features can be found in our documentation.
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Invoke runs a locally hosted web server & React UI with an industry-leading user experience.
Unified Canvas
The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/out-painting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
Workflows & Nodes
Invoke offers a fully featured workflow management solution, enabling users to combine the power of node-based workflows with the easy of a UI. This allows for customizable generation pipelines to be developed and shared by users looking to create specific workflows to support their production use-cases.
Board & Gallery Management
Invoke features an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow.
Other features
- Support for both ckpt and diffusers models
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- Upscaling Tools
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Get started with contributing by reading our contribution documentation, joining the #dev-chat or the GitHub discussion board.
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Thanks
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Original portions of the software are Copyright © 2024 by respective contributors.