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app.py

@@ -33,7 +33,7 @@ image_encoder_path = "google/siglip-so400m-patch14-384"
 
 transformer = SD3Transformer2DModel.from_pretrained(
     model_path, subfolder="transformer", torch_dtype=torch.bfloat16
-).to("cuda")
+)
 
 pipe = StableDiffusion3Pipeline.from_pretrained(
     model_path, transformer=transformer, torch_dtype=torch.bfloat16
@@ -48,18 +48,18 @@ pipe.init_ipadapter(
 
 @spaces.GPU
 def gui_generation(prompt, ref_img, guidance_scale, ipadapter_scale):
-
-    # Ensure the pipeline runs with correct dtype and device
-    image = pipe(
-        width=1024,
-        height=1024,
-        prompt=prompt,
-        negative_prompt="lowres, low quality, worst quality",
-        num_inference_steps=24,
-        guidance_scale=guidance_scale,
-        generator=torch.Generator("cuda").manual_seed(42),
-        clip_image=ref_img.convert('RGB'),
-        ipadapter_scale=ipadapter_scale).images[0]
+    with torch.no_grad():
+        # Ensure the pipeline runs with correct dtype and device
+        image = pipe(
+            width=1024,
+            height=1024,
+            prompt=prompt,
+            negative_prompt="lowres, low quality, worst quality",
+            num_inference_steps=24,
+            guidance_scale=guidance_scale,
+            generator=torch.Generator("cuda").manual_seed(42),
+            clip_image=ref_img.convert('RGB'),
+            ipadapter_scale=ipadapter_scale).images[0]
     return image
 
 # Create Gradio interface