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| import os | |
| import sys | |
| from pathlib import Path | |
| from collections import OrderedDict | |
| import gradio as gr | |
| import shutil | |
| import uuid | |
| import torch | |
| from PIL import Image | |
| # Force CPU usage and disable CUDA completely | |
| torch.backends.cudnn.enabled = False | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '-1' | |
| torch.cuda.is_available = lambda: False | |
| torch.cuda.device_count = lambda: 0 | |
| torch.cuda.get_device_name = lambda x: 'cpu' | |
| torch.cuda.current_device = lambda: 0 | |
| torch.cuda.set_device = lambda x: None | |
| torch.Tensor.cuda = lambda self, device=None: self | |
| torch.nn.Module.cuda = lambda self, device=None: self | |
| demo_path = Path(__file__).resolve().parent | |
| root_path = demo_path | |
| sys.path.append(str(root_path)) | |
| from src import models | |
| from src.methods import rasg, sd, sr | |
| from src.utils import IImage, poisson_blend, image_from_url_text | |
| TMP_DIR = root_path / 'gradio_tmp' | |
| if TMP_DIR.exists(): | |
| shutil.rmtree(str(TMP_DIR)) | |
| TMP_DIR.mkdir(exist_ok=True, parents=True) | |
| os.environ['GRADIO_TEMP_DIR'] = str(TMP_DIR) | |
| on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR" | |
| negative_prompt_str = "text, bad anatomy, bad proportions, blurry, cropped, deformed, disfigured, duplicate, error, extra limbs, gross proportions, jpeg artifacts, long neck, low quality, lowres, malformed, morbid, mutated, mutilated, out of frame, ugly, worst quality" | |
| positive_prompt_str = "Full HD, 4K, high quality, high resolution" | |
| examples_path = root_path / '__assets__/demo/examples' | |
| example_inputs = [ | |
| [f'{examples_path}/images_1024/a40.jpg', f'{examples_path}/images_2048/a40.jpg', 'medieval castle'], | |
| [f'{examples_path}/images_1024/a4.jpg', f'{examples_path}/images_2048/a4.jpg', 'parrot'], | |
| [f'{examples_path}/images_1024/a65.jpg', f'{examples_path}/images_2048/a65.jpg', 'hoodie'], | |
| [f'{examples_path}/images_1024/a54.jpg', f'{examples_path}/images_2048/a54.jpg', 'salad'], | |
| [f'{examples_path}/images_1024/a51.jpg', f'{examples_path}/images_2048/a51.jpg', 'space helmet'], | |
| [f'{examples_path}/images_1024/a46.jpg', f'{examples_path}/images_2048/a46.jpg', 'stack of books'], | |
| [f'{examples_path}/images_1024/a19.jpg', f'{examples_path}/images_2048/a19.jpg', 'antique greek vase'], | |
| [f'{examples_path}/images_1024/a2.jpg', f'{examples_path}/images_2048/a2.jpg', 'sunglasses'], | |
| ] | |
| thumbnails = [ | |
| 'https://lh3.googleusercontent.com/pw/ABLVV87bkFc_SRKrbXuk5BTp18dETNm18MLbjoJo6JvwbIkYtjZXrjU_H1dCJIP799OJjHTZmo19mYVyMCC1RLmwqzoZrgwQzfB-SCtxLa83IbXBQ23xzmKoZgsRlPztxNJD6gmXzFyatdLRzDxHIusBQLUz=w3580-h1150-s-no-gm', | |
| 'https://lh3.googleusercontent.com/pw/ABLVV85RWtrpTf1tMp2p3q37eg5DlFp5znifALK_JTjvxJua8UYMjytVoEy2GUW2cLXgBvQyYKg7GvrWXQ5hkdAsyih5Rf4rFnDq-JoiQYhVZHStCZLKxmeAlQna5ZwMPVTKG1TK63DH_OdK58gvSjWtF2ww=w3580-h1152-s-no-gm', | |
| 'https://lh3.googleusercontent.com/pw/ABLVV84dkaU6SQs9fyDjajpk1X9JkYp_zQBEnPVL67oi11_05U6-Ys5ydQpuny8GBQCMyVbFKxJ5unn9w__gmP9K0cKQ4_IVoT7Hvfmya71klDqSI7vu9Iy_5P2Il5-0giJFpumtffBA3kryn1xtJdR4vSA0=w2924-h1858-s-no-gm', | |
