gradio_demo/demo.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

from cv2 import imencode
import base64

# def encode_pil_to_base64_new(pil_image):
#     print("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA")
#     image_arr = np.asarray(pil_image)[:,:,::-1]
#     _, byte_data = imencode('.png', image_arr)        
#     base64_data = base64.b64encode(byte_data)
#     base64_string_opencv = base64_data.decode("utf-8")
#     return "data:image/png;base64," + base64_string_opencv

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=(320, 320))

# 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)

logo = Image.open("./gradio_demo/logo.png")

pretrained_model_name_or_path="wangqixun/YamerMIX_v8"


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()

# gr.processing_utils.encode_pil_to_base64 = encode_pil_to_base64_new
def remove_tips():
    print("GG")
    return gr.update(visible=False)

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 run_for_prompts1(face_file,style,progress=gr.Progress(track_tqdm=True)):
    # if email != "":
    p,n = styles.get(style, styles.get(STYLE_NAMES[1]))
    return generate_image(face_file, p[0], n)
    # else:
        # raise gr.Error("Email ID is compulsory")
def run_for_prompts2(face_file,style,progress=gr.Progress(track_tqdm=True)):
    # if email != "":
    p,n = styles.get(style, styles.get(STYLE_NAMES[1]))
    return generate_image(face_file, p[1], n)

def run_for_prompts3(face_file,style,progress=gr.Progress(track_tqdm=True)):
    # if email != "":
    p,n = styles.get(style, styles.get(STYLE_NAMES[1]))
    return generate_image(face_file, p[2], n)

def run_for_prompts4(face_file,style,progress=gr.Progress(track_tqdm=True)):
    # if email != "":
    p,n = styles.get(style, styles.get(STYLE_NAMES[1]))
    return generate_image(face_file, p[3], n)


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=640, min_side=640, size=None, 
            pad_to_max_side=True, mode=PIL.Image.BILINEAR, base_pixel_number=64):

        w, h = input_image.size
        print(w)
        print(h)
        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 generate_image(face_image,prompt,negative_prompt):
    pose_image_path = None
    # prompt = "superman"
    enable_LCM = False
    identitynet_strength_ratio = 0.95
    adapter_strength_ratio = 0.60
    num_steps = 15
    guidance_scale = 8.5
    seed = random.randint(0, MAX_SEED)
    # negative_prompt = ""
    # negative_prompt += neg
    enhance_face_region = 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 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,
        # num_images_per_prompt = 4
    ).images

    return images[0]

def main(pretrained_model_name_or_path="wangqixun/YamerMIX_v8", enable_lcm_arg=False):

    


    ### Description
    title = r"""
    <h1 align="center">Choose your AVATAR</h1>
    """

    description = r"""
    <h2> Powered by IDfy </h2>"""

    article = r""""""

    tips = r""""""
    
    js = '''  '''
    
    css = '''
    .gradio-container {width: 95% !important; background-color: #E6F3FF;} 
    .image-gallery {height: 100vh !important; overflow: auto;}
    .gradio-row .gradio-element { margin: 0 !important; }
    '''


    with gr.Blocks(css=css, js=js) as demo:

        # description
        gr.Markdown(title)
        with gr.Row():
            gr.Image("./gradio_demo/logo.png",scale=0,min_width=50,show_label=False,show_download_button=False)
            gr.Markdown(description)
        with gr.Row():
            with gr.Column():
                style = gr.Dropdown(label="Choose your STYLE", choices=STYLE_NAMES)
                face_file = gr.Image(label="Upload a photo of your face", type="pil",sources="webcam")
                submit = gr.Button("Submit", variant="primary")
            with gr.Column():
                with gr.Row():
                    gallery1 = gr.Image(label="Generated Images")
                    gallery2 = gr.Image(label="Generated Images")
                with gr.Row():
                    gallery3 = gr.Image(label="Generated Images")
                    gallery4 = gr.Image(label="Generated Images")
                email = gr.Textbox(label="Email",
                        info="Enter your email address",
                        value="")
            
            usage_tips = gr.Markdown(label="Usage tips of InstantID", value=tips ,visible=False)

            face_file.upload(
                fn=remove_tips,
                outputs=usage_tips,
                queue=True,
                api_name=False,
                show_progress = "full"
            )

            submit.click(
                fn=remove_tips,
                outputs=usage_tips,
                queue=True,
                api_name=False,
                show_progress = "full"
            ).then(
                fn=run_for_prompts1,
                inputs=[face_file,style],
                outputs=[gallery1]
            )
        
        
        gr.Markdown(article)

    demo.launch(share=True)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--pretrained_model_name_or_path", type=str, default="wangqixun/YamerMIX_v8")
    args = parser.parse_args()

    main(args.pretrained_model_name_or_path, False)