Update app.py

app.py

@@ -1,9 +1,6 @@
-# Import spaces first to control GPU initialization
-import spaces
-
-# Now import other packages
-import torch
 import gradio as gr
+
+
 from PIL import Image
 from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline
 from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref
@@ -14,218 +11,307 @@ 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
+import spaces
 import numpy as np
 from utils_mask import get_mask_location
 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
 
-# Rest of your code remains the same...
-
 
-# Function to convert a PIL image to a binary mask
 def pil_to_binary_mask(pil_image, threshold=0):
-    np_image = np.array(pil_image.convert("L"))
-    mask = (np_image > threshold).astype(np.uint8) * 255
-    return Image.fromarray(mask)
+    np_image = np.array(pil_image)
+    grayscale_image = Image.fromarray(np_image).convert("L")
+    binary_mask = np.array(grayscale_image) > threshold
+    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 :
+                mask[i,j] = 1
+    mask = (mask*255).astype(np.uint8)
+    output_mask = Image.fromarray(mask)
+    return output_mask
 
 
-# Base paths for pre-trained models and examples
 base_path = 'yisol/IDM-VTON'
 example_path = os.path.join(os.path.dirname(__file__), 'example')
 
-# Load the UNet model for try-on
-unet = UNet2DConditionModel.from_pretrained(base_path, subfolder="unet", torch_dtype=torch.float16)
+unet = UNet2DConditionModel.from_pretrained(
+    base_path,
+    subfolder="unet",
+    torch_dtype=torch.float16,
+)
 unet.requires_grad_(False)
-
-# Load tokenizers and other required models
-tokenizer_one = AutoTokenizer.from_pretrained(base_path, subfolder="tokenizer", use_fast=False)
-tokenizer_two = AutoTokenizer.from_pretrained(base_path, subfolder="tokenizer_2", use_fast=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(base_path, 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)
-UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(base_path, subfolder="unet_encoder", torch_dtype=torch.float16)
 
-# Load parsing and openpose models
+text_encoder_one = CLIPTextModel.from_pretrained(
+    base_path,
+    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",
+    torch_dtype=torch.float16,
+)
+
 parsing_model = Parsing(0)
 openpose_model = OpenPose(0)
 
-# Freeze the parameters of the models to avoid gradients
 UNet_Encoder.requires_grad_(False)
 image_encoder.requires_grad_(False)
 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]),
+            ]
+    )
 
-# Image transformation function
-tensor_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
-
-# Initialize the pipeline for try-on
 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
 
