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| import gradio as gr | |
| import os | |
| import shutil | |
| import yaml | |
| import tempfile | |
| import cv2 | |
| import huggingface_hub | |
| import subprocess | |
| import threading | |
| import torch | |
| from subprocess import getoutput | |
| is_shared_ui = True if "fffiloni/MimicMotion" in os.environ['SPACE_ID'] else False | |
| available_property = False if is_shared_ui else True | |
| is_gpu_associated = torch.cuda.is_available() | |
| if is_gpu_associated: | |
| gpu_info = getoutput('nvidia-smi') | |
| if("A10G" in gpu_info): | |
| which_gpu = "A10G" | |
| elif("T4" in gpu_info): | |
| which_gpu = "T4" | |
| else: | |
| which_gpu = "CPU" | |
| def stream_output(pipe): | |
| for line in iter(pipe.readline, ''): | |
| print(line, end='') | |
| pipe.close() | |
| HF_TKN = os.environ.get("GATED_HF_TOKEN") | |
| huggingface_hub.login(token=HF_TKN) | |
| huggingface_hub.hf_hub_download( | |
| repo_id='yzd-v/DWPose', | |
| filename='yolox_l.onnx', | |
| local_dir='./models/DWPose' | |
| ) | |
| huggingface_hub.hf_hub_download( | |
| repo_id='yzd-v/DWPose', | |
| filename='dw-ll_ucoco_384.onnx', | |
| local_dir='./models/DWPose' | |
| ) | |
| huggingface_hub.hf_hub_download( | |
| repo_id='ixaac/MimicMotion', | |
| filename='MimicMotion_1.pth', | |
| local_dir='./models' | |
| ) | |
| def print_directory_contents(path): | |
| for root, dirs, files in os.walk(path): | |
| level = root.replace(path, '').count(os.sep) | |
| indent = ' ' * 4 * (level) | |
| print(f"{indent}{os.path.basename(root)}/") | |
| subindent = ' ' * 4 * (level + 1) | |
| for f in files: | |
| print(f"{subindent}{f}") | |
| def check_outputs_folder(folder_path): | |
| # Check if the folder exists | |
| if os.path.exists(folder_path) and os.path.isdir(folder_path): | |
| # Delete all contents inside the folder | |
| for filename in os.listdir(folder_path): | |
| file_path = os.path.join(folder_path, filename) | |
| try: | |
| if os.path.isfile(file_path) or os.path.islink(file_path): | |
| os.unlink(file_path) # Remove file or link | |
| elif os.path.isdir(file_path): | |
| shutil.rmtree(file_path) # Remove directory | |
| except Exception as e: | |
| print(f'Failed to delete {file_path}. Reason: {e}') | |
| else: | |
| print(f'The folder {folder_path} does not exist.') | |
| def check_for_mp4_in_outputs(): | |
| # Define the path to the outputs folder | |
| outputs_folder = './outputs' | |
| # Check if the outputs folder exists | |
| if not os.path.exists(outputs_folder): | |
| return None | |
| # Check if there is a .mp4 file in the outputs folder | |
| mp4_files = [f for f in os.listdir(outputs_folder) if f.endswith('.mp4')] | |
| # Return the path to the mp4 file if it exists | |
| if mp4_files: | |
| return os.path.join(outputs_folder, mp4_files[0]) | |
| else: | |
| return None | |
| def get_video_fps(video_path): | |
| # Open the video file | |
| video_capture = cv2.VideoCapture(video_path) | |
| if not video_capture.isOpened(): | |
| raise ValueError("Error opening video file") | |
| # Get the FPS value | |
| fps = video_capture.get(cv2.CAP_PROP_FPS) | |
| # Release the video capture object | |
| video_capture.release() | |
| return fps | |
| def load_examples(ref_image_in, ref_video_in): | |
| return "./examples/mimicmotion_result1_example.mp4" | |
| def infer(ref_image_in, ref_video_in, num_inference_steps, guidance_scale, output_frames_per_second, seed, checkpoint_version): | |
| # check if 'outputs' dir exists and empty it if necessary | |
| check_outputs_folder('./outputs') | |
| # Create a temporary directory | |
| with tempfile.TemporaryDirectory() as temp_dir: | |
| print("Temporary directory created:", temp_dir) | |
| # Define the values for the variables | |
| ref_video_path = ref_video_in | |
| ref_image_path = ref_image_in | |
| num_frames = 16 | |
| resolution = 576 | |
| frames_overlap = 6 | |
