Add UI setting for upcasting attention to float32

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.

1 files changed, 1 insertions, 1 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 2d186ba0..a850082d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -611,7 +611,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.n_iter > 1:

                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"

 

-            with devices.autocast(disable=devices.unet_needs_upcast):

+            with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():

                 samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)

 

             x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]