gradio_demo/test.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 pandas as pd

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


# 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")

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

# gr.processing_utils.encode_pil_to_base64 = encode_pil_to_base64_new

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 remove_tips():
        return gr.update(visible=False)

    
    # prompts = [
    #     ["superman","Vibrant Color"], ["japanese anime character with white/neon hair","Watercolor"], 
    #     # ["Suited professional","(No style)"], 
    #     ["Scooba diver","Line art"], ["eskimo","Snow"]
    # ]

    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 validate_and_process(face_file, style, email):
    
#     # Your processing logic here
#     gallery1, gallery2, gallery3, gallery4 = run_for_prompts1(face_file, style), run_for_prompts2(face_file, style), run_for_prompts3(face_file, style), run_for_prompts4(face_file, style)
#     return gallery1, gallery2, gallery3, gallery4

    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 store_images(email, gallery1, gallery2, gallery3, gallery4):
        galleries = []
        for i, img in enumerate([gallery1, gallery2, gallery3, gallery4], start=1):
            if isinstance(img, np.ndarray):
                img = Image.fromarray(img)
            print(f"Gallery {i} type after conversion: {type(img)}")
            galleries.append(img)
        # Create the images directory if it doesn't exist
        if not os.path.exists('images'):
            os.makedirs('images')

        # Define image file paths
        image_paths = []
        for i, img in enumerate(galleries, start=1):
            img_path = f'images/{email}_gallery{i}.png'
            img.save(img_path)
            image_paths.append(img_path)

        # Define the CSV file path
        csv_file_path = 'image_data.csv'

        # Create a DataFrame for the email and image paths
        df = pd.DataFrame({
            'email': [email],
            'img1_path': [image_paths[0]],
            'img2_path': [image_paths[1]],
            'img3_path': [image_paths[2]],
            'img4_path': [image_paths[3]],
        })

        # Write to CSV (append if the file exists, create a new one if it doesn't)
        if not os.path.isfile(csv_file_path):
            df.to_csv(csv_file_path, index=False)
        else:
            df.to_csv(csv_file_path, mode='a', header=False, index=False)


    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
        
        print(images[0])

        return images[0]
    
    
    ### Description
    title = r"""
    <h1 align="center">Choose your AVATAR</h1>
    """

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

    article = r""""""

    tips = r""""""
    
    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) as demo:
        title = "<h1 align='center'>Choose your AVATAR</h1>"
        description = "<h2> Powered by IDfy </h2>"
        
        # 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")
                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="")
                submit1 = gr.Button("STORE", variant="primary")
            usage_tips = gr.Markdown(label="Usage tips of InstantID", value="", visible=False)

            # Image upload and processing chain
            face_file.upload(remove_tips, outputs=usage_tips).then(run_for_prompts1, inputs=[face_file, style], outputs=[gallery1]).then(run_for_prompts2, inputs=[face_file, style], outputs=[gallery2]).then(run_for_prompts3, inputs=[face_file, style], outputs=[gallery3]).then(run_for_prompts4, inputs=[face_file, style], outputs=[gallery4])
            submit.click(remove_tips, outputs=usage_tips).then(run_for_prompts1, inputs=[face_file, style], outputs=[gallery1]).then(run_for_prompts2, inputs=[face_file, style], outputs=[gallery2]).then(run_for_prompts3, inputs=[face_file, style], outputs=[gallery3]).then(run_for_prompts4, inputs=[face_file, style], outputs=[gallery4])

            # Store data on button click
            submit1.click(
                fn=store_images,
                inputs=[email,gallery1,gallery2,gallery3,gallery4],
                outputs=None)

        gr.Markdown("")

    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)