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https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-14 22:13:41 +03:00
Save original copy of image before upscaling or face fixing #103
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hlky
parent
343b4157d4
commit
b9b74329ae
162
webui.py
162
webui.py
@ -487,6 +487,64 @@ def check_prompt_length(prompt, comments):
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comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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def save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode):
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filename_i = os.path.join(sample_path_i, filename)
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if not jpg_sample:
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if opt.save_metadata:
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metadata = PngInfo()
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metadata.add_text("SD:prompt", prompts[i])
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metadata.add_text("SD:seed", str(seeds[i]))
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metadata.add_text("SD:width", str(width))
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metadata.add_text("SD:height", str(height))
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metadata.add_text("SD:steps", str(steps))
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metadata.add_text("SD:cfg_scale", str(cfg_scale))
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metadata.add_text("SD:normalize_prompt_weights", str(normalize_prompt_weights))
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metadata.add_text("SD:GFPGAN", str(use_GFPGAN and GFPGAN is not None))
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image.save(f"{filename_i}.png", pnginfo=metadata)
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else:
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image.save(f"{filename_i}.png")
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else:
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image.save(f"{filename_i}.jpg", 'jpeg', quality=100, optimize=True)
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if write_info_files:
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# toggles differ for txt2img vs. img2img:
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offset = 0 if init_img is None else 2
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toggles = []
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if prompt_matrix:
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toggles.append(0)
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if normalize_prompt_weights:
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toggles.append(1)
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if init_img is not None:
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if uses_loopback:
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toggles.append(2)
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if uses_random_seed_loopback:
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toggles.append(3)
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if not skip_save:
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toggles.append(2 + offset)
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if not skip_grid:
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toggles.append(3 + offset)
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if sort_samples:
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toggles.append(4 + offset)
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if write_info_files:
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toggles.append(5 + offset)
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if use_GFPGAN:
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toggles.append(6 + offset)
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info_dict = dict(
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target="txt2img" if init_img is None else "img2img",
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prompt=prompts[i], ddim_steps=steps, toggles=toggles, sampler_name=sampler_name,
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ddim_eta=ddim_eta, n_iter=n_iter, batch_size=batch_size, cfg_scale=cfg_scale,
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seed=seed, width=width, height=height
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)
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if init_img is not None:
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# Not yet any use for these, but they bloat up the files:
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#info_dict["init_img"] = init_img
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#info_dict["init_mask"] = init_mask
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info_dict["denoising_strength"] = denoising_strength
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info_dict["resize_mode"] = resize_mode
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with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
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yaml.dump(info_dict, f)
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def get_next_sequence_number(path, prefix=''):
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"""
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@ -617,24 +675,56 @@ def process_images(
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if opt.optimized:
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modelFS.to(device)
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x_samples_ddim = (model if not opt.optimized else modelFS).decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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for i, x_sample in enumerate(x_samples_ddim):
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sanitized_prompt = prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})
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if sort_samples:
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sanitized_prompt = sanitized_prompt[:128] #200 is too long
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sample_path_i = os.path.join(sample_path, sanitized_prompt)
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os.makedirs(sample_path_i, exist_ok=True)
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base_count = len([x for x in os.listdir(sample_path_i) if x.endswith(('.png', '.jpg'))]) - 1 # start at 0
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filename = f"{base_count:05}-{seeds[i]}"
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else:
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sample_path_i = sample_path
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base_count = len([x for x in os.listdir(sample_path_i) if x.endswith(('.png', '.jpg'))]) - 1 # start at 0
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sanitized_prompt = sanitized_prompt
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filename = f"{base_count:05}-{seeds[i]}_{sanitized_prompt}"[:128] #same as before
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x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
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x_sample = x_sample.astype(np.uint8)
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if use_GFPGAN and GFPGAN is not None:
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torch_gc()
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original_sample = x_sample
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original_filename = filename
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
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x_sample = restored_img[:,:,::-1]
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image = Image.fromarray(x_sample)
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filename = filename + '-gfpgan'
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save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
