# Copyright (c) 2023-2024, Qi Zuo # # 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 from PIL import Image import numpy as np import gradio as gr import base64 import subprocess import os def install_cuda_toolkit(): # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run" # # CUDA_TOOLKIT_URL = "https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run" # CUDA_TOOLKIT_FILE = "/tmp/%s" % os.path.basename(CUDA_TOOLKIT_URL) # subprocess.call(["wget", "-q", CUDA_TOOLKIT_URL, "-O", CUDA_TOOLKIT_FILE]) # subprocess.call(["chmod", "+x", CUDA_TOOLKIT_FILE]) # subprocess.call([CUDA_TOOLKIT_FILE, "--silent", "--toolkit"]) os.environ["CUDA_HOME"] = "/usr/local/cuda" os.environ["PATH"] = "%s/bin:%s" % (os.environ["CUDA_HOME"], os.environ["PATH"]) os.environ["LD_LIBRARY_PATH"] = "%s/lib:%s" % ( os.environ["CUDA_HOME"], "" if "LD_LIBRARY_PATH" not in os.environ else os.environ["LD_LIBRARY_PATH"], ) # Fix: arch_list[-1] += '+PTX'; IndexError: list index out of range os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6" install_cuda_toolkit() def launch_pretrained(): from huggingface_hub import snapshot_download, hf_hub_download hf_hub_download(repo_id="DyrusQZ/LHM_Runtime", repo_type='model', filename='assets.tar', local_dir="./") os.system("tar -xvf assets.tar && rm assets.tar") hf_hub_download(repo_id="DyrusQZ/LHM_Runtime", repo_type='model', filename='LHM-0.5B.tar', local_dir="./") os.system("tar -xvf LHM-0.5B.tar && rm LHM-0.5B.tar") hf_hub_download(repo_id="DyrusQZ/LHM_Runtime", repo_type='model', filename='LHM_prior_model.tar', local_dir="./") os.system("tar -xvf LHM_prior_model.tar && rm LHM_prior_model.tar") def launch_env_not_compile_with_cuda(): os.system("pip install chumpy") os.system("pip uninstall -y basicsr") os.system("pip install git+https://github.com/hitsz-zuoqi/BasicSR/") os.system("pip install git+https://github.com/hitsz-zuoqi/sam2/") os.system("pip install git+https://github.com/ashawkey/diff-gaussian-rasterization/") os.system("pip install git+https://github.com/camenduru/simple-knn/") os.system("pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt251/download.html") # def launch_env_compile_with_cuda(): # # simple_knn # os.system("wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/simple_knn.zip && wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/simple_knn-0.0.0.dist-info.zip") # os.system("unzip simple_knn.zip && unzip simple_knn-0.0.0.dist-info.zip") # os.system("mv simple_knn /usr/local/lib/python3.10/site-packages/") # os.system("mv simple_knn-0.0.0.dist-info /usr/local/lib/python3.10/site-packages/") # # diff_gaussian # os.system("wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/diff_gaussian_rasterization.zip && wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/diff_gaussian_rasterization-0.0.0.dist-info.zip") # os.system("unzip diff_gaussian_rasterization.zip && unzip diff_gaussian_rasterization-0.0.0.dist-info.zip") # os.system("mv diff_gaussian_rasterization /usr/local/lib/python3.10/site-packages/") # os.system("mv diff_gaussian_rasterization-0.0.0.dist-info /usr/local/lib/python3.10/site-packages/") # # pytorch3d # os.system("wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/pytorch3d.zip && wget oss://virutalbuy-public/share/aigc3d/data/for_lingteng/LHM/pytorch3d-0.7.8.dist-info.zip") # os.system("unzip pytorch3d.zip && unzip pytorch3d-0.7.8.dist-info.zip") # os.system("mv pytorch3d /usr/local/lib/python3.10/site-packages/") # os.system("mv pytorch3d-0.7.8.dist-info /usr/local/lib/python3.10/site-packages/") launch_pretrained() launch_env_not_compile_with_cuda() # launch_env_compile_with_cuda() 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 LHM.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 demo_lhm(infer_impl): def core_fn(image: str, video_params, working_dir): image_raw = os.path.join(working_dir.name, "raw.png") with Image.fromarray(image) as img: img.save(image_raw) base_vid = os.path.basename(video_params).split("_")[0] smplx_params_dir = os.path.join("./assets/sample_motion", base_vid, "smplx_params") dump_video_path = os.path.join(working_dir.name, "output.mp4") dump_image_path = os.path.join(working_dir.name, "output.png") # print(video_params) status = infer_impl( gradio_demo_image=image_raw, gradio_motion_file=smplx_params_dir, gradio_masked_image=dump_image_path, gradio_video_save_path=dump_video_path ) if status: return dump_image_path, dump_video_path else: return None, None _TITLE = '''LHM: Large Animatable Human Model''' _DESCRIPTION = ''' Reconstruct a human avatar in 0.2 seconds with A100! ''' with gr.Blocks(analytics_enabled=False) as demo: # logo_url = "./assets/rgba_logo_new.png" logo_base64 = get_image_base64(logo_url) gr.HTML( f"""

