From b63d0726cd26aa8124e6d2e6f339b474a1563459 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 22 Aug 2022 20:08:32 +0300 Subject: [PATCH] fixed bug with images not resizing for img2img added GFPGAN as an option for img2img added GFPGAN as a tab added autodetection for row counts for grids, enabled by default removed Fixed Code sampling because no one can figure out what it does; maybe someone will be upset by removal and will tell me --- webui.py | 178 ++++++++++++++++++++++++++++++------------------------- 1 file changed, 98 insertions(+), 80 deletions(-) diff --git a/webui.py b/webui.py index b0d67f31..bb53e5ff 100644 --- a/webui.py +++ b/webui.py @@ -13,6 +13,7 @@ from torch import autocast from contextlib import contextmanager, nullcontext import mimetypes import random +import math import k_diffusion as K from ldm.util import instantiate_from_config @@ -31,7 +32,7 @@ parser = argparse.ArgumentParser() parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default=None) parser.add_argument("--skip_grid", action='store_true', help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",) parser.add_argument("--skip_save", action='store_true', help="do not save indiviual samples. For speed measurements.",) -parser.add_argument("--n_rows", type=int, default=0, help="rows in the grid (default: n_samples)",) +parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",) parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",) parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",) parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") @@ -118,6 +119,7 @@ if os.path.exists(GFPGAN_dir): print("Error loading GFPGAN:", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) + config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml") model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt") @@ -125,18 +127,26 @@ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp model = model.half().to(device) -def image_grid(imgs, rows): - cols = len(imgs) // rows +def image_grid(imgs, batch_size): + if opt.n_rows > 0: + rows = opt.n_rows + elif opt.n_rows == 0: + rows = batch_size + else: + rows = round(math.sqrt(len(imgs))) + + cols = math.ceil(len(imgs) / rows) w, h = imgs[0].size - grid = Image.new('RGB', size=(cols * w, rows * h)) + grid = Image.new('RGB', size=(cols * w, rows * h), color='black') for i, img in enumerate(imgs): grid.paste(img, box=(i % cols * w, i // cols * h)) return grid -def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int): + +def dream(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, seed: int, height: int, width: int): torch.cuda.empty_cache() outpath = opt.outdir or "outputs/txt2img-samples" @@ -165,7 +175,6 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use os.makedirs(outpath, exist_ok=True) batch_size = n_samples - n_rows = opt.n_rows if opt.n_rows > 0 else batch_size assert prompt is not None data = [batch_size * [prompt]] @@ -175,15 +184,9 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use base_count = len(os.listdir(sample_path)) grid_count = len(os.listdir(outpath)) - 1 - start_code = None - if fixed_code: - start_code = torch.randn([n_samples, opt_C, height // opt_f, width // opt_f], device=device) - precision_scope = autocast if opt.precision == "autocast" else nullcontext output_images = [] with torch.no_grad(), precision_scope("cuda"), model.ema_scope(): - all_samples = [] - for n in range(n_iter): for batch_index, prompts in enumerate(data): uc = None @@ -204,7 +207,7 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use samples_ddim = K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args={'cond': c, 'uncond': uc, 'cond_scale': cfg_scale}, disable=False) elif sampler is not None: - samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=start_code) + samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=c, batch_size=n_samples, shape=shape, verbose=False, unconditional_guidance_scale=cfg_scale, unconditional_conditioning=uc, eta=ddim_eta, x_T=None) x_samples_ddim = model.decode_first_stage(samples_ddim) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) @@ -224,12 +227,9 @@ def dream(prompt: str, ddim_steps: int, sampler_name: str, fixed_code: bool, use output_images.append(image) base_count += 1 - if not opt.skip_grid: - all_samples.append(x_sample) - if not opt.skip_grid: # additionally, save as grid - grid = image_grid(output_images, rows=n_rows) + grid = image_grid(output_images, batch_size) grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) grid_count += 1 @@ -251,7 +251,6 @@ dream_interface = gr.Interface( gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1), gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50), gr.Radio(label='Sampling method', choices=["DDIM", "PLMS", "k-diffusion"], value="k-diffusion"), - gr.Checkbox(label='Enable Fixed Code sampling', value=False), gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None), 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='Sampling iterations', value=1), @@ -272,7 +271,7 @@ dream_interface = gr.Interface( ) -def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int): +def translation(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, ddim_eta: float, n_iter: int, n_samples: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int): torch.cuda.empty_cache() outpath = opt.outdir or "outputs/img2img-samples" @@ -280,14 +279,11 @@ def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter: if seed == -1: seed = random.randrange(4294967294) - sampler = DDIMSampler(model) - model_wrap = K.external.CompVisDenoiser(model) os.makedirs(outpath, exist_ok=True) batch_size = n_samples - n_rows = opt.n_rows if opt.n_rows > 0 else batch_size assert prompt is not None data = [batch_size * [prompt]] @@ -299,78 +295,68 @@ def translation(prompt: str, init_img, ddim_steps: int, ddim_eta: float, n_iter: seedit = 0 image = init_img.convert("RGB") - w, h = image.size + image = image.resize((width, height), resample=PIL.Image.Resampling.LANCZOS) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) image = torch.from_numpy(image) output_images = [] precision_scope = autocast if opt.precision == "autocast" else nullcontext - with torch.no_grad(): - with precision_scope("cuda"): - init_image = 2. * image - 1. - init_image = init_image.to(device) - init_image = repeat(init_image, '1 ... -> b ...', b=batch_size) - init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space - x0 = init_latent + with torch.no_grad(), precision_scope("cuda"), model.ema_scope(): + init_image = 2. * image - 1. + init_image = init_image.to(device) + init_image = repeat(init_image, '1 ... -> b ...', b=batch_size) + init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space + x0 = init_latent - sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta, verbose=False) + assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' + t_enc = int(denoising_strength * ddim_steps) - assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' - t_enc = int(denoising_strength * ddim_steps) - print(f"target t_enc is {t_enc} steps") - with model.ema_scope(): - all_samples = list() - for n in range(n_iter): - for batch_index, prompts in enumerate(data): - uc = None - if cfg_scale != 1.0: - uc = model.get_learned_conditioning(batch_size * [""]) - if isinstance(prompts, tuple): - prompts = list(prompts) - c = model.get_learned_conditioning(prompts) + for n in range(n_iter): + for batch_index, prompts in enumerate(data): + uc = None + if cfg_scale != 1.0: + uc = model.get_learned_conditioning(batch_size * [""]) + if isinstance(prompts, tuple): + prompts = list(prompts) + c = model.get_learned_conditioning(prompts) - sigmas = model_wrap.get_sigmas(ddim_steps) + sigmas = model_wrap.get_sigmas(ddim_steps) - current_seed = seed + n * len(data) + batch_index - torch.manual_seed(current_seed) + current_seed = seed + n * len(data) + batch_index + torch.manual_seed(current_seed) - noise = torch.randn_like(x0) * sigmas[ddim_steps - t_enc - 1] # for GPU draw - xi = x0 + noise - sigma_sched = sigmas[ddim_steps - t_enc - 1:] - # x = torch.randn([n_samples, *shape]).to(device) * sigmas[0] # for CPU draw - model_wrap_cfg = CFGDenoiser(model_wrap) - extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale} + noise = torch.randn_like(x0) * sigmas[ddim_steps - t_enc - 1] # for GPU draw + xi = x0 + noise + sigma_sched = sigmas[ddim_steps - t_enc - 1:] + model_wrap_cfg = CFGDenoiser(model_wrap) + extra_args = {'cond': c, 'uncond': uc, 'cond_scale': cfg_scale} - samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False) - x_samples_ddim = model.decode_first_stage(samples_ddim) - x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) + samples_ddim = K.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args=extra_args, disable=False) + x_samples_ddim = model.decode_first_stage(samples_ddim) + x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) - if not opt.skip_save: - for x_sample in x_samples_ddim: - x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') - image = Image.fromarray(x_sample.astype(np.uint8)) - image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png")) - output_images.append(image) - base_count += 1 - seedit += 1 + if not opt.skip_save or not opt.skip_grid: + for x_sample in x_samples_ddim: + x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c') + x_sample = x_sample.astype(np.uint8) - if not opt.skip_grid: - all_samples.append(x_samples_ddim) + if use_GFPGAN and GFPGAN is not None: + cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True) + x_sample = restored_img - if not opt.skip_grid: - # additionally, save as grid - grid = torch.stack(all_samples, 0) - grid = rearrange(grid, 'n b c h w -> (n b) c h w') - grid = make_grid(grid, nrow=n_rows) + image = Image.fromarray(x_sample) - # to image - grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy() - Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png')) - Image.fromarray(grid.astype(np.uint8)) - grid_count += 1 + image.save(os.path.join(sample_path, f"{base_count:05}-{current_seed}_{prompt.replace(' ', '_')[:128]}.png")) + output_images.append(image) + base_count += 1 + + if not opt.skip_grid: + # additionally, save as grid + grid = image_grid(output_images, batch_size) + grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) + grid_count += 1 - del sampler return output_images, seed @@ -382,9 +368,10 @@ img2img_interface = gr.Interface( gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1), gr.Image(value="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg", source="upload", interactive=True, type="pil"), 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.Slider(minimum=0.0, maximum=1.0, step=0.01, label="DDIM ETA", value=0.0, visible=False), - gr.Slider(minimum=1, maximum=50, step=1, label='Sampling iterations', value=2), - gr.Slider(minimum=1, maximum=8, step=1, label='Samples per iteration', value=2), + gr.Slider(minimum=1, maximum=16, step=1, label='Sampling iterations', value=1), + gr.Slider(minimum=1, maximum=4, step=1, label='Samples per iteration', value=1), gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='Classifier Free Guidance Scale', value=7.0), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength', value=0.75), gr.Number(label='Seed', value=-1), @@ -399,6 +386,37 @@ img2img_interface = gr.Interface( description="Generate images from images with Stable Diffusion", ) -demo = gr.TabbedInterface(interface_list=[dream_interface, img2img_interface], tab_names=["Dream", "Image Translation"]) +interfaces = [ + (dream_interface, "Dream"), + (img2img_interface, "Image Translation") +] + +def run_GFPGAN(image, strength): + image = image.convert("RGB") + + cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True) + res = Image.fromarray(restored_img) + + if strength < 1.0: + res = PIL.Image.blend(image, res, strength) + + return res + + +if GFPGAN is not None: + interfaces.append((gr.Interface( + run_GFPGAN, + inputs=[ + gr.Image(label="Source", source="upload", interactive=True, type="pil"), + gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength", value=100), + ], + outputs=[ + gr.Image(label="Result"), + ], + title="GFPGAN", + description="Fix faces on images", + ), "GFPGAN")) + +demo = gr.TabbedInterface(interface_list=[x[0] for x in interfaces], tab_names=[x[1] for x in interfaces]) demo.launch()