Update app.py

app.py

@@ -137,13 +137,15 @@ def start_tryon(person_img, pose_img, mask_img, cloth_img, garment_des, denoise_
     # Prepare pose image (already uploaded)
     pose_img = pose_img.resize((768, 1024))
 
-    # Generate text embeddings for garment description
-    prompt = f"model is wearing {garment_des}"
-    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+    
     
     # Embedding generation for prompts
     with torch.no_grad():
         with torch.cuda.amp.autocast():
+            # Generate text embeddings for garment description
+            prompt = f"model is wearing {garment_des}"
+            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+            with torch.inference_mode():
             (
                 prompt_embeds,
                 negative_prompt_embeds,
@@ -155,9 +157,19 @@ def start_tryon(person_img, pose_img, mask_img, cloth_img, garment_des, denoise_
                 do_classifier_free_guidance=True,
                 negative_prompt=negative_prompt,
             )
-
-            prompt_embeds_cloth, _ = pipe.encode_prompt(
-                f"a photo of {garment_des}",
+            prompt = "a photo of " + garment_des
+            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+            if not isinstance(prompt, List):
+                        prompt = [prompt] * 1
+            if not isinstance(negative_prompt, List):
+                        negative_prompt = [negative_prompt] * 1
+            with torch.inference_mode():
+                prompt_embeds_cloth, 
+                _,
+                _,
+                _,
+                )= pipe.encode_prompt(
+                prompt,
                 num_images_per_prompt=1,
                 do_classifier_free_guidance=False,
                 negative_prompt=negative_prompt,
@@ -216,7 +228,6 @@ with image_blocks as demo:
             seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42)
 
         with gr.Column():
-            masked_img = gr.Image(label="Masked Image Output", elem_id="masked-img", show_share_button=False)
             image_out = gr.Image(label="Output Image", elem_id="output-img", show_share_button=False)
 
     try_button = gr.Button(value="Try-on")