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https://github.com/Sygil-Dev/sygil-webui.git
synced 2024-12-15 06:21:34 +03:00
#354 + playground.py update
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hlky
parent
cfc9f00a93
commit
dffdaaaf77
@ -153,7 +153,7 @@ def draw_gradio_ui(opt, img2img=lambda x: x, txt2img=lambda x: x, txt2img_defaul
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value=3, visible=False)
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img2img_resize = gr.Radio(label="Resize mode",
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choices=["Just resize", "Crop and resize", "Resize and fill"],
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choices=["Just resize"],
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type="index",
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value=img2img_resize_modes[img2img_defaults['resize_mode']])
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164
webui.py
164
webui.py
@ -847,13 +847,10 @@ def process_images(
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if use_GFPGAN and GFPGAN is not None and not use_RealESRGAN:
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skip_save = True # #287 >_>
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torch_gc()
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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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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(original_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
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gfpgan_sample = restored_img[:,:,::-1]
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gfpgan_image = Image.fromarray(gfpgan_sample)
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gfpgan_filename = original_filename + '-gfpgan'
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save_sample(image, sample_path_i, original_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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save_sample(gfpgan_image, sample_path_i, gfpgan_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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@ -866,13 +863,10 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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torch_gc()
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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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output, img_mode = RealESRGAN.enhance(original_sample[:,:,::-1])
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esrgan_filename = original_filename + '-esrgan4x'
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esrgan_sample = output[:,:,::-1]
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esrgan_image = Image.fromarray(esrgan_sample)
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save_sample(image, sample_path_i, original_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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save_sample(esrgan_image, sample_path_i, esrgan_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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@ -891,9 +885,6 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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gfpgan_esrgan_filename = original_filename + '-gfpgan-esrgan4x'
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gfpgan_esrgan_sample = output[:,:,::-1]
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gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)
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save_sample(image, sample_path_i, original_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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save_sample(gfpgan_esrgan_image, sample_path_i, gfpgan_esrgan_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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@ -901,7 +892,7 @@ skip_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoisin
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if simple_templating:
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grid_captions.append( captions[i] + "\ngfpgan_esrgan" )
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if not skip_save or (not use_GFPGAN or not use_RealESRGAN):
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if not skip_save:
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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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@ -1126,7 +1117,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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if image_editor_mode == 'Mask':
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init_img = init_info["image"]
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init_img = init_img.convert("RGBA")
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init_img = init_img.convert("RGB")
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init_img = resize_image(resize_mode, init_img, width, height)
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init_mask = init_info["mask"]
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init_mask = init_mask.convert("RGB")
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@ -1134,7 +1125,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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keep_mask = mask_mode == 0
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init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
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else:
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init_img = init_info
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init_img = init_info.convert("RGB")
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init_mask = None
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keep_mask = False
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@ -1150,7 +1141,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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mask_channel = None
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if image_editor_mode == "Uncrop":
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alpha = init_img.convert("RGBA")
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alpha = init_img.convert("RGB")
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alpha = resize_image(resize_mode, alpha, width // 8, height // 8)
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mask_channel = alpha.split()[-1]
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mask_channel = mask_channel.filter(ImageFilter.GaussianBlur(4))
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@ -1229,70 +1220,13 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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return samples_ddim
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try:
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if loopback:
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output_images, info = None, None
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history = []
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initial_seed = None
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for i in range(n_iter):
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output_images, seed, info, stats = process_images(
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outpath=outpath,
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func_init=init,
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name=sampler_name,
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skip_save=skip_save,
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skip_grid=skip_grid,
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batch_size=1,
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n_iter=1,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=prompt_matrix,
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use_GFPGAN=use_GFPGAN,
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use_RealESRGAN=False, # Forcefully disable upscaling when using loopback
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realesrgan_model_name=realesrgan_model_name,
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fp=fp,
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do_not_save_grid=True,
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normalize_prompt_weights=normalize_prompt_weights,
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init_img=init_img,
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init_mask=init_mask,
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keep_mask=keep_mask,
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mask_blur_strength=mask_blur_strength,
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denoising_strength=denoising_strength,
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resize_mode=resize_mode,
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uses_loopback=loopback,
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uses_random_seed_loopback=random_seed_loopback,
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sort_samples=sort_samples,
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write_info_files=write_info_files,
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jpg_sample=jpg_sample,
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)
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if loopback:
