Update app.py

app.py

@@ -9,9 +9,8 @@ from transformers import (
     CLIPTextModel,
     CLIPTextModelWithProjection,
 )
-from diffusers import DDPMScheduler,AutoencoderKL
+from diffusers import DDPMScheduler, AutoencoderKL
 from typing import List
-
 import torch
 import os
 from transformers import AutoTokenizer
@@ -22,10 +21,9 @@ from torchvision import transforms
 import apply_net
 from preprocess.humanparsing.run_parsing import Parsing
 from preprocess.openpose.run_openpose import OpenPose
-from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation
+from detectron2.data.detection_utils import convert_PIL_to_numpy, _apply_exif_orientation
 from torchvision.transforms.functional import to_pil_image
 
-
 def pil_to_binary_mask(pil_image, threshold=0):
     np_image = np.array(pil_image)
     grayscale_image = Image.fromarray(np_image).convert("L")
@@ -33,14 +31,14 @@ def pil_to_binary_mask(pil_image, threshold=0):
     mask = np.zeros(binary_mask.shape, dtype=np.uint8)
     for i in range(binary_mask.shape[0]):
         for j in range(binary_mask.shape[1]):
-            if binary_mask[i,j] == True :
+            if binary_mask[i,j] == True:
                 mask[i,j] = 1
-    mask = (mask*255).astype(np.uint8)
+    mask = (mask * 255).astype(np.uint8)
     output_mask = Image.fromarray(mask)
     return output_mask
 
+base_path = 'yisol/IDM-VTON' 
 
-base_path = 'yisol/IDM-VTON'
 example_path = os.path.join(os.path.dirname(__file__), 'example')
 
 unet = UNet2DConditionModel.from_pretrained(
@@ -49,18 +47,21 @@ unet = UNet2DConditionModel.from_pretrained(
     torch_dtype=torch.float16,
 )
 unet.requires_grad_(False)
+
 tokenizer_one = AutoTokenizer.from_pretrained(
     base_path,
     subfolder="tokenizer",
     revision=None,
     use_fast=False,
 )
+
 tokenizer_two = AutoTokenizer.from_pretrained(
     base_path,
     subfolder="tokenizer_2",
     revision=None,
     use_fast=False,
 )
+
 noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
 
 text_encoder_one = CLIPTextModel.from_pretrained(
@@ -68,22 +69,24 @@ text_encoder_one = CLIPTextModel.from_pretrained(
     subfolder="text_encoder",
     torch_dtype=torch.float16,
 )
+
 text_encoder_two = CLIPTextModelWithProjection.from_pretrained(
     base_path,
     subfolder="text_encoder_2",
     torch_dtype=torch.float16,
 )
+
 image_encoder = CLIPVisionModelWithProjection.from_pretrained(
     base_path,
     subfolder="image_encoder",
     torch_dtype=torch.float16,
-    )
+)
+
 vae = AutoencoderKL.from_pretrained(base_path,
                                     subfolder="vae",
                                     torch_dtype=torch.float16,
 )
 
-# "stabilityai/stable-diffusion-xl-base-1.0",
 UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
     base_path,
     subfolder="unet_encoder",
@@ -99,39 +102,39 @@ vae.requires_grad_(False)
 unet.requires_grad_(False)
 text_encoder_one.requires_grad_(False)
 text_encoder_two.requires_grad_(False)
+
 tensor_transfrom = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-    )
+    [
+        transforms.ToTensor(),
+        transforms.Normalize([0.5], [0.5]),
+    ]
+)
 
 pipe = TryonPipeline.from_pretrained(
-        base_path,
-        unet=unet,
-        vae=vae,
-        feature_extractor= CLIPImageProcessor(),
-        text_encoder = text_encoder_one,
-        text_encoder_2 = text_encoder_two,
-        tokenizer = tokenizer_one,
-        tokenizer_2 = tokenizer_two,
-        scheduler = noise_scheduler,
-        image_encoder=image_encoder,
-        torch_dtype=torch.float16,
+    base_path,
+    unet=unet,
+    vae=vae,
+    feature_extractor=CLIPImageProcessor(),
+    text_encoder=text_encoder_one,
+    text_encoder_2=text_encoder_two,
+    tokenizer=tokenizer_one,
+    tokenizer_2=tokenizer_two,
+    scheduler=noise_scheduler,
+    image_encoder=image_encoder,
+    torch_dtype=torch.float16,
 )
 pipe.unet_encoder = UNet_Encoder
 
