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Merge pull request #7234 from brkirch/fix-full-previews 

Fix full previews and--no-half-vae to work correctly with --upcast-sampling
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AUTOMATIC1111 2023-01-26 14:48:43 +03:00 committed by GitHub
parent 6cff440182 10421f93c3
commit 645f4e7ef8
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2 changed files with 5 additions and 5 deletions
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8
modules/processing.py
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@ -172,7 +172,7 @@ class StableDiffusionProcessing:
midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device) midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size) midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_unet) if devices.unet_needs_upcast else source_image)) conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image.to(devices.dtype_vae) if devices.unet_needs_upcast else source_image))
conditioning_image = conditioning_image.float() if devices.unet_needs_upcast else conditioning_image conditioning_image = conditioning_image.float() if devices.unet_needs_upcast else conditioning_image
conditioning = torch.nn.functional.interpolate( conditioning = torch.nn.functional.interpolate(
self.sd_model.depth_model(midas_in), self.sd_model.depth_model(midas_in),
@ -217,7 +217,7 @@ class StableDiffusionProcessing:
) )
# Encode the new masked image using first stage of network. # Encode the new masked image using first stage of network.
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_unet) if devices.unet_needs_upcast else conditioning_image)) conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image.to(devices.dtype_vae) if devices.unet_needs_upcast else conditioning_image))
# Create the concatenated conditioning tensor to be fed to `c_concat` # Create the concatenated conditioning tensor to be fed to `c_concat`
conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:]) conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
@ -417,7 +417,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
def decode_first_stage(model, x): def decode_first_stage(model, x):
with devices.autocast(disable=x.dtype == devices.dtype_vae): with devices.autocast(disable=x.dtype == devices.dtype_vae):
x = model.decode_first_stage(x) x = model.decode_first_stage(x.to(devices.dtype_vae) if devices.unet_needs_upcast else x)
return x return x
@ -1001,7 +1001,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
image = torch.from_numpy(batch_images) image = torch.from_numpy(batch_images)
image = 2. * image - 1. image = 2. * image - 1.
image = image.to(device=shared.device, dtype=devices.dtype_unet if devices.unet_needs_upcast else None) image = image.to(device=shared.device, dtype=devices.dtype_vae if devices.unet_needs_upcast else None)
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image)) self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))

2
modules/sd_hijack_utils.py
View File

@ -5,7 +5,7 @@ class CondFunc:
self = super(CondFunc, cls).__new__(cls) self = super(CondFunc, cls).__new__(cls)
if isinstance(orig_func, str): if isinstance(orig_func, str):
func_path = orig_func.split('.') func_path = orig_func.split('.')
for i in range(len(func_path)-2, -1, -1): for i in range(len(func_path)-1, -1, -1):
try: try:
resolved_obj = importlib.import_module('.'.join(func_path[:i])) resolved_obj = importlib.import_module('.'.join(func_path[:i]))
break break