import argparse
import os
os.environ['CUDA_HOME'] = '/usr/local/cuda'
os.environ['PATH'] = os.environ['PATH'] + ':/usr/local/cuda/bin'
from datetime import datetime
import spaces
import gradio as gr
import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from huggingface_hub import snapshot_download
from PIL import Image
from model.cloth_masker import AutoMaskerSeg, vis_mask
from model.pipeline import CatVTONPipeline
from utils import init_weight_dtype, resize_and_crop, resize_and_padding
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--base_model_path",
type=str,
default="runwayml/stable-diffusion-inpainting",
help=(
"The path to the base model to use for evaluation. This can be a local path or a model identifier from the Model Hub."
),
)
parser.add_argument(
"--resume_path",
type=str,
default="zhengchong/CatVTON",
help=(
"The Path to the checkpoint of trained tryon model."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="resource/demo/output",
help="The output directory where the model predictions will be written.",
)
parser.add_argument(
"--width",
type=int,
default=768,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--height",
type=int,
default=1024,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--repaint",
action="store_true",
help="Whether to repaint the result image with the original background."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
default=True,
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default="bf16",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
# parser.add_argument(
# "--enable_condition_noise",
# action="store_true",
# default=True,
# help="Whether or not to enable condition noise.",
# )
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
return args
def image_grid(imgs, rows, cols):
assert len(imgs) == rows * cols
w, h = imgs[0].size
grid = Image.new("RGB", size=(cols * w, rows * h))
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
args = parse_args()
repo_path = snapshot_download(repo_id=args.resume_path)
# Pipeline
pipeline = CatVTONPipeline(
base_ckpt=args.base_model_path,
attn_ckpt=repo_path,
attn_ckpt_version="mix",
weight_dtype=init_weight_dtype(args.mixed_precision),
use_tf32=args.allow_tf32,
device='cuda'
)
# AutoMasker
mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
automasker = AutoMaskerSeg(
densepose_ckpt=os.path.join(repo_path, "DensePose"),
segformer_ckpt="mattmdjaga/segformer_b2_clothes",
device='cuda',
)
@spaces.GPU
def submit_function(
person_image,
cloth_image,
cloth_type,
num_inference_steps,
guidance_scale,
seed,
show_type
):
person_image, mask = person_image["background"], person_image["layers"][0]
mask = Image.open(mask).convert("L")
if len(np.unique(np.array(mask))) == 1:
mask = None
else:
mask = np.array(mask)
mask[mask > 0] = 255
mask = Image.fromarray(mask)
tmp_folder = args.output_dir
date_str = datetime.now().strftime("%Y%m%d%H%M%S")
result_save_path = os.path.join(tmp_folder, date_str[:8], date_str[8:] + ".png")
if not os.path.exists(os.path.join(tmp_folder, date_str[:8])):
os.makedirs(os.path.join(tmp_folder, date_str[:8]))
generator = None
if seed != -1:
generator = torch.Generator(device='cuda').manual_seed(seed)
person_image = Image.open(person_image).convert("RGB")
cloth_image = Image.open(cloth_image).convert("RGB")
person_image = resize_and_crop(person_image, (args.width, args.height))
cloth_image = resize_and_padding(cloth_image, (args.width, args.height))
# Process mask
if mask is not None:
mask = resize_and_crop(mask, (args.width, args.height))
else:
mask = automasker(
person_image,
cloth_type
)['mask']
mask = mask_processor.blur(mask, blur_factor=9)
# Inference
# try:
result_image = pipeline(
image=person_image,
condition_image=cloth_image,
mask=mask,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=generator
)[0]
# except Exception as e:
# raise gr.Error(
# "An error occurred. Please try again later: {}".format(e)
# )
# Post-process
masked_person = vis_mask(person_image, mask)
save_result_image = image_grid([person_image, masked_person, cloth_image, result_image], 1, 4)
save_result_image.save(result_save_path)
if show_type == "result only":
return result_image
else:
width, height = person_image.size
if show_type == "input & result":
condition_width = width // 2
conditions = image_grid([person_image, cloth_image], 2, 1)
else:
condition_width = width // 3
conditions = image_grid([person_image, masked_person , cloth_image], 3, 1)
conditions = conditions.resize((condition_width, height), Image.NEAREST)
new_result_image = Image.new("RGB", (width + condition_width + 5, height))
new_result_image.paste(conditions, (0, 0))
new_result_image.paste(result_image, (condition_width + 5, 0))
return new_result_image
def person_example_fn(image_path):
return image_path
HEADER = """
🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
· Thanks to ZeroGPU for providing A100 for this demo.