| 'https://lh3.googleusercontent.com/pw/ABLVV853ZyjvS4LvcPpVMY9BWz-232omt3-hgRiGcky_3ojE6WLKgtsrftsg1jSrUm2ccT_UOa279CulZy6fdnH_Xg1SunyRBxaRjOK0uxAkUFwb60rR1S4hI2MmhLV7KCi3tw1A-oiGi0f9JINyade-322A=w2622-h1858-s-no-gm', | |
| 'https://lh3.googleusercontent.com/pw/ABLVV86AJGUVGjb0i6CPg8zlJlWObNY0xdOzM1x5Bq9gKhP-ZWre5aaexRJDxQUO2gmJtRIyohD88FJDG_aVX2G5M0QOyGRWlZmx7tOVXLh-Kbesobxo9MfD-wqk9Ts9O8NUGtIwkWzo9SEs2opKdu83gB9F=w2528-h1858-s-no-gm', | |
| 'https://lh3.googleusercontent.com/pw/ABLVV87MplTciS7z-4i-eY3B3L0YhaK8UEQ3pTQD6W6uYVGR4hPD9u1WGEGyfg5ddqU-Bx2BrKskDhwxzF746cRhgFU5aPtbYA_-O7KfqXe9IsMxYCgUKxEHBm2ncqy64V-w-N8XOFgUMkAQqcuuNZ8Xapqp=w3580-h1186-s-no-gm', | |
| 'https://lh3.googleusercontent.com/pw/ABLVV877Esi6l2Kuw3akH5QBlmDAbWydZDZEEJqlZ_N-X7g33NQZU8nv_UKdAVETS7q23byTuldIAhW-q99zCycFB8Yfc-5e_WPNIM9icU0p3gd6DUVZR233ZNUtLca384MYGIhMGud9Y_Xed1I3PpiMhrpG=w2846-h1858-s-no-gm', | |
| 'https://lh3.googleusercontent.com/pw/ABLVV85hMQbSB6fCokdyut4ke7xTUqjERhuYygnj7T8IIA1k48e9GkaowDywPZzi5QJzZfj7wU3bgBHzjxop19qK1zOi5XDrjfXkn5bwj4MxicHa3TG-Rc-V-c1uyZVUyviyUlkGZ62FxuVROw2x0aGJIcr0=w3580-h1382-s-no-gm' | |
| ] | |
| example_previews = [ | |
| [thumbnails[0], 'Prompt: medieval castle'], | |
| [thumbnails[1], 'Prompt: parrot'], | |
| [thumbnails[2], 'Prompt: hoodie'], | |
| [thumbnails[3], 'Prompt: salad'], | |
| [thumbnails[4], 'Prompt: space helmet'], | |
| [thumbnails[5], 'Prompt: stack of books'], | |
| [thumbnails[6], 'Prompt: antique greek vase'], | |
| [thumbnails[7], 'Prompt: sunglasses'], | |
| ] | |
| # Monkey patch any remaining CUDA calls in the models | |
| original_to = torch.Tensor.to | |
| def patched_to(self, *args, **kwargs): | |
| if len(args) > 0 and isinstance(args[0], str) and args[0] == 'cuda': | |
| return original_to(self, 'cpu') | |
| if 'device' in kwargs and kwargs['device'] == 'cuda': | |
| kwargs['device'] = 'cpu' | |
| return original_to(self, *args, **kwargs) | |
| torch.Tensor.to = patched_to | |
| # Load models with CPU only | |
| models.pre_download_inpainting_models() | |
| inpainting_models = OrderedDict([ | |
| ("Dreamshaper Inpainting V8", 'ds8_inp'), | |
| ("Stable-Inpainting 2.0", 'sd2_inp'), | |
| ("Stable-Inpainting 1.5", 'sd15_inp') | |
| ]) | |
| # Patch model loading to ensure CPU usage | |
| original_load_model = models.load_inpainting_model | |
| def patched_load_model(*args, **kwargs): | |
| kwargs['device'] = 'cpu' | |
| model = original_load_model(*args, **kwargs) | |
| model.to('cpu') | |
| return model | |
| models.load_inpainting_model = patched_load_model | |
| original_sr_load_model = models.sd2_sr.load_model | |
| def patched_sr_load_model(*args, **kwargs): | |
| kwargs['device'] = 'cpu' | |
| model = original_sr_load_model(*args, **kwargs) | |
| # Handle DDIM object which doesn't have to() method | |
| if hasattr(model, 'model'): # If there's a main model component | |
| model.model.to('cpu') | |
| if hasattr(model, 'diffusion'): # Some diffusion models have this | |
| model.diffusion.to('cpu') | |
| return model | |
| models.sd2_sr.load_model = patched_sr_load_model | |
| original_sam_load_model = models.sam.load_model | |
| def patched_sam_load_model(*args, **kwargs): | |
| kwargs['device'] = 'cpu' | |
| model = original_sam_load_model(*args, **kwargs) | |
| # SAM predictor doesn't have to() method but its model does | |
| if hasattr(model, 'model'): | |
| model.model.to('cpu') | |
| return model | |
| models.sam.load_model = patched_sam_load_model | |
