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| import math | |
| import random | |
| import cv2 | |
| import gradio as gr | |
| import numpy as np | |
| import PIL | |
| import spaces | |
| import torch | |
| from diffusers.models import ControlNetModel | |
| from diffusers.utils import load_image | |
| from insightface.app import FaceAnalysis | |
| from PIL import Image | |
| from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline | |
| from style_template import styles | |
| # 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" | |
| open_template_lists = False | |
| # 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 = "./checkpoints/ip-adapter.bin" | |
| controlnet_path = "./checkpoints/ControlNetModel" | |
| # Load pipeline | |
| controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) | |
| base_model_path = "RunDiffusion/Juggernaut-XL-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 get_style_lists(): | |
| gr.update(visible=True) | |
| return [[x] for x in STYLE_NAMES] | |
| # 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_file, prompt, style, negative_prompt): | |
| # return generate_image(face_file, 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 | |
| def check_input_image(face_image): | |
| if face_image is None: | |
| raise gr.Error("Cannot find any input face image! Please upload the face image") | |
| def generate_image( | |
| face_image_path, | |
| pose_image_path, | |
| 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 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_path) | |
| 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("Cannot find any face in the image! Please upload another person image") | |
| face_info = sorted(face_info, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * x["bbox"][3] - x["bbox"][1])[ | |
| -1 | |
| ] # only use the maximum face | |
| face_emb = face_info["embedding"] | |
| face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info["kps"]) | |
| if pose_image_path is not None: | |
| pose_image = load_image(pose_image_path) | |
| 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("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 | |
| # 面部增强 | |
| 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) | |
| 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[0] | |
| # return images[0], gr.update(visible=True) | |
| ### Description | |
| title = r""" | |
| <h1 align="center">InstantID: Zero-shot Identity-Preserving Generation in Seconds</h1> | |
| """ | |
| description = r""" | |
| <b>Official 🤗 Gradio demo</b> for <a href='https://github.com/InstantID/InstantID' target='_blank'><b>InstantID: Zero-shot Identity-Preserving Generation in Seconds</b></a>.<br> | |
| How to use:<br> | |
| 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 <b>Submit</b> button to start customizing. | |
| 5. Share your customizd photo with your friends, enjoy😊! | |
| """ | |
| article = r""" | |
| --- | |
| 📝 **Citation** | |
| <br> | |
| 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} | |
| } | |
| ``` | |
| 📧 **Contact** | |
| <br> | |
| If you have any questions, please feel free to open an issue or directly reach us out at <b>haofanwang.ai@gmail.com</b>. | |
| """ | |
| 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_file = gr.Image(label="Upload a photo of your face", type="filepath") | |
| # optional: upload a reference pose image | |
| pose_file = gr.Image(label="Upload a reference pose image (optional)", type="filepath") | |
| # 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_event = gr.Checkbox(label="display the style templates", value=open_template_lists) | |
| 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="(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3), (lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, deformed eyes, blur, blurry, deformed cat, deformed, nudity, naked, skimpy, scanty, bare skin, lingerie, swimsuit, exposed, see-through, anthropomorphic cat, monochrome, pet collar, gun, weapon, 3d, drones, drone, 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(): | |
| output_image = gr.Image(label="Generated Image") | |
| style_lists = gr.List(label="style templates list", visible=False) | |
| # style_lists = gr.Dataframe(value=np.array(STYLE_NAMES, dtype=np.str_), type="numpy", row_count=1, label="style templates list", visible=False) | |
| # usage_tips = gr.Markdown(label="Usage tips of InstantID", value=tips, visible=False) | |
| submit.click( | |
| # fn=remove_tips, | |
| # outputs=usage_tips, | |
| # queue=False, | |
| # api_name=False, | |
| # ).then( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=check_input_image, | |
| inputs=face_file, | |
| queue=False, | |
| api_name=False, | |
| ).success( | |
| fn=generate_image, | |
| inputs=[ | |
| face_file, | |
| pose_file, | |
| prompt, | |
| negative_prompt, | |
| style, | |
| enhance_face_region, | |
| num_steps, | |
| identitynet_strength_ratio, | |
| adapter_strength_ratio, | |
| guidance_scale, | |
| seed, | |
| ], | |
| outputs=output_image, | |
| ) | |
| style_event.select( | |
| fn=get_style_lists, | |
| outputs=style_lists, | |
| queue=False, | |
| ) | |
| # gr.Examples( | |
| # examples=get_example(), | |
| # inputs=[face_file, prompt, style, negative_prompt], | |
| # outputs=[output_image, usage_tips], | |
| # fn=run_for_examples, | |
| # cache_examples=True, | |
| # ) | |
| # gr.Markdown(article) | |
| demo.queue() | |
| demo.launch() | |