Update app.py

app.py

@@ -13,15 +13,13 @@ feature_extractor = CLIPFeatureExtractor.from_pretrained(model_id)
 
 pipe = StableDiffusionPipeline.from_pretrained(
   model_id,
-  #torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-  torch_dtype=torch.float16,
+  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
   scheduler=scheduler)
 pipe.enable_xformers_memory_efficient_attention()
 
 pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
   model_id,
-  #torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
-  torch_dtype=torch.float16,
+  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
   scheduler=scheduler,
   requires_safety_checker=False,
   safety_checker=None,
@@ -51,6 +49,10 @@ def inference(prompt, guidance, steps, image_size="Square", seed=0, img=None, st
       height=768
       width=1024  
       #pipe.enable_attention_slicing()
+  elif(image_size=="Highreso."):
+      height=1024
+      width=1024  
+      #pipe.enable_attention_slicing()
   else:
       height=768
       width=768
@@ -199,7 +201,7 @@ with gr.Blocks(css=css) as demo:
               neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
               disable_auto_prompt_correction = gr.Checkbox(label="Disable auto prompt corretion.")
               with gr.Row():
-                image_size=gr.Radio(["Portrait","Landscape","Square"])
+                image_size=gr.Radio(["Portrait","Landscape","Square","Highreso."])
                 image_size.show_label=False
                 image_size.value="Square"