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import os |
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from flask import Flask, request, jsonify |
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from PIL import Image |
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from io import BytesIO |
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import torch |
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import base64 |
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import io |
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import gradio as gr |
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import numpy as np |
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import spaces |
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from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline |
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from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref |
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from src.unet_hacked_tryon import UNet2DConditionModel |
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from transformers import ( |
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CLIPImageProcessor, |
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CLIPVisionModelWithProjection, |
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CLIPTextModel, |
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CLIPTextModelWithProjection, |
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AutoTokenizer, |
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) |
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from diffusers import DDPMScheduler, AutoencoderKL |
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from utils_mask import get_mask_location |
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from torchvision import transforms |
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import apply_net |
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from preprocess.humanparsing.run_parsing import Parsing |
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from preprocess.openpose.run_openpose import OpenPose |
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from detectron2.data.detection_utils import convert_PIL_to_numpy, _apply_exif_orientation |
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from torchvision.transforms.functional import to_pil_image |
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app = Flask(__name__) |
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base_path = 'yisol/IDM-VTON' |
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example_path = os.path.join(os.path.dirname(__file__), 'example') |
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unet = UNet2DConditionModel.from_pretrained( |
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base_path, |
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subfolder="unet", |
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torch_dtype=torch.float16, |
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) |
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unet.requires_grad_(False) |
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tokenizer_one = AutoTokenizer.from_pretrained( |
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base_path, |
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subfolder="tokenizer", |
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revision=None, |
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use_fast=False, |
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) |
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tokenizer_two = AutoTokenizer.from_pretrained( |
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base_path, |
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subfolder="tokenizer_2", |
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revision=None, |
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use_fast=False, |
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) |
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noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler") |
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text_encoder_one = CLIPTextModel.from_pretrained( |
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base_path, |
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subfolder="text_encoder", |
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torch_dtype=torch.float16, |
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) |
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text_encoder_two = CLIPTextModelWithProjection.from_pretrained( |
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base_path, |
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subfolder="text_encoder_2", |
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torch_dtype=torch.float16, |
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) |
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image_encoder = CLIPVisionModelWithProjection.from_pretrained( |
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base_path, |
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subfolder="image_encoder", |
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torch_dtype=torch.float16, |
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) |
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vae = AutoencoderKL.from_pretrained(base_path, |
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subfolder="vae", |
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torch_dtype=torch.float16, |
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) |
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UNet_Encoder = UNet2DConditionModel_ref.from_pretrained( |
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base_path, |
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subfolder="unet_encoder", |
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torch_dtype=torch.float16, |
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) |
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parsing_model = Parsing(0) |
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openpose_model = OpenPose(0) |
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UNet_Encoder.requires_grad_(False) |
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image_encoder.requires_grad_(False) |
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vae.requires_grad_(False) |
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unet.requires_grad_(False) |
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text_encoder_one.requires_grad_(False) |
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text_encoder_two.requires_grad_(False) |
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tensor_transfrom = transforms.Compose( |
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[ |
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transforms.ToTensor(), |
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transforms.Normalize([0.5], [0.5]), |
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] |
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) |
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pipe = TryonPipeline.from_pretrained( |
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base_path, |
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unet=unet, |
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vae=vae, |
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feature_extractor= CLIPImageProcessor(), |
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text_encoder = text_encoder_one, |
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text_encoder_2 = text_encoder_two, |
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tokenizer = tokenizer_one, |
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tokenizer_2 = tokenizer_two, |
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scheduler = noise_scheduler, |
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image_encoder=image_encoder, |
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torch_dtype=torch.float16, |
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) |
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pipe.unet_encoder = UNet_Encoder |
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def pil_to_binary_mask(pil_image, threshold=0): |
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np_image = np.array(pil_image) |
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grayscale_image = Image.fromarray(np_image).convert("L") |
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binary_mask = np.array(grayscale_image) > threshold |
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mask = np.zeros(binary_mask.shape, dtype=np.uint8) |
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for i in range(binary_mask.shape[0]): |
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for j in range(binary_mask.shape[1]): |
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if binary_mask[i, j]: |
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mask[i, j] = 1 |
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mask = (mask * 255).astype(np.uint8) |
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output_mask = Image.fromarray(mask) |
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return output_mask |
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@spaces.GPU |
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def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed): |
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device = "cuda" |
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openpose_model.preprocessor.body_estimation.model.to(device) |
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pipe.to(device) |
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pipe.unet_encoder.to(device) |
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garm_img = garm_img.convert("RGB").resize((768, 1024)) |
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human_img_orig = dict["background"].convert("RGB") |
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if is_checked_crop: |
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width, height = human_img_orig.size |
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target_width = int(min(width, height * (3 / 4))) |
