import argparse
import os
from datetime import datetime
import gradio as gr
import numpy as np
import torch
from PIL import Image
from model.cloth_masker import vis_mask
from utils import init_weight_dtype, resize_and_crop, resize_and_padding
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
HEADER = """
🐈 CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
"""
def make_demo(pipeline, mask_processor, automasker, output_dir):
def submit_function(person_image, cloth_image, cloth_type, num_inference_steps, guidance_scale, seed, show_type):
width = 768
height = 1024
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 = 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="cpu").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, (width, height))
cloth_image = resize_and_padding(cloth_image, (width, height))
# Process mask
if mask is not None:
mask = resize_and_crop(mask, (width, height))
else:
mask = automasker(person_image, cloth_type)["mask"]
mask = mask_processor.blur(mask, blur_factor=9)
# Inference
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]
# 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
with gr.Blocks(title="CatVTON") as demo:
gr.Markdown(HEADER)
with gr.Row():
with gr.Column(scale=1, min_width=350):
with gr.Row():
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(
'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 = "CatVTON/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=person_image,
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=person_image,
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. ')
submit.click(
submit_function,
[
person_image,
cloth_image,
cloth_type,
num_inference_steps,
guidance_scale,
seed,
show_type,
],
result_image,
)
return demo