app.py

#
import gradio as gr
from diffusers import DiffusionPipeline
import torch

# Pipeline global vorinitialisieren für bessere Performance
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev")
pipe.load_lora_weights("enhanceaiteam/Flux-uncensored")

# GPU-Optimierung
if torch.cuda.is_available():
    pipe = pipe.to("cuda")
    pipe.enable_model_cpu_offload()  # Zusätzliche Speicheroptimierung

def generate_image(
    prompt, 
    width=512, 
    height=512, 
    num_inference_steps=50,
    guidance_scale=7.5
):
    try:
        image = pipe(
            prompt=prompt, 
            width=width, 
            height=height,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale
        ).images[0]
        return image
    except Exception as e:
        print(f"Fehler bei Bildgenerierung: {e}")
        return None

# Gradio-Interface mit erweiterten Optionen
def create_gradio_interface():
    with gr.Blocks() as demo:
        gr.Markdown("# Flux Bildgenerator")
        
        with gr.Row():
            prompt = gr.Textbox(label="Bildprompt", lines=2)
            width = gr.Slider(minimum=64, maximum=2048, value=512, label="Breite")
            height = gr.Slider(minimum=64, maximum=2048, value=512, label="Höhe")
        
        with gr.Row():
            steps = gr.Slider(minimum=10, maximum=100, value=50, label="Inference Steps")
            guidance = gr.Slider(minimum=1, maximum=15, value=7.5, label="Guidance Scale")
        
        generate_btn = gr.Button("Bild generieren", variant="primary")
        output_image = gr.Image(label="Generiertes Bild")
        
        generate_btn.click(
            fn=generate_image, 
            inputs=[prompt, width, height, steps, guidance],
            outputs=output_image
        )
        
        # Beispiel-Prompts
        gr.Examples(
            examples=[
                ["Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"],
                ["Cyberpunk city landscape at night"],
                ["Realistic portrait of a wolf in mountain terrain"]
            ],
            inputs=[prompt]
        )
    
    return demo

# Hauptausführung
def main():
    interface = create_gradio_interface()
    interface.launch(
        share=True,  # Für öffentlichen Zugang
        debug=True   # Detaillierte Fehlermeldungen
    )

if __name__ == "__main__":
    main()