import numpy as np
from PIL import Image
from huggingface_hub import snapshot_download
from leffa.transform import LeffaTransform
from leffa.model import LeffaModel
from leffa.inference import LeffaInference
from utils.garment_agnostic_mask_predictor import AutoMasker
from utils.densepose_predictor import DensePosePredictor
from utils.utils import resize_and_center, list_dir
import gradio as gr
# Download checkpoints
snapshot_download(repo_id="franciszzj/Leffa", local_dir="./ckpts")
class LeffaPredictor(object):
def __init__(self):
self.mask_predictor = AutoMasker(
densepose_path="./ckpts/densepose",
schp_path="./ckpts/schp",
)
self.densepose_predictor = DensePosePredictor(
config_path="./ckpts/densepose/densepose_rcnn_R_50_FPN_s1x.yaml",
weights_path="./ckpts/densepose/model_final_162be9.pkl",
)
vt_model = LeffaModel(
pretrained_model_name_or_path="./ckpts/stable-diffusion-inpainting",
pretrained_model="./ckpts/virtual_tryon.pth",
)
self.vt_inference = LeffaInference(model=vt_model)
self.vt_model_type = "viton_hd"
pt_model = LeffaModel(
pretrained_model_name_or_path="./ckpts/stable-diffusion-xl-1.0-inpainting-0.1",
pretrained_model="./ckpts/pose_transfer.pth",
)
self.pt_inference = LeffaInference(model=pt_model)
def change_vt_model(self, vt_model_type):
if vt_model_type == self.vt_model_type:
return
if vt_model_type == "viton_hd":
pretrained_model = "./ckpts/virtual_tryon.pth"
elif vt_model_type == "dress_code":
pretrained_model = "./ckpts/virtual_tryon_dc.pth"
vt_model = LeffaModel(
pretrained_model_name_or_path="./ckpts/stable-diffusion-inpainting",
pretrained_model=pretrained_model,
)
self.vt_inference = LeffaInference(model=vt_model)
self.vt_model_type = vt_model_type
def leffa_predict(self, src_image_path, ref_image_path, control_type, step=50, scale=2.5, seed=42):
assert control_type in [
"virtual_tryon", "pose_transfer"], "Invalid control type: {}".format(control_type)
src_image = Image.open(src_image_path)
ref_image = Image.open(ref_image_path)
src_image = resize_and_center(src_image, 768, 1024)
ref_image = resize_and_center(ref_image, 768, 1024)
src_image_array = np.array(src_image)
# Mask
if control_type == "virtual_tryon":
src_image = src_image.convert("RGB")
mask = self.mask_predictor(src_image, "upper")["mask"]
elif control_type == "pose_transfer":
mask = Image.fromarray(np.ones_like(src_image_array) * 255)
# DensePose
if control_type == "virtual_tryon":
src_image_seg_array = self.densepose_predictor.predict_seg(
src_image_array)
src_image_seg = Image.fromarray(src_image_seg_array)
densepose = src_image_seg
elif control_type == "pose_transfer":
src_image_iuv_array = self.densepose_predictor.predict_iuv(
src_image_array)
src_image_iuv = Image.fromarray(src_image_iuv_array)
densepose = src_image_iuv
# Leffa
transform = LeffaTransform()
data = {
"src_image": [src_image],
"ref_image": [ref_image],
"mask": [mask],
"densepose": [densepose],
}
data = transform(data)
if control_type == "virtual_tryon":
inference = self.vt_inference
elif control_type == "pose_transfer":
inference = self.pt_inference
output = inference(
data,
num_inference_steps=step,
guidance_scale=scale,
seed=seed,)
gen_image = output["generated_image"][0]
# gen_image.save("gen_image.png")
return np.array(gen_image)
def leffa_predict_vt(self, src_image_path, ref_image_path, step, scale, seed):
return self.leffa_predict(src_image_path, ref_image_path, "virtual_tryon", step, scale, seed)
def leffa_predict_pt(self, src_image_path, ref_image_path, step, scale, seed):
return self.leffa_predict(src_image_path, ref_image_path, "pose_transfer", step, scale, seed)
if __name__ == "__main__":
leffa_predictor = LeffaPredictor()
example_dir = "./ckpts/examples"
person1_images = list_dir(f"{example_dir}/person1")
person2_images = list_dir(f"{example_dir}/person2")
garment_images = list_dir(f"{example_dir}/garment")
title = "## Leffa: Learning Flow Fields in Attention for Controllable Person Image Generation"
link = """[📚 Paper](https://arxiv.org/abs/2412.08486) - [🤖 Code](https://github.com/franciszzj/Leffa) - [🔥 Demo](https://huggingface.co/spaces/franciszzj/Leffa) - [🤗 Model](https://huggingface.co/franciszzj/Leffa)
Star ⭐ us if you like it!
