Fix full previews, --no-half-vae (10421f93) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+4 −4

modules/sd_hijack_utils.py

+1 −1

Original line number	Original line	Diff line number	Diff line
	@@ -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

Original line number	Original line	Diff line number	Diff line
	@@ -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))

Admin message