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 = False # 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): # 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 = 25 noise_aug_strength = 0 guidance_scale = 2.0 sample_stride = 2 fps = 16 seed = 42 # Create the data structure data = { 'base_model_path': 'stabilityai/stable-video-diffusion-img2vid-xt-1-1', 'ckpt_path': 'models/MimicMotion_1.pth', '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-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") with gr.Row(): with gr.Column(): if is_shared_ui: top_description = gr.HTML(f'''

Attention: this Space need to be duplicated to work

To make it work, duplicate the Space and run it on your own profile using a private GPU (A10G-large recommended).
A A10G-large costs US$1.50/h.

Duplicate this Space to start experimenting with this demo

''', elem_id="warning-duplicate") else: top_description = gr.HTML(f'''

You have successfully associated a {which_gpu} GPU to the MimicMotion Space 🎉

You will be billed by the minute from when you activated the GPU until when it is turned off.

''', elem_id="warning-ready") 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) 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) 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], outputs = [output_video] ) demo.launch(show_api=False, show_error=False)