app.py

import cv2
import torch
import numpy as np
import gradio as gr
from diffusers import StableDiffusionXLPipeline
from insightface.app import FaceAnalysis
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
import os
import urllib.request
import time

# Allow network access for runtime download
os.environ["HF_HUB_OFFLINE"] = "0"

# Set device to CPU
device = "cpu"
dtype = torch.float32

# Load face encoder (InsightFace handles its own download)
try:
    face_app = FaceAnalysis(providers=["CPUExecutionProvider"])
    face_app.prepare(ctx_id=0, det_size=(480, 480))
    print("InsightFace model loaded successfully.")
except Exception as e:
    raise RuntimeError(f"Failed to load InsightFace model: {e}. Ensure network access for initial download.")

# Define paths for preloaded or downloaded weights
model_path = "./"  # Start with root
ip_adapter_path = "./"

# Debug: List files to confirm preloading or download
print("Files in root directory:", os.listdir("."))
print("Files in ./unet/ directory:", os.listdir("./unet") if os.path.exists("./unet") else "No ./unet/ directory")

# Check if base model weights exist or download them
kolors_weights = model_path + "diffusers_weights.safetensors"
if not os.path.exists(kolors_weights):
    kolors_weights = model_path + "diffusion_pytorch_model.fp16.safetensors"
    if not os.path.exists(kolors_weights):
        kolors_weights_unet = "./unet/diffusion_pytorch_model.fp16.safetensors"
        if not os.path.exists(kolors_weights_unet):
            print("Preloading failed. Attempting runtime download with retry...")
            os.makedirs("./unet", exist_ok=True)
            max_retries = 3
            correct_url = "https://huggingface.co/Kwai-Kolors/Kolors/raw/main/unet/diffusion_pytorch_model.fp16.safetensors"
            for attempt in range(max_retries):
                try:
                    print(f"Download attempt {attempt + 1} of {max_retries}")
                    urllib.request.urlretrieve(correct_url, kolors_weights_unet)
                    print("Kolors base weights downloaded to", kolors_weights_unet)
                    model_path = "./unet/"
                    kolors_weights = kolors_weights_unet
                    break
                except urllib.error.HTTPError as e:
                    print(f"Download attempt {attempt + 1} failed: HTTP Error {e.code} - {e.reason}")
                    if attempt < max_retries - 1:
                        time.sleep(5)
                    else:
                        raise FileNotFoundError(f"Failed to download Kolors base weights after {max_retries} attempts: HTTP Error {e.code} - {e.reason}. Verify the URL or contact support.")
                except Exception as e:
                    print(f"Download attempt {attempt + 1} failed: {e}")
                    if attempt < max_retries - 1:
                        time.sleep(5)
                    else:
                        raise FileNotFoundError(f"Failed to download Kolors base weights after {max_retries} attempts: {e}. Check network access or contact support.")
        else:
            model_path = "./unet/"
            kolors_weights = kolors_weights_unet

# Check if IP-Adapter weights exist (preloaded)
if not os.path.exists(ip_adapter_path + "ipa-faceid-plus.bin"):
    raise FileNotFoundError(f"IP-Adapter weights not found at {ip_adapter_path}")

# Initialize model with empty weights
with init_empty_weights():
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus",
        torch_dtype=dtype,
        safety_checker=None,
    )

# Load and dispatch model with accelerate
pipe = load_checkpoint_and_dispatch(pipe, model_path, device_map="cpu", offload_folder=None)
pipe.load_ip_adapter("Kwai-Kolors/Kolors-IP-Adapter-FaceID-Plus", subfolder=None, weight_name="ipa-faceid-plus.bin")

def generate_image(uploaded_image, prompt):
    img = cv2.cvtColor(np.array(uploaded_image), cv2.COLOR_RGB2BGR)
    faces = face_app.get(img)
    if not faces:
        return "No face detected!", None

    face_info = faces[-1]
    face_emb = face_info["embedding"]

    try:
        image = pipe(
            prompt=prompt,
            image_embeds=face_emb,
            num_inference_steps=20,
            guidance_scale=7.5,
            height=512,
            width=512,
        ).images[0]
        return "Image generated successfully!", image
    except Exception as e:
        return f"Generation failed: {e}", None

interface = gr.Interface(
    fn=generate_image,
    inputs=[gr.Image(type="pil", label="Upload Reference Image"), gr.Textbox(label="Enter Prompt", placeholder="e.g., A photorealistic astronaut in space")],
    outputs=[gr.Textbox(label="Status"), gr.Image(label="Generated Image")],
    title="Face Reference Image Generator (Kolors-IP-Adapter-FaceID-Plus)",
    description="Upload an image with a face, enter a prompt, and generate a new image preserving the reference face."
)

interface.launch()