 @spaces.GPU
-def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_steps,seed):
+def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
     device = "cuda"
-    
     openpose_model.preprocessor.body_estimation.model.to(device)
     pipe.to(device)
     pipe.unet_encoder.to(device)
 
-    garm_img= garm_img.convert("RGB").resize((768,1024))
-    human_img_orig = dict["background"].convert("RGB")    
-    
+    garm_img = garm_img.convert("RGB").resize((768, 1024))
+    human_img_orig = dict["background"].convert("RGB")
+
     if is_checked_crop:
         width, height = human_img_orig.size
         target_width = int(min(width, height * (3 / 4)))
@@ -142,37 +145,29 @@ def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_ste
         bottom = (height + target_height) / 2
         cropped_img = human_img_orig.crop((left, top, right, bottom))
         crop_size = cropped_img.size
-        human_img = cropped_img.resize((768,1024))
+        human_img = cropped_img.resize((768, 1024))
     else:
-        human_img = human_img_orig.resize((768,1024))
-
+        human_img = human_img_orig.resize((768, 1024))
 
     if is_checked:
-        keypoints = openpose_model(human_img.resize((384,512)))
-        model_parse, _ = parsing_model(human_img.resize((384,512)))
+        keypoints = openpose_model(human_img.resize((384, 512)))
+        model_parse, _ = parsing_model(human_img.resize((384, 512)))
         mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints)
-        mask = mask.resize((768,1024))
+        mask = mask.resize((768, 1024))
     else:
         mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
-        # mask = transforms.ToTensor()(mask)
-        # mask = mask.unsqueeze(0)
-    mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
-    mask_gray = to_pil_image((mask_gray+1.0)/2.0)
-
+        mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
+        mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)
 
-    human_img_arg = _apply_exif_orientation(human_img.resize((384,512)))
+    human_img_arg = _apply_exif_orientation(human_img.resize((384, 512)))
     human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
-     
-    
 
     args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda'))
-    # verbosity = getattr(args, "verbosity", None)
-    pose_img = args.func(args,human_img_arg)    
-    pose_img = pose_img[:,:,::-1]    
-    pose_img = Image.fromarray(pose_img).resize((768,1024))
-    
+    pose_img = args.func(args, human_img_arg)
+    pose_img = pose_img[:, :, ::-1]
+    pose_img = Image.fromarray(pose_img).resize((768, 1024))
+
     with torch.no_grad():
-        # Extract the images
         with torch.cuda.amp.autocast():
             with torch.no_grad():
                 prompt = "model is wearing " + garment_des
@@ -189,87 +184,82 @@ def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_ste
                         do_classifier_free_guidance=True,
                         negative_prompt=negative_prompt,
                     )
-                                    
-                    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_c,
-                            _,
-                            _,
-                            _,
-                        ) = pipe.encode_prompt(
-                            prompt,
-                            num_images_per_prompt=1,
-                            do_classifier_free_guidance=False,
-                            negative_prompt=negative_prompt,
-                        )
-
-
-
-                    pose_img =  tensor_transfrom(pose_img).unsqueeze(0).to(device,torch.float16)
-                    garm_tensor =  tensor_transfrom(garm_img).unsqueeze(0).to(device,torch.float16)
-                    generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
-                    images = pipe(
-                        prompt_embeds=prompt_embeds.to(device,torch.float16),
-                        negative_prompt_embeds=negative_prompt_embeds.to(device,torch.float16),
-                        pooled_prompt_embeds=pooled_prompt_embeds.to(device,torch.float16),
-                        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device,torch.float16),
-                        num_inference_steps=denoise_steps,
-                        generator=generator,
-                        strength = 1.0,
-                        pose_img = pose_img.to(device,torch.float16),
-                        text_embeds_cloth=prompt_embeds_c.to(device,torch.float16),
-                        cloth = garm_tensor.to(device,torch.float16),
-                        mask_image=mask,
-                        image=human_img, 
-                        height=1024,
-                        width=768,
-                        ip_adapter_image = garm_img.resize((768,1024)),
-                        guidance_scale=2.0,
-                    )[0]
+
+                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_c,
+                        _,
+                        _,
+                        _,
+                    ) = pipe.encode_prompt(
+                        prompt,
+                        num_images_per_prompt=1,
+                        do_classifier_free_guidance=False,
+                        negative_prompt=negative_prompt,
+                    )
+
+                pose_img = tensor_transfrom(pose_img).unsqueeze(0).to(device, torch.float16)
+                garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device, torch.float16)
+                generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
+                images = pipe(
+                    prompt_embeds=prompt_embeds.to(device, torch.float16),
+                    negative_prompt_embeds=negative_prompt_embeds.to(device, torch.float16),
+                    pooled_prompt_embeds=pooled_prompt_embeds.to(device, torch.float16),
+                    negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device, torch.float16),
+                    num_inference_steps=denoise_steps,
+                    generator=generator,
+                    strength=1.0,
+                    pose_img=pose_img.to(device, torch.float16),
+                    text_embeds_cloth=prompt_embeds_c.to(device, torch.float16),
+                    cloth=garm_tensor.to(device, torch.float16),
+                    mask_image=mask,
+                    image=human_img,
+                    height=1024,
+                    width=768,
+                    ip_adapter_image=garm_img.resize((768, 1024)),
+                    guidance_scale=2.0,
+                )[0]
 