· To adapt to ZeroGPU, we replace SCHP with SegFormer which may result in differences from our own demo.
· This demo and our weights are only open for **Non-commercial Use**.
· SafetyChecker is set to filter NSFW content, but it may block normal results too. Please adjust the `seed` for normal outcomes.
"""
def app_gradio():
with gr.Blocks(title="CatVTON") as demo:
gr.Markdown(HEADER)
with gr.Row():
with gr.Column(scale=1, min_width=350):
with gr.Row():
image_path = gr.Image(
type="filepath",
interactive=True,
visible=False,
)
person_image = gr.ImageEditor(
interactive=True, label="Person Image", type="filepath"
)
with gr.Row():
with gr.Column(scale=1, min_width=230):
cloth_image = gr.Image(
interactive=True, label="Condition Image", type="filepath"
)
with gr.Column(scale=1, min_width=120):
gr.Markdown(
'Two ways to provide Mask:
1. Upload the person image and use the `🖌️` above to draw the Mask (higher priority)
2. Select the `Try-On Cloth Type` to generate automatically '
)
cloth_type = gr.Radio(
label="Try-On Cloth Type",
choices=["upper", "lower", "overall"],
value="upper",
)
submit = gr.Button("Submit")
gr.Markdown(
'!!! Click only Once, Wait for Delay !!!'
)
gr.Markdown(
'Advanced options can adjust details:
1. `Inference Step` may enhance details;
2. `CFG` is highly correlated with saturation;
3. `Random seed` may improve pseudo-shadow.'
)
with gr.Accordion("Advanced Options", open=False):
num_inference_steps = gr.Slider(
label="Inference Step", minimum=10, maximum=100, step=5, value=50
)
# Guidence Scale
guidance_scale = gr.Slider(
label="CFG Strenth", minimum=0.0, maximum=7.5, step=0.5, value=2.5
)
# Random Seed
seed = gr.Slider(
label="Seed", minimum=-1, maximum=10000, step=1, value=42
)
show_type = gr.Radio(
label="Show Type",
choices=["result only", "input & result", "input & mask & result"],
value="input & mask & result",
)
with gr.Column(scale=2, min_width=500):
result_image = gr.Image(interactive=False, label="Result")
with gr.Row():
# Photo Examples
root_path = "resource/demo/example"
with gr.Column():
men_exm = gr.Examples(
examples=[
os.path.join(root_path, "person", "men", _)
for _ in os.listdir(os.path.join(root_path, "person", "men"))
],
examples_per_page=4,
inputs=image_path,
label="Person Examples ①",
)
women_exm = gr.Examples(
examples=[
os.path.join(root_path, "person", "women", _)
for _ in os.listdir(os.path.join(root_path, "person", "women"))
],
examples_per_page=4,
inputs=image_path,
label="Person Examples ②",
)
gr.Markdown(
'*Person examples come from the demos of OOTDiffusion and OutfitAnyone. '
)
with gr.Column():
condition_upper_exm = gr.Examples(
examples=[
os.path.join(root_path, "condition", "upper", _)
for _ in os.listdir(os.path.join(root_path, "condition", "upper"))
],
examples_per_page=4,
inputs=cloth_image,
label="Condition Upper Examples",
)
condition_overall_exm = gr.Examples(
examples=[
os.path.join(root_path, "condition", "overall", _)
for _ in os.listdir(os.path.join(root_path, "condition", "overall"))
],
examples_per_page=4,
inputs=cloth_image,
label="Condition Overall Examples",
)
condition_person_exm = gr.Examples(
examples=[
os.path.join(root_path, "condition", "person", _)
for _ in os.listdir(os.path.join(root_path, "condition", "person"))
],
examples_per_page=4,
inputs=cloth_image,
label="Condition Reference Person Examples",
)
gr.Markdown(
'*Condition examples come from the Internet. '
)
image_path.change(
person_example_fn, inputs=image_path, outputs=person_image
)
submit.click(
submit_function,
[
person_image,
cloth_image,
cloth_type,
num_inference_steps,
guidance_scale,
seed,
show_type,
],
result_image,
)
demo.queue().launch(share=True, show_error=True)
if __name__ == "__main__":
app_gradio()
