Update app.py

app.py

@@ -1,68 +1,78 @@
-import torch
-from diffusers import FluxPipeline
 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_flux_image(
-    prompt,
-    width=512,
-    height=512,
+def generate_image(
+    prompt, 
+    width=512, 
+    height=512, 
     num_inference_steps=50,
-    guidance_scale=7.5,
-    seed=None
+    guidance_scale=7.5
 ):
-    # Zufallsgenerator-Initialisierung
-    if seed is None:
-        generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu")
-    else:
-        generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
-    
-    # Pipeline laden
-    pipeline = FluxPipeline.from_pretrained(
-        "models/enhanceaiteam/Flux-uncensored",  # Korrigierter Pfad
-        torch_dtype=torch.float16
-    )
-    
-    # Überprüfen und ggf. auf GPU verschieben
-    if torch.cuda.is_available():
-        pipeline = pipeline.to("cuda")
-    
-    # Bildgenerierung
-    image = pipeline(
-        prompt=prompt,
-        width=width,
-        height=height,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        generator=generator
-    ).images[0]
-    
-    return image
+    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
+# 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")
+            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")
-            seed = gr.Number(label="Seed (optional)", precision=0)
         
-        generate_btn = gr.Button("Bild generieren")
+        generate_btn = gr.Button("Bild generieren", variant="primary")
         output_image = gr.Image(label="Generiertes Bild")
         
         generate_btn.click(
-            fn=generate_flux_image,
-            inputs=[prompt, width, height, steps, guidance, seed],
+            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
 
-# Interface starten
-if __name__ == "__main__":
+# Hauptausführung
+def main():
     interface = create_gradio_interface()
-    interface.launch(share=True)
+    interface.launch(
+        share=True,  # Für öffentlichen Zugang
+        debug=True   # Detaillierte Fehlermeldungen
+    )
+
+if __name__ == "__main__":
+    main()