import os import cv2 import math import spaces import torch import random import numpy as np import PIL from PIL import Image import diffusers from diffusers.utils import load_image from diffusers.models import ControlNetModel import insightface from insightface.app import FaceAnalysis from style_template import styles from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline import gradio as gr # global variable MAX_SEED = np.iinfo(np.int32).max device = "cuda" if torch.cuda.is_available() else "cpu" STYLE_NAMES = list(styles.keys()) DEFAULT_STYLE_NAME = "Watercolor" # download checkpoints from huggingface_hub import hf_hub_download hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints") hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints") hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints") # Load face encoder app = FaceAnalysis(name='antelopev2', root='./', providers=['CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) # Path to InstantID models face_adapter = f'./checkpoints/ip-adapter.bin' controlnet_path = f'./checkpoints/ControlNetModel' # Load pipeline controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) base_model_path = 'wangqixun/YamerMIX_v8' pipe = StableDiffusionXLInstantIDPipeline.from_pretrained( base_model_path, controlnet=controlnet, torch_dtype=torch.float16, safety_checker=None, feature_extractor=None, ) pipe.cuda() pipe.load_ip_adapter_instantid(face_adapter) pipe.image_proj_model.to('cuda') pipe.unet.to('cuda') def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed def swap_to_gallery(images): return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False) def upload_example_to_gallery(images, prompt, style, negative_prompt): return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False) def remove_back_to_files(): return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) def remove_tips(): return gr.update(visible=False) def get_example(): case = [ [ ['./examples/yann-lecun_resize.jpg'], "a man", "Snow", "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green", ], [ ['./examples/musk_resize.jpeg'], "a man", "Mars", "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green", ], [ ['./examples/sam_resize.png'], "a man", "Jungle", "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, gree", ], [ ['./examples/schmidhuber_resize.png'], "a man", "Neon", "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green", ], [ ['./examples/kaifu_resize.png'], "a man", "Vibrant Color", "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green", ], ] return case def run_for_examples(face_files, prompt, style, negative_prompt): return generate_image(face_files, None, prompt, negative_prompt, style, True, 30, 0.8, 0.8, 5, 42) def convert_from_cv2_to_image(img: np.ndarray) -> Image: return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) def convert_from_image_to_cv2(img: Image) -> np.ndarray: return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]): stickwidth = 4 limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]]) kps = np.array(kps) w, h = image_pil.size out_img = np.zeros([h, w, 3]) for i in range(len(limbSeq)): index = limbSeq[i] color = color_list[index[0]] x = kps[index][:, 0] y = kps[index][:, 1] length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1])) polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1) out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color) out_img = (out_img * 0.6).astype(np.uint8) for idx_kp, kp in enumerate(kps): color = color_list[idx_kp] x, y = kp out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1) out_img_pil = Image.fromarray(out_img.astype(np.uint8)) return out_img_pil def resize_img(input_image, max_side=1280, min_side=1024, size=None, pad_to_max_side=False, mode=PIL.Image.BILINEAR, base_pixel_number=64): w, h = input_image.size if size is not None: w_resize_new, h_resize_new = size else: ratio = min_side / min(h, w) w, h = round(ratio*w), round(ratio*h) ratio = max_side / max(h, w) input_image = input_image.resize([round(ratio*w), round(ratio*h)], mode) w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number input_image = input_image.resize([w_resize_new, h_resize_new], mode) if pad_to_max_side: res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255 offset_x = (max_side - w_resize_new) // 2 offset_y = (max_side - h_resize_new) // 2 res[offset_y:offset_y+h_resize_new, offset_x:offset_x+w_resize_new] = np.array(input_image) input_image = Image.fromarray(res) return input_image def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]: p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) return p.replace("{prompt}", positive), n + ' ' + negative @spaces.GPU def generate_image(face_image, pose_image, prompt, negative_prompt, style_name, enhance_face_region, num_steps, identitynet_strength_ratio, adapter_strength_ratio, guidance_scale, seed, progress=gr.Progress(track_tqdm=True)): if face_image is None: raise gr.Error(f"Cannot find any input face image! Please upload the face image") if prompt is None: prompt = "a person" # apply the style template prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) face_image = load_image(face_image[0]) face_image = resize_img(face_image) face_image_cv2 = convert_from_image_to_cv2(face_image) height, width, _ = face_image_cv2.shape # Extract face features face_info = app.get(face_image_cv2) if len(face_info) == 0: raise gr.Error(f"Cannot find any face in the image! Please upload another person image") face_info = face_info[-1] face_emb = face_info['embedding'] face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info['kps']) if pose_image is not None: pose_image = load_image(pose_image[0]) pose_image = resize_img(pose_image) pose_image_cv2 = convert_from_image_to_cv2(pose_image) face_info = app.get(pose_image_cv2) if len(face_info) == 0: raise gr.Error(f"Cannot find any face in the reference image! Please upload another person image") face_info = face_info[-1] face_kps = draw_kps(pose_image, face_info['kps']) width, height = face_kps.size if enhance_face_region: control_mask = np.zeros([height, width, 3]) x1, y1, x2, y2 = face_info['bbox'] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) control_mask[y1:y2, x1:x2] = 255 control_mask = Image.fromarray(control_mask.astype(np.uint8)) else: control_mask = None generator = torch.Generator(device=device).manual_seed(seed) print("Start inference...") print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}") pipe.set_ip_adapter_scale(adapter_strength_ratio) images = pipe( prompt=prompt, negative_prompt=negative_prompt, image_embeds=face_emb, image=face_kps, control_mask=control_mask, controlnet_conditioning_scale=float(identitynet_strength_ratio), num_inference_steps=num_steps, guidance_scale=guidance_scale, height=height, width=width, generator=generator ).images return images, gr.update(visible=True) ### Description title = r"""

InstantID: Zero-shot Identity-Preserving Generation in Seconds

""" description = r""" Official πŸ€— Gradio demo for InstantID: Zero-shot Identity-Preserving Generation in Seconds.
