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from torchvision import transforms
from diffusers import DDPMScheduler, AutoencoderKL
from transformers import (
CLIPImageProcessor,
CLIPVisionModelWithProjection,
CLIPTextModel,
CLIPTextModelWithProjection,
AutoTokenizer,
)
from src.tryon.src.tryon_pipeline import (
StableDiffusionXLInpaintPipeline as TryonPipeline,
)
from src.tryon.src.unet_hacked_garmnet import (
UNet2DConditionModel as UNet2DConditionModel_ref,
)
from src.tryon.src.unet_hacked_tryon import UNet2DConditionModel
from src.tryon.preprocess.humanparsing.run_parsing import Parsing
from src.tryon.preprocess.openpose.run_openpose import OpenPose
from src.tryon.utils_mask import get_mask_location
from torchvision.transforms.functional import to_pil_image
from src.tryon.detectron2.data.detection_utils import (
convert_PIL_to_numpy,
_apply_exif_orientation,
)
import torch
from PIL import Image
from src.tryon import apply_net
pipe = None
UNet_Encoder = None
parsing_model = None
openpose_model = None
tensor_transform = None
def load_model():
global pipe, UNet_Encoder, parsing_model, openpose_model, tensor_transform
if pipe is not None:
return {
"pipe": pipe,
"unet_encoder": UNet_Encoder,
"parsing_model": parsing_model,
"openpose_model": openpose_model,
"tensor_transform": tensor_transform,
}
device = "cuda" if torch.cuda.is_available() else "cpu"
base_path = "yisol/IDM-VTON"
# Load sub-models
unet = (
UNet2DConditionModel.from_pretrained(
base_path, subfolder="unet", torch_dtype=torch.float16
)
.eval()
.requires_grad_(False)
)
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
)
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
)
UNet_Encoder.requires_grad_(False)
parsing_model = Parsing(0)
openpose_model = OpenPose(0)
tensor_transform = 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
pipe.to(device)
return {
"pipe": pipe,
"unet_encoder": UNet_Encoder,
"parsing_model": parsing_model,
"openpose_model": openpose_model,
"tensor_transform": tensor_transform,
}
def unload_model():
global pipe, UNet_Encoder, parsing_model, openpose_model, tensor_transform
pipe = None
UNet_Encoder = None
parsing_model = None
openpose_model = None
tensor_transform = None
if torch.cuda.is_available():
torch.cuda.empty_cache()
def predict(
# pipe,
# UNet_Encoder,
# parsing_model,
# openpose_model,
# tensor_transform,
dict_img,
garm_img,
garment_des,
is_checked,
is_checked_crop,
denoise_steps,
seed,
category,
):
device = "cuda" if torch.cuda.is_available() else "cpu"
openpose_model.preprocessor.body_estimation.model.to(device)
pipe.unet_encoder.to(device)
pipe.to(device)
# Resize and convert
garm_img = garm_img.convert("RGB").resize((768, 1024))
human_img_orig = dict_img["background"].convert("RGB")
orig_size = human_img_orig.size
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))
# Mask generation
if is_checked:
keypoints = openpose_model(human_img.resize((384, 512)))
model_parse, _ = parsing_model(human_img.resize((384, 512)))
mask, _ = get_mask_location("hd", category, model_parse, keypoints)
mask = mask.resize((768, 1024))
else:
if dict_img["layers"][0] is not None:
mask = dict_img["layers"][0].convert("RGB").resize((768, 1024))
mask = Image.fromarray(
(torch.tensor(mask) > 0).numpy().astype("uint8") * 255
)
else:
mask = Image.new("RGB", (768, 1024), color="white")
# Mask gray
mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transform(human_img)
mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)
# Pose estimation
human_np = convert_PIL_to_numpy(
_apply_exif_orientation(human_img.resize((384, 512))), format="BGR"
)
args = apply_net.create_argument_parser().parse_args(
(
"show",
"./tryon/configs/densepose_rcnn_R_50_FPN_s1x.yaml",
"./tryon/ckpt/densepose/model_final_162be9.pkl",
"dp_segm",
"-v",
"--opts",
"MODEL.DEVICE",
device,
)
)
pose_img = args.func(args, human_np)
pose_img = Image.fromarray(pose_img[:, :, ::-1]).resize((768, 1024))
# Inference
with torch.no_grad(), torch.cuda.amp.autocast():
prompt = f"((best quality, masterpiece, ultra-detailed, high quality photography, photo realistic)), the model is wearing {garment_des}"
neg_prompt = "monochrome, lowres, bad anatomy, worst quality, normal quality, low quality, blurry, jpeg artifacts, sketch"
prompt_embeds, neg_embeds, pooled_prompt_embeds, neg_pooled_prompt_embeds = (
pipe.encode_prompt(
prompt,
num_images_per_prompt=1,
do_classifier_free_guidance=True,
negative_prompt=neg_prompt,
)
)
garment_prompt = f"((best quality, masterpiece, ultra-detailed, high quality photography, photo realistic)), a photo of {garment_des}"
prompt_embeds_c, *_ = pipe.encode_prompt(
[garment_prompt],
num_images_per_prompt=1,
do_classifier_free_guidance=False,
negative_prompt=[neg_prompt],
)
pose_tensor = 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 != -1 else None
images = pipe(
prompt_embeds=prompt_embeds.to(device),
negative_prompt_embeds=neg_embeds.to(device),
pooled_prompt_embeds=pooled_prompt_embeds.to(device),
negative_pooled_prompt_embeds=neg_pooled_prompt_embeds.to(device),
num_inference_steps=denoise_steps,
generator=generator,
strength=1.0,
pose_img=pose_tensor,
text_embeds_cloth=prompt_embeds_c.to(device),
cloth=garm_tensor,
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:
return images[0].resize(crop_size), mask_gray.resize(crop_size)
else:
return images[0].resize(orig_size), mask_gray.resize(orig_size)
# from PIL import Image
# import torch
# from tryon_model import load_model, predict # replace 'tryon_model' with your actual filename/module
# # Load the models
# pipe, UNet_Encoder, parsing_model, openpose_model, tensor_transform = load_model()
# # Load input images
# human_image = Image.open("path_to_human_image.jpg").convert("RGB")
# garment_image = Image.open("path_to_garment_image.jpg").convert("RGB")
# # Create mock dict_img
# dict_img = {
# "background": human_image,
# "layers": [None] # or use a mask PIL.Image
# }
# # Set parameters
# garment_des = "a red floral dress with puff sleeves"
# is_checked = True # Auto-generate mask
# is_checked_crop = True # Crop input to 3:4 ratio
# denoise_steps = 30 # Diffusion steps
# seed = 42 # Set -1 for random
# category = "upper" # Use correct clothing type
# # Run prediction
# result_img, result_mask = predict(
# pipe=pipe,
# UNet_Encoder=UNet_Encoder,
# parsing_model=parsing_model,
# openpose_model=openpose_model,
# tensor_transform=tensor_transform,
# dict_img=dict_img,
# garm_img=garment_image,
# garment_des=garment_des,
# is_checked=is_checked,
# is_checked_crop=is_checked_crop,
# denoise_steps=denoise_steps,
# seed=seed,
# category=category
# )
# # Save or show results
# result_img.save("tryon_output.png")
# result_mask.save("tryon_mask.png")
# print("Prediction complete. Saved output and mask.")
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