From 3324f31e8463434796028c7bee6e695ef95c0681 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Mon, 22 Aug 2022 17:15:46 +0300 Subject: [PATCH] first --- README.md | 60 +++++++- screenshot.png | Bin 0 -> 885798 bytes webui.py | 404 +++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 462 insertions(+), 2 deletions(-) create mode 100644 screenshot.png create mode 100644 webui.py diff --git a/README.md b/README.md index 95514721a..41730012c 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,58 @@ -# stable-diffusion-webui -Stable Diffusion web UI +# Stable Diffusion web UI +A browser interface based on Gradio library for Stable Diffusion. + +Original script with Gradio UI was written by a kind anonymopus user. This is a modification. + +![](screenshot.png) + +## Stable Diffusion + +This script assumes that you already have main Stable Diffusion sutff installed, assumed to be in directory `/sd`. +If you don't have it installed, follow the guide: + +- https://rentry.org/kretard + +This repository's `webgui.py` is a replacement for `kdiff.py` from the guide. + +Particularly, following files must exist: + +- `/sd/configs/stable-diffusion/v1-inference.yaml` +- `/sd/models/ldm/stable-diffusion-v1/model.ckpt` +- `/sd/ldm/util.py` +- `/sd/k_diffusion/__init__.py` + +## GFPGAN + +If you want to use GFPGAN to improve generated faces, you need to install it separately. +Follow instructions from https://github.com/TencentARC/GFPGAN, but when cloning it, do so into Stable Diffusion main directory, `/sd`. +After that download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and put it +into the `/sd/GFPGAN/experiments/pretrained_models` directory. If you're getting troubles with GFPGAN support, follow instructions +from the GFPGAN's repository until `inference_gfpgan.py` script works. + +The following files must exist: + +- `/sd/GFPGAN/inference_gfpgan.py` +- `/sd/GFPGAN/experiments/pretrained_models/GFPGANv1.3.pth` + +If the GFPGAN directory does not exist, you will not get the option to use GFPGAN in the UI. If it does exist, you will either be able +to use it, or there will be a message in console with an error related to GFPGAN. + +## Web UI + +Run the script as: + +`python webui.py` + +When running the script, you must be in the main Stable Diffusion directory, `/sd`. If you cloned this repository into a subdirectory +of `/sd`, say, the `stable-diffusion-webui` directory, you will run it as: + +`python stable-diffusion-webui/webui.py` + +When launching, you may get a very long warning message related to some weights not being used. You may freely ignore it. +After a while, you will get a message like this: + +``` +Running on local URL: http://127.0.0.1:7860/ +``` + +Open the URL in browser, and you are good to go. diff --git a/screenshot.png b/screenshot.png new file mode 100644 index 0000000000000000000000000000000000000000..7e13a0de8fc0313477e3af84efdd9a3b8b8b5c67 GIT binary patch literal 885798 zcmeFZXIxWj6EC_D1r-$)6_qBUV4*08h(MyEpdds=dJ_Q=DbdhN5){OWpfr&Z6$KUP zkQhp$B2t0`q!S<@2|WY?Nk~6o@9q1#&%Gb+r+a?CbIu1yS!>N&&&)G3|7m$}!PrWWq;ZU_d z<*(~-2q@X|g=Y3A5wDUfz?J=XNC|)Lrp7B@fX|m!Vif+|bV* z-blYbC)y*Pl3IRF zpB)^GEnZvC$NZ_sw`Z#XW8I+mtfLDL9Gsec*8na$)2t+3gAk6J*gR~S)LUwK%f0I_lV)1{3JQ*+N{PXcnMn8 zVvgGzrUq{>hjk%Fsj(vTW6gyeWIDA$}?Q>2K}3Y+(T+Sm7l%cr!;6Ry-gp0i#FDC6fXu>9Nh z6gqog-*{GT#JJ1e^g$!%Aw*OWpTq8H&hQ~7bs)oX2Nt5Kj?A1M%7ZB3F1!)_l3_sA zIGGh3cs533Ku>gu^JA5LbKfd}YyQ;Bqp)XX;l+VsNG z2101(IqT7=BD$gdphkI1&p{Y*t9|n#+3hun2_s9{8+)MyC-FqwyziE^k>b_Zqx<$E zlXy7`+mx27oB|_%g+v_auI#nHc<0Ye&%Jt93OL=GOvL%@aGWmjiN3`hD{GMkX{)$< 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zqz=^7_2|Fgio6CE>_Mxa`w58A?|=5nN+mVu*IfIhUZ3F1z3ICzl!L2BpTFw5bP$T! xfB#9t199zNId${@U)dK0@aO-aG{Dbyym8IOz8x%|Uhj3Ntf+n~|K 0 and verbose: + print("missing keys:") + print(m) + if len(u) > 0 and verbose: + print("unexpected keys:") + print(u) + + model.cuda() + model.eval() + return model + + +def load_img_pil(img_pil): + image = img_pil.convert("RGB") + w, h = image.size + print(f"loaded input image of size ({w}, {h})") + w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 + image = image.resize((w, h), resample=PIL.Image.LANCZOS) + print(f"cropped image to size ({w}, {h})") + image = np.array(image).astype(np.float32) / 255.0 + image = image[None].transpose(0, 3, 1, 2) + image = torch.from_numpy(image) + return 2. * image - 1. + + +def load_img(path): + return load_img_pil(Image.open(path)) + + +class CFGDenoiser(nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + + def forward(self, x, sigma, uncond, cond, cond_scale): + x_in = torch.cat([x] * 2) + sigma_in = torch.cat([sigma] * 2) + cond_in = torch.cat([uncond, cond]) + uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2) + return uncond + (cond - uncond) * cond_scale + + +def load_GFPGAN(): + model_name = 'GFPGANv1.3' + model_path = os.path.join(GFPGAN_dir, 'experiments/pretrained_models', model_name + '.pth') + if not os.path.isfile(model_path): + raise