Currently, if a graph partially fails validation (i.e. some outputs are
valid while others have links from missing nodes), the execution loop
could get an exception resulting in server lockup.
This isn't actually possible to reproduce via the default UI, but is a
potential issue for people using the API to construct invalid graphs.
This code automatically forces upcasting attention for MacOS versions 14.5 and 14.6. My computer returns the string "14.6.1" for `platform.mac_ver()[0]`, so this generalizes the comparison to catch more versions.
I am running MacOS Sonoma 14.6.1 (latest version) and was seeing black image generation on previously functional workflows after recent software updates. This PR solved the issue for me.
See comfyanonymous/ComfyUI#3521
This change fixes a bug where non-constant values could be passed to the
IS_CHANGED function. This would result in workflows taking an extra
execution before they acted as if they were cached.
The actual change is like 4 characters -- the rest is adding unit tests.
When generating images with fp8_e4_m3 Flux and batch size >1, using --fast, ComfyUI throws a "view size is not compatible with input tensor's size and stride" error pointing at the first of these two calls to view.
As reshape is semantically equivalent to view except for working on a broader set of inputs, there should be no downside to changing this. The only difference is that it clones the underlying data in cases where .view would error out. I have confirmed that the output still looks as expected, but cannot confirm that no mutable use is made of the tensors anywhere.
Note that --fast is only marginally faster than the default.
* Create internal route table.
* List files.
* Add GET /internal/files.
Retrieves list of files in models, output, and user directories.
* Refactor file names.
* Use typing_extensions for Python 3.8
* Fix tests.
* Remove print statements.
* Update README.
* Add output and user to valid directory test.
* Add missing type hints.
Optimizations that might break things/lower quality will be put behind
this flag first and might be enabled by default in the future.
Currently the only optimization is float8_e4m3fn matrix multiplication on
4000/ADA series Nvidia cards or later. If you have one of these cards you
will see a speed boost when using fp8_e4m3fn flux for example.
comfyanonymous
Alex "mcmonkey" Goodwin
Dr.Lt.Data
Chenlei Hu
Robin Huang
guill
Simon Lui
Scorpinaus
David
Svein Ove Aas