app.py

import os
import json
import torch
import gc
import numpy as np
import gradio as gr
from PIL import Image
from diffusers import StableDiffusionXLPipeline
import open_clip
from huggingface_hub import hf_hub_download
from IP_Composer.IP_Adapter.ip_adapter import IPAdapterXL
from IP_Composer.perform_swap import compute_dataset_embeds_svd, get_modified_images_embeds_composition

device = "cuda" if torch.cuda.is_available() else "cpu"

# Initialize SDXL pipeline
base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = StableDiffusionXLPipeline.from_pretrained(
    base_model_path,
    torch_dtype=torch.float16,
    add_watermarker=False,
)

# Initialize IP-Adapter
image_encoder_repo = 'h94/IP-Adapter'
image_encoder_subfolder = 'models/image_encoder'
ip_ckpt = hf_hub_download('h94/IP-Adapter', subfolder="sdxl_models", filename='ip-adapter_sdxl_vit-h.bin')
ip_model = IPAdapterXL(pipe, image_encoder_repo, image_encoder_subfolder, ip_ckpt, device)

# Initialize CLIP model
clip_model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:laion/CLIP-ViT-H-14-laion2B-s32B-b79K')
clip_model.to(device)
print("Models initialized successfully!")

def get_image_embeds(pil_image, model=clip_model, preproc=preprocess, dev=device):
    """Get CLIP image embeddings for a given PIL image"""
    image = preproc(pil_image)[np.newaxis, :, :, :]
    with torch.no_grad():
        embeds = model.encode_image(image.to(dev))
    return embeds.cpu().detach().numpy()

def process_images(
    base_image, 
    concept_image1, concept_desc1,
    concept_image2=None, concept_desc2=None,
    concept_image3=None, concept_desc3=None,
    rank1=10, rank2=10, rank3=10,
    prompt=None, 
    scale=1.0,
    seed=420
):
    """Process the base image and concept images to generate modified images"""
    # Process base image
    base_image_pil = Image.fromarray(base_image).convert("RGB")
    base_embed = get_image_embeds(base_image_pil)
    
    # Process concept images
    concept_images = []
    concept_descriptions = []
    
    # Add first concept (required)
    if concept_image1 is not None:
        concept_images.append(concept_image1)
        concept_descriptions.append(concept_desc1 if concept_desc1 else "Concept 1")
    else:
        return None, "Please upload at least one concept image"
    
    # Add second concept (optional)
    if concept_image2 is not None:
        concept_images.append(concept_image2)
        concept_descriptions.append(concept_desc2 if concept_desc2 else "Concept 2")
    
    # Add third concept (optional)
    if concept_image3 is not None:
        concept_images.append(concept_image3)
        concept_descriptions.append(concept_desc3 if concept_desc3 else "Concept 3")
    
    # Get all ranks
    ranks = [rank1]
    if concept_image2 is not None:
        ranks.append(rank2)
    if concept_image3 is not None:
        ranks.append(rank3)
    
    concept_embeds = []
    for img in concept_images:
        if img is not None:
            img_pil = Image.fromarray(img).convert("RGB")
            concept_embeds.append(get_image_embeds(img_pil))
    
    # Compute projection matrices
    projection_matrices = []
    for i, embed in enumerate(concept_embeds):
        # For a single image, we need to reshape to have the same format as a collection
        single_embed = embed.reshape(1, *embed.shape)
        projection_matrix = compute_dataset_embeds_svd(single_embed, ranks[i])
        projection_matrices.append(projection_matrix)
    
    # Create projection data structure for the composition
    projections_data = [
        {
            "embed": embed,
            "projection_matrix": proj_matrix
        }
        for embed, proj_matrix in zip(concept_embeds, projection_matrices)
    ]
    
    # Generate modified images - 
    modified_images = get_modified_images_embeds_composition(
        base_embed, 
        projections_data, 
        ip_model, 
        prompt=prompt, 
        scale=scale, 
        num_samples=1, 
        seed=seed
    )
    
    return modified_images

def process_and_display(
    base_image, 
    concept_image1, concept_desc1,
    concept_image2=None, concept_desc2=None,
    concept_image3=None, concept_desc3=None,
    rank1=10, rank2=10, rank3=10,
    prompt=None, scale=1.0, seed=420
):
    """Wrapper for process_images that handles UI updates"""
    if base_image is None:
        return None, "Please upload a base image"
    
    if concept_image1 is None:
        return None, "Please upload at least one concept image"
    
    modified_images = process_images(
        base_image, 
        concept_image1, concept_desc1,
        concept_image2, concept_desc2,
        concept_image3, concept_desc3,
        rank1, rank2, rank3, 
        prompt, scale, seed
    )
    
    # # Clean up memory
    # torch.cuda.empty_cache()
    # gc.collect()
    
    return modified_images

with gr.Blocks(title="Image Concept Composition") as demo:
        gr.Markdown("# IP Composer")
        gr.Markdown("")
        
        with gr.Row():
            with gr.Column():
                base_image = gr.Image(label="Base Image (Required)", type="numpy")
                
                with gr.Row():
                    with gr.Column(scale=2):
                        concept_image1 = gr.Image(label="Concept Image 1 (Required)", type="numpy")
                    with gr.Column(scale=1):
                        concept_desc1 = gr.Textbox(label="Concept 1 Description", placeholder="Describe this concept")
                        rank1 = gr.Slider(minimum=1, maximum=50, value=10, step=1, label="Rank 1")
                
                with gr.Row():
                    with gr.Column(scale=2):
                        concept_image2 = gr.Image(label="Concept Image 2 (Optional)", type="numpy")
                    with gr.Column(scale=1):
                        concept_desc2 = gr.Textbox(label="Concept 2 Description", placeholder="Describe this concept")
                        rank2 = gr.Slider(minimum=1, maximum=50, value=10, step=1, label="Rank 2")
                
                with gr.Row():
                    with gr.Column(scale=2):
                        concept_image3 = gr.Image(label="Concept Image 3 (Optional)", type="numpy")
                    with gr.Column(scale=1):
                        concept_desc3 = gr.Textbox(label="Concept 3 Description", placeholder="Describe this concept")
                        rank3 = gr.Slider(minimum=1, maximum=50, value=10, step=1, label="Rank 3")
                
                prompt = gr.Textbox(label="Guidance Prompt (Optional)", placeholder="Optional text prompt to guide generation")
                
                with gr.Row():
                    scale = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Scale")
                    seed = gr.Number(value=420, label="Seed", precision=0)
                
                submit_btn = gr.Button("Generate")
            
            with gr.Column():
                output_image = gr.Image(label="composed output", show_label=True)
        
        submit_btn.click(
            fn=process_and_display,
            inputs=[
                base_image, 
                concept_image1, concept_desc1,
                concept_image2, concept_desc2,
                concept_image3, concept_desc3,
                rank1, rank2, rank3, 
                prompt, scale, seed
            ],
            outputs=[output_image]
        )
    
    

    demo.launch()