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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 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 torchvision.transforms.functional import to_pil_image
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)
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
@spaces.GPU
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"
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():
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
)
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(show_api=True)
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