Large Animatable Human Model

""" ) gr.HTML( """

Notes: Please input full-body image in case of detection errors.

""" ) # DISPLAY with gr.Row(): with gr.Column(variant='panel', scale=1): with gr.Tabs(elem_id="openlrm_input_image"): with gr.TabItem('Input Image'): with gr.Row(): input_image = gr.Image(label="Input Image", image_mode="RGBA", height=480, width=270, sources="upload", type="numpy", elem_id="content_image") # EXAMPLES with gr.Row(): examples = [ ['assets/sample_input/joker.jpg'], ['assets/sample_input/anime.png'], ['assets/sample_input/basket.png'], ['assets/sample_input/ai_woman1.JPG'], ['assets/sample_input/anime2.JPG'], ['assets/sample_input/anime3.JPG'], ['assets/sample_input/boy1.png'], ['assets/sample_input/choplin.jpg'], ['assets/sample_input/eins.JPG'], ['assets/sample_input/girl1.png'], ['assets/sample_input/girl2.png'], ['assets/sample_input/robot.jpg'], ] gr.Examples( examples=examples, inputs=[input_image], examples_per_page=20, ) with gr.Column(): with gr.Tabs(elem_id="openlrm_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/danaotiangong/danaotiangong_origin.mp4', './assets/sample_motion/ex5/ex5_origin.mp4', './assets/sample_motion/girl2/girl2_origin.mp4', './assets/sample_motion/jntm/jntm_origin.mp4', './assets/sample_motion/mimo1/mimo1_origin.mp4', './assets/sample_motion/mimo2/mimo2_origin.mp4', './assets/sample_motion/mimo4/mimo4_origin.mp4', './assets/sample_motion/mimo5/mimo5_origin.mp4', './assets/sample_motion/mimo6/mimo6_origin.mp4', './assets/sample_motion/nezha/nezha_origin.mp4', './assets/sample_motion/taiji/taiji_origin.mp4' ] gr.Examples( examples=examples, inputs=[video_input], examples_per_page=20, ) with gr.Column(variant='panel', scale=1): with gr.Tabs(elem_id="openlrm_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="openlrm_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): submit = gr.Button('Generate', elem_id="openlrm_generate", variant='primary') 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=core_fn, inputs=[input_image, video_input, working_dir], # video_params refer to smpl dir outputs=[processed_image, output_video], ) demo.queue() demo.launch() def launch_gradio_app(): os.environ.update({ "APP_ENABLED": "1", "APP_MODEL_NAME": "./exps/releases/video_human_benchmark/human-lrm-500M/step_060000/", "APP_INFER": "./configs/inference/human-lrm-500M.yaml", "APP_TYPE": "infer.human_lrm", "NUMBA_THREADING_LAYER": 'omp', }) from LHM.runners import REGISTRY_RUNNERS RunnerClass = REGISTRY_RUNNERS[os.getenv("APP_TYPE")] with RunnerClass() as runner: demo_lhm(infer_impl=runner.infer) if __name__ == '__main__': launch_gradio_app()