Update app.py

app.py

@@ -229,82 +229,83 @@ def submit_function(
     return new_result_image
 
 
-@spaces.GPU(duration=120)
-def submit_function_flux(
-    person_image,
-    cloth_image,
-    cloth_type,
-    num_inference_steps,
-    guidance_scale,
-    seed,
-    show_type
-):
-
-    # Process image editor input
-    person_image, mask = person_image["background"], person_image["layers"][0]
-    mask = Image.open(mask).convert("L")
-    if len(np.unique(np.array(mask))) == 1:
-        mask = None
-    else:
-        mask = np.array(mask)
-        mask[mask > 0] = 255
-        mask = Image.fromarray(mask)
-
-    # Set random seed
-    generator = None
-    if seed != -1:
-        generator = torch.Generator(device='cuda').manual_seed(seed)
-
-    # Process input images
-    person_image = Image.open(person_image).convert("RGB")
-    cloth_image = Image.open(cloth_image).convert("RGB")
+# @spaces.GPU(duration=120)
+# def submit_function_flux(
+#     person_image,
+#     cloth_image,
+#     cloth_type,
+#     num_inference_steps,
+#     guidance_scale,
+#     seed,
+#     show_type
+# ):
+
+#     # Process image editor input
+#     person_image, mask = person_image["background"], person_image["layers"][0]
+#     mask = Image.open(mask).convert("L")
+#     if len(np.unique(np.array(mask))) == 1:
+#         mask = None
+#     else:
+#         mask = np.array(mask)
+#         mask[mask > 0] = 255
+#         mask = Image.fromarray(mask)
+
+#     # Set random seed
+#     generator = None
+#     if seed != -1:
+#         generator = torch.Generator(device='cuda').manual_seed(seed)
+
+#     # Process input images
+#     person_image = Image.open(person_image).convert("RGB")
+#     cloth_image = Image.open(cloth_image).convert("RGB")
     
-    # Adjust image sizes
-    person_image = resize_and_crop(person_image, (args.width, args.height))
-    cloth_image = resize_and_padding(cloth_image, (args.width, args.height))
-
-    # Process mask
-    if mask is not None:
-        mask = resize_and_crop(mask, (args.width, args.height))
-    else:
-        mask = automasker(
-            person_image,
-            cloth_type
-        )['mask']
-    mask = mask_processor.blur(mask, blur_factor=9)
-
-    # Inference
-    result_image = pipeline_flux(
-        image=person_image,
-        condition_image=cloth_image,
-        mask_image=mask,
-        width=args.width,
-        height=args.height,
-        num_inference_steps=num_inference_steps,
-        guidance_scale=guidance_scale,
-        generator=generator
-    ).images[0]
-
-    # Post-processing
-    masked_person = vis_mask(person_image, mask)
-
-    # Return result based on show type
-    if show_type == "result only":
-        return result_image
-    else:
-        width, height = person_image.size
-        if show_type == "input & result":
-            condition_width = width // 2
-            conditions = image_grid([person_image, cloth_image], 2, 1)
-        else:
-            condition_width = width // 3
-            conditions = image_grid([person_image, masked_person, cloth_image], 3, 1)
+#     # Adjust image sizes
+#     person_image = resize_and_crop(person_image, (args.width, args.height))
+#     cloth_image = resize_and_padding(cloth_image, (args.width, args.height))
+
+#     # Process mask
+#     if mask is not None:
+#         mask = resize_and_crop(mask, (args.width, args.height))
+#     else:
+#         mask = automasker(
+#             person_image,
+#             cloth_type
+#         )['mask']
+#     mask = mask_processor.blur(mask, blur_factor=9)
+
+#     # Inference
+#     result_image = pipeline_flux(
+#         image=person_image,
+#         condition_image=cloth_image,
+#         mask_image=mask,
+#         width=args.width,
+#         height=args.height,
+#         num_inference_steps=num_inference_steps,
+#         guidance_scale=guidance_scale,
+#         generator=generator
+#     ).images[0]
+
+#     # Post-processing
+#     masked_person = vis_mask(person_image, mask)
+
+#     # Return result based on show type
+#     if show_type == "result only":
+#         return result_image
+#     else:
+#         width, height = person_image.size
+#         if show_type == "input & result":
+#             condition_width = width // 2
+#             conditions = image_grid([person_image, cloth_image], 2, 1)
+#         else:
+#             condition_width = width // 3
+#             conditions = image_grid([person_image, masked_person, cloth_image], 3, 1)
         
-        conditions = conditions.resize((condition_width, height), Image.NEAREST)
-        new_result_image = Image.new("RGB", (width + condition_width + 5, height))
-        new_result_image.paste(conditions, (0, 0))
-        new_result_image.paste(result_image, (condition_width + 5, 0))
-        return new_result_image
+#         conditions = conditions.resize((condition_width, height), Image.NEAREST)
+#         new_result_image = Image.new("RGB", (width + condition_width + 5, height))
+#         new_result_image.paste(conditions, (0, 0))
+#         new_result_image.paste(result_image, (condition_width + 5, 0))
+#         return new_result_image
+
 
 
 def person_example_fn(image_path):