Update app.py

app.py

@@ -51,15 +51,14 @@ pipe_edit.to("cuda")
 
 # Image Generator
 # Keep the models loaded globally for reuse
-if torch.cuda.is_available():
-    pipe = StableDiffusionXLPipeline.from_pretrained(
+pipe = StableDiffusionXLPipeline.from_pretrained(
         "fluently/Fluently-XL-v4",
         torch_dtype=torch.float16,
         use_safetensors=True,
     ).to("cuda")
-    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-    pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
-    pipe.set_adapters("dalle")
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
+pipe.set_adapters("dalle")
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
@@ -87,7 +86,6 @@ def king(type = "Image Generation",
         text_cfg_scale = text_cfg_scale
         image_cfg_scale = image_cfg_scale
         input_image = input_image
-
         steps=steps
         generator = torch.manual_seed(seed)
         output_image = pipe_edit(
@@ -97,7 +95,7 @@ def king(type = "Image Generation",
         return seed, output_image
     else :
         seed = int(randomize_seed_fn(seed, randomize_seed))
-        generator = torch.Generator(device="cuda").manual_seed(seed) # Move generator to cuda for speed
+        generator = torch.Generator().manual_seed(seed) # Move generator to cuda for speed
 
         options = {
             "prompt":instruction,
@@ -110,8 +108,7 @@ def king(type = "Image Generation",
             "output_type":"pil",
         }
         
-        with torch.autocast("cuda"): # Use autocast for faster inference
-            output_image = pipe(**options).images[0]
+        output_image = pipe(**options).images[0]
         return seed, output_image
 
 # Prompt classifier