| # Load models with CPU | |
| sr_model = models.sd2_sr.load_model(device='cpu') | |
| sam_predictor = models.sam.load_model(device='cpu') | |
| inp_model_name = list(inpainting_models.keys())[0] | |
| inp_model = models.load_inpainting_model( | |
| inpainting_models[inp_model_name], device='cpu', cache=True) | |
| def set_model_from_name(new_inp_model_name): | |
| global inp_model | |
| global inp_model_name | |
| if new_inp_model_name != inp_model_name: | |
| print(f"Activating Inpaintng Model: {new_inp_model_name}") | |
| inp_model = models.load_inpainting_model( | |
| inpainting_models[new_inp_model_name], device='cpu', cache=True) | |
| inp_model_name = new_inp_model_name | |
| def save_user_session(hr_image, hr_mask, lr_results, prompt, session_id=None): | |
| if session_id == '': | |
| session_id = str(uuid.uuid4()) | |
| session_dir = TMP_DIR / session_id | |
| session_dir.mkdir(exist_ok=True, parents=True) | |
| hr_image.save(session_dir / 'hr_image.png') | |
| hr_mask.save(session_dir / 'hr_mask.png') | |
| lr_results_dir = session_dir / 'lr_results' | |
| if lr_results_dir.exists(): | |
| shutil.rmtree(lr_results_dir) | |
| lr_results_dir.mkdir(parents=True) | |
| for i, lr_result in enumerate(lr_results): | |
| lr_result.save(lr_results_dir / f'{i}.png') | |
| with open(session_dir / 'prompt.txt', 'w') as f: | |
| f.write(prompt) | |
| return session_id | |
| def recover_user_session(session_id): | |
| if session_id == '': | |
| return None, None, [], '' | |
| session_dir = TMP_DIR / session_id | |
| lr_results_dir = session_dir / 'lr_results' | |
| hr_image = Image.open(session_dir / 'hr_image.png') | |
| hr_mask = Image.open(session_dir / 'hr_mask.png') | |
| lr_result_paths = list(lr_results_dir.glob('*.png')) | |
| gallery = [] | |
| for lr_result_path in sorted(lr_result_paths): | |
| gallery.append(Image.open(lr_result_path)) | |
| with open(session_dir / 'prompt.txt', "r") as f: | |
| prompt = f.read() | |
| return hr_image, hr_mask, gallery, prompt | |
| def inpainting_run(model_name, use_rasg, use_painta, prompt, imageMask, | |
| hr_image, seed, eta, negative_prompt, positive_prompt, ddim_steps, | |
| guidance_scale=7.5, batch_size=1, session_id='' | |
| ): | |
| set_model_from_name(model_name) | |
| method = ['default'] | |
| if use_painta: method.append('painta') | |
| if use_rasg: method.append('rasg') | |
| method = '-'.join(method) | |
| if use_rasg: | |
| inpainting_f = rasg.run | |
| else: | |
| inpainting_f = sd.run | |
| seed = int(seed) | |
| batch_size = max(1, min(int(batch_size), 4)) | |
| image = IImage(hr_image).resize(512) | |
| mask = IImage(imageMask['mask']).rgb().resize(512) | |
| method = ['default'] | |
| if use_painta: method.append('painta') | |
| method = '-'.join(method) | |
| inpainted_images = [] | |
| blended_images = [] | |
| for i in range(batch_size): | |
| seed = seed + i * 1000 | |
| inpainted_image = inpainting_f( | |
| ddim=inp_model, | |
| method=method, | |
| prompt=prompt, | |
| image=image, | |
| mask=mask, | |
| seed=seed, | |
| eta=eta, | |
| negative_prompt=negative_prompt, | |
| positive_prompt=positive_prompt, | |
| num_steps=ddim_steps, | |
| guidance_scale=guidance_scale | |
| ).crop(image.size) | |
| blended_image = poisson_blend( | |
| orig_img=image.data[0], | |
| fake_img=inpainted_image.data[0], | |
