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from huggingface_hub import hf_hub_download |
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import math |
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import random |
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import cv2 |
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import gradio as gr |
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import numpy as np |
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import PIL |
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import spaces |
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import torch |
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from diffusers.models import ControlNetModel |
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from diffusers.utils import load_image |
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from insightface.app import FaceAnalysis |
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from PIL import Image |
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from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline |
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from style_template import styles |
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MAX_SEED = np.iinfo(np.int32).max |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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STYLE_NAMES = list(styles.keys()) |
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DEFAULT_STYLE_NAME = "Watercolor" |
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hf_hub_download(repo_id="InstantX/InstantID", |
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filename="ControlNetModel/config.json", local_dir="./checkpoints") |
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hf_hub_download( |
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repo_id="InstantX/InstantID", |
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filename="ControlNetModel/diffusion_pytorch_model.safetensors", |
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local_dir="./checkpoints", |
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) |
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hf_hub_download(repo_id="InstantX/InstantID", |
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filename="ip-adapter.bin", local_dir="./checkpoints") |
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app = FaceAnalysis(name="antelopev2", root="./", |
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providers=["CPUExecutionProvider"]) |
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app.prepare(ctx_id=0, det_size=(640, 640)) |
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face_adapter = "./checkpoints/ip-adapter.bin" |
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controlnet_path = "./checkpoints/ControlNetModel" |
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controlnet = ControlNetModel.from_pretrained( |
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controlnet_path, torch_dtype=torch.float16) |
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base_model_path = "SG161222/RealVisXL_V3.0" |
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pipe = StableDiffusionXLInstantIDPipeline.from_pretrained( |
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base_model_path, |
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controlnet=controlnet, |
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torch_dtype=torch.float16, |
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safety_checker=None, |
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feature_extractor=None, |
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) |
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pipe.cuda() |
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pipe.load_ip_adapter_instantid(face_adapter) |
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pipe.image_proj_model.to("cuda") |
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pipe.unet.to("cuda") |
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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return seed |
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def remove_tips(): |
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return gr.update(visible=False) |
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def get_example(): |
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case = [ |
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[ |
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"./examples/yann-lecun_resize.jpg", |
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"a man", |
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"Snow", |
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"(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", |
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], |
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[ |
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"./examples/musk_resize.jpeg", |
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"a man", |
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"Mars", |
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"(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", |
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], |
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[ |
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"./examples/sam_resize.png", |
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"a man", |
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"Jungle", |
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"(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", |
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], |
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[ |
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"./examples/schmidhuber_resize.png", |
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"a man", |
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"Neon", |
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"(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", |
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], |
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[ |
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"./examples/kaifu_resize.png", |
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"a man", |
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"Vibrant Color", |
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"(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", |
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], |
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] |
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return case |
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def run_for_examples(face_file, prompt, style, negative_prompt): |
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return generate_image(face_file, None, prompt, negative_prompt, style, True, 30, 0.8, 0.8, 5, 42) |
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def convert_from_cv2_to_image(img: np.ndarray) -> Image: |
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return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) |
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def convert_from_image_to_cv2(img: Image) -> np.ndarray: |
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return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) |
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def draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]): |
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stickwidth = 4 |
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limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]]) |
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kps = np.array(kps) |
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w, h = image_pil.size |
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out_img = np.zeros([h, w, 3]) |
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for i in range(len(limbSeq)): |
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index = limbSeq[i] |
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color = color_list[index[0]] |
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x = kps[index][:, 0] |
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y = kps[index][:, 1] |
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length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5 |
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angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1])) |
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polygon = cv2.ellipse2Poly( |
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(int(np.mean(x)), int(np.mean(y))), (int( |
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length / 2), stickwidth), int(angle), 0, 360, 1 |
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) |
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out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color) |
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out_img = (out_img * 0.6).astype(np.uint8) |
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for idx_kp, kp in enumerate(kps): |
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color = color_list[idx_kp] |
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x, y = kp |
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out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1) |
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out_img_pil = Image.fromarray(out_img.astype(np.uint8)) |
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return out_img_pil |
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def resize_img( |
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input_image, |
