Spaces:
Runtime error
Runtime error
| from share import * | |
| import config | |
| import cv2 | |
| import einops | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import random | |
| from pytorch_lightning import seed_everything | |
| from annotator.util import resize_image, HWC3 | |
| from cldm.model import create_model, load_state_dict | |
| from cldm.ddim_hacked import DDIMSampler | |
| import dlib | |
| from PIL import Image, ImageDraw | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| else: | |
| device = torch.device("cpu") | |
| model = create_model('./models/cldm_v15.yaml').cpu() | |
| model.load_state_dict(load_state_dict( | |
| './models/control_sd15_landmarks.pth', location='cpu')) | |
| model = model.to(device) | |
| ddim_sampler = DDIMSampler(model) | |
| detector = dlib.get_frontal_face_detector() | |
| predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") | |
| canvas_html = "<face-canvas id='canvas-root' data-mode='points' style='display:flex;max-width: 500px;margin: 0 auto;'></face-canvas>" | |
| load_js = """ | |
| async () => { | |
| const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/face-canvas.js" | |
| fetch(url) | |
| .then(res => res.text()) | |
| .then(text => { | |
| const script = document.createElement('script'); | |
| script.type = "module" | |
| script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' })); | |
| document.head.appendChild(script); | |
| }); | |
| } | |
| """ | |
| get_js_image = """ | |
| async (input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta, image_file_live_opt) => { | |
| const canvasEl = document.getElementById("canvas-root"); | |
| const imageData = canvasEl? canvasEl._data : null; | |
| if(image_file_live_opt === 'webcam'){ | |
| input_image = imageData['image'] | |
| landmark_direct_mode = true | |
| } | |
| return [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta, image_file_live_opt] | |
| } | |
| """ | |
| def draw_landmarks(image, landmarks, color="white", radius=2.5): | |
| draw = ImageDraw.Draw(image) | |
| for dot in landmarks: | |
| x, y = dot | |
| draw.ellipse((x-radius, y-radius, x+radius, y+radius), fill=color) | |
| def get_68landmarks_img(img): | |
| gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
| faces = detector(gray) | |
| landmarks = [] | |
| for face in faces: | |
| shape = predictor(gray, face) | |
| for i in range(68): | |
| x = shape.part(i).x | |
| y = shape.part(i).y | |
| landmarks.append((x, y)) | |
| con_img = Image.new('RGB', (img.shape[1], img.shape[0]), color=(0, 0, 0)) | |
| draw_landmarks(con_img, landmarks) | |
| con_img = np.array(con_img) | |
| return con_img | |
| def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta, image_file_live_opt="file"): | |
| input_image = input_image.convert('RGB') | |
| input_image = np.array(input_image) | |
| input_image = np.flip(input_image, axis=2) | |
| print('input_image.shape', input_image.shape) | |
| # Limit the number of samples to 2 for Spaces only | |
| num_samples = min(num_samples, 2) | |
| with torch.no_grad(): | |
| img = resize_image(HWC3(input_image), image_resolution) | |
| H, W, C = img.shape | |
| if landmark_direct_mode: | |
| detected_map = img | |
| else: | |
| detected_map = get_68landmarks_img(img) | |
| detected_map = HWC3(detected_map) | |
| control = torch.from_numpy( | |
| detected_map.copy()).float().to(device) / 255.0 | |
| control = torch.stack([control for _ in range(num_samples)], dim=0) | |
| control = einops.rearrange(control, 'b h w c -> b c h w').clone() | |
| if seed == -1: | |
| seed = random.randint(0, 2**32 - 1) | |
| seed_everything(seed) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| cond = {"c_concat": [control], "c_crossattn": [ | |
| model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} | |
| un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [ | |
| model.get_learned_conditioning([n_prompt] * num_samples)]} | |
| shape = (4, H // 8, W // 8) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=True) | |
| model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ( | |
| [strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01 | |
| samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples, | |
| shape, cond, verbose=False, eta=eta, | |
| unconditional_guidance_scale=scale, | |
| unconditional_conditioning=un_cond) | |
| if config.save_memory: | |
| model.low_vram_shift(is_diffusing=False) | |
| x_samples = model.decode_first_stage(samples) | |
| x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') | |
| * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8) | |
| results = [x_samples[i] for i in range(num_samples)] | |
| return [255 - detected_map] + results | |
| def toggle(choice): | |
| if choice == "file": | |
| return gr.update(visible=True, value=None), gr.update(visible=False, value=None) | |
| elif choice == "webcam": | |
| return gr.update(visible=False, value=None), gr.update(visible=True, value=canvas_html) | |
| block = gr.Blocks().queue() | |
| with block: | |
| live_conditioning = gr.JSON(value={}, visible=False) | |
| with gr.Row(): | |
| gr.Markdown("## Control Stable Diffusion with Face Landmarks") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_file_live_opt = gr.Radio(["file", "webcam"], value="file", | |
| label="How would you like to upload your image?") | |
| input_image = gr.Image(source="upload", visible=True, type="pil") | |
| canvas = gr.HTML(None, elem_id="canvas_html", visible=False) | |
| image_file_live_opt.change(fn=toggle, | |
| inputs=[image_file_live_opt], | |
| outputs=[input_image, canvas], | |
| queue=False) | |
| prompt = gr.Textbox(label="Prompt") | |
| run_button = gr.Button(label="Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| num_samples = gr.Slider( | |
| label="Images", minimum=1, maximum=2, value=1, step=1) | |
| image_resolution = gr.Slider( | |
| label="Image Resolution", minimum=256, maximum=768, value=512, step=64) | |
| strength = gr.Slider( | |
| label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) | |
| guess_mode = gr.Checkbox(label='Guess Mode', value=False) | |
| landmark_direct_mode = gr.Checkbox( | |
| label='Input Landmark Directly', value=False) | |
| ddim_steps = gr.Slider( | |
| label="Steps", minimum=1, maximum=100, value=20, step=1) | |
| scale = gr.Slider(label="Guidance Scale", | |
| minimum=0.1, maximum=30.0, value=9.0, step=0.1) | |
| seed = gr.Slider(label="Seed", minimum=-1, | |
| maximum=2147483647, step=1, randomize=True) | |
| eta = gr.Number(label="eta (DDIM)", value=0.0) | |
| a_prompt = gr.Textbox( | |
| label="Added Prompt", value='best quality, extremely detailed') | |
| n_prompt = gr.Textbox(label="Negative Prompt", | |
| value='cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') | |
| with gr.Column(): | |
| result_gallery = gr.Gallery( | |
| label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') | |
| ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, | |
| ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta] | |
| gr.Examples(fn=process, examples=[ | |
| ["examples/image0.jpg", "a silly clown face", "best quality, extremely detailed", | |
| "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0], | |
| ["examples/image1.png", "a photo of a woman wearing glasses", "best quality, extremely detailed", | |
| "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0], | |
| ["examples/image2.png", "a silly portrait of man with head tilted and a beautiful hair on the side", "best quality, extremely detailed", | |
| "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0], | |
| ["examples/image3.png", "portrait handsome men", "best quality, extremely detailed", | |
| "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0], | |
| ["examples/image4.jpg", "a beautiful woman looking at the sky", "best quality, extremely detailed", | |
| "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0], | |
| ], inputs=ips, outputs=[result_gallery], cache_examples=True) | |
| run_button.click(fn=process, inputs=ips + [image_file_live_opt], | |
| outputs=[result_gallery], _js=get_js_image) | |
| block.load(None, None, None, _js=load_js) | |
| block.launch() | |