API Update

app.py

@@ -136,30 +136,6 @@ automasker = AutoMasker(
 )
 
 
-# Flux-based CatVTON
-# access_token = os.getenv("HUGGING_FACE_HUB_TOKEN")
-# flux_repo = "black-forest-labs/FLUX.1-Fill-dev"
-# pipeline_flux = FluxTryOnPipeline.from_pretrained(flux_repo, use_auth_token=access_token)
-# pipeline_flux.load_lora_weights(
-#     os.path.join(repo_path, "flux-lora"), 
-#     weight_name='pytorch_lora_weights.safetensors'
-# )
-# pipeline_flux.to("cuda", init_weight_dtype(args.mixed_precision))
-
-
-# Mask-free CatVTON
-# catvton_mf_repo = "zhengchong/CatVTON-MaskFree"
-# repo_path_mf = snapshot_download(repo_id=catvton_mf_repo, use_auth_token=access_token)
-# pipeline_p2p = CatVTONPix2PixPipeline(
-#     base_ckpt=args.p2p_base_model_path,
-#     attn_ckpt=repo_path_mf,
-#     attn_ckpt_version="mix-48k-1024",
-#     weight_dtype=init_weight_dtype(args.mixed_precision),
-#     use_tf32=args.allow_tf32,
-#     device='cuda'
-# )
-
-
 @spaces.GPU(duration=120)
 def submit_function(
     person_image,
@@ -170,8 +146,8 @@ def submit_function(
     seed,
     show_type
 ):
-    person_image, mask = person_image["background"], person_image["layers"][0]
-    mask = Image.open(mask).convert("L")
+    person_image = person_image if isinstance(person_image, str) else person_image["background"]
+    mask = None  # Ignore the mask if not provided
     if len(np.unique(np.array(mask))) == 1:
         mask = None
     else:
@@ -239,126 +215,6 @@ def submit_function(
         new_result_image.paste(result_image, (condition_width + 5, 0))
     return new_result_image
 
-# @spaces.GPU(duration=120)
-# def submit_function_p2p(
-#     person_image,
-#     cloth_image,
-#     num_inference_steps,
-#     guidance_scale,
-#     seed):
-#     person_image= person_image["background"]
-
-#     tmp_folder = args.output_dir
-#     date_str = datetime.now().strftime("%Y%m%d%H%M%S")
-#     result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
-#     if not os.path.exists(os.path.join(tmp_folder, date_str[:8])):
-#         os.makedirs(os.path.join(tmp_folder, date_str[:8]))
-
-#     generator = None
-#     if seed != -1:
-#         generator = torch.Generator(device='cuda').manual_seed(seed)
-
-#     person_image = Image.open(person_image).convert("RGB")
-#     cloth_image = Image.open(cloth_image).convert("RGB")
-#     person_image = resize_and_crop(person_image, (args.width, args.height))
-#     cloth_image = resize_and_padding(cloth_image, (args.width, args.height))
-
-#     # Inference
-#     try:
-#         result_image = pipeline_p2p(
-#             image=person_image,
-#             condition_image=cloth_image,
-#             num_inference_steps=num_inference_steps,
-#             guidance_scale=guidance_scale,
-#             generator=generator
-#         )[0]
-#     except Exception as e:
-#         raise gr.Error(
-#             "An error occurred. Please try again later: {}".format(e)
-#         )
-    
-#     # Post-process
-#     save_result_image = image_grid([person_image, cloth_image, result_image], 1, 3)
-#     save_result_image.save(result_save_path)
-#     return 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")
-    
-#     # 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
-
 