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filename = original_filename
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x_sample = original_sample
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if use_RealESRGAN and RealESRGAN is not None:
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torch_gc()
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original_sample = x_sample
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original_filename = filename
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if RealESRGAN.model.name != realesrgan_model_name:
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try_loading_RealESRGAN(realesrgan_model_name)
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output, img_mode = RealESRGAN.enhance(x_sample[:,:,::-1])
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x_sample = output[:,:,::-1]
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image = Image.fromarray(x_sample)
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filename = filename + '-esrgan'
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save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
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filename = original_filename
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x_sample = original_sample
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image = Image.fromarray(x_sample)
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if init_mask:
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@ -656,74 +746,10 @@ def process_images(
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init_mask = init_mask.convert('L')
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image = Image.composite(init_img, image, init_mask)
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sanitized_prompt = prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})
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if sort_samples:
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sanitized_prompt = sanitized_prompt[:128] #200 is too long
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sample_path_i = os.path.join(sample_path, sanitized_prompt)
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os.makedirs(sample_path_i, exist_ok=True)
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base_count = get_next_sequence_number(sample_path_i)
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filename = f"{base_count:05}-{seeds[i]}"
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else:
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sample_path_i = sample_path
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base_count = get_next_sequence_number(sample_path_i)
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sanitized_prompt = sanitized_prompt
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filename = f"{base_count:05}-{seeds[i]}_{sanitized_prompt}"[:128] #same as before
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if not skip_save:
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filename_i = os.path.join(sample_path_i, filename)
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if not jpg_sample:
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if opt.save_metadata:
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metadata = PngInfo()
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metadata.add_text("SD:prompt", prompts[i])
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metadata.add_text("SD:seed", str(seeds[i]))
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metadata.add_text("SD:width", str(width))
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metadata.add_text("SD:height", str(height))
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metadata.add_text("SD:steps", str(steps))
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metadata.add_text("SD:cfg_scale", str(cfg_scale))
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metadata.add_text("SD:normalize_prompt_weights", str(normalize_prompt_weights))
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metadata.add_text("SD:GFPGAN", str(use_GFPGAN and GFPGAN is not None))
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image.save(f"{filename_i}.png", pnginfo=metadata)
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else:
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image.save(f"{filename_i}.png")
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else:
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image.save(f"{filename_i}.jpg", 'jpeg', quality=100, optimize=True)
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if write_info_files:
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# toggles differ for txt2img vs. img2img:
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offset = 0 if init_img is None else 2
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toggles = []
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if prompt_matrix:
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toggles.append(0)
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if normalize_prompt_weights:
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toggles.append(1)
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if init_img is not None:
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if uses_loopback:
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toggles.append(2)
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if uses_random_seed_loopback:
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toggles.append(3)
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if not skip_save:
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toggles.append(2 + offset)
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if not skip_grid:
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toggles.append(3 + offset)
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if sort_samples:
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toggles.append(4 + offset)
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if write_info_files:
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toggles.append(5 + offset)
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if use_GFPGAN:
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toggles.append(6 + offset)
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info_dict = dict(
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target="txt2img" if init_img is None else "img2img",
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prompt=prompts[i], ddim_steps=steps, toggles=toggles, sampler_name=sampler_name,
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ddim_eta=ddim_eta, n_iter=n_iter, batch_size=batch_size, cfg_scale=cfg_scale,
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seed=seed, width=width, height=height
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)
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if init_img is not None:
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# Not yet any use for these, but they bloat up the files:
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#info_dict["init_img"] = init_img
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#info_dict["init_mask"] = init_mask
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info_dict["denoising_strength"] = denoising_strength
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info_dict["resize_mode"] = resize_mode
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with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
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yaml.dump(info_dict, f)
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save_sample(image, sample_path_i, filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
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normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
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skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)
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output_images.append(image)
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