fix broken api for checkpoint list

fix some style inconsistenices

Fix full previews and--no-half-vae to work correctly with --upcast-sampling

add edit_image_conditioning from my earlier edits in case there's an attempt to inegrate pix2pix properly
this allows to use pix2pix model in img2img though it won't work well this way

allow symlinks in the textual inversion embeddings folder

Update default Mac command line arguments to use --upcast-sampling instead of --no-half

i have no idea what commit message should be

Add upcast options, full precision sampling from float16 UNet and upcasting attention for inference using SD 2.1 models without --no-half

Fix extra network thumbs label color

Fix Approx NN previews changing first generation result

Add instruct-pix2pix hijack

Update requirement for transformers to version 4.25.1 to allow instruct-pix2pix demo code to work

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 X/Y/Z Grid Script

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().

add PIP_INSTALLER_LOCATION env var to install pip if it's not installed
remove accidental call to accelerate when venv is disabled
add another env var to skip venv - SKIP_VENV