 def person_example_fn(image_path):
     return image_path
@@ -502,247 +358,6 @@ def app_gradio():
                     ],
                     result_image,
                 )
-
-        # with gr.Tab("Mask-based & Flux.1 Fill Dev"):
-        #     with gr.Row():
-        #         with gr.Column(scale=1, min_width=350):
-        #             with gr.Row():
-        #                 image_path_flux = gr.Image(
-        #                     type="filepath",
-        #                     interactive=True,
-        #                     visible=False,
-        #                 )
-        #                 person_image_flux = gr.ImageEditor(
-        #                     interactive=True, label="Person Image", type="filepath"
-        #                 )
-                    
-        #             with gr.Row():
-        #                 with gr.Column(scale=1, min_width=230):
-        #                     cloth_image_flux = gr.Image(
-        #                         interactive=True, label="Condition Image", type="filepath"
-        #                     )
-        #                 with gr.Column(scale=1, min_width=120):
-        #                     gr.Markdown(
-        #                         '<span style="color: #808080; font-size: small;">Two ways to provide Mask:<br>1. Upload the person image and use the `🖌️` above to draw the Mask (higher priority)<br>2. Select the `Try-On Cloth Type` to generate automatically </span>'
-        #                     )
-        #                     cloth_type = gr.Radio(
-        #                         label="Try-On Cloth Type",
-        #                         choices=["upper", "lower", "overall"],
-        #                         value="upper",
-        #                     )
-
-        #             submit_flux = gr.Button("Submit")
-        #             gr.Markdown(
-        #                 '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>'
-        #             )
-                    
-        #             with gr.Accordion("Advanced Options", open=False):
-        #                 num_inference_steps_flux = gr.Slider(
-        #                     label="Inference Step", minimum=10, maximum=100, step=5, value=50
-        #                 )
-        #                 # Guidence Scale
-        #                 guidance_scale_flux = gr.Slider(
-        #                     label="CFG Strenth", minimum=0.0, maximum=50, step=0.5, value=30
-        #                 )
-        #                 # Random Seed
-        #                 seed_flux = gr.Slider(
-        #                     label="Seed", minimum=-1, maximum=10000, step=1, value=42
-        #                 )
-        #                 show_type = gr.Radio(
-        #                     label="Show Type",
-        #                     choices=["result only", "input & result", "input & mask & result"],
-        #                     value="input & mask & result",
-        #                 )
-                    
-        #         with gr.Column(scale=2, min_width=500):
-        #             result_image_flux = gr.Image(interactive=False, label="Result")
-        #             with gr.Row():
-        #                 # Photo Examples
-        #                 root_path = "resource/demo/example"
-        #                 with gr.Column():
-        #                     gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "person", "men", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "person", "men"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=image_path_flux,
-        #                         label="Person Examples ①",
-        #                     )
-        #                     gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "person", "women", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "person", "women"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=image_path_flux,
-        #                         label="Person Examples ②",
-        #                     )
-        #                     gr.Markdown(
-        #                         '<span style="color: #808080; font-size: small;">*Person examples come from the demos of <a href="https://huggingface.co/spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a>. </span>'
-        #                     )
-        #                 with gr.Column():
-        #                     gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "condition", "upper", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "condition", "upper"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=cloth_image_flux,
-        #                         label="Condition Upper Examples",
-        #                     )
-        #                     gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "condition", "overall", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "condition", "overall"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=cloth_image_flux,
-        #                         label="Condition Overall Examples",
-        #                     )
-        #                     condition_person_exm = gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "condition", "person", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "condition", "person"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=cloth_image_flux,
-        #                         label="Condition Reference Person Examples",
-        #                     )
-        #                     gr.Markdown(
-        #                         '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>'
-        #                     )
-
-                
-        #         image_path_flux.change(
-        #             person_example_fn, inputs=image_path_flux, outputs=person_image_flux
-        #         )
-
-        #         submit_flux.click(
-        #             submit_function_flux,
-        #             [person_image_flux, cloth_image_flux, cloth_type, num_inference_steps_flux, guidance_scale_flux, seed_flux, show_type],
