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 gradio as gr
import spaces
import numpy as np
import torch
from diffusers.image_processor import VaeImageProcessor
from huggingface_hub import snapshot_download
from PIL import Image
torch.jit.script = lambda f: f
from model.cloth_masker import AutoMasker, vis_mask
from model.pipeline import CatVTONPipeline, CatVTONPix2PixPipeline
from model.flux.pipeline_flux_tryon import FluxTryOnPipeline
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="booksforcharlie/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(
"--p2p_base_model_path",
type=str,
default="timbrooks/instruct-pix2pix",
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."
),
)
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()
# Mask-based CatVTON
catvton_repo = "zhengchong/CatVTON"
repo_path = snapshot_download(repo_id=catvton_repo)
# 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 = AutoMasker(
densepose_ckpt=os.path.join(repo_path, "DensePose"),
schp_ckpt=os.path.join(repo_path, "SCHP"),
device='cuda',
)
# # Flux-based CatVTON
# access_token = os.getenv("HUGGING_FACE_HUB_TOKEN")
# flux_repo = "black-forest-labs/FLUX.1-Fill-dev"
# pipeline_flux = FluxTryOnPipeline.from_pretrained(flux_repo, use_auth_token=access_token)
# pipeline_flux.load_lora_weights(
# os.path.join(repo_path, "flux-lora"),
# weight_name='pytorch_lora_weights.safetensors'
# )
# pipeline_flux.to("cuda", init_weight_dtype(args.mixed_precision))
@spaces.GPU(duration=120)
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
# @spaces.GPU(duration=120)
# def submit_function_flux(
# person_image,
# cloth_image,
# cloth_type,
# num_inference_steps,
# guidance_scale,
# seed,
# show_type
# ):
# # Process image editor input
# 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)
# # Set random seed
# generator = None
# if seed != -1:
# generator = torch.Generator(device='cuda').manual_seed(seed)
# # Process input images
# person_image = Image.open(person_image).convert("RGB")
# cloth_image = Image.open(cloth_image).convert("RGB")
# # Adjust image sizes
# 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
# result_image = pipeline_flux(
# image=person_image,
# condition_image=cloth_image,
# mask_image=mask,
# width=args.width,
# height=args.height,
# num_inference_steps=num_inference_steps,
# guidance_scale=guidance_scale,
# generator=generator
# ).images[0]
# # Post-processing
# masked_person = vis_mask(person_image, mask)
# # Return result based on show type
# 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 = ""
def app_gradio():
with gr.Blocks(title="CatVTON") as demo:
gr.Markdown(HEADER)
with gr.Tab("Virtual Try on"):
with gr.Row():
# define root_path
root_path = "resource/demo/example"
# First column ==============================
with gr.Column(scale=1, min_width=350):
# Person image
image_path = gr.Image(
type="filepath",
interactive=True,
visible=False,
)
person_image = gr.ImageEditor(
interactive=True, label="Person Image", type="filepath"
)
# Mask instruction
with gr.Row():
with gr.Column(scale = 2, min_width=80):
gr.Markdown(
'NOTE: The model image must fully show the body parts in the area where you want to try on the clothes
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 '
)
with gr.Column(scale = 1, min_width=80):
cloth_type = gr.Radio(
label="Try-On Cloth Type",
choices=["upper", "lower", "overall"],
value="upper",
)
# Model column examples
# Men examples
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 examples
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",
)
# Markdown: component display text in Gradio
gr.Markdown(
'*Person examples come from the demos of OOTDiffusion and OutfitAnyone. '
)
# Second column ==========================================
with gr.Column(scale=1, min_width=350):
# Clothes image
cloth_image = gr.Image(
interactive=True, label="Clothes Image", type="filepath"
)
# Clothes column examples
# Upper clothes examples
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="Upper clothes",
)
# Lower clothes examples
condition_upper_exm = gr.Examples(
examples=[
os.path.join(root_path, "condition", "lower", _)
for _ in os.listdir(os.path.join(root_path, "condition", "lower"))
],
examples_per_page=4,
inputs=cloth_image,
label="Lower clothes",
)
# Full-body clothes 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="Full-body clothes",
)
# Below ===============================================================
with gr.Row():
with gr.Column():
# Result pennal
result_image = gr.Image(interactive=False, label="Result")
# Submit button
submit = gr.Button("Submit")
gr.Markdown(
'!!! Click only Once, Wait for Delay !!!'
)
# Advance options setting
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="result only",
)
# event listener for changes to the image_path input component. Whenever the value of image_path changes (e.g., when a new image is uploaded or selected)
image_path.change(
person_example_fn, inputs=image_path, outputs=person_image
)
# when submit button clicked
submit.click(
submit_function,
[
person_image,
cloth_image,
cloth_type,
num_inference_steps,
guidance_scale,
seed,
show_type,
],
result_image,
)
# with gr.Tab("Mask-based & Flux.1 Fill Dev"):
# with gr.Row():
# with gr.Column(scale=1, min_width=350):
# with gr.Row():
# image_path_flux = gr.Image(
# type="filepath",
# interactive=True,
# visible=False,
# )
# person_image_flux = gr.ImageEditor(
# interactive=True, label="Person Image", type="filepath"
# )
# with gr.Row():
# with gr.Column(scale=1, min_width=230):
# cloth_image_flux = 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_flux = gr.Button("Submit")
# gr.Markdown(
# '!!! Click only Once, Wait for Delay !!!'
# )
# with gr.Accordion("Advanced Options", open=False):
# num_inference_steps_flux = gr.Slider(
# label="Inference Step", minimum=10, maximum=100, step=5, value=50
# )
# # Guidence Scale
# guidance_scale_flux = gr.Slider(
# label="CFG Strenth", minimum=0.0, maximum=50, step=0.5, value=30
# )
# # Random Seed
# seed_flux = 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_flux = gr.Image(interactive=False, label="Result")
# with gr.Row():
# # Photo Examples
# root_path = "resource/demo/example"
# with gr.Column():
# 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_flux,
# label="Person Examples ①",
# )
# 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_flux,
# label="Person Examples ②",
# )
# gr.Markdown(
# '*Person examples come from the demos of OOTDiffusion and OutfitAnyone. '
# )
# with gr.Column():
# 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_flux,
# label="Condition Upper Examples",
# )
# 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_flux,
# 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_flux,
# label="Condition Reference Person Examples",
# )
# gr.Markdown(
# '*Condition examples come from the Internet. '
# )
# image_path_flux.change(
# person_example_fn, inputs=image_path_flux, outputs=person_image_flux
# )
# submit_flux.click(
# submit_function_flux,
# [person_image_flux, cloth_image_flux, cloth_type, num_inference_steps_flux, guidance_scale_flux, seed_flux, show_type],
# result_image_flux,
# )
demo.queue().launch(share=True, show_error=True)
if __name__ == "__main__":
app_gradio()