# Copyright (c) 2024-2025, Yisheng He, Yuan Dong # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os os.system("rm -rf /data-nvme/zerogpu-offload/") os.system("pip install chumpy") # os.system("pip uninstall -y basicsr") os.system("pip install Cython") os.system("pip install ./new_wheels/diff_gaussian_rasterization-0.0.0-cp310-cp310-linux_x86_64.whl") os.system("pip install ./wheels/simple_knn-0.0.0-cp310-cp310-linux_x86_64.whl") os.system("pip install ./wheels/nvdiffrast-0.3.3-cp310-cp310-linux_x86_64.whl --force-reinstall") os.system( "pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt240/download.html") os.system("pip install numpy==1.23.0") # FBX SDK 및 추가 패키지 설치 (업로드한 wheel 파일 사용) print("Installing FBX SDK for download functionality...") os.system("pip install trimesh") # trimesh 의존성 추가 os.system("pip install ./wheels/fbx-2020.3.4-cp310-cp310-manylinux1_x86_64.whl") os.system("pip install ./wheels/pytorch3d-0.7.8-cp310-cp310-linux_x86_64.whl --force-reinstall") print("FBX SDK installation completed") import cv2 import sys import base64 import subprocess import argparse from glob import glob import gradio as gr import numpy as np from PIL import Image from omegaconf import OmegaConf import torch import moviepy.editor as mpy from lam.runners.infer.head_utils import prepare_motion_seqs, preprocess_image from lam.utils.ffmpeg_utils import images_to_video import spaces import shutil import time from pathlib import Path def compile_module(subfolder, script): try: # Save the current working directory current_dir = os.getcwd() # Change directory to the subfolder os.chdir(os.path.join(current_dir, subfolder)) # Run the compilation command result = subprocess.run( ["sh", script], capture_output=True, text=True, check=True ) # Print the compilation output print("Compilation output:", result.stdout) except Exception as e: # Print any error that occurred print(f"An error occurred: {e}") finally: # Ensure returning to the original directory os.chdir(current_dir) print("Returned to the original directory.") # compile flame_tracking dependence submodule compile_module("external/landmark_detection/FaceBoxesV2/utils/", "make.sh") from flame_tracking_single_image import FlameTrackingSingleImage def launch_pretrained(): from huggingface_hub import snapshot_download, hf_hub_download # launch pretrained for flame tracking. hf_hub_download(repo_id='yuandong513/flametracking_model', repo_type='model', filename='pretrain_model.tar', local_dir='./') os.system('tar -xf pretrain_model.tar && rm pretrain_model.tar') # launch human model files hf_hub_download(repo_id='3DAIGC/LAM-assets', repo_type='model', filename='LAM_human_model.tar', local_dir='./') os.system('tar -xf LAM_human_model.tar && rm LAM_human_model.tar') # launch pretrained for LAM model_dir = hf_hub_download(repo_id="3DAIGC/LAM-20K", repo_type="model", local_dir="./exps/releases/lam/lam-20k/step_045500/", filename="config.json") print(model_dir) model_dir = hf_hub_download(repo_id="3DAIGC/LAM-20K", repo_type="model", local_dir="./exps/releases/lam/lam-20k/step_045500/", filename="model.safetensors") print(model_dir) model_dir = hf_hub_download(repo_id="3DAIGC/LAM-20K", repo_type="model", local_dir="./exps/releases/lam/lam-20k/step_045500/", filename="README.md") print(model_dir) # launch example for LAM hf_hub_download(repo_id='3DAIGC/LAM-assets', repo_type='model', filename='LAM_assets.tar', local_dir='./') os.system('tar -xf LAM_assets.tar && rm LAM_assets.tar') hf_hub_download(repo_id='3DAIGC/LAM-assets', repo_type='model', filename='config.json', local_dir='./tmp/') def launch_env_not_compile_with_cuda(): os.system('pip install chumpy') os.system('pip install numpy==1.23.0') os.system( 'pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt251/download.html' ) def assert_input_image(input_image): if input_image is None: raise gr.Error('No image selected or uploaded!') def prepare_working_dir(): import tempfile working_dir = tempfile.TemporaryDirectory() return working_dir def init_preprocessor(): from lam.utils.preprocess import Preprocessor global preprocessor preprocessor = Preprocessor() def preprocess_fn(image_in: np.ndarray, remove_bg: bool, recenter: bool, working_dir): image_raw = os.path.join(working_dir.name, 'raw.png') with Image.fromarray(image_in) as img: img.save(image_raw) image_out = os.path.join(working_dir.name, 'rembg.png') success = preprocessor.preprocess(image_path=image_raw, save_path=image_out, rmbg=remove_bg, recenter=recenter) assert success, f'Failed under preprocess_fn!' return image_out def get_image_base64(path): with open(path, 'rb') as image_file: encoded_string = base64.b64encode(image_file.read()).decode() return f'data:image/png;base64,{encoded_string}' def save_imgs_2_video(imgs, v_pth, fps=30): # moviepy example from moviepy.editor import ImageSequenceClip, VideoFileClip images = [image.astype(np.uint8) for image in imgs] clip = ImageSequenceClip(images, fps=fps) # final_duration = len(images) / fps # clip = clip.subclip(0, final_duration) clip = clip.subclip(0, len(images) / fps) clip.write_videofile(v_pth, codec='libx264') import cv2 cap = cv2.VideoCapture(v_pth) nf = cap.get(cv2.CAP_PROP_FRAME_COUNT) if nf != len(images): print("="*100+f"\n{v_pth} moviepy saved video frame error."+"\n"+"="*100) print(f"Video saved successfully at {v_pth}") def add_audio_to_video(video_path, out_path, audio_path, fps=30): # Import necessary modules from moviepy from moviepy.editor import VideoFileClip, AudioFileClip # Load video file into VideoFileClip object video_clip = VideoFileClip(video_path) # Load audio file into AudioFileClip object audio_clip = AudioFileClip(audio_path) # Hard code clip audio if audio_clip.duration > 10: audio_clip = audio_clip.subclip(0, 10) # Attach audio clip to video clip (replaces existing audio) video_clip_with_audio = video_clip.set_audio(audio_clip) # Export final video with audio using standard codecs video_clip_with_audio.write_videofile(out_path, codec='libx264', audio_codec='aac', fps=fps) print(f"Audio added successfully at {out_path}") def parse_configs(): parser = argparse.ArgumentParser() parser.add_argument("--config", type=str) parser.add_argument("--infer", type=str) args, unknown = parser.parse_known_args() cfg = OmegaConf.create() cli_cfg = OmegaConf.from_cli(unknown) # parse from ENV if os.environ.get("APP_INFER") is not None: args.infer = os.environ.get("APP_INFER") if os.environ.get("APP_MODEL_NAME") is not None: cli_cfg.model_name = os.environ.get("APP_MODEL_NAME") args.config = args.infer if args.config is None else args.config if args.config is not None: cfg_train = OmegaConf.load(args.config) cfg.source_size = cfg_train.dataset.source_image_res try: cfg.src_head_size = cfg_train.dataset.src_head_size except: cfg.src_head_size = 112 cfg.render_size = cfg_train.dataset.render_image.high _relative_path = os.path.join( cfg_train.experiment.parent, cfg_train.experiment.child, os.path.basename(cli_cfg.model_name).split("_")[-1], ) cfg.save_tmp_dump = os.path.join("exps", "save_tmp", _relative_path) cfg.image_dump = os.path.join("exps", "images", _relative_path) cfg.video_dump = os.path.join("exps", "videos", _relative_path) # output path if args.infer is not None: cfg_infer = OmegaConf.load(args.infer) cfg.merge_with(cfg_infer) cfg.setdefault( "save_tmp_dump", os.path.join("exps", cli_cfg.model_name, "save_tmp") ) cfg.setdefault("image_dump", os.path.join("exps", cli_cfg.model_name, "images")) cfg.setdefault( "video_dump", os.path.join("dumps", cli_cfg.model_name, "videos") ) cfg.setdefault("mesh_dump", os.path.join("dumps", cli_cfg.model_name, "meshes")) cfg.motion_video_read_fps = 30 cfg.merge_with(cli_cfg) cfg.setdefault("logger", "INFO") assert cfg.model_name is not None, "model_name is required" return cfg, cfg_train def upload2oss(enable_oac_file, filepath): print(f"Upload to OSS: enable_oac_file={enable_oac_file}, filepath={filepath}") if(enable_oac_file): print(f"ZIP file ready for download: {filepath}") return "Upload completed" def demo_lam(flametracking, lam, cfg): @spaces.GPU(duration=80) def core_fn(image_path: str, video_params, working_dir, enable_oac_file): image_raw = os.path.join(working_dir.name, "raw.png") with Image.open(image_path).convert('RGB') as img: img.save(image_raw) base_vid = os.path.basename(video_params).split(".")[0] flame_params_dir = os.path.join("./assets/sample_motion/export", base_vid, "flame_param") base_iid = os.path.basename(image_path).split('.')[0] image_path = os.path.join("./assets/sample_input", base_iid, "images/00000_00.png") dump_video_path = os.path.join(working_dir.name, "output.mp4") dump_image_path = os.path.join(working_dir.name, "output.png") # prepare dump paths omit_prefix = os.path.dirname(image_raw) image_name = os.path.basename(image_raw) uid = image_name.split(".")[0] subdir_path = os.path.dirname(image_raw).replace(omit_prefix, "") subdir_path = ( subdir_path[1:] if subdir_path.startswith("/") else subdir_path ) print("subdir_path and uid:", subdir_path, uid) motion_seqs_dir = flame_params_dir dump_image_dir = os.path.dirname(dump_image_path) os.makedirs(dump_image_dir, exist_ok=True) print(image_raw, motion_seqs_dir, dump_image_dir, dump_video_path) dump_tmp_dir = dump_image_dir if os.path.exists(dump_video_path): return dump_image_path, dump_video_path motion_img_need_mask = cfg.get("motion_img_need_mask", False) # False vis_motion = cfg.get("vis_motion", False) # False # preprocess input image: segmentation, flame params estimation # """ return_code = flametracking.preprocess(image_raw) assert (return_code == 0), "flametracking preprocess failed!" return_code = flametracking.optimize() assert (return_code == 0), "flametracking optimize failed!" return_code, output_dir = flametracking.export() assert (return_code == 0), "flametracking export failed!" image_path = os.path.join(output_dir, "images/00000_00.png") # """ mask_path = image_path.replace("/images/", "/fg_masks/").replace(".jpg", ".png") print(image_path, mask_path) aspect_standard = 1.0 / 1.0 source_size = cfg.source_size render_size = cfg.render_size render_fps = 30 # prepare reference image image, _, _, shape_param = preprocess_image(image_path, mask_path=mask_path, intr=None, pad_ratio=0, bg_color=1., max_tgt_size=None, aspect_standard=aspect_standard, enlarge_ratio=[1.0, 1.0], render_tgt_size=source_size, multiply=14, need_mask=True, get_shape_param=True) # save masked image for vis save_ref_img_path = os.path.join(dump_tmp_dir, "output.png") vis_ref_img = (image[0].permute(1, 2, 0).cpu().detach().numpy() * 255).astype(np.uint8) Image.fromarray(vis_ref_img).save(save_ref_img_path) # prepare motion seq src = image_path.split('/')[-3] driven = motion_seqs_dir.split('/')[-2] src_driven = [src, driven] motion_seq = prepare_motion_seqs(motion_seqs_dir, None, save_root=dump_tmp_dir, fps=render_fps, bg_color=1., aspect_standard=aspect_standard, enlarge_ratio=[1.0, 1, 0], render_image_res=render_size, multiply=16, need_mask=motion_img_need_mask, vis_motion=vis_motion, shape_param=shape_param, test_sample=False, cross_id=False, src_driven=src_driven, max_squen_length=300) # start inference motion_seq["flame_params"]["betas"] = shape_param.unsqueeze(0) device, dtype = "cuda", torch.float32 print("start to inference...................") with torch.no_grad(): # TODO check device and dtype res = lam.infer_single_view(image.unsqueeze(0).to(device, dtype), None, None, render_c2ws=motion_seq["render_c2ws"].to(device), render_intrs=motion_seq["render_intrs"].to(device), render_bg_colors=motion_seq["render_bg_colors"].to(device), flame_params={k: v.to(device) for k, v in motion_seq["flame_params"].items()}) rgb = res["comp_rgb"].detach().cpu().numpy() # [Nv, H, W, 3], 0-1 mask = res["comp_mask"].detach().cpu().numpy() # [Nv, H, W, 3], 0-1 mask[mask < 0.5] = 0.0 rgb = rgb * mask + (1 - mask) * 1 rgb = (np.clip(rgb, 0, 1.0) * 255).astype(np.uint8) if vis_motion: vis_ref_img = np.tile( cv2.resize(vis_ref_img, (rgb[0].shape[1], rgb[0].shape[0]), interpolation=cv2.INTER_AREA)[None, :, :, :], (rgb.shape[0], 1, 1, 1), ) rgb = np.concatenate([vis_ref_img, rgb, motion_seq["vis_motion_render"]], axis=2) os.makedirs(os.path.dirname(dump_video_path), exist_ok=True) print("==="*36, "\nrgb length:", rgb.shape, render_fps, "==="*36) save_imgs_2_video(rgb, dump_video_path, render_fps) # images_to_video(rgb, output_path=dump_video_path, fps=30, gradio_codec=False, verbose=True) audio_path = os.path.join("./assets/sample_motion/export", base_vid, base_vid + ".wav") dump_video_path_wa = dump_video_path.replace(".mp4", "_audio.mp4") add_audio_to_video(dump_video_path, dump_video_path_wa, audio_path) output_zip_path = '' download_command = '' # ZIP 생성 로직 if enable_oac_file: try: from generateARKITGLBWithBlender import generate_glb base_iid_zip = f"chatting_avatar_{int(time.time())}" oac_dir = os.path.join('./', base_iid_zip) os.makedirs(oac_dir, exist_ok=True) # 1. 실제 얼굴 mesh 저장 - 원본 로직 그대로 구현 import trimesh # save_shaped_mesh 메소드가 없는 경우 원본 로직대로 구현 if hasattr(lam.renderer.flame_model, 'save_shaped_mesh'): # 메소드가 있으면 그대로 사용 saved_head_path = lam.renderer.flame_model.save_shaped_mesh(shape_param.unsqueeze(0).cuda(), fd=oac_dir) else: # 메소드가 없으면 원본 코드의 정확한 로직 구현 print("⚠️ save_shaped_mesh 메소드가 없어서 원본 로직으로 구현") # 원본: blend_shapes 함수 정의 (flame.py에서 가져옴) def blend_shapes(betas, shape_disps): """Blend shapes based on parameters""" blend_shape = torch.einsum('bl,mkl->bmk', [betas, shape_disps]) return blend_shape # 원본 로직 그대로 구현 flame_model = lam.renderer.flame_model batch_size = shape_param.shape[0] # faces와 vertices 찾기 if