Update app.py

app.py

@@ -1,26 +1,25 @@
-# app.py - FIX FÜR BILD-ANZEIGE
+# app.py - FIX FÜR TIMEOUT PROBLEM
 import gradio as gr
 from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 import torch
 from PIL import Image
-import io
+import time
 
 # === OPTIMIERTE EINSTELLUNGEN ===
 device = "cuda" if torch.cuda.is_available() else "cpu"
 torch_dtype = torch.float16 if device == "cuda" else torch.float32
-IMG_SIZE = 320
-STEPS = 20
+IMG_SIZE = 256  # Noch kleiner für schnellere Generierung
+STEPS = 15      # Noch weniger Schritte
 
 print(f"Running on: {device}")
 
 # === PIPELINES ===
 pipe_txt2img = None
-pipe_img2img = None
 
-def load_txt2img():
+def load_pipeline():
     global pipe_txt2img
     if pipe_txt2img is None:
-        print("Loading Text-to-Image model...")
+        print("Loading model...")
         pipe_txt2img = StableDiffusionPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5",
             torch_dtype=torch_dtype,
@@ -31,108 +30,68 @@ def load_txt2img():
         pipe_txt2img.enable_attention_slicing()
     return pipe_txt2img
 
-def load_img2img():
-    global pipe_img2img
-    if pipe_img2img is None:
-        print("Loading Img2Img model...")
-        pipe_img2img = StableDiffusionImg2ImgPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
-            torch_dtype=torch_dtype,
-            use_safetensors=True,
-            safety_checker=None,
-            requires_safety_checker=False
-        ).to(device)
-        pipe_img2img.enable_attention_slicing()
-    return pipe_img2img
-
 # === FUNKTIONEN ===
 def text_to_image(prompt):
     try:
-        if not prompt.strip():
+        if not prompt or not prompt.strip():
             return None
             
-        print(f"Generating: {prompt}")
-        pipe = load_txt2img()
+        print(f"Starting generation for: {prompt}")
+        start_time = time.time()
         
-        # Direkte Generierung ohne komplexe Parameter
+        pipe = load_pipeline()
+        
+        # Schnellste mögliche Generierung
         image = pipe(
             prompt=prompt,
             height=IMG_SIZE,
             width=IMG_SIZE,
             num_inference_steps=STEPS,
-            guidance_scale=7.5
+            guidance_scale=7.0,  # Niedriger für schnellere Konvergenz
         ).images[0]
         
-        print("✅ Bild erfolgreich generiert")
-        return image
-        
-    except Exception as e:
-        print(f"❌ Fehler: {e}")
-        return None
-
-def img_to_image(image, prompt="", strength=0.75):
-    try:
-        if image is None:
-            return None
-            
-        pipe = load_img2img()
-        img_resized = image.convert("RGB").resize((IMG_SIZE, IMG_SIZE))
-        
-        image = pipe(
-            prompt=prompt or "beautiful landscape",
-            image=img_resized,
-            strength=strength,
-            num_inference_steps=STEPS,
-            guidance_scale=7.5
-        ).images[0]
+        end_time = time.time()
+        print(f"✅ Bild generiert in {end_time - start_time:.2f} Sekunden")
         
+        # Sofortige Rückgabe
         return image
         
     except Exception as e:
         print(f"❌ Fehler: {e}")
+        import traceback
+        traceback.print_exc()
         return None
 
-# === UI ===
-with gr.Blocks(title="AI Bild Generator") as app:
+# === UI MIT EXPLIZITER QUEUE ===
+with gr.Blocks() as app:
     gr.Markdown("# 🎨 AI Bild Generator")
     
     with gr.Tab("Text zu Bild"):
-        gr.Markdown("**Eingabe:**")
+        gr.Markdown("**Eingabe (Englisch):**")
         txt_input = gr.Textbox(
-            label="Beschreibung",
-            placeholder="Beschreibe dein Bild auf Englisch...",
+            placeholder="z.B. a red apple on a table",
             lines=2
         )
-        generate_btn = gr.Button("🎨 Bild generieren", variant="primary")
-        txt_output = gr.Image(label="Ergebnis", show_label=True)
+        generate_btn = gr.Button("🎨 Generieren", variant="primary")
+        txt_output = gr.Image(label="Ergebnis")
         
         generate_btn.click(
             fn=text_to_image,
             inputs=txt_input,
-            outputs=txt_output
-        )
-    
-    with gr.Tab("Bild zu Bild"):
-        gr.Markdown("**Bild hochladen und verändern:**")
-        img_input = gr.Image(type="pil", label="Eingabebild")
-        img_prompt = gr.Textbox(
-            label="Transformations-Prompt",
-            placeholder="Wie soll das Bild verändert werden?",
-            lines=2
-        )
-        strength_slider = gr.Slider(0.6, 0.9, 0.75, label="Stärke der Veränderung")
-        transform_btn = gr.Button("🔄 Bild transformieren", variant="primary")
-        img_output = gr.Image(label="Transformiertes Bild", show_label=True)
-        
-        transform_btn.click(
-            fn=img_to_image,
-            inputs=[img_input, img_prompt, strength_slider],
-            outputs=img_output
+            outputs=txt_output,
+            # Wichtig: Queue konfigurieren
+            concurrency_limit=1
         )
 
-# SIMPLER APP START
+# === LAUNCH MIT OPTIMIERTEN TIMEOUTS ===
 app.launch(
     share=True,
-    server_name="0.0.0.0", 
-    server_port=7860
+    server_name="0.0.0.0",
+    server_port=7860,
+    # Wichtige Timeout-Einstellungen:
+    max_file_size="10MB",
+    # Verhindert "Connection re-established":
+    ssl_verify=False,
+    quiet=True,
+    show_error=True
 )