import os
import argparse
import gradio as gr
from datetime import datetime
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 AutoMasker, vis_mask
from model.flux.pipeline_flux_tryon import FluxTryOnPipeline
from utils import resize_and_crop, resize_and_padding
def parse_args():
parser = argparse.ArgumentParser(description="FLUX Try-On Demo")
parser.add_argument(
"--base_model_path",
type=str,
default="black-forest-labs/FLUX.1-Fill-dev",
# default="Models/FLUX.1-Fill-dev",
help="The path to the base model to use for evaluation."
)
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(
"--mixed_precision",
type=str,
default="bf16",
choices=["no", "fp16", "bf16"],
help="Whether to use mixed precision."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
default=True,
help="Whether or not to allow TF32 on Ampere GPUs."
)
parser.add_argument(
"--width",
type=int,
default=768,
help="The width of the input image."
)
parser.add_argument(
"--height",
type=int,
default=1024,
help="The height of the input image."
)
return parser.parse_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
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,
height=args.height,
width=args.width,
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
def app_gradio():
with gr.Blocks(title="CatVTON with FLUX.1-Fill-dev") as demo:
gr.Markdown("# CatVTON with 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)
# 解析参数
args = parse_args()
# 加载模型
repo_path = snapshot_download(repo_id=args.resume_path)
pipeline_flux = FluxTryOnPipeline.from_pretrained(args.base_model_path)
pipeline_flux.load_lora_weights(
os.path.join(repo_path, "flux-lora"),
weight_name='pytorch_lora_weights.safetensors'
)
pipeline_flux.to("cuda", torch.bfloat16)
# 初始化 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'
)
if __name__ == "__main__":
app_gradio()