| mask=mask.data[0], | |
| dilation=12 | |
| ) | |
| blended_images.append(blended_image) | |
| inpainted_images.append(inpainted_image.pil()) | |
| session_id = save_user_session( | |
| hr_image, imageMask['mask'], inpainted_images, prompt, session_id=session_id) | |
| return blended_images, session_id | |
| def upscale_run( | |
| ddim_steps, seed, use_sam_mask, session_id, img_index, | |
| negative_prompt='', positive_prompt='high resolution professional photo' | |
| ): | |
| hr_image, hr_mask, gallery, prompt = recover_user_session(session_id) | |
| if len(gallery) == 0: | |
| return Image.open(root_path / '__assets__/demo/sr_info.png') | |
| seed = int(seed) | |
| img_index = int(img_index) | |
| img_index = 0 if img_index < 0 else img_index | |
| img_index = len(gallery) - 1 if img_index >= len(gallery) else img_index | |
| inpainted_image = gallery[img_index if img_index >= 0 else 0] | |
| output_image = sr.run( | |
| sr_model, | |
| sam_predictor, | |
| inpainted_image, | |
| hr_image, | |
| hr_mask, | |
| prompt=f'{prompt}, {positive_prompt}', | |
| noise_level=20, | |
| blend_trick=True, | |
| blend_output=True, | |
| negative_prompt=negative_prompt, | |
| seed=seed, | |
| use_sam_mask=use_sam_mask | |
| ) | |
| return output_image | |
| with gr.Blocks(css=demo_path / 'style.css') as demo: | |
| gr.HTML( | |
| """ | |
| <div style="text-align: center; max-width: 1200px; margin: 20px auto;"> | |
| <h1 style="font-weight: 900; font-size: 3rem; margin-bottom: 0.5rem"> | |
| 🧑🎨 HD-Painter Demo (CPU Mode) | |
| </h1> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| Hayk Manukyan<sup>1*</sup>, Andranik Sargsyan<sup>1*</sup>, Barsegh Atanyan<sup>1</sup>, Zhangyang Wang<sup>1,2</sup>, Shant Navasardyan<sup>1</sup> | |
| and <a href="https://www.humphreyshi.com/home">Humphrey Shi</a><sup>1,3</sup> | |
| </h2> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| <sup>1</sup>Picsart AI Resarch (PAIR), <sup>2</sup>UT Austin, <sup>3</sup>Georgia Tech | |
| </h2> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem"> | |
| [<a href="https://arxiv.org/abs/2312.14091" style="color:blue;">arXiv</a>] | |
| [<a href="https://github.com/Picsart-AI-Research/HD-Painter" style="color:blue;">GitHub</a>] | |
| </h2> | |
| <h2 style="font-weight: 450; font-size: 1rem; margin: 0.7rem auto; max-width: 1000px"> | |
| <b>HD-Painter</b> enables prompt-faithfull and high resolution (up to 2k) image inpainting upon any diffusion-based image inpainting method. | |
| <br/><b>Note:</b> Running on CPU may be slower than GPU. | |
| </h2> | |
| </div> | |
| """) | |
| if on_huggingspace: | |
| gr.HTML(""" | |
| <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to the suggested GPU in settings. | |
| <br/> | |
| <a href="https://huggingface.co/spaces/PAIR/HD-Painter?duplicate=true"> | |
| <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| </p>""") | |
| with open(demo_path / 'script.js', 'r') as f: | |
| js_str = f.read() | |
| demo.load(_js=js_str) | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_picker = gr.Dropdown( | |
| list(inpainting_models.keys()), | |
| value=list(inpainting_models.keys())[0], | |
| label="Please select a model!", | |
| ) | |
| with gr.Column(): | |
| use_painta = gr.Checkbox(value=True, label="Use PAIntA") | |
| use_rasg = gr.Checkbox(value=True, label="Use RASG") | |