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target_height = int(min(height, width * (4 / 3))) |
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left = (width - target_width) / 2 |
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top = (height - target_height) / 2 |
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right = (width + target_width) / 2 |
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bottom = (height + target_height) / 2 |
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cropped_img = human_img_orig.crop((left, top, right, bottom)) |
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crop_size = cropped_img.size |
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human_img = cropped_img.resize((768, 1024)) |
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else: |
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human_img = human_img_orig.resize((768, 1024)) |
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if is_checked: |
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keypoints = openpose_model(human_img.resize((384, 512))) |
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model_parse, _ = parsing_model(human_img.resize((384, 512))) |
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mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints) |
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mask = mask.resize((768, 1024)) |
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else: |
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mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024))) |
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mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transfrom(human_img) |
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mask_gray = to_pil_image((mask_gray + 1.0) / 2.0) |
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human_img_arg = _apply_exif_orientation(human_img.resize((384, 512))) |
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human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR") |
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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')) |
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pose_img = args.func(args, human_img_arg) |
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pose_img = pose_img[:, :, ::-1] |
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pose_img = Image.fromarray(pose_img).resize((768, 1024)) |
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with torch.no_grad(): |
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with torch.cuda.amp.autocast(): |
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prompt = "model is wearing " + garment_des |
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" |
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with torch.inference_mode(): |
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( |
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prompt_embeds, |
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negative_prompt_embeds, |
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pooled_prompt_embeds, |
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negative_pooled_prompt_embeds, |
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) = pipe.encode_prompt( |
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prompt, |
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num_images_per_prompt=1, |
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do_classifier_free_guidance=True, |
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negative_prompt=negative_prompt, |
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) |
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prompt = "a photo of " + garment_des |
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" |
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if not isinstance(prompt, list): |
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prompt = [prompt] * 1 |
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if not isinstance(negative_prompt, list): |
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negative_prompt = [negative_prompt] * 1 |
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with torch.inference_mode(): |
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( |
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prompt_embeds_c, |
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_, |
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_, |
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_, |
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) = pipe.encode_prompt( |
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prompt, |
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num_images_per_prompt=1, |
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do_classifier_free_guidance=False, |
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negative_prompt=negative_prompt, |
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) |
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pose_img = tensor_transfrom(pose_img).unsqueeze(0).to(device, torch.float16) |
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garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device, torch.float16) |
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generator = torch.Generator(device).manual_seed(seed) if seed is not None else None |
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images = pipe( |
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prompt_embeds=prompt_embeds.to(device, torch.float16), |
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negative_prompt_embeds=negative_prompt_embeds.to(device, torch.float16), |
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pooled_prompt_embeds=pooled_prompt_embeds.to(device, torch.float16), |
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device, torch.float16), |
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num_inference_steps=denoise_steps, |
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generator=generator, |
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strength=1.0, |
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pose_img=pose_img.to(device, torch.float16), |
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text_embeds_cloth=prompt_embeds_c.to(device, torch.float16), |
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cloth=garm_tensor.to(device, torch.float16), |
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mask_image=mask, |
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image=human_img, |
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height=1024, |
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width=768, |
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ip_adapter_image=garm_img.resize((768, 1024)), |
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guidance_scale=2.0, |
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)[0] |
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if is_checked_crop: |
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out_img = images[0].resize(crop_size) |
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human_img_orig.paste(out_img, (int(left), int(top))) |
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return human_img_orig, mask_gray |
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else: |
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return images[0], mask_gray |
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def decode_image_from_base64(image_data): |
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image_data = base64.b64decode(image_data) |
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image = Image.open(io.BytesIO(image_data)) |
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return image |
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def encode_image_to_base64(image): |
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buffered = BytesIO() |
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image.save(buffered, format="PNG") |
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return base64.b64encode(buffered.getvalue()).decode('utf-8') |
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@app.route('/tryon', methods=['POST']) |
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def tryon(): |
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data = request.json |
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human_image = decode_image_from_base64(data['human_image']) |
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garment_image = decode_image_from_base64(data['garment_image']) |
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description = data.get('description') |
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use_auto_mask = data.get('use_auto_mask', True) |
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use_auto_crop = data.get('use_auto_crop', False) |
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denoise_steps = int(data.get('denoise_steps', 30)) |
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seed = int(data.get('seed', 42)) |
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human_dict = { |
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'background': human_image, |
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'layers': [human_image] if not use_auto_mask else None, |
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'composite': None |
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} |
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output_image, mask_image = start_tryon(human_dict, garment_image, description, use_auto_mask, use_auto_crop, denoise_steps, seed) |
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output_base64 = encode_image_to_base64(output_image) |
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mask_base64 = encode_image_to_base64(mask_image) |
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return jsonify({ |
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'output_image': output_base64, |
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'mask_image': mask_base64 |
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}) |
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if __name__ == "__main__": |
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app.run(debug=True, host="0.0.0.0", port=7860) |
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