app.py

import spaces
import gradio as gr
import random
import os
import time
import torch
from diffusers import FluxPipeline

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {DEVICE}")

DEFAULT_HEIGHT = 1024
DEFAULT_WIDTH = 1024
DEFAULT_GUIDANCE_SCALE = 3.5
DEFAULT_NUM_INFERENCE_STEPS = 15
DEFAULT_MAX_SEQUENCE_LENGTH = 512
HF_TOKEN = os.environ.get("HF_ACCESS_TOKEN")

# Cache for the pipeline
CACHED_PIPE = None

def load_bnb_4bit_pipeline():
    """Load the 4-bit quantized pipeline"""
    global CACHED_PIPE
    if CACHED_PIPE is not None:
        return CACHED_PIPE
    
    print("Loading 4-bit BNB pipeline...")
    MODEL_ID = "derekl35/FLUX.1-dev-nf4"
    
    start_time = time.time()
    try:
        pipe = FluxPipeline.from_pretrained(
            MODEL_ID,
            torch_dtype=torch.bfloat16
        )
        pipe.enable_model_cpu_offload()
        end_time = time.time()
        mem_reserved = torch.cuda.memory_reserved(0)/1024**3 if DEVICE == "cuda" else 0
        print(f"4-bit BNB pipeline loaded in {end_time - start_time:.2f}s. Memory reserved: {mem_reserved:.2f} GB")
        CACHED_PIPE = pipe
        return pipe
    except Exception as e:
        print(f"Error loading 4-bit BNB pipeline: {e}")
        raise

@spaces.GPU(duration=240)
def generate_image(prompt, progress=gr.Progress(track_tqdm=True)):
    """Generate image using 4-bit quantized model"""
    if not prompt:
        return None, "Please enter a prompt."
    
    progress(0.2, desc="Loading 4-bit quantized model...")
    
    try:
        # Load the 4-bit pipeline
        pipe = load_bnb_4bit_pipeline()
        
        # Set up generation parameters
        pipe_kwargs = {
            "prompt": prompt,
            "height": DEFAULT_HEIGHT,
            "width": DEFAULT_WIDTH,
            "guidance_scale": DEFAULT_GUIDANCE_SCALE,
            "num_inference_steps": DEFAULT_NUM_INFERENCE_STEPS,
            "max_sequence_length": DEFAULT_MAX_SEQUENCE_LENGTH,
        }
        
        # Generate seed
        seed = random.getrandbits(64)
        print(f"Using seed: {seed}")
        
        progress(0.5, desc="Generating image...")
        
        # Generate image
        gen_start_time = time.time()
        image = pipe(**pipe_kwargs, generator=torch.manual_seed(seed)).images[0]
        gen_end_time = time.time()
        
        print(f"Image generated in {gen_end_time - gen_start_time:.2f} seconds")
        mem_reserved = torch.cuda.memory_reserved(0)/1024**3 if DEVICE == "cuda" else 0
        print(f"Memory reserved: {mem_reserved:.2f} GB")
        
        return image, f"Generation complete! (Seed: {seed})"
        
    except Exception as e:
        print(f"Error during generation: {e}")
        return None, f"Error: {e}"

# Create Gradio interface
with gr.Blocks(title="FLUXllama", theme=gr.themes.Soft()) as demo:
    gr.HTML(
        """
        <div style='text-align: center; margin-bottom: 20px;'>
            <h1>FLUXllama</h1>
            <p>FLUX.1-dev 4-bit Quantized Version</p>
        </div>
        """
    )
    
    gr.HTML(
        """
        <div class='container' style='display:flex; justify-content:center; gap:12px; margin-bottom: 20px;'>
            <a href="https://huggingface.co/spaces/openfree/Best-AI" target="_blank">
                <img src="https://img.shields.io/static/v1?label=OpenFree&message=BEST%20AI%20Services&color=%230000ff&labelColor=%23000080&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="OpenFree badge">
            </a>
    
            <a href="https://discord.gg/openfreeai" target="_blank">
                <img src="https://img.shields.io/static/v1?label=Discord&message=Openfree%20AI&color=%230000ff&labelColor=%23800080&logo=discord&logoColor=white&style=for-the-badge" alt="Discord badge">
            </a>
        </div>
        """
    )
    
    with gr.Row():
        prompt_input = gr.Textbox(
            label="Enter your prompt",
            placeholder="e.g., A photorealistic portrait of an astronaut on Mars",
            lines=2,
            scale=4
        )
        generate_button = gr.Button("Generate", variant="primary", scale=1)
    
    output_image = gr.Image(
        label="Generated Image (4-bit Quantized)",
        type="pil",
        height=600
    )
    
    status_text = gr.Textbox(
        label="Status",
        interactive=False,
        lines=1
    )
    
    # Connect components
    generate_button.click(
        fn=generate_image,
        inputs=[prompt_input],
        outputs=[output_image, status_text]
    )
    
    # Enter key to submit
    prompt_input.submit(
        fn=generate_image,
        inputs=[prompt_input],
        outputs=[output_image, status_text]
    )
    
    # Example prompts
    gr.Examples(
        examples=[
            "A photorealistic portrait of an astronaut on Mars",
            "Water-color painting of a cat wearing sunglasses",
            "Neo-tokyo cyberpunk cityscape at night, rain-soaked streets, 8K",
            "A majestic dragon flying over a medieval castle at sunset",
            "Abstract art representing the concept of time and space",
            "Detailed oil painting of a steampunk clockwork city",
            "Underwater scene with bioluminescent creatures in deep ocean",
            "Japanese garden in autumn with falling maple leaves"
        ],
        inputs=prompt_input
    )

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