app.py

from preprocess.detectron2.projects.DensePose.apply_net_gradio import DensePose4Gradio
from preprocess.humanparsing.run_parsing import Parsing
from preprocess.openpose.run_openpose import OpenPose
import pytorch_lightning as pl
import os
import sys
import time
from glob import glob
from os.path import join as opj
from pathlib import Path

import gradio as gr
import torch
from omegaconf import OmegaConf
from PIL import Image
import spaces

from cldm.model import create_model
from cldm.plms_hacked import PLMSSampler
from utils_stableviton import get_batch, get_mask_location, tensor2img
print("pip import done")

PROJECT_ROOT = Path(__file__).absolute().parents[1].absolute()
sys.path.insert(0, str(PROJECT_ROOT))

IMG_H = 512
IMG_W = 384

openpose_model_hd = OpenPose(0)
openpose_model_hd.preprocessor.body_estimation.model.to('cuda')
parsing_model_hd = Parsing(0)
densepose_model_hd = DensePose4Gradio(
    cfg='preprocess/detectron2/projects/DensePose/configs/densepose_rcnn_R_50_FPN_s1x.yaml',
    model='https://dl.fbaipublicfiles.com/densepose/densepose_rcnn_R_50_FPN_s1x/165712039/model_final_162be9.pkl',
)

category_dict = ['upperbody', 'lowerbody', 'dress']
category_dict_utils = ['upper_body', 'lower_body', 'dresses']

# #### model init >>>>
config = OmegaConf.load("./configs/VITON.yaml")
config.model.params.img_H = IMG_H
config.model.params.img_W = IMG_W
params = config.model.params

model = create_model(config_path=None, config=config)
model.load_state_dict(torch.load("./checkpoints/VITONHD.ckpt", map_location="cpu")["state_dict"])
model = model.cuda()
model.eval()
sampler = PLMSSampler(model)
# #### model init <<<<


def stable_viton_model_hd(
        batch,
        n_steps,
):
    z, cond = model.get_input(batch, params.first_stage_key)
    bs = z.shape[0]
    c_crossattn = cond["c_crossattn"][0][:bs]
    if c_crossattn.ndim == 4:
        c_crossattn = model.get_learned_conditioning(c_crossattn)
        cond["c_crossattn"] = [c_crossattn]
    uc_cross = model.get_unconditional_conditioning(bs)
    uc_full = {"c_concat": cond["c_concat"], "c_crossattn": [uc_cross]}
    uc_full["first_stage_cond"] = cond["first_stage_cond"]
    for k, v in batch.items():
        if isinstance(v, torch.Tensor):
            batch[k] = v.cuda()
    sampler.model.batch = batch

    ts = torch.full((1,), 999, device=z.device, dtype=torch.long)
    start_code = model.q_sample(z, ts)

    output, _, _ = sampler.sample(
        n_steps,
        bs,
        (4, IMG_H // 8, IMG_W // 8),
        cond,
        x_T=start_code,
        verbose=False,
        eta=0.0,
        unconditional_conditioning=uc_full,
    )

    output = model.decode_first_stage(output)
    output = tensor2img(output)
    pil_output = Image.fromarray(output)
    return pil_output

@spaces.GPU  # TODO: turn on when final upload
@torch.no_grad()
def process_hd(vton_img, garm_img, n_steps):
    model_type = 'hd'
    category = 0  # 0:upperbody; 1:lowerbody; 2:dress

    stt = time.time()
    print('load images... ', end='')
    garm_img = Image.open(garm_img).resize((IMG_W, IMG_H))
    vton_img = Image.open(vton_img).resize((IMG_W, IMG_H))
    print('%.2fs' % (time.time() - stt))

    stt = time.time()
    print('get agnostic map... ', end='')
    keypoints = openpose_model_hd(vton_img.resize((IMG_W, IMG_H)))
    model_parse, _ = parsing_model_hd(vton_img.resize((IMG_W, IMG_H)))
    mask, mask_gray = get_mask_location(model_type, category_dict_utils[category], model_parse, keypoints)
    mask = mask.resize((IMG_W, IMG_H), Image.NEAREST)
    mask_gray = mask_gray.resize((IMG_W, IMG_H), Image.NEAREST)
    masked_vton_img = Image.composite(mask_gray, vton_img, mask)  # agnostic map
    print('%.2fs' % (time.time() - stt))

    stt = time.time()
    print('get densepose... ', end='')
    vton_img = vton_img.resize((IMG_W, IMG_H))  # size for densepose
    densepose = densepose_model_hd.execute(vton_img)  # densepose

    # human_img_arg = _apply_exif_orientation(vton_img.resize((IMG_W, IMG_H)))
    # 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((IMG_W, IMG_H))

    print('%.2fs' % (time.time() - stt))

    batch = get_batch(
        vton_img,
        garm_img,
        densepose,
        masked_vton_img,
        mask,
        IMG_H,
        IMG_W
    )

    sample = stable_viton_model_hd(
        batch,
        n_steps
    )
    return sample


example_path = opj(os.path.dirname(__file__), 'examples')
example_model_ps = sorted(glob(opj(example_path, "model/*")))
example_garment_ps = sorted(glob(opj(example_path, "garment/*")))

with gr.Blocks(css='style.css') as demo:
    gr.HTML(
        """
        <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
            <div>
                <h1>StableVITON Demo 👕👔👗</h1>
                <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
                    <a href='https://arxiv.org/abs/2312.01725'>
                        <img src="https://img.shields.io/badge/arXiv-2312.01725-red">
                    </a>
                    &nbsp;
                    <a href='https://rlawjdghek.github.io/StableVITON/'>
                        <img src='https://img.shields.io/badge/page-github.io-blue.svg'>
                    </a>
                    &nbsp;
                    <a href='https://github.com/rlawjdghek/StableVITON'>
                        <img src='https://img.shields.io/github/stars/rlawjdghek/StableVITON'>
                    </a>
                    &nbsp;
                    <a href='https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode'>
                        <img src='https://img.shields.io/badge/license-CC_BY--NC--SA_4.0-lightgrey'>
                    </a>
                </div>
            </div>
        </div>
        """
    )
    with gr.Row():
        gr.Markdown("## Experience virtual try-on with your own images!")
    with gr.Row():
        with gr.Column():
            vton_img = gr.Image(label="Model", type="filepath", height=384, value=example_model_ps[0])
            example = gr.Examples(
                inputs=vton_img,
                examples_per_page=14,
                examples=example_model_ps)
        with gr.Column():
            garm_img = gr.Image(label="Garment", type="filepath", height=384, value=example_garment_ps[0])
            example = gr.Examples(
                inputs=garm_img,
                examples_per_page=14,
                examples=example_garment_ps)
        with gr.Column():
            result_gallery = gr.Image(label='Output', show_label=False, scale=1)
            # result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", scale=1)
    with gr.Column():
        run_button = gr.Button(value="Run")
        # TODO: change default values (important!)
        # n_samples = gr.Slider(label="Images", minimum=1, maximum=4, value=1, step=1)
        n_steps = gr.Slider(label="Steps", minimum=20, maximum=70, value=25, step=1)
        # guidance_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=5.0, value=2.0, step=0.1)
        # seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=-1)

    ips = [vton_img, garm_img, n_steps]
    run_button.click(fn=process_hd, inputs=ips, outputs=[result_gallery])

demo.queue().launch()