hasattr(flame_model, 'faces_up'): faces = flame_model.faces_up.cpu().numpy() template_vertices = flame_model.v_template_up.unsqueeze(0).expand(batch_size, -1, -1) shapedirs = flame_model.shapedirs_up elif hasattr(flame_model, 'face_upsampled'): faces = flame_model.face_upsampled template_vertices = flame_model.v_template.unsqueeze(0).expand(batch_size, -1, -1) shapedirs = flame_model.shapedirs else: faces = flame_model.faces.cpu().numpy() template_vertices = flame_model.v_template.unsqueeze(0).expand(batch_size, -1, -1) shapedirs = flame_model.shapedirs # shape blend (shape_param이 1차원인 경우 2차원으로 변환) n_shape_params = flame_model.n_shape_params if hasattr(flame_model, 'n_shape_params') else 10 # shape_param을 올바른 차원으로 변환 if shape_param.dim() == 1: shape_param_2d = shape_param.unsqueeze(0) # (num_betas) -> (1, num_betas) else: shape_param_2d = shape_param # 모든 텐서를 같은 디바이스로 통일 (CUDA) device = shape_param_2d.device template_vertices = template_vertices.to(device) shapedirs_subset = shapedirs[:, :, :n_shape_params].to(device) v_shaped = template_vertices + blend_shapes(shape_param_2d.to(device), shapedirs_subset) # mesh 저장 mesh = trimesh.Trimesh(vertices=v_shaped.squeeze(0).cpu().numpy(), faces=faces) saved_head_path = os.path.join(oac_dir, "nature.obj") mesh.export(saved_head_path) print(f"✅ 실제 얼굴 mesh 저장: {saved_head_path}") # 2. offset.ply 생성 res['cano_gs_lst'][0].save_ply(os.path.join(oac_dir, "offset.ply"), rgb2sh=False, offset2xyz=True) print(f"✅ offset.ply 생성 완료") # 3. skin.glb 생성 (Blender 사용) generate_glb( input_mesh=Path(saved_head_path), template_fbx=Path("./assets/sample_oac/template_file.fbx"), output_glb=Path(os.path.join(oac_dir, "skin.glb")), blender_exec=Path("./blender-4.0.2-linux-x64/blender") ) print(f"✅ skin.glb 생성 완료") # 4. animation.glb 복사 shutil.copy( src='./assets/sample_oac/animation.glb', dst=os.path.join(oac_dir, 'animation.glb') ) print(f"✅ animation.glb 복사 완료") # 5. 임시 mesh 파일 삭제 os.remove(saved_head_path) # 6. ZIP 파일 생성 output_zip_path = os.path.join('./', base_iid_zip + '.zip') if os.path.exists(output_zip_path): os.remove(output_zip_path) os.system('zip -r {} {}'.format(output_zip_path, oac_dir)) # 7. 디렉토리 정리 shutil.rmtree(oac_dir) # 8. HuggingFace용 다운로드 명령어 download_command = f'wget https://ych144-lam2.hf.space/file={output_zip_path}\n✅ ZIP file generated: {os.path.basename(output_zip_path)}' print(f"✅ ZIP 생성 완료: {output_zip_path}") except Exception as e: output_zip_path = f"Archive creation failed: {str(e)}" download_command = f"❌ ZIP 생성 실패: {str(e)}" print(f"❌ ZIP 생성 실패: {e}") return dump_image_path, dump_video_path_wa, output_zip_path, download_command def core_fn_space(image_path: str, video_params, working_dir): return core_fn(image_path, video_params, working_dir, False) with gr.Blocks(analytics_enabled=False, delete_cache=[3600, 3600]) as demo: logo_url = './assets/images/logo.jpeg' logo_base64 = get_image_base64(logo_url) gr.HTML(f"""

Large Avatar Model for One-shot Animatable Gaussian Head

""") gr.HTML( """
arXiv Paper Project Page badge-github-stars Video
""" ) gr.HTML("""

Notes1: Inputing front-face images or face orientation close to the driven signal gets better results.

Notes2: Due to computational constraints with Hugging Face's ZeroGPU infrastructure, 3D avatar generation requires ~1 minute per instance.

Notes3: Using LAM-20K model (lower quality than premium LAM-80K) to mitigate processing latency.