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max_side=1280, |
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min_side=1024, |
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size=None, |
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pad_to_max_side=False, |
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mode=PIL.Image.BILINEAR, |
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base_pixel_number=64, |
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): |
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w, h = input_image.size |
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if size is not None: |
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w_resize_new, h_resize_new = size |
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else: |
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ratio = min_side / min(h, w) |
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w, h = round(ratio * w), round(ratio * h) |
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ratio = max_side / max(h, w) |
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input_image = input_image.resize( |
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[round(ratio * w), round(ratio * h)], mode) |
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w_resize_new = (round(ratio * w) // base_pixel_number) * \ |
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base_pixel_number |
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h_resize_new = (round(ratio * h) // base_pixel_number) * \ |
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base_pixel_number |
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input_image = input_image.resize([w_resize_new, h_resize_new], mode) |
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if pad_to_max_side: |
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res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255 |
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offset_x = (max_side - w_resize_new) // 2 |
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offset_y = (max_side - h_resize_new) // 2 |
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res[offset_y: offset_y + h_resize_new, offset_x: offset_x + |
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w_resize_new] = np.array(input_image) |
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input_image = Image.fromarray(res) |
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return input_image |
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def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]: |
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
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return p.replace("{prompt}", positive), n + " " + negative |
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def check_input_image(face_image): |
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if face_image is None: |
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raise gr.Error( |
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"Cannot find any input face image! Please upload the face image") |
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@spaces.GPU |
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def generate_image( |
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face_image_path, |
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pose_image_path, |
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prompt, |
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negative_prompt, |
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style_name, |
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enhance_face_region, |
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num_steps, |
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identitynet_strength_ratio, |
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adapter_strength_ratio, |
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guidance_scale, |
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seed, |
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progress=gr.Progress(track_tqdm=True), |
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): |
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if prompt is None: |
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prompt = "a person" |
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prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) |
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face_image = load_image(face_image_path) |
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face_image = resize_img(face_image) |
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face_image_cv2 = convert_from_image_to_cv2(face_image) |
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height, width, _ = face_image_cv2.shape |
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face_info = app.get(face_image_cv2) |
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if len(face_info) == 0: |
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raise gr.Error( |
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"Cannot find any face in the image! Please upload another person image") |
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face_info = sorted(face_info, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * x["bbox"][3] - x["bbox"][1])[ |
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-1 |
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] |
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face_emb = face_info["embedding"] |
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face_kps = draw_kps(convert_from_cv2_to_image( |
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face_image_cv2), face_info["kps"]) |
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if pose_image_path is not None: |
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pose_image = load_image(pose_image_path) |
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pose_image = resize_img(pose_image) |
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pose_image_cv2 = convert_from_image_to_cv2(pose_image) |
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face_info = app.get(pose_image_cv2) |
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if len(face_info) == 0: |
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raise gr.Error( |
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"Cannot find any face in the reference image! Please upload another person image") |
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face_info = face_info[-1] |
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face_kps = draw_kps(pose_image, face_info["kps"]) |
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width, height = face_kps.size |
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if enhance_face_region: |
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control_mask = np.zeros([height, width, 3]) |
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x1, y1, x2, y2 = face_info["bbox"] |
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x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) |
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control_mask[y1:y2, x1:x2] = 255 |
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control_mask = Image.fromarray(control_mask.astype(np.uint8)) |
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else: |
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control_mask = None |
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generator = torch.Generator(device=device).manual_seed(seed) |
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print("Start inference...") |
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print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}") |
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pipe.set_ip_adapter_scale(adapter_strength_ratio) |
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images = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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image_embeds=face_emb, |
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image=face_kps, |
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control_mask=control_mask, |
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controlnet_conditioning_scale=float(identitynet_strength_ratio), |
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num_inference_steps=num_steps, |
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guidance_scale=guidance_scale, |
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height=height, |
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width=width, |
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generator=generator, |
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).images |
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return images[0], gr.update(visible=True) |
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title = r""" |
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<h1 align="center">InstantID: Zero-shot Identity-Preserving Generation in Seconds</h1> |
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""" |
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description = r""" |
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<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> |
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How to use:<br> |
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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. |
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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. |
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3. Enter a text prompt as done in normal text-to-image models. |
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4. Click the <b>Submit</b> button to start customizing. |
