import base64 from io import BytesIO 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 def b64_to_pil(base64_string): # Decode the base64 string image_data = base64.b64decode(base64_string) # Create a PIL Image object from the decoded image data image = Image.open(BytesIO(image_data)) return image def pil_to_b64(pil_img): buffered = BytesIO() pil_img.save(buffered, format="PNG") img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") return img_str def prepare_pipeline(): pass 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) 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, ) pipe.unet_encoder = UNet_Encoder def get_result(human_img,garm_img, body_part="upper_body",denoise_steps=30,seed=42,is_checked_crop=False,garment_des=""): 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 = human_img 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)) keypoints = openpose_model(human_img.resize((384,512))) model_parse, _ = parsing_model(human_img.resize((384,512))) mask, mask_gray = get_mask_location('hd', body_part, model_parse, keypoints) mask = mask.resize((768,1024)) 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" 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