     if is_checked_crop:
-        out_img = images[0].resize(crop_size)        
-        human_img_orig.paste(out_img, (int(left), int(top)))    
+        out_img = images[0].resize(crop_size)
+        human_img_orig.paste(out_img, (int(left), int(top)))
         return human_img_orig, mask_gray
     else:
         return images[0], mask_gray
-    # return images[0], mask_gray
 
-garm_list = os.listdir(os.path.join(example_path,"cloth"))
-garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
+garm_list = os.listdir(os.path.join(example_path, "cloth"))
+garm_list_path = [os.path.join(example_path, "cloth", garm) for garm in garm_list]
 
-human_list = os.listdir(os.path.join(example_path,"human"))
-human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
+human_list = os.listdir(os.path.join(example_path, "human"))
+human_list_path = [os.path.join(example_path, "human", human) for human in human_list]
 
 human_ex_list = []
 for ex_human in human_list_path:
-    ex_dict= {}
+    ex_dict = {}
     ex_dict['background'] = ex_human
     ex_dict['layers'] = None
     ex_dict['composite'] = None
     human_ex_list.append(ex_dict)
 
-##default human
-
-
 image_blocks = gr.Blocks().queue()
+
 with image_blocks as demo:
-    gr.Markdown("## IDM-VTON 👕👔👚")
-    gr.Markdown("Virtual Try-on with your image and garment image. Check out the [source codes](https://github.com/yisol/IDM-VTON) and the [model](https://huggingface.co/yisol/IDM-VTON)")
+    gr.Markdown("## FashionFit 👚 👕 👖 👗 👔 👙 🧥")
+    gr.Markdown("Experience your style with AI-powered fashion fitting..")
+    
     with gr.Row():
         with gr.Column():
             imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
             with gr.Row():
-                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
+                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)", value=True)
             with gr.Row():
-                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
-
+                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing", value=False)
             example = gr.Examples(
                 inputs=imgs,
                 examples_per_page=10,
@@ -284,30 +274,23 @@ with image_blocks as demo:
             example = gr.Examples(
                 inputs=garm_img,
                 examples_per_page=8,
-                examples=garm_list_path)
-        with gr.Column():
-            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-            masked_img = gr.Image(label="Masked image output", elem_id="masked-img",show_share_button=False)
-        with gr.Column():
-            # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-            image_out = gr.Image(label="Output", elem_id="output-img",show_share_button=False)
-
-
-
-
-    with gr.Column():
-        try_button = gr.Button(value="Try-on")
-        with gr.Accordion(label="Advanced Settings", open=False):
-            with gr.Row():
-                denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=40, value=30, step=1)
-                seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42)
-
-
+                examples=garm_list_path
+            )
 
-    try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked,is_checked_crop, denoise_steps, seed], outputs=[image_out,masked_img], api_name='tryon')
+        with gr.Column():
+            # Only the following line is changed to hide the masked image element
+            masked_img = gr.Image(label="Masked image output", elem_id="masked-img", show_share_button=False, visible=False)
 
-            
+        with gr.Column():
+            image_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False)
 
+        with gr.Column():
+            try_button = gr.Button(value="Try-on")
+            with gr.Accordion(label="Advanced Settings", open=False):
+                with gr.Row():
+                    denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=40, value=30, step=1)
+                    seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42)
 
-image_blocks.launch()
+    try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked, is_checked_crop, denoise_steps, seed], outputs=[image_out, masked_img], api_name='tryon')
 
+image_blocks.launch()