-
-# Main function for try-on with error handling
 @spaces.GPU
-def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
-    try:
-        device = "cuda"
-
-        # Prepare the device and models for computation
-        openpose_model.preprocessor.body_estimation.model.to(device)
-        pipe.to(device)
-        pipe.unet_encoder.to(device)
-
-        # Prepare the images
-        garm_img = garm_img.convert("RGB").resize((768, 1024))
-        human_img_orig = dict["background"].convert("RGB")
-
-        # Handle cropping if needed
-        if is_checked_crop:
-            width, height = human_img_orig.size
-            target_width = int(min(width, height * (3 / 4)))
-            target_height = int(min(height, width * (4 / 3)))
-            left = (width - target_width) / 2
-            top = (height - target_height) / 2
-            right = (width + target_width) / 2
-            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))
-        else:
-            human_img = human_img_orig.resize((768, 1024))
-
-        # Apply masking if selected
-        if is_checked:
-            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))
-        else:
-            mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
-
-        mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transform(human_img)
-        mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)
-
-        # Apply pose estimation
-        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')
-        )
-        pose_img = args.func(args, human_img_arg)
-        pose_img = pose_img[:, :, ::-1]
-        pose_img = Image.fromarray(pose_img).resize((768, 1024))
-
-        # Generate the try-on image
-        with torch.no_grad():
-            with torch.cuda.amp.autocast():
+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")    
+    
+    if is_checked_crop:
+        width, height = human_img_orig.size
+        target_width = int(min(width, height * (3 / 4)))
+        target_height = int(min(height, width * (4 / 3)))
+        left = (width - target_width) / 2
+        top = (height - target_height) / 2
+        right = (width + target_width) / 2
+        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))
+    else:
+        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)))
+        mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints)
+        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)
+
+
+    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))
+    
+    with torch.no_grad():
+        # Extract the images
+        with torch.cuda.amp.autocast():
+            with torch.no_grad():
                 prompt = "model is wearing " + garment_des
                 negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-                prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = pipe.encode_prompt(
-                    prompt, num_images_per_prompt=1, do_classifier_free_guidance=True, negative_prompt=negative_prompt
-                )
-
-                # Cloth prompt embedding
-                prompt = "a photo of " + garment_des
-                prompt_embeds_c, _, _, _ = pipe.encode_prompt(
-                    prompt, num_images_per_prompt=1, do_classifier_free_guidance=False, negative_prompt=negative_prompt
-                )
-
-                # Convert pose image and garment to tensors
-                pose_img = tensor_transform(pose_img).unsqueeze(0).to(device, torch.float16)
-                garm_tensor = tensor_transform(garm_img).unsqueeze(0).to(device, torch.float16)
-                generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
-
-                # Run the pipeline
-                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)))
-            return human_img_orig, mask_gray
-        else:
-            return images[0], mask_gray
-
-    except Exception as e:
-        print(f"Error during try-on: {e}")
-        return None, None
-
-
-# Gradio interface setup
-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_ex_list = [{"background": ex_human, "layers": None, "composite": None} for ex_human in human_list_path]
-
-# Gradio blocks UI
-with gr.Blocks() as image_blocks:
-    with gr.Column():
-        with gr.Row():
-            # imgs = gr.Image(source='upload', type="pil", label='Person Image')
-            imgs = gr.Image(type="pil", label='Person Image')  # Remove the 'source' argument
-            is_checked = gr.Checkbox(label="Check if mask needed")
-            is_checked_crop = gr.Checkbox(label="Check to crop")
-            ex_img = gr.Examples(inputs=imgs, examples_per_page=9, examples=human_ex_list)
+                with torch.inference_mode():
+                    (
+                        prompt_embeds,
+                        negative_prompt_embeds,
+                        pooled_prompt_embeds,
+                        negative_pooled_prompt_embeds,
+                    ) = pipe.encode_prompt(
+                        prompt,
+                        num_images_per_prompt=1,
+                        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]
+
+    if is_checked_crop:
+        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]
+
+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['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)")
+    with gr.Row():
         with gr.Column():
-            garm_img = gr.Image(source='upload', type="pil", label='Cloth')
-            garment_des = gr.Textbox(label="Garment Description", value='garment,shirt')
-            ex_garm = gr.Examples(inputs=garm_img, examples_per_page=9, examples=garm_list_path)
-        with gr.Row():
-            denoise_steps = gr.Slider(label="denoise steps", minimum=1, maximum=50, step=1, value=25)
-            seed = gr.Slider(label="Seed (for reproducible results)", minimum=0, maximum=2147483647, step=1)
-        with gr.Row():
-            try_button = gr.Button("Try it on")
-        with gr.Row():
-            out_img = gr.Image(label="Generated tryon output")
-            masked_img = gr.Image(label="Mask")
-
-    try_button.click(
-        start_tryon,
-        inputs=[imgs, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed],
-        outputs=[out_img, masked_img]
-    )
+            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)
+            with gr.Row():
+                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
+
+            example = gr.Examples(
+                inputs=imgs,
+                examples_per_page=10,
+                examples=human_ex_list
+            )
 
-# Launch Gradio app
+        with gr.Column():
+            garm_img = gr.Image(label="Garment", sources='upload', type="pil")
+            with gr.Row(elem_id="prompt-container"):
+                with gr.Row():
+                    prompt = gr.Textbox(placeholder="Description of garment ex) Short Sleeve Round Neck T-shirts", show_label=False, elem_id="prompt")
+            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)
+
+
+
+    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(share=True)
 image_blocks.launch(server_name="0.0.0.0", server_port=7860)
+
+