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5. Share your customizd photo with your friends, enjoy😊! |
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""" |
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article = r""" |
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--- |
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📝 **Citation** |
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<br> |
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If our work is helpful for your research or applications, please cite us via: |
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```bibtex |
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@article{wang2024instantid, |
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title={InstantID: Zero-shot Identity-Preserving Generation in Seconds}, |
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author={Wang, Qixun and Bai, Xu and Wang, Haofan and Qin, Zekui and Chen, Anthony}, |
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journal={arXiv preprint arXiv:2401.07519}, |
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year={2024} |
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} |
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``` |
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📧 **Contact** |
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<br> |
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If you have any questions, please feel free to open an issue or directly reach us out at <b>haofanwang.ai@gmail.com</b>. |
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""" |
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tips = r""" |
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### Usage tips of InstantID |
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1. If you're unsatisfied with the similarity, increase the weight of controlnet_conditioning_scale (IdentityNet) and ip_adapter_scale (Adapter). |
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2. If the generated image is over-saturated, decrease the ip_adapter_scale. If not work, decrease controlnet_conditioning_scale. |
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3. If text control is not as expected, decrease ip_adapter_scale. |
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4. Find a good base model always makes a difference. |
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""" |
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css = """ |
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.gradio-container {width: 85% !important} |
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""" |
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with gr.Blocks(css=css) as demo: |
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gr.Markdown(title) |
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gr.Markdown(description) |
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with gr.Row(): |
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with gr.Column(): |
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face_file = gr.Image( |
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label="Upload a photo of your face", type="filepath") |
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pose_file = gr.Image( |
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label="Upload a reference pose image (optional)", type="filepath") |
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prompt = gr.Textbox( |
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label="Prompt", |
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info="Give simple prompt is enough to achieve good face fedility", |
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placeholder="A photo of a person", |
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value="", |
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) |
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submit = gr.Button("Submit", variant="primary") |
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style = gr.Dropdown(label="Style template", |
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choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) |
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identitynet_strength_ratio = gr.Slider( |
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label="IdentityNet strength (for fedility)", |
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minimum=0, |
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maximum=1.5, |
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step=0.05, |
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value=0.80, |
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) |
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adapter_strength_ratio = gr.Slider( |
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label="Image adapter strength (for detail)", |
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minimum=0, |
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maximum=1.5, |
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step=0.05, |
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value=0.80, |
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) |
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with gr.Accordion(open=False, label="Advanced Options"): |
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negative_prompt = gr.Textbox( |
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label="Negative Prompt", |
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placeholder="low quality", |
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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", |
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) |
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num_steps = gr.Slider( |
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label="Number of sample steps", |
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minimum=20, |
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maximum=100, |
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step=1, |
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value=30, |
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) |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.1, |
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maximum=10.0, |
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step=0.1, |
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value=5, |
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) |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=42, |
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) |
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randomize_seed = gr.Checkbox( |
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label="Randomize seed", value=True) |
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enhance_face_region = gr.Checkbox( |
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label="Enhance non-face region", value=True) |
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with gr.Column(): |
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output_image = gr.Image(label="Generated Image") |
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usage_tips = gr.Markdown( |
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label="Usage tips of InstantID", value=tips, visible=False) |
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submit.click( |
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fn=remove_tips, |
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outputs=usage_tips, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=check_input_image, |
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inputs=face_file, |
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queue=False, |
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api_name=False, |
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).success( |
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fn=generate_image, |
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inputs=[ |
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face_file, |
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pose_file, |
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prompt, |
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negative_prompt, |
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style, |
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enhance_face_region, |
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num_steps, |
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identitynet_strength_ratio, |
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adapter_strength_ratio, |
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guidance_scale, |
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seed, |
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], |
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outputs=[output_image, usage_tips], |
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) |
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gr.Examples( |
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examples=get_example(), |
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inputs=[face_file, prompt, style, negative_prompt], |
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outputs=[output_image, usage_tips], |
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fn=run_for_examples, |
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cache_examples=True, |
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) |
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gr.Markdown(article) |
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demo.queue(api_open=False) |
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demo.launch() |
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