Update app.py

app.py

@@ -75,7 +75,7 @@ current_model_path = current_model.path
 if is_colab:
   pipe = StableDiffusionPipeline.from_pretrained(
       current_model.path,
-      torch_dtype=torch.get_default_dtype(),
+      torch_dtype=torch.float16,
       scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
       safety_checker=lambda images, clip_input: (images, False)
       )
@@ -83,13 +83,13 @@ if is_colab:
 else:
   pipe = StableDiffusionPipeline.from_pretrained(
       current_model.path,
-      torch_dtype=torch.get_default_dtype(),
+      torch_dtype=torch.float16,
       scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
       )
-
-    if torch.cuda.is_available():
-        pipe = pipe.to("cuda")
-        pipe.enable_xformers_memory_efficient_attention()
+    
+if torch.cuda.is_available():
+  pipe = pipe.to("cuda")
+  pipe.enable_xformers_memory_efficient_attention()
 
 device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
 
@@ -164,14 +164,14 @@ def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width,
         if is_colab or current_model == custom_model:
           pipe = StableDiffusionPipeline.from_pretrained(
               current_model_path,
-              torch_dtype=torch.get_default_dtype(),
+              torch_dtype=torch.float16,
               scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
               safety_checker=lambda images, clip_input: (images, False)
               )
         else:
           pipe = StableDiffusionPipeline.from_pretrained(
               current_model_path,
-              torch_dtype=torch.get_default_dtype(),
+              torch_dtype=torch.float16,
               scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
               )
           # pipe = pipe.to("cpu")
@@ -213,19 +213,19 @@ def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance
         if is_colab or current_model == custom_model:
           pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
               current_model_path,
-              torch_dtype=torch.get_default_dtype(),
+              torch_dtype=torch.float16,
               scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
               safety_checker=lambda images, clip_input: (images, False)
               )
         else:
           pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
               current_model_path,
-              torch_dtype=torch.get_default_dtype(),
+              torch_dtype=torch.float16,
               scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
               )
           # pipe = pipe.to("cpu")
           # pipe = current_model.pipe_i2i
-
+        
         if torch.cuda.is_available():
           pipe = pipe.to("cuda")
           pipe.enable_xformers_memory_efficient_attention()
@@ -285,8 +285,8 @@ with gr.Blocks(css="style.css") as demo:
             </div>
         """
     )
-    with gr.Row():
-        
+
+    with gr.Row():       
         with gr.Column(scale=55):
           with gr.Group():
               model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)