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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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loopback mode for img2img
commandline options for grid filetypes and max batch count
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AUTOMATIC
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images/loopback.jpg
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images/loopback.jpg
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96
webui.py
96
webui.py
@ -49,6 +49,8 @@ parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=(
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parser.add_argument("--no-verify-input", action='store_true', help="do not verify input to check if it's too long")
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--grid-format", type=str, default='png', help="file format for saved grids; can be png or jpg")
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opt = parser.parse_args()
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GFPGAN_dir = opt.gfpgan_dir
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@ -156,8 +158,10 @@ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp
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model = (model if opt.no_half else model.half()).to(device)
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def image_grid(imgs, batch_size, round_down=False):
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if opt.n_rows > 0:
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def image_grid(imgs, batch_size, round_down=False, force_n_rows=None):
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if force_n_rows is not None:
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rows = force_n_rows
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elif opt.n_rows > 0:
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rows = opt.n_rows
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elif opt.n_rows == 0:
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rows = batch_size
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@ -296,7 +300,7 @@ 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 process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN):
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def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False):
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"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
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assert prompt is not None
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@ -387,7 +391,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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output_images.append(image)
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base_count += 1
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if prompt_matrix or not opt.skip_grid:
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if (prompt_matrix or not opt.skip_grid) and not do_not_save_grid:
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grid = image_grid(output_images, batch_size, round_down=prompt_matrix)
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if prompt_matrix:
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@ -401,7 +405,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
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output_images.insert(0, grid)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.{opt.grid_format}'))
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grid_count += 1
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info = f"""
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@ -506,7 +510,7 @@ txt2img_interface = gr.Interface(
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
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gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False),
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gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=opt.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
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gr.Number(label='Seed', value=-1),
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@ -524,13 +528,12 @@ txt2img_interface = gr.Interface(
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)
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def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
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def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
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outpath = opt.outdir or "outputs/img2img-samples"
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sampler = KDiffusionSampler(model)
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assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
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t_enc = int(denoising_strength * ddim_steps)
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def init():
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image = init_img.convert("RGB")
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@ -547,6 +550,8 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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return init_latent,
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def sample(init_data, x, conditioning, unconditional_conditioning):
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t_enc = int(denoising_strength * ddim_steps)
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x0, = init_data
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sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
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@ -558,22 +563,62 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
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return samples_ddim
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output_images, seed, info = 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='k-diffusion',
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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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)
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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 = 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='k-diffusion',
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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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do_not_save_grid=True
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)
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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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seed = seed + 1
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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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grid_count = len(os.listdir(outpath)) - 1
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grid = image_grid(history, batch_size, force_n_rows=1)
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grid.save(os.path.join(outpath, f'grid-{grid_count:04}.{opt.grid_format}'))
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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 = 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='k-diffusion',
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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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)
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del sampler
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@ -591,7 +636,8 @@ img2img_interface = gr.Interface(
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
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gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
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gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),
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gr.Slider(minimum=1, maximum=opt.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale (how strongly the image should follow the prompt)', value=7.0),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),
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