"""
news = """## News
- 18/Dec/2024, thanks to @[StartHua](https://github.com/StartHua) for integrating Leffa into ComfyUI! Here is the [repo](https://github.com/StartHua/Comfyui_leffa)!
- 16/Dec/2024, the virtual try-on [model](https://huggingface.co/franciszzj/Leffa/blob/main/virtual_tryon_dc.pth) trained on DressCode is released.
- 12/Dec/2024, the HuggingFace [demo](https://huggingface.co/spaces/franciszzj/Leffa) and [models](https://huggingface.co/franciszzj/Leffa) (virtual try-on model trained on VITON-HD and pose transfer model trained on DeepFashion) are released.
- 11/Dec/2024, the [arXiv](https://arxiv.org/abs/2412.08486) version of the paper is released.
"""
description = "Leffa is a unified framework for controllable person image generation that enables precise manipulation of both appearance (i.e., virtual try-on) and pose (i.e., pose transfer)."
note = "Note: The models used in the demo are trained solely on academic datasets. Virtual try-on uses VITON-HD/DressCode, and pose transfer uses DeepFashion."
with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.pink, secondary_hue=gr.themes.colors.red)).queue() as demo:
gr.Markdown(title)
gr.Markdown(link)
gr.Markdown(news)
gr.Markdown(description)
with gr.Tab("Control Appearance (Virtual Try-on)"):
with gr.Row():
with gr.Column():
gr.Markdown("#### Person Image")
vt_src_image = gr.Image(
sources=["upload"],
type="filepath",
label="Person Image",
width=512,
height=512,
)
gr.Examples(
inputs=vt_src_image,
examples_per_page=10,
examples=person1_images,
)
with gr.Column():
gr.Markdown("#### Garment Image")
vt_ref_image = gr.Image(
sources=["upload"],
type="filepath",
label="Garment Image",
width=512,
height=512,
)
gr.Examples(
inputs=vt_ref_image,
examples_per_page=10,
examples=garment_images,
)
with gr.Column():
gr.Markdown("#### Generated Image")
vt_gen_image = gr.Image(
label="Generated Image",
width=512,
height=512,
)
with gr.Row():
vt_gen_button = gr.Button("Generate")
with gr.Accordion("Advanced Options", open=False):
vt_step = gr.Number(
label="Inference Steps", minimum=30, maximum=100, step=1, value=50)
vt_scale = gr.Number(
label="Guidance Scale", minimum=0.1, maximum=5.0, step=0.1, value=2.5)
vt_seed = gr.Number(
label="Random Seed", minimum=-1, maximum=2147483647, step=1, value=42)
vt_gen_button.click(fn=leffa_predictor.leffa_predict_vt, inputs=[
vt_src_image, vt_ref_image, vt_step, vt_scale, vt_seed], outputs=[vt_gen_image])
with gr.Tab("Control Pose (Pose Transfer)"):
with gr.Row():
with gr.Column():
gr.Markdown("#### Person Image")
pt_ref_image = gr.Image(
sources=["upload"],
type="filepath",
label="Person Image",
width=512,
height=512,
)
gr.Examples(
inputs=pt_ref_image,
examples_per_page=10,
examples=person1_images,
)
with gr.Column():
gr.Markdown("#### Target Pose Person Image")
pt_src_image = gr.Image(
sources=["upload"],
type="filepath",
label="Target Pose Person Image",
width=512,
height=512,
)
gr.Examples(
inputs=pt_src_image,
examples_per_page=10,
examples=person2_images,
)
with gr.Column():
gr.Markdown("#### Generated Image")
pt_gen_image = gr.Image(
label="Generated Image",
width=512,
height=512,
)
with gr.Row():
pose_transfer_gen_button = gr.Button("Generate")
with gr.Accordion("Advanced Options", open=False):
pt_step = gr.Number(
label="Inference Steps", minimum=30, maximum=100, step=1, value=50)
pt_scale = gr.Number(
label="Guidance Scale", minimum=0.1, maximum=5.0, step=0.1, value=2.5)
pt_seed = gr.Number(
label="Random Seed", minimum=-1, maximum=2147483647, step=1, value=42)
pose_transfer_gen_button.click(fn=leffa_predictor.leffa_predict_pt, inputs=[
pt_src_image, pt_ref_image, pt_step, pt_scale, pt_seed], outputs=[pt_gen_image])
gr.Markdown(note)
demo.launch(share=True, server_port=7860)