Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 from diffusers import StableDiffusionInpaintPipeline, AutoencoderKL
+from diffusers import DPMSolverMultistepScheduler, PNDMScheduler
 from controlnet_module import controlnet_processor
 import torch
 from PIL import Image, ImageDraw
@@ -23,7 +24,8 @@ MODEL_CONFIGS = {
         "description": "Universal model, good all-rounder, reliable results",
         "requires_vae": False,
         "recommended_steps": 35,
-        "recommended_cfg": 7.5
+        "recommended_cfg": 7.5,
+        "supports_fp16": True  # Offizielles Modell hat FP16 Variante
     },
     "SG161222/Realistic_Vision_V6.0_B1_noVAE": {
         "name": "👤 Realistic Vision V6.0 (Portraits)",
@@ -31,28 +33,32 @@ MODEL_CONFIGS = {
         "requires_vae": True,
         "vae_model": "stabilityai/sd-vae-ft-mse",
         "recommended_steps": 40,
-        "recommended_cfg": 7.0
+        "recommended_cfg": 7.0,
+        "supports_fp16": False  # Custom Model, keine FP16 Variante
     },
     "RunDiffusion/Juggernaut-X-v10": {
         "name": "🏢 Juggernaut X (Business)",
         "description": "Ideal for corporate images, team photos, professional settings",
         "requires_vae": False,
         "recommended_steps": 35,
-        "recommended_cfg": 7.5
+        "recommended_cfg": 7.5,
+        "supports_fp16": False  # Custom Model, keine FP16 Variante
     },
     "Lykon/DreamShaper": {
         "name": "🎨 DreamShaper (Artistic)",
         "description": "Creative interpretations, artistic styles, illustrations",
         "requires_vae": False,
         "recommended_steps": 40,
-        "recommended_cfg": 8.0
+        "recommended_cfg": 8.0,
+        "supports_fp16": False  # Custom Model, keine FP16 Variante
     },
     "nitrosocke/redshift-diffusion": {
         "name": "🖼️ Redshift Diffusion (Design)",
         "description": "Clean CGI style, product visuals, design mockups",
         "requires_vae": False,
         "recommended_steps": 30,
-        "recommended_cfg": 8.5
+        "recommended_cfg": 8.5,
+        "supports_fp16": False  # Custom Model, keine FP16 Variante
     }
 }
 
@@ -68,7 +74,7 @@ def auto_negative_prompt(positive_prompt):
     # Personen / Portraits
     if any(w in p for w in ["person", "man", "woman", "face", "portrait", "team", "employee", "people", "crowd"]):
         negatives.append(
-            "bad anatomy, malformed hands, extra fingers, uneven eyes, distorted face, unrealistic skin, mutated"
+            "bad anatomy, malformed hands, extra fingers, uneven eyes, distorted face, unrealistic skin, mutated, deformed, ugly, disfigured, poorly drawn face, missing limbs, extra limbs, fused fingers, too many fingers, long neck"
         )
 
     # Business / Corporate
@@ -162,7 +168,7 @@ def load_txt2img(model_id):
     
     print(f"🔄 Lade Modell: {model_id}")
     
-    config = MODEL_CONFIGS[model_id]
+    config = MODEL_CONFIGS.get(model_id, MODEL_CONFIGS["runwayml/stable-diffusion-v1-5"])
     print(f"📋 Modell-Konfiguration: {config['name']}")
     print(f"📝 Beschreibung: {config['description']}")
     
@@ -171,59 +177,127 @@ def load_txt2img(model_id):
         vae = None
         if config.get("requires_vae", False):
             print(f"🔧 Lade externe VAE: {config['vae_model']}")
-            vae = AutoencoderKL.from_pretrained(
-                config["vae_model"],
-                torch_dtype=torch_dtype
-            ).to(device)
-            print("✅ VAE erfolgreich geladen")
+            try:
+                vae = AutoencoderKL.from_pretrained(
+                    config["vae_model"],
+                    torch_dtype=torch_dtype
+                ).to(device)
+                print("✅ VAE erfolgreich geladen")
+            except Exception as vae_error:
+                print(f"⚠️ Fehler beim Laden der VAE: {vae_error}")
+                print("ℹ️ Versuche ohne VAE weiter...")
+                vae = None
+        
+        # Modellparameter basierend auf Modelltyp
+        model_params = {
+            "torch_dtype": torch_dtype,
+            "safety_checker": None,
+            "requires_safety_checker": False,
+            "add_watermarker": False,
+            "use_safetensors": True,
+        }
+        
+        # NUR FP16 Variante laden wenn Modell sie unterstützt UND wir auf GPU sind
+        if config.get("supports_fp16", False) and torch_dtype == torch.float16:
+            model_params["variant"] = "fp16"
+            print("ℹ️ Verwende FP16 Variante")
+        else:
+            print("ℹ️ Verwende Standard Variante (kein FP16)")
+        
+        # VAE nur wenn nicht None
+        if vae is not None:
+            model_params["vae"] = vae
         
