Update app.py

app.py

@@ -3,12 +3,20 @@ import gradio as gr
 from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 import torch
 from PIL import Image
+import traceback
+import logging
+
+# Logging aktivieren
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # === AUTO HARDWARE ===
 device = "cuda" if torch.cuda.is_available() else "cpu"
 torch_dtype = torch.float16 if device == "cuda" else torch.float32
-IMG_SIZE = 512 if device == "cuda" else 384
-STEPS = 50 if device == "cuda" else 40
+IMG_SIZE = 384  # Konservative Größe für Stabilität
+STEPS = 30 if device == "cuda" else 25
+
+print(f"Device: {device}, Torch dtype: {torch_dtype}")
 
 # === GLOBALE VARIABLEN ===
 pipe_txt2img = None
@@ -17,32 +25,52 @@ pipe_img2img = None
 def load_txt2img():
     global pipe_txt2img
     if pipe_txt2img is None:
-        print("Lade Text-to-Image Pipeline...")
-        pipe_txt2img = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", 
-            torch_dtype=torch_dtype,
-            # Kompatibilität mit diffusers 0.29.2
-            use_safetensors=True
-        ).to(device)
-        pipe_txt2img.enable_attention_slicing()
+        try:
+            print("Lade Text-to-Image Pipeline...")
+            pipe_txt2img = StableDiffusionPipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5", 
+                torch_dtype=torch_dtype,
+                use_safetensors=True,
+                variant="fp16" if device == "cuda" else None
+            )
+            pipe_txt2img = pipe_txt2img.to(device)
+            pipe_txt2img.enable_attention_slicing()
+            print("Text-to-Image Pipeline erfolgreich geladen")
+        except Exception as e:
+            print(f"Fehler beim Laden der Pipeline: {e}")
+            traceback.print_exc()
+            raise
     return pipe_txt2img
 
 def load_img2img():
     global pipe_img2img
     if pipe_img2img is None:
-        print("Lade Img2Img Pipeline...")
-        pipe_img2img = StableDiffusionImg2ImgPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", 
-            torch_dtype=torch_dtype,
-            use_safetensors=True
-        ).to(device)
-        pipe_img2img.enable_attention_slicing()
+        try:
+            print("Lade Img2Img Pipeline...")
+            pipe_img2img = StableDiffusionImg2ImgPipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5", 
+                torch_dtype=torch_dtype,
+                use_safetensors=True,
+                variant="fp16" if device == "cuda" else None
+            )
+            pipe_img2img = pipe_img2img.to(device)
+            pipe_img2img.enable_attention_slicing()
+            print("Img2Img Pipeline erfolgreich geladen")
+        except Exception as e:
+            print(f"Fehler beim Laden der Img2Img Pipeline: {e}")
+            traceback.print_exc()
+            raise
     return pipe_img2img
 
 # === FUNKTIONEN ===
 def text_to_image(prompt):
     try:
+        if not prompt or prompt.strip() == "":
+            return None
+            
+        print(f"Generiere Bild für Prompt: {prompt}")
         pipe = load_txt2img()
+        
         result = pipe(
             prompt=prompt,
             height=IMG_SIZE,
@@ -50,15 +78,23 @@ def text_to_image(prompt):
             num_inference_steps=STEPS,
             guidance_scale=7.5,
         )
+        
+        print("Bild erfolgreich generiert")
         return result.images[0]
+        
     except Exception as e:
         print(f"Fehler in text_to_image: {e}")
+        traceback.print_exc()
         return None
 
 def img_to_image(image, prompt="", neg_prompt="", strength=0.75):
     try:
+        if image is None:
+            return None
+            
         pipe = load_img2img()
         img = image.convert("RGB").resize((IMG_SIZE, IMG_SIZE))
+        
         result = pipe(
             prompt=prompt or "snowy winter landscape",
             negative_prompt=neg_prompt or "blur, furniture, grass, table",
@@ -67,36 +103,73 @@ def img_to_image(image, prompt="", neg_prompt="", strength=0.75):
             guidance_scale=7.5,
             num_inference_steps=STEPS
         )
+        
         return result.images[0]
+        
     except Exception as e:
         print(f"Fehler in img_to_image: {e}")
+        traceback.print_exc()
         return None
 
 # === UI ===
 with gr.Blocks() as app:
-    gr.Markdown("# Text-to-Image + Img2Img\n**Optimierte Version für bessere Ergebnisse**")
+    gr.Markdown("# Text-to-Image + Img2Img\n**Stabile Version für dein Environment**")
     
     with gr.Tab("Text to Image"):
-        gr.Markdown("### Tipp: Verwende englische Prompts für beste Ergebnisse")
-        txt_in = gr.Textbox(
-            placeholder="z.B. 'a snowman in a forest, winter, snowy trees, highly detailed'",
-            lines=2,
-            value="a snowman in a forest, winter, snowy trees, highly detailed"
+        gr.Markdown("### Wichtige Tipps:")
+        gr.Markdown("- Verwende englische Prompts")
+        gr.Markdown("- Erste Generation kann länger dauern (Modell-Download)")
+        
+        with gr.Row():
+            txt_in = gr.Textbox(
+                label="Prompt (englisch)",
+                placeholder="a snowman in a forest, winter, snowy trees, highly detailed",
+                lines=2,
+                value="a snowman in a forest, winter, snowy trees, highly detailed"
+            )
+        with gr.Row():
+            txt_out = gr.Image(label="Ergebnis", height=400)
+        with gr.Row():
+            txt_btn = gr.Button("✨ Bild generieren", variant="primary")
+        
+        txt_btn.click(
+            fn=text_to_image,
+            inputs=txt_in,
+            outputs=txt_out
         )
-        txt_out = gr.Image(label="Ergebnis")
-        txt_btn = gr.Button("Generieren")
-        txt_btn.click(text_to_image, txt_in, txt_out)
     
     with gr.Tab("Bild zu Schneelandschaft"):
-        img_in = gr.Image(type="pil")
-        prompt = gr.Textbox(
-            value="girl hugging snowman in snowy landscape, realistic, winter",
-            lines=2
+        with gr.Row():
+            img_in = gr.Image(type="pil", label="Eingabebild")
+        with gr.Row():
+            prompt = gr.Textbox(
+                label="Prompt",
+                value="girl hugging snowman in snowy landscape, realistic, winter",
+                lines=2
+            )
+        with gr.Row():
+            neg = gr.Textbox(
+                label="Negative Prompts", 
+                value="table, tarp, grass, blur, furniture", 
+                lines=2
+            )
+        with gr.Row():
+            strength = gr.Slider(0.6, 0.9, 0.75, label="Strength")
+        with gr.Row():
+            img_out = gr.Image(label="Ergebnis", height=400)
+        with gr.Row():
+            img_btn = gr.Button("✨ Bild transformieren", variant="primary")
+        
+        img_btn.click(
+            fn=img_to_image,
+            inputs=[img_in, prompt, neg, strength],
+            outputs=img_out
         )
-        neg = gr.Textbox(value="table, tarp, grass, blur, furniture", lines=2)
-        strength = gr.Slider(0.6, 0.9, 0.75)
-        img_out = gr.Image(label="Ergebnis")
-        img_btn = gr.Button("Generieren")
-        img_btn.click(img_to_image, [img_in, prompt, neg, strength], img_out)
 
-app.launch(share=True)
+# Server-Konfiguration für bessere Stabilität
+app.launch(
+    share=True,
+    server_name="0.0.0.0",
+    server_port=7860,
+    show_error=True
+)