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 import gradio as gr # Download checkpoints snapshot_download(repo_id="franciszzj/Leffa", local_dir="./ckpts") mask_predictor = AutoMasker( densepose_path="./ckpts/densepose", schp_path="./ckpts/schp", ) 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", ) vt_inference = LeffaInference(model=vt_model) pt_model = LeffaModel( pretrained_model_name_or_path="./ckpts/stable-diffusion-xl-1.0-inpainting-0.1", pretrained_model="./ckpts/pose_transfer.pth", ) pt_inference = LeffaInference(model=pt_model) 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 = resize_and_center(src_image, 768, 1024) ref_image = resize_and_center(ref_image, 768, 1024) src_image_array = np.array(src_image) ref_image_array = np.array(ref_image) # Mask if control_type == "virtual_tryon": src_image = src_image.convert("RGB") mask = mask_predictor(src_image, "upper")["mask"] elif control_type == "pose_transfer": mask = Image.fromarray(np.ones_like(src_image_array) * 255) # DensePose 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() data = { "src_image": [src_image], "ref_image": [ref_image], "mask": [mask], "densepose": [densepose], } data = transform(data) if control_type == "virtual_tryon": inference = vt_inference elif control_type == "pose_transfer": inference = pt_inference output = inference(data) gen_image = output["generated_image"][0] # gen_image.save("gen_image.png") return np.array(gen_image) def leffa_predict_vt(src_image_path, ref_image_path): return leffa_predict(src_image_path, ref_image_path, "virtual_tryon") def leffa_predict_pt(src_image_path, ref_image_path): return leffa_predict(src_image_path, ref_image_path, "pose_transfer") 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) title = "## Leffa: Learning Flow Fields in Attention for Controllable Person Image Generation" 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)." 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(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=5, examples=["./ckpts/examples/person1/01320_00.jpg", "./ckpts/examples/person1/01350_00.jpg", "./ckpts/examples/person1/01365_00.jpg", "./ckpts/examples/person1/01376_00.jpg", "./ckpts/examples/person1/01416_00.jpg",], ) 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=5, examples=["./ckpts/examples/garment/01449_00.jpg", "./ckpts/examples/garment/01486_00.jpg", "./ckpts/examples/garment/01853_00.jpg", "./ckpts/examples/garment/02070_00.jpg", "./ckpts/examples/garment/03553_00.jpg",], ) 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") vt_gen_button.click(fn=leffa_predict_vt, inputs=[ vt_src_image, vt_ref_image], 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=5, examples=["./ckpts/examples/person1/01320_00.jpg", "./ckpts/examples/person1/01350_00.jpg", "./ckpts/examples/person1/01365_00.jpg", "./ckpts/examples/person1/01376_00.jpg", "./ckpts/examples/person1/01416_00.jpg",], ) 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=5, examples=["./ckpts/examples/person2/01850_00.jpg", "./ckpts/examples/person2/01875_00.jpg", "./ckpts/examples/person2/02532_00.jpg", "./ckpts/examples/person2/02902_00.jpg", "./ckpts/examples/person2/05346_00.jpg",], ) 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") pose_transfer_gen_button.click(fn=leffa_predict_pt, inputs=[ pt_src_image, pt_ref_image], outputs=[pt_gen_image]) demo.launch(share=True, server_port=7860)