How to use:
1. Upload a person image. For multiple person images, we will only detect the biggest face. Make sure face is not too small and not significantly blocked or blurred. 2. (Optionally) upload another person image as reference pose. If not uploaded, we will use the first person image to extract landmarks. If you use a cropped face at step1, it is recommeneded to upload it to extract a new pose. 3. Enter a text prompt as done in normal text-to-image models. 4. Click the Submit button to start customizing. 5. Share your customizd photo with your friends, enjoy😊! """ article = r""" --- πŸ“ **Citation**
If our work is helpful for your research or applications, please cite us via: ```bibtex @article{wang2024instantid, title={InstantID: Zero-shot Identity-Preserving Generation in Seconds}, author={Wang, Qixun and Bai, Xu and Wang, Haofan and Qin, Zekui and Chen, Anthony}, journal={arXiv preprint arXiv:2401.07519}, year={2024} } ``` πŸ“§ **Contact**
If you have any questions, please feel free to open an issue or directly reach us out at haofanwang.ai@gmail.com. """ tips = r""" ### Usage tips of InstantID 1. If you're unsatisfied with the similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter). 2. If the generated image is over-saturated, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale. 3. If text control is not as expected, decrease ip_adapter_scale. 4. Find a good base model always makes a difference. """ css = ''' .gradio-container {width: 85% !important} ''' with gr.Blocks(css=css) as demo: # description gr.Markdown(title) gr.Markdown(description) with gr.Row(): with gr.Column(): # upload face image face_files = gr.Files( label="Upload a photo of your face", file_types=["image"] ) uploaded_faces = gr.Gallery(label="Your images", visible=False, columns=1, rows=1, height=512) with gr.Column(visible=False) as clear_button_face: remove_and_reupload_faces = gr.ClearButton(value="Remove and upload new ones", components=face_files, size="sm") # optional: upload a reference pose image pose_files = gr.Files( label="Upload a reference pose image (optional)", file_types=["image"] ) uploaded_poses = gr.Gallery(label="Your images", visible=False, columns=1, rows=1, height=512) with gr.Column(visible=False) as clear_button_pose: remove_and_reupload_poses = gr.ClearButton(value="Remove and upload new ones", components=pose_files, size="sm") # prompt prompt = gr.Textbox(label="Prompt", info="Give simple prompt is enough to achieve good face fedility", placeholder="A photo of a person", value="") submit = gr.Button("Submit", variant="primary") style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) # strength identitynet_strength_ratio = gr.Slider( label="IdentityNet strength (for fedility)", minimum=0, maximum=1.5, step=0.05, value=0.80, ) adapter_strength_ratio = gr.Slider( label="Image adapter strength (for detail)", minimum=0, maximum=1.5, step=0.05, value=0.80, ) with gr.Accordion(open=False, label="Advanced Options"): negative_prompt = gr.Textbox( label="Negative Prompt", placeholder="low quality", value="(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green", ) num_steps = gr.Slider( label="Number of sample steps", minimum=20, maximum=100, step=1, value=30, ) guidance_scale = gr.Slider( label="Guidance scale", minimum=0.1, maximum=10.0, step=0.1, value=5, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) enhance_face_region = gr.Checkbox(label="Enhance non-face region", value=True) with gr.Column(): gallery = gr.Gallery(label="Generated Images") usage_tips = gr.Markdown(label="Usage tips of InstantID", value=tips ,visible=False) face_files.upload(fn=swap_to_gallery, inputs=face_files, outputs=[uploaded_faces, clear_button_face, face_files]) pose_files.upload(fn=swap_to_gallery, inputs=pose_files, outputs=[uploaded_poses, clear_button_pose, pose_files]) remove_and_reupload_faces.click(fn=remove_back_to_files, outputs=[uploaded_faces, clear_button_face, face_files]) remove_and_reupload_poses.click(fn=remove_back_to_files, outputs=[uploaded_poses, clear_button_pose, pose_files]) submit.click( fn=remove_tips, outputs=usage_tips, ).then( fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False, ).then( fn=generate_image, inputs=[face_files, pose_files, prompt, negative_prompt, style, enhance_face_region, num_steps, identitynet_strength_ratio, adapter_strength_ratio, guidance_scale, seed], outputs=[gallery, usage_tips] ) gr.Examples( examples=get_example(), inputs=[face_files, prompt, style, negative_prompt], run_on_click=True, fn=upload_example_to_gallery, outputs=[uploaded_faces, clear_button_face, face_files], cache_examples=True ) gr.Markdown(article) demo.queue(api_open=False) demo.launch()