Upload app.py

app.py

@@ -0,0 +1,645 @@
+# 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")
+
+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
+                saved_head_path = lam.renderer.flame_model.save_shaped_mesh(shape_param.unsqueeze(0).cuda(), fd=oac_dir)
+                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"""
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+            <div>
+                <h1> <img src="{logo_base64}" style='height:35px; display:inline-block;'/>  Large Avatar Model for One-shot Animatable Gaussian Head</h1>
+            </div>
+            </div>
+            """)
+        
+        gr.HTML(
+            """
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center; margin: 20px; gap: 10px;">
+                <a class="flex-item" href="https://arxiv.org/abs/2502.17796" target="_blank">
+                    <img src="https://img.shields.io/badge/Paper-arXiv-darkred.svg" alt="arXiv Paper">
+                </a>                      
+                <a class="flex-item" href="https://aigc3d.github.io/projects/LAM/" target="_blank">
+                    <img src="https://img.shields.io/badge/Project-LAM-blue" alt="Project Page">
+                </a>
+                <a class="flex-item" href="https://github.com/aigc3d/LAM" target="_blank">
+                    <img src="https://img.shields.io/github/stars/aigc3d/LAM?label=Github%20★&logo=github&color=C8C" alt="badge-github-stars">
+                </a>
+                <a class="flex-item" href="https://youtu.be/FrfE3RYSKhk" target="_blank">
+                    <img src="https://img.shields.io/badge/Youtube-Video-red.svg" alt="Video">
+                </a>
+            </div>
+            """
+        )
+
+        
+        gr.HTML("""<div style="margin-top: -10px">
+            <p style="margin: 4px 0; line-height: 1.2"><h4 style="color: black; margin: 2px 0">Notes1: Inputing front-face images or face orientation close to the driven signal gets better results.</h4></p>
+            <p style="margin: 4px 0; line-height: 1.2"><h4 style="color: black; margin: 2px 0">Notes2: Due to computational constraints with Hugging Face's ZeroGPU infrastructure, 3D avatar generation requires ~1 minute per instance.</h4></p>
+            <p style="margin: 4px 0; line-height: 1.2"><h4 style="color: black; margin: 2px 0">Notes3: Using LAM-20K model (lower quality than premium LAM-80K) to mitigate processing latency.</h4></p>
+        </div>""")
+
+
+
+
+        # 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()