| prompt = gr.Textbox(label="Inpainting Prompt") | |
| with gr.Row(): | |
| with gr.Column(): | |
| imageMask = gr.ImageMask(label="Input Image", brush_color='#ff0000', elem_id="inputmask", type="pil") | |
| hr_image = gr.Image(visible=False, type="pil") | |
| hr_image.change(fn=None, _js="function() {setTimeout(imageMaskResize, 200);}", inputs=[], outputs=[]) | |
| imageMask.upload( | |
| fn=None, | |
| _js="async function (a) {hr_img = await resize_b64_img(a['image'], 2048); dp_img = await resize_b64_img(hr_img, 1024); return [hr_img, {image: dp_img, mask: null}]}", | |
| inputs=[imageMask], | |
| outputs=[hr_image, imageMask], | |
| ) | |
| with gr.Row(): | |
| inpaint_btn = gr.Button("Inpaint", scale=0) | |
| with gr.Accordion('Advanced options', open=False): | |
| guidance_scale = gr.Slider(minimum=0, maximum=30, value=7.5, label="Guidance Scale") | |
| eta = gr.Slider(minimum=0, maximum=1, value=0.1, label="eta") | |
| ddim_steps = gr.Slider(minimum=10, maximum=100, value=50, step=1, label='Number of diffusion steps') | |
| with gr.Row(): | |
| seed = gr.Number(value=49123, label="Seed") | |
| batch_size = gr.Number(value=1, label="Batch size", minimum=1, maximum=4) | |
| negative_prompt = gr.Textbox(value=negative_prompt_str, label="Negative prompt", lines=3) | |
| positive_prompt = gr.Textbox(value=positive_prompt_str, label="Positive prompt", lines=1) | |
| with gr.Column(): | |
| with gr.Row(): | |
| output_gallery = gr.Gallery( | |
| [], | |
| columns=4, | |
| preview=True, | |
| allow_preview=True, | |
| object_fit='scale-down', | |
| elem_id='outputgallery' | |
| ) | |
| with gr.Row(): | |
| upscale_btn = gr.Button("Send to Inpainting-Specialized Super-Resolution (x4)", scale=1) | |
| with gr.Row(): | |
| use_sam_mask = gr.Checkbox(value=False, label="Use SAM mask for background preservation (for SR only, experimental feature)") | |
| with gr.Row(): | |
| hires_image = gr.Image(label="Hi-res Image") | |
| label = gr.Markdown("## High-Resolution Generation Samples (2048px large side)") | |
| with gr.Column(): | |
| example_container = gr.Gallery( | |
| example_previews, | |
| columns=4, | |
| preview=True, | |
| allow_preview=True, | |
| object_fit='scale-down' | |
| ) | |
| gr.Examples( | |
| [example_inputs[i] + [[example_previews[i]]] | |
| for i in range(len(example_previews))], | |
| [imageMask, hr_image, prompt, example_container], | |
| elem_id='examples' | |
| ) | |
| session_id = gr.Textbox(value='', visible=False) | |
| html_info = gr.HTML(elem_id=f'html_info', elem_classes="infotext") | |
| inpaint_btn.click( | |
| fn=inpainting_run, | |
| inputs=[ | |
| model_picker, | |
| use_rasg, | |
| use_painta, | |
| prompt, | |
| imageMask, | |
| hr_image, | |
| seed, | |
| eta, | |
| negative_prompt, | |
| positive_prompt, | |
| ddim_steps, | |
| guidance_scale, | |
| batch_size, | |
| session_id | |
| ], | |
| outputs=[output_gallery, session_id], | |
| api_name="inpaint" | |
| ) | |
| upscale_btn.click( | |
| fn=upscale_run, | |
| inputs=[ | |
| ddim_steps, | |
| seed, | |
| use_sam_mask, | |
| session_id, | |
| html_info | |
| ], | |
| outputs=[hires_image], | |
| api_name="upscale", | |
| _js="function(a, b, c, d, e){ return [a, b, c, d, selected_gallery_index()] }", | |
| ) | |
| demo.queue(max_size=20) | |
| demo.launch(share=True, allowed_paths=[str(TMP_DIR)]) | |