| num_inference_steps = num_inference_steps # 25 | |
| noise_aug_strength = 0 | |
| guidance_scale = guidance_scale # 2.0 | |
| sample_stride = 2 | |
| fps = output_frames_per_second # 16 | |
| seed = seed # 42 | |
| # Create the data structure | |
| data = { | |
| 'base_model_path': 'stabilityai/stable-video-diffusion-img2vid-xt-1-1', | |
| 'ckpt_path': f'models/{checkpoint_version}', | |
| 'test_case': [ | |
| { | |
| 'ref_video_path': ref_video_path, | |
| 'ref_image_path': ref_image_path, | |
| 'num_frames': num_frames, | |
| 'resolution': resolution, | |
| 'frames_overlap': frames_overlap, | |
| 'num_inference_steps': num_inference_steps, | |
| 'noise_aug_strength': noise_aug_strength, | |
| 'guidance_scale': guidance_scale, | |
| 'sample_stride': sample_stride, | |
| 'fps': fps, | |
| 'seed': seed | |
| } | |
| ] | |
| } | |
| # Define the file path | |
| file_path = os.path.join(temp_dir, 'config.yaml') | |
| # Write the data to a YAML file | |
| with open(file_path, 'w') as file: | |
| yaml.dump(data, file, default_flow_style=False) | |
| print("YAML file 'config.yaml' created successfully in", file_path) | |
| # Execute the inference command | |
| command = ['python', 'inference.py', '--inference_config', file_path] | |
| process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1) | |
| # Create threads to handle stdout and stderr | |
| stdout_thread = threading.Thread(target=stream_output, args=(process.stdout,)) | |
| stderr_thread = threading.Thread(target=stream_output, args=(process.stderr,)) | |
| # Start the threads | |
| stdout_thread.start() | |
| stderr_thread.start() | |
| # Wait for the process to complete and the threads to finish | |
| process.wait() | |
| stdout_thread.join() | |
| stderr_thread.join() | |
| print("Inference script finished with return code:", process.returncode) | |
| # Print the outputs directory contents | |
| print_directory_contents('./outputs') | |
| # Call the function and print the result | |
| mp4_file_path = check_for_mp4_in_outputs() | |
| print(mp4_file_path) | |
| return mp4_file_path | |
| output_video = gr.Video(label="Output Video") | |
| css = """ | |
| div#warning-duplicate { | |
| background-color: #ebf5ff; | |
| padding: 0 16px 16px; | |
| margin: 20px 0; | |
| color: #030303!important; | |
| } | |
| div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p { | |
| color: #0f4592!important; | |
| } | |
| div#warning-duplicate strong { | |
| color: #0f4592; | |
| } | |
| p.actions { | |
| display: flex; | |
| align-items: center; | |
| margin: 20px 0; | |
| } | |
| div#warning-duplicate .actions a { | |
| display: inline-block; | |
| margin-right: 10px; | |
| } | |
| div#warning-setgpu { | |
| background-color: #fff4eb; | |
| padding: 0 16px 16px; | |
| margin: 20px 0; | |
| color: #030303!important; | |
| } | |
| div#warning-setgpu > .gr-prose > h2, div#warning-setgpu > .gr-prose > p { | |
| color: #92220f!important; | |
| } | |
| div#warning-setgpu a, div#warning-setgpu b { | |
| color: #91230f; | |
| } | |
| div#warning-setgpu p.actions > a { | |
| display: inline-block; | |
| background: #1f1f23; | |
| border-radius: 40px; | |
| padding: 6px 24px; | |
| color: antiquewhite; | |
| text-decoration: none; | |
| font-weight: 600; | |
| font-size: 1.2em; | |
| } | |
| div#warning-ready { | |
| background-color: #ecfdf5; | |
| padding: 0 16px 16px; | |
| margin: 20px 0; | |
| color: #030303!important; | |
| } | |
| div#warning-ready > .gr-prose > h2, div#warning-ready > .gr-prose > p { | |
| color: #057857!important; | |
| } | |
| .custom-color { | |
| color: #030303 !important; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(): | |
| gr.Markdown("# MimicMotion") | |
| gr.Markdown("High-quality human motion video generation with pose-guided control") | |
| gr.HTML(""" | |
| <div style="display:flex;column-gap:4px;"> | |
| <a href='http://tencent.github.io/MimicMotion'> | |
