Previously, dependency cycles that were created during node expansion
would cause the application to quit (due to an uncaught exception). Now,
we'll throw a proper error to the UI. We also make an attempt to 'blame'
the most relevant node in the UI.
- ComfyUI can now load EXR files.
- There are new arithmetic nodes for floats and integers.
- EXR nodes can load depth maps and be remapped with
ImageApplyColormap. This allows end users to use ground truth depth
data from video game engines or 3D graphics tools and recolor it to
the format expected by depth ControlNets: grayscale inverse depth
maps and "inferno" colored inverse depth maps.
- Fixed license notes.
- Added an additional known ControlNet model.
- Because CV2 is now used to read OpenEXR files, an environment
variable must be set early on in the application, before CV2 is
imported. This file, main_pre, is now imported early on in more
places.
- Removed /api/v1/images because you should use your own CDN style
image host and /view for maximum compatibility
- The /api/v1/prompts POST application/json response will now return
the outputs dictionary
- Caching has been removed
- More tests
- Subdirectory prefixes are now supported
- Fixed an issue where a Linux frontend and Windows backend would have
paths that could not interact with each other correctly
Additionally, if `VALIDATE_INPUTS` takes an argument named `input_types`,
that variable will be a dictionary of the socket type of all incoming
connections. If that argument exists, normal socket type validation will
not occur. This removes the last hurdle for enabling variant types
entirely from custom nodes, so I've removed that command-line option.
I've added appropriate unit tests for these changes.
- Run comfyui workflows directly inside other python applications using
EmbeddedComfyClient.
- Optional telemetry in prompts and models using anonymity preserving
Plausible self-hosted or hosted.
- Better OpenAPI schema
- Basic support for distributed ComfyUI backends. Limitations: no
progress reporting, no easy way to start your own distributed
backend, requires RabbitMQ as a message broker.
* Add inference tests
* Clean up
* Rename test graph file
* Add readme for tests
* Separate server fixture
* test file name change
* Assert images are generated
* Clean up comments
* Add __init__.py so tests can run with command line `pytest`
* Fix command line args for pytest
* Loop all samplers/schedulers in test_inference.py
* Ci quality workflows compare (#1)
* Add image comparison tests
* Comparison tests do not pass with empty metadata
* Ensure tests are run in correct order
* Save image files with test name
* Update tests readme
* Reduce step counts in tests to ~halve runtime
* Ci quality workflows build (#2)
* Add build test github workflow
