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Merge branch 'master' of github.com:AUTOMATIC1111/stable-diffusion-webui into gamepad

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unknown 2023-02-03 20:39:42 -06:00
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6 changed files with 69 additions and 56 deletions
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2
javascript/hints.js
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@ -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",

49
modules/devices.py
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@ -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)

2
modules/shared.py
View File

@ -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"),

4
modules/ui.py
View File

@ -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")

43
scripts/prompt_matrix.py
View File

@ -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"))
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]
return [put_at_start, different_seeds]
def run(self, p, put_at_start, different_seeds):
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))

21
scripts/xyz_grid.py
View File

@ -286,23 +286,24 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
return Processed(p, [])
grids = [None] * len(zs)
sub_grids = [None] * len(zs)
for i in range(len(zs)):
start_index = i * len(xs) * len(ys)
end_index = start_index + len(xs) * len(ys)
grid = images.image_grid(image_cache[start_index:end_index], rows=len(ys))
if draw_legend:
grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
grids[i] = grid
sub_grids[i] = grid
if include_sub_grids and len(zs) > 1:
processed_result.images.insert(i+1, grid)
original_grid_size = grids[0].size
grids = images.image_grid(grids, rows=1)
processed_result.images[0] = images.draw_grid_annotations(grids, original_grid_size[0], original_grid_size[1], title_texts, [[images.GridAnnotation()]])
sub_grid_size = sub_grids[0].size
z_grid = images.image_grid(sub_grids, rows=1)
if draw_legend:
z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
processed_result.images[0] = z_grid
return processed_result
return processed_result, sub_grids
class SharedSettingsStackHelper(object):
@ -576,7 +577,7 @@ class Script(scripts.Script):
return res
with SharedSettingsStackHelper():
processed = draw_xyz_grid(
processed, sub_grids = draw_xyz_grid(
p,
xs=xs,
ys=ys,
@ -592,6 +593,10 @@ class Script(scripts.Script):
second_axes_processed=second_axes_processed
)
if opts.grid_save and len(sub_grids) > 1:
for sub_grid in sub_grids:
images.save_image(sub_grid, p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
if opts.grid_save:
images.save_image(processed.images[0], p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)