Update app.py

app.py

@@ -1,386 +1,12 @@
-import spaces
-import logging
-import math
 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
-from src.unet_hacked_tryon import UNet2DConditionModel
-from transformers import (
-    CLIPImageProcessor,
-    CLIPVisionModelWithProjection,
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-)
-from diffusers import DDPMScheduler,AutoencoderKL
-from typing import List
-
-import torch
-import os
-from transformers import AutoTokenizer
-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 torchvision.transforms.functional import to_pil_image
-from src.background_processor import BackgroundProcessor
-
-def pil_to_binary_mask(pil_image, threshold=0):
-    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_path = 'yisol/IDM-VTON'
-example_path = os.path.join(os.path.dirname(__file__), 'example')
-
-unet = UNet2DConditionModel.from_pretrained(
-    base_path,
-    subfolder="unet",
-    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(
-    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)
+def greet(name, intensity):
+    return "Hello, " + name + "!" * int(intensity)
 
-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]),
-            ]
-    )
-
-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,
+demo = gr.Interface(
+    fn=greet,
+    inputs=["text", "slider"],
+    outputs=["text"],
 )
-pipe.unet_encoder = UNet_Encoder
-
-# Standard size of shein images
-#WIDTH = int(4160/5)
-#HEIGHT = int(6240/5)
-# Standard size on which model is trained
-WIDTH = int(768)
-HEIGHT = int(1024)
-POSE_WIDTH = int(WIDTH/2)  # int(WIDTH/2)
-POSE_HEIGHT = int(HEIGHT/2)  #int(HEIGHT/2)
-ARM_WIDTH = "dc" # "hd" # hd -> full sleeve, dc for half sleeve 
-CATEGORY = "upper_body" # "lower_body"
-
-
-def is_cropping_required(width, height):
-    # If aspect ratio is 1.33, which is same as standard 3x4 ( 768x1024 ), then no need to crop, else crop
-    aspect_ratio = round(height/width, 2)
-    if aspect_ratio == 1.33:
-        return False
-    return True
-
-
-@spaces.GPU
-def start_tryon(human_img_dict,garm_img,garment_des, background_img, is_checked,is_checked_crop,denoise_steps,seed):
-    logging.info("Starting try on")
-    #device = "cuda"
-    device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
-    
-    openpose_model.preprocessor.body_estimation.model.to(device)
-    pipe.to(device)
-    pipe.unet_encoder.to(device)
-    
-    human_img_orig = human_img_dict["background"].convert("RGB")   # ImageEditor
-    #human_img_orig = human_img_dict.convert("RGB")     # Image
-    
-
-    """
-    # Derive HEIGHT & WIDTH such that width is not more than 1000. This will cater to both Shein images (4160x6240) of 2:3 AR and model standard images ( 768x1024 ) of 3:4 AR
-    WIDTH, HEIGHT = human_img_orig.size
-    division_factor = math.ceil(WIDTH/1000)
-    WIDTH = int(WIDTH/division_factor)
-    HEIGHT = int(HEIGHT/division_factor)
-    POSE_WIDTH = int(WIDTH/2)
-    POSE_HEIGHT = int(HEIGHT/2)
-    """
-    # is_checked_crop as True if original AR is not same as 2x3 as expected by model
-    w, h = human_img_orig.size
-    is_checked_crop = is_cropping_required(w, h)
-
-    garm_img= garm_img.convert("RGB").resize((WIDTH,HEIGHT))
-    if is_checked_crop:
-        # This will crop the image to make it Aspect Ratio of 3 x 4. And then at the end revert it back to original dimentions
-        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
-        right = (width + target_width) / 2
-        # for Landmark, model sizes are 594x879, so we need to reduce the height. In some case the garment on the model is
-        # also getting removed when reducing size from bottom. So we will only reduce height from top for now
-        top = (height - target_height) #top = (height - target_height) / 2        
-        bottom = height #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((WIDTH, HEIGHT))
-    else:
-        human_img = human_img_orig.resize((WIDTH, HEIGHT))
-
-    # Commenting out naize harmonization for now. We will have to integrate with Deep Learning based Harmonization methods
-    # Do color transfer from background image for better image harmonization
-    #if background_img:
-    #    human_img = BackgroundProcessor.intensity_transfer(human_img, background_img)
-
-
-    if is_checked:
-        # internally openpose_model is resizing human_img to resolution 384 if not passed as input
-        keypoints = openpose_model(human_img.resize((POSE_WIDTH, POSE_HEIGHT)))
-        model_parse, _ = parsing_model(human_img.resize((POSE_WIDTH, POSE_HEIGHT)))
-        # internally get mask location function is resizing model_parse to 384x512 if width & height not passed
-        mask, mask_gray = get_mask_location(ARM_WIDTH, CATEGORY, model_parse, keypoints)
-        mask = mask.resize((WIDTH, HEIGHT))
-        logging.info("Mask location on model identified")
-    else:
-        mask = pil_to_binary_mask(human_img_dict['layers'][0].convert("RGB").resize((WIDTH, HEIGHT)))
-        # 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((POSE_WIDTH,POSE_HEIGHT)))
-    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', device))
-    # 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((WIDTH,HEIGHT))
-    
-    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"
-                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=HEIGHT,
-                        width=WIDTH,
-                        ip_adapter_image = garm_img.resize((WIDTH,HEIGHT)),
-                        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)))    
-        final_image = human_img_orig
-        # return human_img_orig, mask_gray
-    else:
-        final_image = images[0]
-        # return images[0], mask_gray
-    
-    # apply background to final image
-    if background_img:
-       logging.info("Adding background")
-       final_image = BackgroundProcessor.replace_background_with_removebg(final_image, background_img) 
-    return final_image, 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 = []
-#human_ex_list = human_list_path # Image
-#""" if using ImageEditor instead of Image while taking input, use this - ImageEditor
-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
-
-
-# api_open=True will allow this API to be hit using curl
-image_blocks = gr.Blocks().queue(api_open=True)
-with image_blocks as demo:
-    gr.Markdown("## Virtual Try-On 👕👔👚")
-    gr.Markdown("Upload an image of a person and an image of a garment ✨.")
-    with gr.Row():
-        with gr.Column():
-            # changing from ImageEditor to Image to allow easy passing of data through API
-            # instead of passing {"dictionary": <>} ( which is failing ), we can directly pass the image
-            imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
-            #imgs = gr.Image(sources='upload', type='pil',label='Human. Mask with pen or use auto-masking')
-            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
-            )
-
-        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():
-           background_img = gr.Image(label="Background", sources='upload', type="pil")
-
-        with gr.Column():
-            with gr.Row():
-               image_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False) 
-            with gr.Row():
-               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)
-            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, background_img, is_checked,is_checked_crop, denoise_steps, seed], outputs=[image_out,masked_img], api_name='tryon')
 
-            
-image_blocks.launch()
+demo.launch()