Exception("GFPGAN model not found at path "+model_path) + + sys.path.append(os.path.abspath(GFPGAN_dir)) + from gfpgan import GFPGANer + + return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None) + + +GFPGAN = None +if os.path.exists(GFPGAN_dir): + try: + GFPGAN = load_GFPGAN() + print("Loaded GFPGAN") + except Exception: + import traceback + 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") + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") +model = model.half().to(device) + + +def image_grid(imgs, rows): + cols = len(imgs) // rows + + w, h = imgs[0].size + grid = Image.new('RGB', size=(cols * w, rows * h)) + + 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): + torch.cuda.empty_cache() + + outpath = opt.outdir or "outputs/txt2img-samples" + + if seed == -1: + seed = random.randrange(4294967294) + + seed = int(seed) + + is_PLMS = sampler_name == 'PLMS' + is_DDIM = sampler_name == 'DDIM' + is_Kdif = sampler_name == 'k-diffusion' + + sampler = None + if is_PLMS: + sampler = PLMSSampler(model) + elif is_DDIM: + sampler = DDIMSampler(model) + elif is_Kdif: + pass + else: + raise Exception("Unknown sampler: " + sampler_name) + + 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]] + + sample_path = os.path.join(outpath, "samples") + os.makedirs(sample_path, exist_ok=True) + 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 + 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) + shape = [opt_C, height // opt_f, width // opt_f] + + current_seed = seed + n * len(data) + batch_index + torch.manual_seed(current_seed) + + if is_Kdif: + sigmas = model_wrap.get_sigmas(ddim_steps) + x = torch.randn([n_samples, *shape], device=device) * sigmas[0] # for GPU draw + model_wrap_cfg = CFGDenoiser(model_wrap) + 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) + + 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 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 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 + + image = Image.fromarray(x_sample) + + 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: + all_samples.append(x_sample) + + if not opt.skip_grid: + # additionally, save as grid + grid = image_grid(output_images, rows=n_rows) + grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png')) + grid_count += 1 + + + if sampler is not None: + del sampler + + info = f""" +{prompt} +Steps: {ddim_steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''} + """.strip() + + return output_images, seed, info + + +dream_interface = gr.Interface( + dream, + inputs=[ + 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), + 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.Number(label='Seed', value=-1), + gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512), + gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512), + ], + outputs=[ + gr.Gallery(label="Images"), + gr.Number(label='Seed'), + gr.Textbox(label="Copy-paste generation parameters"), + ], + title="Stable Diffusion Text-to-Image K", + description="Generate images from text with Stable Diffusion (using K-LMS)", + allow_flagging="never" +) + + +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): + torch.cuda.empty_cache() + + outpath = opt.outdir or "outputs/img2img-samples" + + 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]] + + sample_path = os.path.join(outpath, "samples") + os.makedirs(sample_path, exist_ok=True) + base_count = len(os.listdir(sample_path)) + grid_count = len(os.listdir(outpath)) - 1 + seedit = 0 + + image = init_img.convert("RGB") + w, h = image.size + 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 + + 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) + 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) + + sigmas = model_wrap.get_sigmas(ddim_steps) + + 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} + + 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_grid: + all_samples.append(x_samples_ddim) + + 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) + + # 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 + + del sampler + return output_images, seed + + +# prompt, init_img, ddim_steps, plms, ddim_eta, n_iter, n_samples, cfg_scale, denoising_strength, seed + +img2img_interface = gr.Interface( + translation, + inputs=[ + 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.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=1.0, 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), + gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Height", value=512), + gr.Slider(minimum=64, maximum=2048, step=64, label="Resize Width", value=512), + ], + outputs=[ + gr.Gallery(), + gr.Number(label='Seed') + ], + title="Stable Diffusion Image-to-Image", + description="Generate images from images with Stable Diffusion", +) + +demo = gr.TabbedInterface(interface_list=[dream_interface, img2img_interface], tab_names=["Dream", "Image Translation"]) + +demo.launch()

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