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output_images, info = None, None
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history = []
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initial_seed = None
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if initial_seed is None:
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initial_seed = seed
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init_img = output_images[0]
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if not random_seed_loopback:
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seed = seed + 1
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else:
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seed = seed_to_int(None)
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denoising_strength = max(denoising_strength * 0.95, 0.1)
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history.append(init_img)
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if not skip_grid:
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grid_count = get_next_sequence_number(outpath, 'grid-')
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grid = image_grid(history, batch_size, force_n_rows=1)
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grid_file = f"grid-{grid_count:05}-{seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.{grid_ext}"
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grid.save(os.path.join(outpath, grid_file), grid_format, quality=grid_quality, lossless=grid_lossless, optimize=True)
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output_images = history
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seed = initial_seed
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else:
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for i in range(n_iter):
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output_images, seed, info, stats = process_images(
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outpath=outpath,
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func_init=init,
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@ -1302,17 +1236,18 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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sampler_name=sampler_name,
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skip_save=skip_save,
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skip_grid=skip_grid,
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batch_size=batch_size,
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n_iter=n_iter,
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batch_size=1,
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n_iter=1,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=prompt_matrix,
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use_GFPGAN=use_GFPGAN,
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use_RealESRGAN=use_RealESRGAN,
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use_RealESRGAN=False, # Forcefully disable upscaling when using loopback
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realesrgan_model_name=realesrgan_model_name,
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fp=fp,
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do_not_save_grid=True,
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normalize_prompt_weights=normalize_prompt_weights,
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init_img=init_img,
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init_mask=init_mask,
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@ -1321,22 +1256,71 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
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denoising_strength=denoising_strength,
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resize_mode=resize_mode,
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uses_loopback=loopback,
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uses_random_seed_loopback=random_seed_loopback,
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sort_samples=sort_samples,
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write_info_files=write_info_files,
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jpg_sample=jpg_sample,
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)
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del sampler
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if initial_seed is None:
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initial_seed = seed
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init_img = output_images[0]
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if not random_seed_loopback:
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seed = seed + 1
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else:
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seed = seed_to_int(None)
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denoising_strength = max(denoising_strength * 0.95, 0.1)
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history.append(init_img)
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if not skip_grid:
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grid_count = get_next_sequence_number(outpath, 'grid-')
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grid = image_grid(history, batch_size, force_n_rows=1)
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grid_file = f"grid-{grid_count:05}-{seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.{grid_ext}"
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grid.save(os.path.join(outpath, grid_file), grid_format, quality=grid_quality, lossless=grid_lossless, optimize=True)
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output_images = history
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seed = initial_seed
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else:
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output_images, seed, info, stats = process_images(
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outpath=outpath,
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func_init=init,
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name=sampler_name,
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skip_save=skip_save,
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skip_grid=skip_grid,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=prompt_matrix,
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use_GFPGAN=use_GFPGAN,
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use_RealESRGAN=use_RealESRGAN,
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realesrgan_model_name=realesrgan_model_name,
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fp=fp,
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normalize_prompt_weights=normalize_prompt_weights,
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init_img=init_img,
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init_mask=init_mask,
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keep_mask=keep_mask,
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mask_blur_strength=mask_blur_strength,
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denoising_strength=denoising_strength,
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resize_mode=resize_mode,
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uses_loopback=loopback,
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sort_samples=sort_samples,
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write_info_files=write_info_files,
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jpg_sample=jpg_sample,
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)
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del sampler
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return output_images, seed, info, stats
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return output_images, seed, info, stats
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except RuntimeError as e:
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err = e
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err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
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stats = err_msg
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return [], seed, 'err', stats
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finally:
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if err:
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crash(err, '!!Runtime error (img2img)!!')
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prompt_parser = re.compile("""
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(?P<prompt> # capture group for 'prompt'
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@ -100,7 +100,9 @@ txt2img_defaults = {
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'height': 512,
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'width': 512,
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'fp': None,
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'submit_on_enter': 'Yes'
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'submit_on_enter': 'Yes',
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'variant_amount': 0,
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'variant_seed': ''
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}
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if 'txt2img' in user_defaults:
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