| # # 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 spaces | |
| # 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 -e ./third_party/sam2") | |
| # os.system("pip install numpy==1.23.0") | |
| # # 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_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") | |
| # status = spaces.GPU(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 = ''' | |
| # <strong>Reconstruct a human avatar in 0.2 seconds with A100!</strong> | |
| # ''' | |
| # with gr.Blocks(analytics_enabled=False) as demo: | |
| # # </div> | |
| # logo_url = "./assets/rgba_logo_new.png" | |
| # 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 Animatable Human Model </h1> | |
| # </div> | |
| # </div> | |
| # """ | |
| # ) | |
| # gr.HTML( | |
| # """<p><h4 style="color: red;"> Notes: Please input full-body image in case of detection errors.</h4></p>""" | |
| # ) | |
| # # 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_pretrained() | |
| # # launch_env_not_compile_with_cuda() | |
| # launch_gradio_app() | |
| import gradio as gr | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| demo = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| demo.launch() |