app.py

import spaces
import logging
import random
import warnings
import os
import shutil
import subprocess
import torch
import numpy as np
from diffusers import FluxControlNetModel
from diffusers.pipelines import FluxControlNetPipeline
from PIL import Image
from huggingface_hub import snapshot_download, login
import io
import base64
from fastapi import FastAPI, File, UploadFile,Form
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from concurrent.futures import ThreadPoolExecutor
import uvicorn
import asyncio
import time  # Import time module for measuring execution time

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# FastAPI app for image processing
app = FastAPI()
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])

# ThreadPoolExecutor for managing image processing threads
executor = ThreadPoolExecutor()

#Determine the device (GPU or CPU)
if torch.cuda.is_available():
    device = "cuda"
    logger.info("CUDA is available. Using GPU.")
else:
    device = "cpu"
    logger.info("CUDA is not available. Using CPU.")

# Load model from Huggingface Hub
huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
if huggingface_token:
    login(token=huggingface_token)
    logger.info("Hugging Face token found and logged in.")
else:
    logger.warning("Hugging Face token not found in environment variables.")

# Download model using snapshot_download
model_path = snapshot_download(
    repo_id="black-forest-labs/FLUX.1-dev",
    repo_type="model",
    ignore_patterns=["*.md", "*..gitattributes"],
    local_dir="FLUX.1-dev",
    token=huggingface_token
)
logger.info("Model downloaded to: %s", model_path)

# Load pipeline
logger.info('Loading ControlNet model.')
cache_dir = "./model_cache"  
controlnet = FluxControlNetModel.from_pretrained(
    "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16,cache_dir=cache_dir
).to(device)
logger.info("ControlNet model loaded successfully.")

logger.info('Loading pipeline.')
pipe = FluxControlNetPipeline.from_pretrained(
    model_path, controlnet=controlnet, torch_dtype=torch.bfloat16,cache_dir=cache_dir
).to(device)
logger.info("Pipeline loaded successfully.")

MAX_SEED = 1000000
MAX_PIXEL_BUDGET = 1024 * 1024

#@spaces.GPU
def process_input(input_image, upscale_factor):
    w, h = input_image.size
    aspect_ratio = w / h
    was_resized = False

    # Resize if input size exceeds the maximum pixel budget
    if w * h * upscale_factor**2 > MAX_PIXEL_BUDGET:
        warnings.warn("Requested output image is too large. Resizing to fit within pixel budget.")
        input_image = input_image.resize(
            (
                int(aspect_ratio * MAX_PIXEL_BUDGET**0.5 // upscale_factor),
                int(MAX_PIXEL_BUDGET**0.5 // aspect_ratio // upscale_factor),
            )
        )
        was_resized = True

    # Adjust dimensions to be a multiple of 8
    w, h = input_image.size
    w = w - w % 8
    h = h - h % 8

    return input_image.resize((w, h)), was_resized

#@spaces.GPU
def run_inference(input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale):
    logger.info("Processing inference.")
    input_image, was_resized = process_input(input_image, upscale_factor)

    # Rescale image for ControlNet processing
    w, h = input_image.size
    control_image = input_image.resize((w * upscale_factor, h * upscale_factor))

    # Set the random generator for inference
    generator = torch.Generator().manual_seed(seed)

    # Perform inference using the pipeline
    logger.info("Running pipeline.")
    image = pipe(
        prompt="",
        control_image=control_image,
        controlnet_conditioning_scale=controlnet_conditioning_scale,
        num_inference_steps=num_inference_steps,
        guidance_scale=3.5,
        height=control_image.size[1],
        width=control_image.size[0],
        generator=generator,
    ).images[0]

    # Resize output image back to the original dimensions if needed
    if was_resized:
        original_size = (input_image.width * upscale_factor, input_image.height * upscale_factor)
        image = image.resize(original_size)

    # Convert the output image to base64
    buffered = io.BytesIO()
    image.save(buffered, format="JPEG")
    image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")

    return image_base64

@app.post("/infer")
async def infer(input_image: UploadFile = File(...), 
                upscale_factor: int = Form(4),  # Default value of 4
                seed: int = Form(42),            # Default value of 42
                num_inference_steps: int = Form(28),  # Default value of 28
                controlnet_conditioning_scale: float = Form(0.6)):
    logger.info("Received request for inference.")
    
    # Start timing the entire inference process
    start_time = time.time()

    # Read the uploaded image
    contents = await input_image.read()
    print(type(contents))
    contents = bytes(contents)
    image = Image.open(io.BytesIO(contents))

    # Get the current event loop
    loop = asyncio.get_event_loop()

    # Run inference in a separate thread
    base64_image = await loop.run_in_executor(executor, run_inference, image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale)

    # Calculate the time taken
    time_taken = time.time() - start_time

    return JSONResponse(content={"base64_image": base64_image, "time_taken": time_taken})

if __name__ == "__main__":
    # Start FastAPI server
    uvicorn.run(app, host="0.0.0.0", port=7860)