-        #             result_image_flux,
-        #         )
-        
-            
-        # with gr.Tab("Mask-free & SD1.5"):
-        #     with gr.Row():
-        #         with gr.Column(scale=1, min_width=350):
-        #             with gr.Row():
-        #                 image_path_p2p = gr.Image(
-        #                     type="filepath",
-        #                     interactive=True,
-        #                     visible=False,
-        #                 )
-        #                 person_image_p2p = gr.ImageEditor(
-        #                     interactive=True, label="Person Image", type="filepath"
-        #                 )
-
-        #             with gr.Row():
-        #                 with gr.Column(scale=1, min_width=230):
-        #                     cloth_image_p2p = gr.Image(
-        #                         interactive=True, label="Condition Image", type="filepath"
-        #                     )
-
-        #             submit_p2p = gr.Button("Submit")
-        #             gr.Markdown(
-        #                 '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>'
-        #             )
-                    
-        #             gr.Markdown(
-        #                 '<span style="color: #808080; font-size: small;">Advanced options can adjust details:<br>1. `Inference Step` may enhance details;<br>2. `CFG` is highly correlated with saturation;<br>3. `Random seed` may improve pseudo-shadow.</span>'
-        #             )
-        #             with gr.Accordion("Advanced Options", open=False):
-        #                 num_inference_steps_p2p = gr.Slider(
-        #                     label="Inference Step", minimum=10, maximum=100, step=5, value=50
-        #                 )
-        #                 # Guidence Scale
-        #                 guidance_scale_p2p = gr.Slider(
-        #                     label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5
-        #                 )
-        #                 # Random Seed
-        #                 seed_p2p = gr.Slider(
-        #                     label="Seed", minimum=-1, maximum=10000, step=1, value=42
-        #                 )
-        #                 # show_type = gr.Radio(
-        #                 #     label="Show Type",
-        #                 #     choices=["result only", "input & result", "input & mask & result"],
-        #                 #     value="input & mask & result",
-        #                 # )
-
-        #         with gr.Column(scale=2, min_width=500):
-        #             result_image_p2p = gr.Image(interactive=False, label="Result")
-        #             with gr.Row():
-        #                 # Photo Examples
-        #                 root_path = "resource/demo/example"
-        #                 with gr.Column():
-        #                     gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "person", "men", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "person", "men"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=image_path_p2p,
-        #                         label="Person Examples ①",
-        #                     )
-        #                     gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "person", "women", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "person", "women"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=image_path_p2p,
-        #                         label="Person Examples ②",
-        #                     )
-        #                     gr.Markdown(
-        #                         '<span style="color: #808080; font-size: small;">*Person examples come from the demos of <a href="https://huggingface.co/spaces/levihsu/OOTDiffusion">OOTDiffusion</a> and <a href="https://www.outfitanyone.org">OutfitAnyone</a>. </span>'
-        #                     )
-        #                 with gr.Column():
-        #                     gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "condition", "upper", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "condition", "upper"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=cloth_image_p2p,
-        #                         label="Condition Upper Examples",
-        #                     )
-        #                     gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "condition", "overall", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "condition", "overall"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=cloth_image_p2p,
-        #                         label="Condition Overall Examples",
-        #                     )
-        #                     condition_person_exm = gr.Examples(
-        #                         examples=[
-        #                             os.path.join(root_path, "condition", "person", _)
-        #                             for _ in os.listdir(os.path.join(root_path, "condition", "person"))
-        #                         ],
-        #                         examples_per_page=4,
-        #                         inputs=cloth_image_p2p,
-        #                         label="Condition Reference Person Examples",
-        #                     )
-        #                     gr.Markdown(
-        #                         '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>'
-        #                     )
-
-        #         image_path_p2p.change(
-        #             person_example_fn, inputs=image_path_p2p, outputs=person_image_p2p
-        #         )
-
-        #         submit_p2p.click(
-        #             submit_function_p2p,
-        #             [
-        #                 person_image_p2p,
-        #                 cloth_image_p2p,
-        #                 num_inference_steps_p2p,
-        #                 guidance_scale_p2p,
-        #                 seed_p2p],
-        #             result_image_p2p,
-        #         )
         
     demo.queue().launch(share=True, show_error=True)