app.py

import spaces
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 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

# Functions unchanged
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]:
                mask[i, j] = 1
    mask = (mask * 255).astype(np.uint8)
    output_mask = Image.fromarray(mask)
    return output_mask

# Paths
base_path = 'yisol/IDM-VTON'
example_path = os.path.join(os.path.dirname(__file__), 'example')

# Load models
unet = UNet2DConditionModel.from_pretrained(
    base_path,
    subfolder="unet",
    torch_dtype=torch.float16,
).cpu()  # Start on CPU
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,
).cpu()
text_encoder_two = CLIPTextModelWithProjection.from_pretrained(
    base_path,
    subfolder="text_encoder_2",
    torch_dtype=torch.float16,
).cpu()
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
    base_path,
    subfolder="image_encoder",
    torch_dtype=torch.float16,
).cpu()
vae = AutoencoderKL.from_pretrained(
    base_path,
    subfolder="vae",
    torch_dtype=torch.float16,
).cpu()

UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
    base_path,
    subfolder="unet_encoder",
    torch_dtype=torch.float16,
).cpu()

parsing_model = Parsing(0)  # Parsing runs on CPU
openpose_model = OpenPose(0)  # OpenPose runs on CPU

# Offloading setup
def move_model_to_device(model, device):
    if model is not None:
        model.to(device)

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,
).cpu()  # Start pipeline on CPU

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"

    # Move required models to GPU
    move_model_to_device(pipe.unet, device)
    move_model_to_device(pipe.vae, device)
    move_model_to_device(pipe.unet_encoder, device)
    move_model_to_device(pipe.text_encoder, device)
    move_model_to_device(pipe.text_encoder_2, device)
    move_model_to_device(pipe.image_encoder, device)
    openpose_model.preprocessor.body_estimation.model.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_gray = (1 - transforms.ToTensor()(mask)) * transforms.ToTensor()(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'))
    pose_img = args.func(args, human_img_arg)
    pose_img = pose_img[:, :, ::-1]
    pose_img = Image.fromarray(pose_img).resize((768, 1024))

    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"
            (
                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,
            )

            garm_tensor = transforms.ToTensor()(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=transforms.ToTensor()(pose_img).unsqueeze(0).to(device, torch.float16),
                cloth=garm_tensor,
                mask_image=mask,
                image=human_img,
                height=1024,
                width=768,
                guidance_scale=2.0,
            )[0]

    # Return unused models to CPU
    move_model_to_device(pipe.unet, "cpu")
    move_model_to_device(pipe.vae, "cpu")
    move_model_to_device(pipe.unet_encoder, "cpu")
    move_model_to_device(pipe.text_encoder, "cpu")
    move_model_to_device(pipe.text_encoder_2, "cpu")
    move_model_to_device(pipe.image_encoder, "cpu")

    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

# Gradio UI setup
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]