Usage: `--gradio-auth-path {PATH}`
It adds the credentials to the already existing `--gradio-auth` credentials. It can also handle line breaks.
The file should look like:
`{u1}:{p1},{u2}:{p2}`
or
```
{u1}:{p1},
{u2}:{p2}
```
Will gradio handle duplicate credentials if it happens?
previously module attributes like __file__ where not set correctly,
leading to scripts getting the directory of the stable-diffusion repo
location instead of their own script.
This causes problem when loading user data from an external location
using the --data-dir flag, as extensions would look for their own code
in the stable-diffusion repo location instead of the data dir location.
Using pythons importlib functions sets the modules specs correctly and
executes them. But this will break extensions if they build paths based
on the previously incorrect __file__ attribute.
Allows loading instruct-pix2pix models via same method as inpainting models in sd_models.py and sd_hijack_ip2p.py
Adds ddpm_edit.py necessary for instruct-pix2pix
Adds "Upcast cross attention layer to float32" option in Stable Diffusion settings. This allows for generating images using SD 2.1 models without --no-half or xFormers.
In order to make upcasting cross attention layer optimizations possible it is necessary to indent several sections of code in sd_hijack_optimizations.py so that a context manager can be used to disable autocast. Also, even though Stable Diffusion (and Diffusers) only upcast q and k, unfortunately my findings were that most of the cross attention layer optimizations could not function unless v is upcast also.
This also handles type casting so that ROCm and MPS torch devices work correctly without --no-half. One cast is required for deepbooru in deepbooru_model.py, some explicit casting is required for img2img and inpainting. depth_model can't be converted to float16 or it won't work correctly on some systems (it's known to have issues on MPS) so in sd_models.py model.depth_model is removed for model.half().
The loading of the model for approx nn live previews can change the internal state of PyTorch, resulting in a different image. This can be avoided by preloading the approx nn model in advance.
