ruff auto fixes (028d3f64) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/LDSR/sd_hijack_autoencoder.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -288,5 +288,5 @@ class VQModelInterface(VQModel):
		dec = self.decoder(quant)
		return dec

		setattr(ldm.models.autoencoder, "VQModel", VQModel)
		setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
		ldm.models.autoencoder.VQModel = VQModel
		ldm.models.autoencoder.VQModelInterface = VQModelInterface

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -1116,7 +1116,7 @@ class LatentDiffusionV1(DDPMV1):
		if cond is not None:
		if isinstance(cond, dict):
		cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
		list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
		[x[:batch_size] for x in cond[key]] for key in cond}
		else:
		cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]

		@@ -1215,7 +1215,7 @@ class LatentDiffusionV1(DDPMV1):
		if cond is not None:
		if isinstance(cond, dict):
		cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
		list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
		[x[:batch_size] for x in cond[key]] for key in cond}
		else:
		cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
		return self.p_sample_loop(cond,
		@@ -1437,7 +1437,7 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
		logs['bbox_image'] = cond_img
		return logs

		setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
		setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
		setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
		setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
		ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
		ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
		ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
		ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1

extensions-builtin/Lora/lora.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -172,7 +172,7 @@ def load_lora(name, filename):
		else:
		print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
		continue
		assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
		raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")

		with torch.no_grad():
		module.weight.copy_(weight)
		@@ -184,7 +184,7 @@ def load_lora(name, filename):
		elif lora_key == "lora_down.weight":
		lora_module.down = module
		else:
		assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
		raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")

		if len(keys_failed_to_match) > 0:
		print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
		@@ -202,7 +202,7 @@ def load_loras(names, multipliers=None):
		loaded_loras.clear()

		loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
		if any([x is None for x in loras_on_disk]):
		if any(x is None for x in loras_on_disk):
		list_available_loras()

		loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
		@@ -309,7 +309,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu

		print(f'failed to calculate lora weights for layer {lora_layer_name}')

		setattr(self, "lora_current_names", wanted_names)
		self.lora_current_names = wanted_names


		def lora_forward(module, input, original_forward):
		@@ -343,8 +343,8 @@ def lora_forward(module, input, original_forward):


		def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
		setattr(self, "lora_current_names", ())
		setattr(self, "lora_weights_backup", None)
		self.lora_current_names = ()
		self.lora_weights_backup = None


		def lora_Linear_forward(self, input):

extensions-builtin/Lora/scripts/lora_script.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -53,7 +53,7 @@ script_callbacks.on_infotext_pasted(lora.infotext_pasted)


		shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
		"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
		"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + list(lora.available_loras)}, refresh=lora.list_available_loras),
		}))

modules/config_states.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -35,7 +35,7 @@ def list_config_states():
		j["filepath"] = path
		config_states.append(j)

		config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True))
		config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)

		for cs in config_states:
		timestamp = time.asctime(time.gmtime(cs["created_at"]))

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