Merge branch 'master' of github.com:AUTOMATIC1111/stable-diffusion-webui into gamepad (5e1f4f74) · Commits · github_fork / Stable Diffusion Webui

javascript/hints.js

+1 −1

Original line number	Diff line number	Diff line
		@@ -17,7 +17,7 @@ titles = {
		"\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
		"\u{1f4c2}": "Open images output directory",
		"\u{1f4be}": "Save style",
		"\U0001F5D1": "Clear prompt",
		"\u{1f5d1}": "Clear prompt",
		"\u{1f4cb}": "Apply selected styles to current prompt",
		"\u{1f4d2}": "Paste available values into the field",
		"\u{1f3b4}": "Show extra networks",

modules/devices.py

+15 −34

Original line number	Diff line number	Diff line
		@@ -2,6 +2,7 @@ import sys, os, shlex
		import contextlib
		import torch
		from modules import errors
		from modules.sd_hijack_utils import CondFunc
		from packaging import version


		@@ -156,36 +157,7 @@ def test_for_nans(x, where):
		raise NansException(message)


		# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
		orig_tensor_to = torch.Tensor.to
		def tensor_to_fix(self, args, *kwargs):
		if self.device.type != 'mps' and \
		((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
		(isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
		self = self.contiguous()
		return orig_tensor_to(self, args, *kwargs)


		# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
		orig_layer_norm = torch.nn.functional.layer_norm
		def layer_norm_fix(args, *kwargs):
		if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
		args = list(args)
		args[0] = args[0].contiguous()
		return orig_layer_norm(args, *kwargs)


		# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
		orig_tensor_numpy = torch.Tensor.numpy
		def numpy_fix(self, args, *kwargs):
		if self.requires_grad:
		self = self.detach()
		return orig_tensor_numpy(self, args, *kwargs)


		# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
		orig_cumsum = torch.cumsum
		orig_Tensor_cumsum = torch.Tensor.cumsum
		def cumsum_fix(input, cumsum_func, args, *kwargs):
		if input.device.type == 'mps':
		output_dtype = kwargs.get('dtype', input.dtype)
		@@ -199,11 +171,20 @@ def cumsum_fix(input, cumsum_func, args, *kwargs):
		if has_mps():
		if version.parse(torch.__version__) < version.parse("1.13"):
		# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
		torch.Tensor.to = tensor_to_fix
		torch.nn.functional.layer_norm = layer_norm_fix
		torch.Tensor.numpy = numpy_fix

		# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
		CondFunc('torch.Tensor.to', lambda orig_func, self, args, kwargs: orig_func(self.contiguous(), args, **kwargs),
		lambda _, self, args, *kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
		# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
		CondFunc('torch.nn.functional.layer_norm', lambda orig_func, args, kwargs: orig_func(([args[0].contiguous()] + list(args[1:])), **kwargs),
		lambda _, args, *kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
		# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
		CondFunc('torch.Tensor.numpy', lambda orig_func, self, args, kwargs: orig_func(self.detach(), args, *kwargs), lambda _, self, args, **kwargs: self.requires_grad)
		elif version.parse(torch.__version__) > version.parse("1.13.1"):
		cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
		cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))
		torch.cumsum = lambda input, args, kwargs: ( cumsum_fix(input, orig_cumsum, args, **kwargs) )
		torch.Tensor.cumsum = lambda self, args, kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, args, **kwargs) )
		cumsum_fix_func = lambda orig_func, input, args, kwargs: cumsum_fix(input, orig_func, args, **kwargs)
		CondFunc('torch.cumsum', cumsum_fix_func, None)
		CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
		CondFunc('torch.narrow', lambda orig_func, args, kwargs: orig_func(args, **kwargs).clone(), None)

modules/shared.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -327,7 +327,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
		"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
		"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),

		"use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
		"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
		"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
		"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
		"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),

modules/ui.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -479,8 +479,8 @@ def create_ui():
		width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
		height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")

		if opts.dimensions_and_batch_together:
		res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
		if opts.dimensions_and_batch_together:
		with gr.Column(elem_id="txt2img_column_batch"):
		batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
		batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
		@@ -757,8 +757,8 @@ def create_ui():
		width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
		height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")

		if opts.dimensions_and_batch_together:
		res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
		if opts.dimensions_and_batch_together:
		with gr.Column(elem_id="img2img_column_batch"):
		batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
		batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")

scripts/prompt_matrix.py

+38 −11

Original line number	Diff line number	Diff line
		@@ -45,15 +45,39 @@ class Script(scripts.Script):
		return "Prompt matrix"

		def ui(self, is_img2img):
		put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False, elem_id=self.elem_id("put_at_start"))
		different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))

		return [put_at_start, different_seeds]

		def run(self, p, put_at_start, different_seeds):
		gr.HTML('<br />')
		with gr.Row():
		with gr.Column():
		put_at_start = gr.Checkbox(label='Put variable parts at start of prompt',
		value=False, elem_id=self.elem_id("put_at_start"))
		with gr.Column():
		# Radio buttons for selecting the prompt between positive and negative
		prompt_type = gr.Radio(["positive", "negative"], label="Select prompt",
		elem_id=self.elem_id("prompt_type"), value="positive")
		with gr.Row():
		with gr.Column():
		different_seeds = gr.Checkbox(
		label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
		with gr.Column():
		# Radio buttons for selecting the delimiter to use in the resulting prompt
		variations_delimiter = gr.Radio(["comma", "space"], label="Select delimiter", elem_id=self.elem_id(
		"variations_delimiter"), value="comma")
		return [put_at_start, different_seeds, prompt_type, variations_delimiter]

		def run(self, p, put_at_start, different_seeds, prompt_type, variations_delimiter):
		modules.processing.fix_seed(p)
		# Raise error if promp type is not positive or negative
		if prompt_type not in ["positive", "negative"]:
		raise ValueError(f"Unknown prompt type {prompt_type}")
		# Raise error if variations delimiter is not comma or space
		if variations_delimiter not in ["comma", "space"]:
		raise ValueError(f"Unknown variations delimiter {variations_delimiter}")

		original_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
		prompt = p.prompt if prompt_type == "positive" else p.negative_prompt
		original_prompt = prompt[0] if type(prompt) == list else prompt
		positive_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt

		delimiter = ", " if variations_delimiter == "comma" else " "

		all_prompts = []
		prompt_matrix_parts = original_prompt.split("\|")
		@@ -66,16 +90,19 @@ class Script(scripts.Script):
		else:
		selected_prompts = [prompt_matrix_parts[0]] + selected_prompts

		all_prompts.append(", ".join(selected_prompts))
		all_prompts.append(delimiter.join(selected_prompts))

		p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
		p.do_not_save_grid = True

		print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")

		if prompt_type == "positive":
		p.prompt = all_prompts
		else:
		p.negative_prompt = all_prompts
		p.seed = [p.seed + (i if different_seeds else 0) for i in range(len(all_prompts))]
		p.prompt_for_display = original_prompt
		p.prompt_for_display = positive_prompt
		processed = process_images(p)

		grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))

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