| <img src='https://img.shields.io/badge/Project-Page-Green'> | |
| </a> | |
| <a href='https://arxiv.org/abs/2406.19680'> | |
| <img src='https://img.shields.io/badge/Paper-Arxiv-red'> | |
| </a> | |
| </div> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| if is_shared_ui: | |
| top_description = gr.HTML(f''' | |
| <div class="gr-prose"> | |
| <h2 class="custom-color"><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg> | |
| Attention: this Space need to be duplicated to work</h2> | |
| <p class="main-message custom-color"> | |
| To make it work, <strong>duplicate the Space</strong> and run it on your own profile using a <strong>private</strong> GPU (A10G-large recommended).<br /> | |
| A A10G-large costs <strong>US$1.50/h</strong>. You'll also need to set your own secret hf_token to access gated stabilityai/stable-video-diffusion-img2vid-xt-1-1 repo. | |
| </p> | |
| <p class="actions custom-color"> | |
| <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true"> | |
| <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" /> | |
| </a> | |
| to start experimenting with this demo | |
| </p> | |
| </div> | |
| ''', elem_id="warning-duplicate") | |
| else: | |
| if(is_gpu_associated): | |
| top_description = gr.HTML(f''' | |
| <div class="gr-prose"> | |
| <h2 class="custom-color"><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg> | |
| You have successfully associated a {which_gpu} GPU to this Space 🎉</h2> | |
| <p class="custom-color"> | |
| You will be billed by the minute from when you activated the GPU until when it is turned off. | |
| </p> | |
| </div> | |
| ''', elem_id="warning-ready") | |
| else: | |
| top_description = gr.HTML(f''' | |
| <div class="gr-prose"> | |
| <h2 class="custom-color"><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg> | |
| You have successfully duplicated the MimicMotion Space 🎉</h2> | |
| <p class="custom-color">There's only one step left before you can properly play with this demo: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a GPU</b> to it (via the Settings tab)</a> and run the app below. | |
| You will be billed by the minute from when you activate the GPU until when it is turned off.</p> | |
| <p class="actions custom-color"> | |
| <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings">🔥 Set recommended GPU</a> | |
| </p> | |
| </div> | |
| ''', elem_id="warning-setgpu") | |
| with gr.Row(): | |
| ref_image_in = gr.Image(label="Person Image Reference", type="filepath") | |
| ref_video_in = gr.Video(label="Person Video Reference") | |
| with gr.Accordion("Advanced Settings", open=False): | |
| num_inference_steps = gr.Slider(label="num inference steps", minimum=12, maximum=50, value=25, step=1, interactive=available_property) | |
| guidance_scale = gr.Slider(label="guidance scale", minimum=0.1, maximum=10, value=2, step=0.1, interactive=available_property) | |
| with gr.Row(): | |
| output_frames_per_second = gr.Slider(label="fps", minimum=1, maximum=60, value=16, step=1, interactive=available_property) | |
| seed = gr.Number(label="Seed", value=42, interactive=available_property) | |
| checkpoint_version = gr.Dropdown(label="Checkpoint Version", choices=["MimicMotion_1.pth", "MimicMotion_1-1.pth"], value="MimicMotion_1.pth", interactive=available_property, filterable=False) | |
| submit_btn = gr.Button("Submit", interactive=available_property) | |
| gr.Examples( | |
| examples = [ | |
| ["./examples/demo1.jpg", "./examples/preview_1.mp4"] | |
| ], | |
| fn = load_examples, | |
| inputs = [ref_image_in, ref_video_in], | |
| outputs = [output_video], | |
| run_on_click = True, | |
| cache_examples = False | |
| ) | |
| output_video.render() | |
| submit_btn.click( | |
| fn = infer, | |
| inputs = [ref_image_in, ref_video_in, num_inference_steps, guidance_scale, output_frames_per_second, seed, checkpoint_version], | |
| outputs = [output_video] | |
| ) | |
| demo.launch(show_api=False, show_error=False) |