Update app.py

app.py

@@ -16,14 +16,14 @@ def read_content(file_path: str) -> str:
 
 
 def predict(prompt, negative_prompt, guidance_scale, num_inference_steps,model, scheduler, lora, lora_weight):
-    pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", vae=vae)
+    pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", vae=vae).to("cuda")
     pipeline.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
     if model == "Realistic_V5.1":
-        pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", vae=vae)
+        pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", vae=vae).to("cuda")
     if model == "Realistic_V5.0":
-        pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE", vae=vae)
+        pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE", vae=vae).to("cuda")
     if model == "EpicRealism":
-        pipeline = diffusers.DiffusionPipeline.from_pretrained("emilianJR/epiCRealism", vae=vae)
+        pipeline = diffusers.DiffusionPipeline.from_pretrained("emilianJR/epiCRealism", vae=vae).to("cuda")
         pipeline.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
     
     scheduler_class_name = scheduler.split("-")[0]