colab_demo.py

import torch
from torch import autocast
from diffusers import StableDiffusionInpaintPipeline
import gradio as gr
import traceback
import base64
from io import BytesIO
import os
import PIL
import json
import requests
import logging
import time
import warnings
warnings.filterwarnings("ignore")

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger('looks.studio')

# Model paths
SEGFORMER_MODEL = "mattmdjaga/segformer_b2_clothes"
STABLE_DIFFUSION_MODEL = "stabilityai/stable-diffusion-2-inpainting"

# Global variables for models
parser = None
model = None
inpainter = None

def get_device():
    if torch.cuda.is_available():
        device = "cuda"
        logger.info("Using GPU")
    else:
        device = "cpu"
        logger.info("Using CPU")
    return device

def init():
    global parser
    global model
    global inpainter

    start_time = time.time()
    logger.info("Starting application initialization")

    try:
        device = get_device()
        
        # Initialize Segformer parser
        logger.info("Initializing Segformer parser...")
        from parser.segformer_parser import SegformerParser
        parser = SegformerParser(SEGFORMER_MODEL)
        
        # Initialize Stable Diffusion model
        logger.info("Initializing Stable Diffusion model...")
        model = StableDiffusionInpaintPipeline.from_pretrained(
            STABLE_DIFFUSION_MODEL,
            safety_checker=None,
            revision="fp16" if device == "cuda" else None,
            torch_dtype=torch.float16 if device == "cuda" else torch.float32
        ).to(device)
        
        # Initialize inpainter
        logger.info("Initializing inpainter...")
        inpainter = ClothingInpainter(model=model, parser=parser)
        
        logger.info(f"Application initialized in {time.time() - start_time:.2f} seconds")
    except Exception as e:
        logger.error(f"Error initializing application: {str(e)}")
        raise e

class ClothingInpainter:
    def __init__(self, model_path=None, model=None, parser=None):
        self.device = get_device()
        
        if model_path is None and model is None:
            raise ValueError('No model provided!')
        if model_path is not None:
            self.pipe = StableDiffusionInpaintPipeline.from_pretrained(
                model_path,
                safety_checker=None,
                revision="fp16" if self.device == "cuda" else None,
                torch_dtype=torch.float16 if self.device == "cuda" else torch.float32
            ).to(self.device)
        else:
            self.pipe = model

        self.parser = parser

    def make_square(self, im, min_size=256, fill_color=(0, 0, 0, 0)):
        x, y = im.size
        size = max(min_size, x, y)
        new_im = PIL.Image.new('RGBA', (size, size), fill_color)
        new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))
        return new_im.convert('RGB')

    def unmake_square(self, init_im, op_im, min_size=256, rs_size=512):
        x, y = init_im.size
        size = max(min_size, x, y)
        factor = rs_size/size
        return op_im.crop((int((size-x) * factor / 2), int((size-y) * factor / 2),\
                                int((size+x) * factor / 2), int((size+y) * factor / 2)))

    def inpaint(self, prompt, init_image, parser=None) -> dict:
        image = self.make_square(init_image).resize((512,512))

        if self.parser is not None:
            mask = self.parser.get_image_mask(image)
            mask = mask.resize((512,512))
        elif parser is not None:
            mask = parser.get_image_mask(image)
            mask = mask.resize((512,512))
        else:
            raise ValueError('Image Parser is Missing')
        logger.info(f'[generated required mask(s) at {time.time()}]')

        # Run the model
        guidance_scale=7.5
        num_samples = 3
        with autocast("cuda"), torch.inference_mode():
            images = self.pipe(
                num_inference_steps = 50,
                prompt=prompt['pos'],
                image=image,
                mask_image=mask,
                guidance_scale=guidance_scale,
                num_images_per_prompt=num_samples,
            ).images
        
        images_output = []
        for img in images:
            ch = PIL.Image.composite(img,image, mask.convert('L'))
            fin_img = self.unmake_square(init_image, ch)
            images_output.append(fin_img)

        return images_output

def process_image(prompt, image):
    start_time = time.time()
    logger.info(f"Processing new request - Prompt: {prompt}, Image size: {image.size if image else 'None'}")
    
    try:
        if image is None:
            logger.error("No image provided")
            raise gr.Error("Please upload an image")
        if not prompt:
            logger.error("No prompt provided")
            raise gr.Error("Please enter a prompt")
            
        prompt_dict = {'pos': prompt}
        logger.info("Starting inpainting process")
        images = inpainter.inpaint(prompt_dict, image)
        
        if not images:
            logger.error("Inpainting failed to produce results")
            raise gr.Error("Failed to generate images. Please try again.")
            
        logger.info(f"Request processed in {time.time() - start_time:.2f} seconds")
        return images
    except Exception as e:
        logger.error(f"Error processing image: {str(e)}")
        raise gr.Error(f"Error processing image: {str(e)}")

# Initialize the model
init()

# Create Gradio interface
with gr.Blocks(title="Looks.Studio - AI Clothing Inpainting") as demo:
    gr.Markdown("# Looks.Studio - AI Clothing Inpainting")
    gr.Markdown("Upload an image and describe the clothing you want to generate")
    
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(
                type="pil", 
                label="Upload Image",
                height=512
            )
            prompt = gr.Textbox(label="Describe the clothing you want to generate")
            generate_btn = gr.Button("Generate")
        
        with gr.Column():
            gallery = gr.Gallery(
                label="Generated Images", 
                show_label=False,
                columns=2,
                height=512
            )
    
    generate_btn.click(
        fn=process_image,
        inputs=[prompt, input_image],
        outputs=gallery
    )

if __name__ == "__main__":
    demo.launch(share=True)