""") # DISPLAY with gr.Row(): with gr.Column(variant='panel', scale=1): with gr.Tabs(elem_id='lam_input_image'): with gr.TabItem('Input Image'): with gr.Row(): input_image = gr.Image(label='Input Image', image_mode='RGB', height=480, width=270, sources='upload', type='filepath', elem_id='content_image') # EXAMPLES with gr.Row(): examples = [ ['assets/sample_input/messi.png'], ['assets/sample_input/status.png'], ['assets/sample_input/james.png'], ['assets/sample_input/cluo.jpg'], ['assets/sample_input/dufu.jpg'], ['assets/sample_input/libai.jpg'], ['assets/sample_input/barbara.jpg'], ['assets/sample_input/pop.png'], ['assets/sample_input/musk.jpg'], ['assets/sample_input/speed.jpg'], ['assets/sample_input/zhouxingchi.jpg'], ] gr.Examples( examples=examples, inputs=[input_image], examples_per_page=20 ) with gr.Column(): with gr.Tabs(elem_id='lam_input_video'): with gr.TabItem('Input Video'): with gr.Row(): video_input = gr.Video(label='Input Video', height=480, width=270, interactive=False) examples = ['./assets/sample_motion/export/Speeding_Scandal/Speeding_Scandal.mp4', './assets/sample_motion/export/Look_In_My_Eyes/Look_In_My_Eyes.mp4', './assets/sample_motion/export/D_ANgelo_Dinero/D_ANgelo_Dinero.mp4', './assets/sample_motion/export/Michael_Wayne_Rosen/Michael_Wayne_Rosen.mp4', './assets/sample_motion/export/I_Am_Iron_Man/I_Am_Iron_Man.mp4', './assets/sample_motion/export/Anti_Drugs/Anti_Drugs.mp4', './assets/sample_motion/export/Pen_Pineapple_Apple_Pen/Pen_Pineapple_Apple_Pen.mp4', './assets/sample_motion/export/Joe_Biden/Joe_Biden.mp4', './assets/sample_motion/export/Donald_Trump/Donald_Trump.mp4', './assets/sample_motion/export/Taylor_Swift/Taylor_Swift.mp4', './assets/sample_motion/export/GEM/GEM.mp4', './assets/sample_motion/export/The_Shawshank_Redemption/The_Shawshank_Redemption.mp4' ] print("Video example list {}".format(examples)) gr.Examples( examples=examples, inputs=[video_input], examples_per_page=20, ) with gr.Column(variant='panel', scale=1): with gr.Tabs(elem_id='lam_processed_image'): with gr.TabItem('Processed Image'): with gr.Row(): processed_image = gr.Image( label='Processed Image', image_mode='RGBA', type='filepath', elem_id='processed_image', height=480, width=270, interactive=False) with gr.Column(variant='panel', scale=1): with gr.Tabs(elem_id='lam_render_video'): with gr.TabItem('Rendered Video'): with gr.Row(): output_video = gr.Video(label='Rendered Video', format='mp4', height=480, width=270, autoplay=True) # SETTING with gr.Row(): with gr.Column(variant='panel', scale=1): enable_oac_file = gr.Checkbox(label="Export ZIP file for Chatting Avatar", value=False, interactive=True) submit = gr.Button('Generate', elem_id='lam_generate', variant='primary') download_command = gr.Textbox( label="📦 Download ZIP Command", interactive=False, placeholder="Check 'Export ZIP file' and generate to get download link...", ) main_fn = core_fn output_zip_textbox = gr.Textbox(visible=False) working_dir = gr.State() submit.click( fn=assert_input_image, inputs=[input_image], queue=False, ).success( fn=prepare_working_dir, outputs=[working_dir], queue=False, ).success( fn=main_fn, inputs=[input_image, video_input, working_dir, enable_oac_file], # video_params refer to smpl dir outputs=[processed_image, output_video, output_zip_textbox, download_command], ).success( fn=upload2oss, inputs=[enable_oac_file, output_zip_textbox] ) demo.queue() demo.launch() def _build_model(cfg): from lam.models import model_dict from lam.utils.hf_hub import wrap_model_hub hf_model_cls = wrap_model_hub(model_dict["lam"]) model = hf_model_cls.from_pretrained(cfg.model_name) return model def launch_gradio_app(): os.environ.update({ 'APP_ENABLED': '1', 'APP_MODEL_NAME': './exps/releases/lam/lam-20k/step_045500/', 'APP_INFER': './configs/inference/lam-20k-8gpu.yaml', 'APP_TYPE': 'infer.lam', 'NUMBA_THREADING_LAYER': 'omp', }) cfg, _ = parse_configs() lam = _build_model(cfg) lam.to('cuda') flametracking = FlameTrackingSingleImage(output_dir='tracking_output', alignment_model_path='./pretrain_model/68_keypoints_model.pkl', vgghead_model_path='./pretrain_model/vgghead/vgg_heads_l.trcd', human_matting_path='./pretrain_model/matting/stylematte_synth.pt', facebox_model_path='./pretrain_model/FaceBoxesV2.pth', detect_iris_landmarks=False) demo_lam(flametracking, lam, cfg) if __name__ == '__main__': launch_pretrained() launch_gradio_app()