Update app.py

app.py

@@ -1,34 +1,229 @@
 import argparse
 import os
+os.environ['CUDA_HOME'] = '/usr/local/cuda'
+os.environ['PATH'] = os.environ['PATH'] + ':/usr/local/cuda/bin'
 from datetime import datetime
+
 import gradio as gr
+import spaces
 import numpy as np
 import torch
 from diffusers.image_processor import VaeImageProcessor
 from huggingface_hub import snapshot_download
 from PIL import Image
+torch.jit.script = lambda f: f
 from model.cloth_masker import AutoMasker, vis_mask
 from model.pipeline import CatVTONPipeline
 from utils import init_weight_dtype, resize_and_crop, resize_and_padding
 
-# ... (rest of your imports and function definitions remain unchanged)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--base_model_path",
+        type=str,
+        default="booksforcharlie/stable-diffusion-inpainting",
+        help=(
+            "The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub."
+        ),
+    )
+    parser.add_argument(
+        "--resume_path",
+        type=str,
+        default="zhengchong/CatVTON",
+        help=(
+            "The Path to the checkpoint of trained tryon model."
+        ),
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="resource/demo/output",
+        help="The output directory where the model predictions will be written.",
+    )
+
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=768,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=1024,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--repaint", 
+        action="store_true", 
+        help="Whether to repaint the result image with the original background."
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        default=True,
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="bf16",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+def image_grid(imgs, rows, cols):
+    assert len(imgs) == rows * cols
+
+    w, h = imgs[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+args = parse_args()
+repo_path = snapshot_download(repo_id=args.resume_path)
+# Pipeline
+pipeline = CatVTONPipeline(
+    base_ckpt=args.base_model_path,
+    attn_ckpt=repo_path,
+    attn_ckpt_version="mix",
+    weight_dtype=init_weight_dtype(args.mixed_precision),
+    use_tf32=args.allow_tf32,
+    device='cuda'
+)
+# AutoMasker
+mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
+automasker = AutoMasker(
+    densepose_ckpt=os.path.join(repo_path, "DensePose"),
+    schp_ckpt=os.path.join(repo_path, "SCHP"),
+    device='cuda', 
+)
+
+@spaces.GPU(duration=120)
+def submit_function(
+    person_image,
+    cloth_image,
+    cloth_type,
+    num_inference_steps,
+    guidance_scale,
+    seed,
+    show_type
+):
+    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)
+
+    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))
+    
+    # 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
+    # try:
+    result_image = pipeline(
+        image=person_image,
+        condition_image=cloth_image,
+        mask=mask,
+        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
+    masked_person = vis_mask(person_image, mask)
+    save_result_image = image_grid([person_image, masked_person, cloth_image, result_image], 1, 4)
+    save_result_image.save(result_save_path)
+    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
 
 HEADER = """
-<p style="text-align: center;">
-    <img src="resource/DeXFIT.png" alt="DeX Logo" style="height: 100px;">
-</p>
-<h1 style="text-align: center; color: #101820;"> DEX FIT Virtual Try-On with Diffusion Models </h1>
+<h1 style="text-align: center;"> DeX Fit - Try-On </h1>
 <br>
-<p style="color: #101820;">· SafetyChecker is set to filter NSFW content, but it may block normal results too. Please adjust the <span style="color: #00685E;">seed</span> for normal outcomes.</p>
+
+· SafetyChecker is set to filter NSFW content, but it may block normal results too. Please adjust the <span>`seed`</span> for normal outcomes.<br> 
 """
 
 def app_gradio():
-    with gr.Blocks(title="CatVTON", css="#main {background-color: #F4F4F1;}") as demo:
+    with gr.Blocks(title="CatVTON") as demo:
         gr.Markdown(HEADER)
-        
         with gr.Row():
             with gr.Column(scale=1, min_width=350):
                 with gr.Row():
+                    image_path = gr.Image(
+                        type="filepath",
+                        interactive=True,
+                        visible=False,
+                    )
                     person_image = gr.ImageEditor(
                         interactive=True, label="Person Image", type="filepath"
                     )
@@ -46,10 +241,10 @@ def app_gradio():
                             label="Try-On Cloth Type",
                             choices=["upper", "lower", "overall"],
                             value="upper",
-                            label_style={"color": "#101820"}
                         )
 
-                submit = gr.Button("Submit", elem_id="submit-button", style={"background-color": "#00685E", "color": "#FFFFFF"})
+
+                submit = gr.Button("Submit")
                 gr.Markdown(
                     '<center><span style="color: #FF0000">!!! Click only Once, Wait for Delay !!!</span></center>'
                 )
@@ -61,9 +256,11 @@ def app_gradio():
                     num_inference_steps = gr.Slider(
                         label="Inference Step", minimum=10, maximum=100, step=5, value=50
                     )
+                    # Guidence Scale
                     guidance_scale = gr.Slider(
-                        label="CFG Strength", minimum=0.0, maximum=7.5, step=0.5, value=2.5
+                        label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5
                     )
+                    # Random Seed
                     seed = gr.Slider(
                         label="Seed", minimum=-1, maximum=10000, step=1, value=1000
                     )
@@ -76,29 +273,30 @@ def app_gradio():
             with gr.Column(scale=2, min_width=500):
                 result_image = gr.Image(interactive=False, label="Result")
                 with gr.Row():
+                    # Photo Examples
                     root_path = "resource/demo/example"
-with gr.Column():
-    men_exm = 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=person_image,
-        label="Person Examples ①",
-    )
-    women_exm = 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=person_image,
-        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" style="color: #00685E;">OOTDiffusion</a> and <a href="https://www.outfitanyone.org" style="color: #00685E;">OutfitAnyone</a>.</span>'
-    )
+                    with gr.Column():
+                        men_exm = 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,
+                            label="Person Examples ①",
+                        )
+                        women_exm = 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,
+                            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():
                         condition_upper_exm = gr.Examples(
                             examples=[
@@ -128,30 +326,28 @@ with gr.Column():
                             label="Condition Reference Person Examples",
                         )
                         gr.Markdown(
-                            '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet.</span>'
+                            '<span style="color: #808080; font-size: small;">*Condition examples come from the Internet. </span>'
                         )
 
-                # Update the image path change function
-                person_image.change(
-                    person_example_fn, inputs=person_image, outputs=person_image
-                )
-
-                # Connect the submit button to the function
-                submit.click(
-                    submit_function,
-                    [
-                        person_image,
-                        cloth_image,
-                        cloth_type,
-                        num_inference_steps,
-                        guidance_scale,
-                        seed,
-                        show_type,
-                    ],
-                    result_image,
-                )
+            image_path.change(
+                person_example_fn, inputs=image_path, outputs=person_image
+            )
 
+            submit.click(
+                submit_function,
+                [
+                    person_image,
+                    cloth_image,
+                    cloth_type,
+                    num_inference_steps,
+                    guidance_scale,
+                    seed,
+                    show_type,
+                ],
+                result_image,
+            )
     demo.queue().launch(share=True, show_error=True)
 
+
 if __name__ == "__main__":
     app_gradio()