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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=( parser.add_argument("--no-verify-input", action='store_true', help="do not verify input to check if it's too long") parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats") 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)") +parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") +parser.add_argument("--grid-format", type=str, default='png', help="file format for saved grids; can be png or jpg") opt = parser.parse_args() GFPGAN_dir = opt.gfpgan_dir @@ -156,8 +158,10 @@ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp model = (model if opt.no_half else model.half()).to(device) -def image_grid(imgs, batch_size, round_down=False): - if opt.n_rows > 0: +def image_grid(imgs, batch_size, round_down=False, force_n_rows=None): + if force_n_rows is not None: + rows = force_n_rows + elif opt.n_rows > 0: rows = opt.n_rows elif opt.n_rows == 0: rows = batch_size @@ -296,7 +300,7 @@ def check_prompt_length(prompt, comments): comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") -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): +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): """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""" assert prompt is not None @@ -387,7 +391,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, output_images.append(image) base_count += 1 - if prompt_matrix or not opt.skip_grid: + if (prompt_matrix or not opt.skip_grid) and not do_not_save_grid: grid = image_grid(output_images, batch_size, round_down=prompt_matrix) if prompt_matrix: @@ -401,7 +405,7 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, output_images.insert(0, grid) - grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) + grid.save(os.path.join(outpath, f'grid-{grid_count:04}.{opt.grid_format}')) grid_count += 1 info = f""" @@ -506,7 +510,7 @@ txt2img_interface = gr.Interface( gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None), gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False), - gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1), + gr.Slider(minimum=1, maximum=opt.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1), gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1), 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), gr.Number(label='Seed', value=-1), @@ -524,13 +528,12 @@ txt2img_interface = gr.Interface( ) -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): +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): outpath = opt.outdir or "outputs/img2img-samples" sampler = KDiffusionSampler(model) assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' - t_enc = int(denoising_strength * ddim_steps) def init(): image = init_img.convert("RGB") @@ -547,6 +550,8 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat return init_latent, def sample(init_data, x, conditioning, unconditional_conditioning): + t_enc = int(denoising_strength * ddim_steps) + x0, = init_data sigmas = sampler.model_wrap.get_sigmas(ddim_steps) @@ -558,22 +563,62 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat 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) return samples_ddim - output_images, seed, info = process_images( - outpath=outpath, - func_init=init, - func_sample=sample, - prompt=prompt, - seed=seed, - sampler_name='k-diffusion', - batch_size=batch_size, - n_iter=n_iter, - steps=ddim_steps, - cfg_scale=cfg_scale, - width=width, - height=height, - prompt_matrix=prompt_matrix, - use_GFPGAN=use_GFPGAN - ) + if loopback: + output_images, info = None, None + history = [] + initial_seed = None + + for i in range(n_iter): + output_images, seed, info = process_images( + outpath=outpath, + func_init=init, + func_sample=sample, + prompt=prompt, + seed=seed, + sampler_name='k-diffusion', + batch_size=1, + n_iter=1, + steps=ddim_steps, + cfg_scale=cfg_scale, + width=width, + height=height, + prompt_matrix=prompt_matrix, + use_GFPGAN=use_GFPGAN, + do_not_save_grid=True + ) + + if initial_seed is None: + initial_seed = seed + + init_img = output_images[0] + seed = seed + 1 + denoising_strength = max(denoising_strength * 0.95, 0.1) + history.append(init_img) + + grid_count = len(os.listdir(outpath)) - 1 + grid = image_grid(history, batch_size, force_n_rows=1) + grid.save(os.path.join(outpath, f'grid-{grid_count:04}.{opt.grid_format}')) + + output_images = history + seed = initial_seed + + else: + output_images, seed, info = process_images( + outpath=outpath, + func_init=init, + func_sample=sample, + prompt=prompt, + seed=seed, + sampler_name='k-diffusion', + batch_size=batch_size, + n_iter=n_iter, + steps=ddim_steps, + cfg_scale=cfg_scale, + width=width, + height=height, + prompt_matrix=prompt_matrix, + use_GFPGAN=use_GFPGAN + ) del sampler @@ -591,7 +636,8 @@ img2img_interface = gr.Interface( gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50), gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None), gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False), - gr.Slider(minimum=1, maximum=16, step=1, label='Batch count (how many batches of images to generate)', value=1), + gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False), + gr.Slider(minimum=1, maximum=opt.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1), gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1), 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), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75),