├── .github └── workflows │ └── publish_action.yml ├── .gitignore ├── LICENSE ├── README-CN.md ├── README.md ├── __init__.py ├── docker-compose-lite.yml ├── heygem_node.py ├── heygem_utils.py ├── images └── 2025-05-22_22-41-52.png ├── pyproject.toml └── requirements.txt /.github/workflows/publish_action.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - master 7 | - main 8 | paths: 9 | - "pyproject.toml" 10 | 11 | jobs: 12 | publish-node: 13 | name: Publish Custom Node to registry 14 | runs-on: ubuntu-latest 15 | steps: 16 | - name: Check out code 17 | uses: actions/checkout@v4 18 | - name: Publish Custom Node 19 | uses: Comfy-Org/publish-node-action@main 20 | with: 21 | ## Add your own personal access token to your Github Repository secrets and reference it here. 22 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 23 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; 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Basically capable of generating full-body, dynamic, and arbitrary resolution digital humans. **The input video frame rate should be consistent with the synthesized frame rate.** 6 | 7 | ![image](https://github.com/billwuhao/Comfyui_HeyGem/blob/main/images/2025-05-22_22-41-52.png) 8 | 9 | ## 📣 Updates 10 | 11 | [2025-05-25]⚒️: Fixed bugs, automatically capture long videos to match audio duration. 12 | 13 | [2025-05-23]⚒️: Optimize CPU usage for long videos, allowing short videos to be repeated or pingPong to audio duration. 14 | 15 | [2025-05-22]⚒️: Released v1.0.0. 16 | 17 | ## Node Installation 18 | 19 | ``` 20 | cd ComfyUI/custom_nodes 21 | git clone https://github.com/billwuhao/Comfyui_HeyGem.git 22 | ``` 23 | 24 | ## WSL and Docker Installation 25 | 26 | - Windows (taking X64 as an example): 27 | 28 | 1, Install Windows Subsystem for Linux: https://github.com/microsoft/WSL/releases (`wsl.2.5.7.0.x64.msi`). If already installed, run `wsl --update` with administrator privileges to update. 29 | 30 | 2, Install Docker: https://www.docker.com/ (download `AMD64` version). After installation, start it: 31 | 32 | ![](https://github.com/duixcom/Duix.Heygem/raw/main/README.assets/8.png) 33 | ![](https://github.com/duixcom/Duix.Heygem/raw/main/README.assets/13.png) 34 | ![](https://github.com/duixcom/Duix.Heygem/raw/main/README.assets/3.png) 35 | 36 | The image is downloaded to drive C by default **(requires about 14GB of space)**. You can change it to another drive in the settings: 37 | 38 | ![](https://github.com/duixcom/Duix.Heygem/raw/main/README.assets/7.png) 39 | 40 | OK! You are ready. Start Docker before running the node each time. The first time you run the node, it needs to download the image, which takes about 30 minutes, depending on your network speed. The installation is not complicated; just click a few times to install the software. Docker is an isolated image environment, so you don't need to worry about compatibility issues, and it rarely reports errors, making it simpler than installing other plugins. 41 | 42 | - Linux (taking Ubuntu as an example): 43 | 44 | 1, Install Docker: Run `docker --version` to check if it is installed. If not installed, run the following commands to install it. 45 | ``` 46 | sudo apt update 47 | sudo apt install docker.io 48 | sudo apt install docker-compose 49 | ``` 50 | 51 | 2, Install Drivers: Run `nvidia-smi` to check if they are installed. If not installed, refer to the official documentation (https://www.nvidia.cn/drivers/lookup/) for installation. 