-        # Modell laden
         print(f"📥 Lade Hauptmodell von Hugging Face...")
         pipe_txt2img = StableDiffusionPipeline.from_pretrained(
             model_id,
-            torch_dtype=torch_dtype,
-            safety_checker=None,
-            requires_safety_checker=False,
-            add_watermarker=False,
-            use_safetensors=True,
-            variant="fp16" if torch_dtype == torch.float16 else None,
-            vae=vae
+            **model_params
         ).to(device)
         
-        # Scheduler konfigurieren
-        from diffusers import DPMSolverMultistepScheduler
-        pipe_txt2img.scheduler = DPMSolverMultistepScheduler.from_config(
-            pipe_txt2img.scheduler.config,
-            use_karras_sigmas=True,
-            algorithm_type="sde-dpmsolver++"
-        )
+        # SICHERER SCHEDULER-HANDLING
+        print("⚙️ Konfiguriere Scheduler...")
+        
+        # Prüfe ob Scheduler existiert
+        if pipe_txt2img.scheduler is None:
+            print("⚠️ Scheduler ist None, setze Standard-Scheduler")
+            pipe_txt2img.scheduler = PNDMScheduler.from_pretrained(
+                model_id,
+                subfolder="scheduler"
+            )
+        
+        # Versuche DPM-Solver zu verwenden (bessere Ergebnisse)
+        try:
+            # Hole die Scheduler-Konfiguration
+            if hasattr(pipe_txt2img.scheduler, 'config'):
+                scheduler_config = pipe_txt2img.scheduler.config
+            else:
+                # Fallback-Konfiguration für Scheduler
+                scheduler_config = {
+                    "beta_start": 0.00085,
+                    "beta_end": 0.012,
+                    "beta_schedule": "scaled_linear",
+                    "num_train_timesteps": 1000,
+                    "prediction_type": "epsilon",
+                    "steps_offset": 1
+                }
+                print("⚠️ Keine Scheduler-Konfig gefunden, verwende Standard")
+            
+            # Setze DPM-Solver Scheduler
+            pipe_txt2img.scheduler = DPMSolverMultistepScheduler.from_config(
+                scheduler_config,
+                use_karras_sigmas=True,
+                algorithm_type="sde-dpmsolver++"
+            )
+            print("✅ DPM-Solver Multistep Scheduler konfiguriert")
+            
+        except Exception as scheduler_error:
+            print(f"⚠️ Konnte DPM-Scheduler nicht setzen: {scheduler_error}")
+            print("ℹ️ Verwende Standard-Scheduler weiter")
+            # Behalte den aktuellen Scheduler bei
         
         # Optimierungen
         pipe_txt2img.enable_attention_slicing()
-        pipe_txt2img.enable_vae_slicing()
-        if hasattr(pipe_txt2img, 'vae'):
-            pipe_txt2img.vae.enable_slicing()
+        print("✅ Attention Slicing aktiviert")
+        
+        # VAE Slicing nur wenn VAE existiert
+        if hasattr(pipe_txt2img, 'vae') and pipe_txt2img.vae is not None:
+            try:
+                pipe_txt2img.enable_vae_slicing()
+                if hasattr(pipe_txt2img.vae, 'enable_slicing'):
+                    pipe_txt2img.vae.enable_slicing()
+                print("✅ VAE Slicing aktiviert")
+            except Exception as vae_slice_error:
+                print(f"⚠️ VAE Slicing nicht möglich: {vae_slice_error}")
         
         current_pipe_model_id = model_id
         print(f"✅ {config['name']} erfolgreich geladen")
+        print(f"📊 Modell-Dtype: {pipe_txt2img.dtype}")
+        print(f"📊 Scheduler: {type(pipe_txt2img.scheduler).__name__}")
         print(f"⚙️ Empfohlene Einstellungen: Steps={config['recommended_steps']}, CFG={config['recommended_cfg']}")
         
         return pipe_txt2img
         
     except Exception as e:
-        print(f"❌ Fehler beim Laden von {model_id}: {e}")
+        print(f"❌ Fehler beim Laden von {model_id}: {str(e)[:200]}...")
+        import traceback
+        traceback.print_exc()
         print("🔄 Fallback auf SD 1.5...")
         
         # Fallback auf Standard SD 1.5
-        pipe_txt2img = StableDiffusionPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
-            torch_dtype=torch_dtype,
-            use_safetensors=True,
-        ).to(device)
-        pipe_txt2img.enable_attention_slicing()
-        current_pipe_model_id = "runwayml/stable-diffusion-v1-5"
-        
-        return pipe_txt2img
+        try:
+            pipe_txt2img = StableDiffusionPipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5",
+                torch_dtype=torch_dtype,
+                use_safetensors=True,
+            ).to(device)
+            pipe_txt2img.enable_attention_slicing()
+            current_pipe_model_id = "runwayml/stable-diffusion-v1-5"
+            print("✅ Fallback auf SD 1.5 erfolgreich")
+            
+            return pipe_txt2img
+        except Exception as fallback_error:
+            print(f"❌ Auch Fallback fehlgeschlagen: {fallback_error}")
+            raise
 
 def load_img2img():
     global pipe_img2img