output directory options
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2d5689a051
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78
webui.py
78
webui.py
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@ -36,6 +36,7 @@ from collections import namedtuple
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from contextlib import nullcontext
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from contextlib import nullcontext
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import signal
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import signal
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import tqdm
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import tqdm
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import re
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import k_diffusion.sampling
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import k_diffusion.sampling
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from ldm.util import instantiate_from_config
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from ldm.util import instantiate_from_config
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@ -187,7 +188,14 @@ class Options:
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data = None
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data = None
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data_labels = {
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data_labels = {
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"outdir": OptionInfo("", "Output dictectory; if empty, defaults to 'outputs/*'"),
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"outdir_samples": OptionInfo("", "Output dictectory for images; if empty, defaults to two directories below"),
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"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output dictectory for txt2img images'),
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"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output dictectory for img2img images'),
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"outdir_grids": OptionInfo("", "Output dictectory for grids; if empty, defaults to two directories below"),
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"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output dictectory for txt2img grids'),
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"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output dictectory for img2img grids'),
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"save_to_dirs": OptionInfo(False, "When writing images/grids, create a directory with name derived from the prompt"),
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"save_to_dirs_prompt_len": OptionInfo(10, "When using above, how many words from prompt to put into directory name", gr.Slider, {"minimum": 1, "maximum": 32, "step": 1}),
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"samples_save": OptionInfo(True, "Save indiviual samples"),
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"samples_save": OptionInfo(True, "Save indiviual samples"),
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"samples_format": OptionInfo('png', 'File format for indiviual samples'),
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"samples_format": OptionInfo('png', 'File format for indiviual samples'),
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"grid_save": OptionInfo(True, "Save image grids"),
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"grid_save": OptionInfo(True, "Save image grids"),
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@ -342,11 +350,12 @@ def torch_gc():
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def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False):
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def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False):
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if short_filename or prompt is None or seed is None:
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if short_filename or prompt is None or seed is None:
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filename = f"{basename}"
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file_decoration = ""
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elif opts.save_to_dirs:
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file_decoration = f"-{seed}"
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else:
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else:
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filename = f"{basename}-{seed}-{sanitize_filename_part(prompt)[:128]}"
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file_decoration = f"-{seed}-{sanitize_filename_part(prompt)[:128]}"
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if extension == 'png' and opts.enable_pnginfo and info is not None:
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if extension == 'png' and opts.enable_pnginfo and info is not None:
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pnginfo = PngImagePlugin.PngInfo()
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pnginfo = PngImagePlugin.PngInfo()
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@ -354,8 +363,26 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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else:
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else:
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pnginfo = None
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pnginfo = None
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if opts.save_to_dirs:
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words = re.findall(r'\w+', prompt or "")
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if len(words) == 0:
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words = ["empty"]
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dirname = " ".join(words[0:opts.save_to_dirs_prompt_len])
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path = os.path.join(path, dirname)
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os.makedirs(path, exist_ok=True)
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os.makedirs(path, exist_ok=True)
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fullfn = os.path.join(path, f"{filename}.{extension}")
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filecount = len(os.listdir(path))
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fullfn = "a.png"
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fullfn_without_extension = "a"
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for i in range(100):
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fn = f"{filecount:05}" if basename == '' else f"{basename}-{filecount:04}"
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fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
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fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
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if not os.path.exists(fullfn):
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break
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image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
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image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
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target_side_length = 4000
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target_side_length = 4000
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@ -368,7 +395,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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elif oversize:
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elif oversize:
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image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
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image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
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image.save(os.path.join(path, f"{filename}.jpg"), quality=opts.jpeg_quality, pnginfo=pnginfo)
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image.save(os.path.join(path, f"{fullfn_without_extension}.jpg"), quality=opts.jpeg_quality, pnginfo=pnginfo)
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@ -824,8 +851,9 @@ class EmbeddingsWithFixes(nn.Module):
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class StableDiffusionProcessing:
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class StableDiffusionProcessing:
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def __init__(self, outpath=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
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def __init__(self, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
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self.outpath: str = outpath
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self.outpath_samples: str = outpath_samples
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self.outpath_grids: str = outpath_grids
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self.prompt: str = prompt
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self.prompt: str = prompt
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self.negative_prompt: str = (negative_prompt or "")
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self.negative_prompt: str = (negative_prompt or "")
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self.seed: int = seed
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self.seed: int = seed
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@ -969,10 +997,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
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seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
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sample_path = os.path.join(p.outpath, "samples")
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os.makedirs(p.outpath_samples, exist_ok=True)
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os.makedirs(sample_path, exist_ok=True)
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os.makedirs(p.outpath_grids, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(p.outpath)) - 1