52 | 53 | 3, Install NVIDIA Container Toolkit: 54 | - Add the NVIDIA package repository: 55 | ``` 56 | distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ 57 | && curl -s -L https://nvidia.github.io/libnvidia-container/gpgkey | sudo apt-key add - \ 58 | && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list 59 | ``` 60 | 61 | - Install the NVIDIA Container Toolkit: 62 | ``` 63 | sudo apt-get update 64 | sudo apt-get install -y nvidia-container-toolkit 65 | ``` 66 | 67 | - Configure the NVIDIA container runtime: 68 | ``` 69 | sudo nvidia-ctk runtime configure --runtime=docker 70 | ``` 71 | 72 | - Restart the Docker daemon to apply changes: 73 | ``` 74 | sudo systemctl restart docker 75 | ``` 76 | 77 | OK! You are ready. The first time you run the node, it needs to download the image, which takes about 30 minutes, depending on your network speed. 78 | 79 | ## Acknowledgements 80 | 81 | - [Duix.Heygem](https://github.com/duixcom/Duix.Heygem) 82 | - https://github.com/duixcom/Duix.Heygem/blob/main/LICENSE -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .heygem_node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 2 | 3 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] 4 | -------------------------------------------------------------------------------- /docker-compose-lite.yml: -------------------------------------------------------------------------------- 1 | networks: 2 | ai_network: 3 | driver: bridge 4 | 5 | services: 6 | heygem-gen-video: 7 | image: guiji2025/heygem.ai 8 | container_name: heygem-gen-video 9 | restart: always 10 | runtime: nvidia 11 | privileged: true 12 | volumes: 13 | - ${HEGEM_FACE2FACE_DATA_PATH}:/code/data 14 | environment: 15 | - PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512 16 | deploy: 17 | resources: 18 | reservations: 19 | devices: 20 | - capabilities: [gpu] 21 | shm_size: '8g' 22 | ports: 23 | - '8383:8383' 24 | command: python /code/app_local.py 25 | networks: 26 | - ai_network 27 | -------------------------------------------------------------------------------- /heygem_node.py: -------------------------------------------------------------------------------- 1 | import os 2 | import time 3 | import uuid 4 | import torchaudio 5 | import tempfile 6 | from typing import Optional 7 | import torch 8 | import requests 9 | import json 10 | from tqdm import tqdm 11 | 12 | from .heygem_utils import ( 13 | start_heygem_service, 14 | stop_heygem_service, 15 | process_tensor_by_duration, 16 | save_tensor_as_video, 17 | video_to_tensor 18 | ) 19 | import folder_paths 20 | temp_dir = folder_paths.get_temp_directory() 21 | 22 | TEMP_DIR = os.path.join(temp_dir, 'heygem') 23 | os.makedirs(os.path.join(TEMP_DIR, 'temp'), exist_ok=True) 24 | 25 | 26 | def cache_audio_tensor( 27 | cache_dir, 28 | audio_tensor: torch.Tensor, 29 | sample_rate: int, 30 | filename_prefix: str = "cached_audio_", 31 | audio_format: Optional[str] = ".wav" 32 | ) -> str: 33 | try: 34 | with tempfile.NamedTemporaryFile( 35 | prefix=filename_prefix, 36 | suffix=audio_format, 37 | dir=cache_dir, 38 | delete=False 39 | ) as tmp_file: 40 | temp_filepath = tmp_file.name 41 | 42 | torchaudio.save(temp_filepath, audio_tensor, sample_rate) 43 | 44 | return temp_filepath 45 | except Exception as e: 46 | raise Exception(f"Error caching audio tensor: {e}") 47 | 48 | 49 | SERVICE_DICT = { 50 | 9999: 'System abnormality', 51 | 10000: 'Succeeded', 52 | 10001: 'Engaged in another task', 53 | 10002: 'Parameter exception', 54 | 10003: 'Get lock exception', 55 | 10004: 'Task does not exist', 56 | 10005: 'Task processing timeout', 