app.py

import sys

sys.path.append('./')

from typing import Tuple

import os
import cv2
import math
import torch
import random
import numpy as np
import argparse

import PIL
from PIL import Image

import diffusers
from diffusers.utils import load_image
from diffusers.models import ControlNetModel
from diffusers import LCMScheduler

from huggingface_hub import hf_hub_download

import insightface
from insightface.app import FaceAnalysis

from style_template import styles
from pipeline_stable_diffusion_xl_instantid_full import StableDiffusionXLInstantIDPipeline
from model_util import load_models_xl, get_torch_device, torch_gc

import gradio as gr

# global variable
MAX_SEED = np.iinfo(np.int32).max
device = get_torch_device()
dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "Watercolor"

# Load face encoder
app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', '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=dtype)


def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=False):
    if pretrained_model_name_or_path.endswith(
            ".ckpt"
    ) or pretrained_model_name_or_path.endswith(".safetensors"):
        scheduler_kwargs = hf_hub_download(
            repo_id="wangqixun/YamerMIX_v8",
            subfolder="scheduler",
            filename="scheduler_config.json",
        )

        (tokenizers, text_encoders, unet, _, vae) = load_models_xl(
            pretrained_model_name_or_path=pretrained_model_name_or_path,
            scheduler_name=None,
            weight_dtype=dtype,
        )

        scheduler = diffusers.EulerDiscreteScheduler.from_config(scheduler_kwargs)
        pipe = StableDiffusionXLInstantIDPipeline(
            vae=vae,
            text_encoder=text_encoders[0],
            text_encoder_2=text_encoders[1],
            tokenizer=tokenizers[0],
            tokenizer_2=tokenizers[1],
            unet=unet,
            scheduler=scheduler,
            controlnet=controlnet,
        ).to(device)

    else:
        pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
            pretrained_model_name_or_path,
            controlnet=controlnet,
            torch_dtype=dtype,
            safety_checker=None,
            feature_extractor=None,
        ).to(device)

        pipe.scheduler = diffusers.EulerDiscreteScheduler.from_config(pipe.scheduler.config)

    pipe.load_ip_adapter_instantid(face_adapter)
    # load and disable LCM
    pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
    pipe.disable_lora()

    def toggle_lcm_ui(value):
        if value:
            return (
                gr.update(minimum=0, maximum=100, step=1, value=5),
                gr.update(minimum=0.1, maximum=20.0, step=0.1, value=1.5)
            )
        else:
            return (
                gr.update(minimum=5, maximum=100, step=1, value=30),
                gr.update(minimum=0.1, maximum=20.0, step=0.1, value=5)
            )

    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, 30, 0.8, 0.8, 5, 42, False, True)

    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 generate_image(face_image_path, pose_image_path, prompt, negative_prompt, style_name, num_steps,
                       identitynet_strength_ratio, adapter_strength_ratio, guidance_scale, seed, enable_LCM,
                       enhance_face_region, progress=gr.Progress(track_tqdm=True)):
        if enable_LCM:
            pipe.enable_lora()
            pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
        else:
            pipe.disable_lora()
            pipe.scheduler = diffusers.EulerDiscreteScheduler.from_config(pipe.scheduler.config)

        if face_image_path 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_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(f"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(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[0], gr.update(visible=True)

    ### Description
    title = r"""
    <h1 align="center">InstantID: 1张照片，无需训练，秒级生成个人写真</h1>
    """

    description = r"""
    <b>官方Gradio demo</b> for <a href='https://github.com/InstantID/InstantID' target='_blank'><b>InstantID: 1张照片，无需训练，秒级生成个人写真</b></a>.<br>
    用户指南:<br>
    1. 上传人物图片。 对于多人图像，我们只会检测最大的脸部。 确保脸部不要太小，并且没有明显遮挡或模糊。
    2. （可选）上传另一个人的图像作为参考姿势。 如果没有上传，我们将使用第一张图像来提取姿势。 如果您在步骤1中使用了裁剪后的脸部，建议上传它以提取新的姿势。
    3. （可选）输入文本prompt提示词，就像所有文生图应用中所做的那样.
    4. 点击 <b>Submit</b> 按钮开始定制.
    5. 分享美图给你的好友吧, 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 not satisfied with the similarity, try increasing the weight of "IdentityNet Strength" and "Adapter Strength."    
    2. If you feel that the saturation is too high, first decrease the Adapter strength. If it remains too high, then decrease the IdentityNet strength.
    3. If you find that text control is not as expected, decrease Adapter strength.
    4. If you find that realistic style is not good enough, go for our Github repo and use a more realistic base model.
    """

    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 fidelity",
                                    placeholder="A photo of a person",
                                    value="")

                submit = gr.Button("Submit", variant="primary")

                enable_LCM = gr.Checkbox(
                    label="Enable Fast Inference with LCM", value=enable_lcm_arg,
                    info="LCM speeds up the inference step, the trade-off is the quality of the generated image. It performs better with portrait face images rather than distant faces",
                )
                style = gr.Dropdown(label="Style template", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)

                # strength
                identitynet_strength_ratio = gr.Slider(
                    label="IdentityNet strength (for fidelity)",
                    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=5 if enable_lcm_arg else 30,
                    )
                    guidance_scale = gr.Slider(
                        label="Guidance scale",
                        minimum=0.1,
                        maximum=10.0,
                        step=0.1,
                        value=0 if enable_lcm_arg else 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.Image(label="Generated Images")
                usage_tips = gr.Markdown(label="Usage tips of InstantID", value=tips, visible=False)

            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_file, pose_file, prompt, negative_prompt, style, num_steps, identitynet_strength_ratio,
                        adapter_strength_ratio, guidance_scale, seed, enable_LCM, enhance_face_region],
                outputs=[gallery, usage_tips]
            )

            enable_LCM.input(fn=toggle_lcm_ui, inputs=[enable_LCM], outputs=[num_steps, guidance_scale], queue=False)

        gr.Examples(
            examples=get_example(),
            inputs=[face_file, prompt, style, negative_prompt],
            run_on_click=True,
            fn=run_for_examples,
            outputs=[gallery, usage_tips],
            cache_examples=True,
        )

        gr.Markdown(article)

    demo.queue().launch(server_name='0.0.0.0', share=True)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--pretrained_model_name_or_path", type=str, default="wangqixun/YamerMIX_v8")
    parser.add_argument("--enable_LCM", type=bool, default=os.environ.get("ENABLE_LCM", False))

    args = parser.parse_args()

    main(args.pretrained_model_name_or_path, args.enable_LCM)