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comments = []
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comments = []
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@ -1071,10 +1097,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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image = image.convert('RGB')
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image = image.convert('RGB')
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if not p.do_not_save_samples:
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if not p.do_not_save_samples:
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save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opts.samples_format, info=infotext())
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save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext())
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output_images.append(image)
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output_images.append(image)
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base_count += 1
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unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
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unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
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if (p.prompt_matrix or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
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if (p.prompt_matrix or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
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@ -1097,8 +1122,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if return_grid:
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if return_grid:
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output_images.insert(0, grid)
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output_images.insert(0, grid)
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save_image(grid, p.outpath, f"grid-{grid_count:04}", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
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save_image(grid, p.outpath_grids, "grid", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
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grid_count += 1
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torch_gc()
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torch_gc()
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return Processed(output_images, seed, infotext())
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return Processed(output_images, seed, infotext())
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@ -1114,11 +1138,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
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samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
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return samples_ddim
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return samples_ddim
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def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
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outpath = opts.outdir or "outputs/txt2img-samples"
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def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
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p = StableDiffusionProcessingTxt2Img(
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p = StableDiffusionProcessingTxt2Img(
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outpath=outpath,
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outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
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outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
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prompt=prompt,
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prompt=prompt,
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negative_prompt=negative_prompt,
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negative_prompt=negative_prompt,
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seed=seed,
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seed=seed,
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@ -1138,6 +1162,7 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, u
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p.do_not_save_samples = True
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p.do_not_save_samples = True
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display_result_data = [[], -1, ""]
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display_result_data = [[], -1, ""]
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def display(imgs, s=display_result_data[1], i=display_result_data[2]):
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def display(imgs, s=display_result_data[1], i=display_result_data[2]):
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display_result_data[0] = imgs
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display_result_data[0] = imgs
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display_result_data[1] = s
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display_result_data[1] = s
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@ -1422,8 +1447,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
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def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
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outpath = opts.outdir or "outputs/img2img-samples"
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is_classic = mode == 0
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is_classic = mode == 0
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is_inpaint = mode == 1
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is_inpaint = mode == 1
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is_loopback = mode == 2
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is_loopback = mode == 2
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@ -1439,7 +1462,8 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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p = StableDiffusionProcessingImg2Img(
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p = StableDiffusionProcessingImg2Img(
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outpath=outpath,
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outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
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outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
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prompt=prompt,
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prompt=prompt,
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seed=seed,
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seed=seed,
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sampler_index=sampler_index,
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sampler_index=sampler_index,
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@ -1483,10 +1507,9 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
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p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
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history.append(processed.images[0])
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history.append(processed.images[0])
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grid_count = len(os.listdir(outpath)) - 1
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grid = image_grid(history, batch_size, rows=1)
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grid = image_grid(history, batch_size, rows=1)
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save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
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save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
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processed = Processed(history, initial_seed, initial_info)
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processed = Processed(history, initial_seed, initial_info)
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@ -1535,8 +1558,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
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combined_image = combine_grid(grid)
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combined_image = combine_grid(grid)
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grid_count = len(os.listdir(outpath)) - 1
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save_image(combined_image, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
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save_image(combined_image, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
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processed = Processed([combined_image], initial_seed, initial_info)
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processed = Processed([combined_image], initial_seed, initial_info)
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@ -1708,9 +1730,7 @@ def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_in
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if have_realesrgan and RealESRGAN_upscaling != 1.0:
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if have_realesrgan and RealESRGAN_upscaling != 1.0:
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image = upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
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image = upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
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os.makedirs(outpath, exist_ok=True)
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save_image(image, outpath, "", None, '', opts.samples_format, short_filename=True)
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base_count = len(os.listdir(outpath))
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save_image(image, outpath, f"{base_count:05}", None, '', opts.samples_format, short_filename=True)
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return image, 0, ''
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return image, 0, ''
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