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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" | |
| torch_dtype = torch.float16 | |
| if torch.backends.mps.is_available(): | |
| device = "mps" | |
| torch_dtype = torch.float32 | |
| elif torch.cuda.is_available(): | |
| device = "cuda" | |
| else: | |
| device = "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") | |
| hf_hub_download(repo_id="latent-consistency/lcm-lora-sdxl", filename="pytorch_lora_weights.safetensors", 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" | |
| lcm_lora_path = "./checkpoints/pytorch_lora_weights.safetensors" | |
| # Load pipeline | |
| #controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) | |
| controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch_dtype) | |
| base_model_path = "wangqixun/YamerMIX_v8" | |
| pipe = StableDiffusionXLInstantIDPipeline.from_pretrained( | |
| base_model_path, | |
| controlnet=controlnet, | |
| #torch_dtype=torch.float16, | |
| torch_dtype=torch_dtype, | |
| safety_checker=None, | |
| feature_extractor=None, | |
| ) | |
| #pipe.cuda() | |
| num_inference_steps = 30 | |
| guidance_scale = 5 | |
| +# LCM | |
| if os.environ.get("LCM"): | |
| num_inference_steps = 10 | |
| guidance_scale = 0 | |
| pipe.load_lora_weights(lcm_lora_path) | |
| pipe.fuse_lora() | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
| if device == 'mps': | |
| pipe.to("mps", torch_dtype) | |
| pipe.enable_attention_slicing() | |
| elif device == 'cuda': | |
| pipe.cuda() | |
| pipe.load_ip_adapter_instantid(face_adapter) | |
| #pipe.image_proj_model.to("cuda") | |
| #pipe.unet.to("cuda") | |
| if device == 'mps' or device == 'cuda': | |
| pipe.image_proj_model.to(device) | |
| pipe.unet.to(device) | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| 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") | |
| #@spaces.GPU | |
| 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 | |
| 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[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 = 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, nudity,naked, bikini, skimpy, scanty, bare skin, lingerie, swimsuit, exposed, see-through, 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=num_inference_steps, | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.1, | |
| maximum=10.0, | |
| step=0.1, | |
| value=guidance_scale, | |
| ) | |
| 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") | |
| 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, usage_tips], | |
| ) | |
| 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(api_open=False) | |
| demo.launch() | |