Merge pull request #3923 from random-thoughtss/master (2cf3d2ac) · Commits · github_fork / Stable Diffusion Webui

modules/masking.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -49,7 +49,7 @@ def expand_crop_region(crop_region, processing_width, processing_height, image_w
		ratio_processing = processing_width / processing_height

		if ratio_crop_region > ratio_processing:
		desired_height = (x2 - x1) * ratio_processing
		desired_height = (x2 - x1) / ratio_processing
		desired_height_diff = int(desired_height - (y2-y1))
		y1 -= desired_height_diff//2
		y2 += desired_height_diff - desired_height_diff//2

modules/processing.py

+13 −14

Original line number	Diff line number	Diff line
		@@ -134,11 +134,7 @@ class StableDiffusionProcessing():
		# Dummy zero conditioning if we're not using inpainting model.
		# Still takes up a bit of memory, but no encoder call.
		# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
		return torch.zeros(
		x.shape[0], 5, 1, 1,
		dtype=x.dtype,
		device=x.device
		)
		return x.new_zeros(x.shape[0], 5, 1, 1)

		height = height or self.height
		width = width or self.width
		@@ -156,11 +152,7 @@ class StableDiffusionProcessing():
		def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
		if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
		# Dummy zero conditioning if we're not using inpainting model.
		return torch.zeros(
		latent_image.shape[0], 5, 1, 1,
		dtype=latent_image.dtype,
		device=latent_image.device
		)
		return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)

		# Handle the different mask inputs
		if image_mask is not None:
		@@ -174,11 +166,11 @@ class StableDiffusionProcessing():
		# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
		conditioning_mask = torch.round(conditioning_mask)
		else:
		conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])
		conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])

		# Create another latent image, this time with a masked version of the original input.
		# Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
		conditioning_mask = conditioning_mask.to(source_image.device)
		conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
		conditioning_image = torch.lerp(
		source_image,
		source_image * (1.0 - conditioning_mask),
		@@ -675,6 +667,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		if opts.use_scale_latent_for_hires_fix:
		samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

		# Avoid making the inpainting conditioning unless necessary as
		# this does need some extra compute to decode / encode the image again.
		if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
		image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
		else:
		image_conditioning = self.txt2img_image_conditioning(samples)

		for i in range(samples.shape[0]):
		save_intermediate(samples, i)
		else:
		@@ -700,14 +699,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

		samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

		image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)

		shared.state.nextjob()

		self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)

		noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

		image_conditioning = self.txt2img_image_conditioning(x)

		# GC now before running the next img2img to prevent running out of memory
		x = None
		devices.torch_gc()

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