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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
import gradio as gr
# Download checkpoints
snapshot_download(repo_id="franciszzj/Leffa", local_dir="./")
def leffa_predict(src_image_path, ref_image_path, control_type):
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_array = np.array(src_image)
ref_image_array = np.array(ref_image)
# Mask
if control_type == "virtual_tryon":
automasker = AutoMasker(
densepose_path="./ckpts/densepose",
schp_path="./ckpts/schp",
)
src_image = src_image.convert("RGB")
mask = automasker(src_image, "upper")["mask"]
elif control_type == "pose_transfer":
mask = Image.fromarray(np.ones_like(src_image_array) * 255)
# DensePose
densepose_predictor = DensePosePredictor(
config_path="./ckpts/densepose/densepose_rcnn_R_50_FPN_s1x.yaml",
weights_path="./ckpts/densepose/model_final_162be9.pkl",
)
src_image_iuv_array = densepose_predictor.predict_iuv(src_image_array)
src_image_seg_array = densepose_predictor.predict_seg(src_image_array)
src_image_iuv = Image.fromarray(src_image_iuv_array)
src_image_seg = Image.fromarray(src_image_seg_array)
if control_type == "virtual_tryon":
densepose = src_image_seg
elif control_type == "pose_transfer":
densepose = src_image_iuv
# Leffa
transform = LeffaTransform()
if control_type == "virtual_tryon":
pretrained_model_name_or_path = "./ckpts/stable-diffusion-inpainting"
pretrained_model = "./ckpts/virtual_tryon.pth"
elif control_type == "pose_transfer":
pretrained_model_name_or_path = "./ckpts/stable-diffusion-xl-1.0-inpainting-0.1"
pretrained_model = "./ckpts/pose_transfer.pth"
model = LeffaModel(
pretrained_model_name_or_path=pretrained_model_name_or_path,
pretrained_model=pretrained_model,
)
inference = LeffaInference(model=model)
data = {
"src_image": [src_image],
"ref_image": [ref_image],
"mask": [mask],
"densepose": [densepose],
}
data = transform(data)
output = inference(data)
gen_image = output["generated_image"][0]
# gen_image.save("gen_image.png")
return np.array(gen_image)
if __name__ == "__main__":
# import sys
# src_image_path = sys.argv[1]
# ref_image_path = sys.argv[2]
# control_type = sys.argv[3]
# leffa_predict(src_image_path, ref_image_path, control_type)
with gr.Blocks().queue() as demo:
gr.Markdown(
"## Leffa: Learning Flow Fields in Attention for Controllable Person Image Generation")
gr.Markdown("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).")
with gr.Row():
with gr.Column():
src_image = gr.Image(
sources=["upload"],
type="filepath",
label="Source Person Image",
width=384,
height=512,
)
with gr.Row():
control_type = gr.Dropdown(
["virtual_tryon", "pose_transfer"], label="Control Type")
example = gr.Examples(
inputs=src_image,
examples_per_page=10,
examples=["./examples/14684_00_person.jpg",
"./examples/14092_00_person.jpg"],
)
with gr.Column():
ref_image = gr.Image(
sources=["upload"],
type="filepath",
label="Reference Image",
width=384,
height=512,
)
with gr.Row():
gen_button = gr.Button("Generate")
example = gr.Examples(
inputs=ref_image,
examples_per_page=10,
examples=["./examples/04181_00_garment.jpg",
"./examples/14684_00_person.jpg"],
)
with gr.Column():
gen_image = gr.Image(
label="Generated Person Image",
width=384,
height=512,
)
gen_button.click(fn=leffa_predict, inputs=[
src_image, ref_image, control_type], outputs=[gen_image])
demo.launch(share=True, server_port=7860)
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