57 | 10006: 'Unable to submit task, please check service status', 58 | } 59 | 60 | class HeyGemRun: 61 | @classmethod 62 | def INPUT_TYPES(s): 63 | return {"required": 64 | { 65 | "audio": ("AUDIO",), 66 | "video": ("IMAGE", ), 67 | "mode": (['pingpong', 'repeat'], {"default": "pingpong"}), 68 | }, 69 | "optional": { 70 | "stop_heygem": ("BOOLEAN", {"default": False}), 71 | "fps": ("FLOAT", {"default": 24.0, "min": 1.0, "max": 60.0, "step": 1.0}), 72 | }, 73 | } 74 | 75 | CATEGORY = "🎤MW/MW-HeyGem" 76 | RETURN_TYPES = ("IMAGE",) 77 | RETURN_NAMES = ("VIDEO",) 78 | 79 | FUNCTION = "run" 80 | 81 | def run(self, video, audio, mode, fps, stop_heygem=False): 82 | start_heygem_service(TEMP_DIR) 83 | 84 | audio_path = cache_audio_tensor( 85 | cache_dir=os.path.join(TEMP_DIR, "temp"), 86 | audio_tensor=audio["waveform"].squeeze(0), 87 | sample_rate=audio["sample_rate"] 88 | ) 89 | 90 | duration = audio["waveform"].shape[-1] / audio["sample_rate"] 91 | 92 | taskcode = f"{uuid.uuid4()}" 93 | video_path = os.path.join(TEMP_DIR, "temp", f"{taskcode}.mkv") 94 | 95 | processed_tensor = process_tensor_by_duration(video, duration, mode, fps) 96 | save_tensor_as_video(processed_tensor, video_path, fps) 97 | 98 | docker_video_path = os.path.join("/code/data/temp/", f"{taskcode}.mkv") 99 | docker_audio_path = os.path.join("/code/data/temp/", os.path.basename(audio_path)) 100 | data = { 101 | "audio_url": docker_audio_path, 102 | "video_url": docker_video_path, 103 | "code": taskcode, 104 | 'watermark_switch': 0, 105 | 'digital_auth': 0, 106 | 'chaofen': 0, 107 | 'pn': 0 108 | } 109 | 110 | data = json.dumps(data) 111 | waite_sec = 0 112 | while waite_sec < 120: 113 | try: 114 | post_response = requests.post(url="http://127.0.0.1:8383/easy/submit",data=data) 115 | if post_response.status_code == 200: 116 | result = post_response.json() 117 | if result['code'] != 10000: 118 | raise ValueError(SERVICE_DICT[result['code']]) 119 | break 120 | else: 121 | raise Exception(f"Request failed, status code: {post_response.status_code}") 122 | except requests.exceptions.RequestException as e: 123 | print(f"Request failed: {e}... \nWait a moment... The service is not ready, try again...") 124 | 125 | time.sleep(3) 126 | waite_sec += 3 127 | 128 | tq_bar = tqdm(desc="\nHeyGem...") 129 | 130 | while True: 131 | try: 132 | get_response = requests.get(url=f"http://127.0.0.1:8383/easy/query?code={taskcode}") 133 | if get_response.status_code == 200: 134 | result = get_response.json() 135 | print(result) 136 | if result['code'] != 10000: 137 | raise ValueError(SERVICE_DICT[result['code']]) 138 | try: 139 | if result['data']['status'] == 2: 140 | break 141 | except: 142 | pass 143 | 144 | else: 145 | raise Exception(f"Request failed, status code: {get_response.status_code}") 146 | except requests.exceptions.RequestException as e: 147 | raise Exception(f"Request failed: {e}") 148 | 149 | time.sleep(3) 150 | tq_bar.update(1) 151 | 152 | res_viedo = os.path.join(TEMP_DIR, "temp", f"{taskcode}-r.mp4") 153 | 154 | images_tensor = video_to_tensor(res_viedo) 155 | if stop_heygem: 156 | stop_heygem_service() 157 | return (images_tensor,) 158 | 159 | 160 | 161 | NODE_CLASS_MAPPINGS = { 162 | "HeyGemRun": HeyGemRun, 163 | } 164 | 165 | NODE_DISPLAY_NAME_MAPPINGS = { 166 | "HeyGemRun": "HeyGem AI Avatar", 167 | } -------------------------------------------------------------------------------- /heygem_utils.py: -------------------------------------------------------------------------------- 1 | import imageio 2 | import numpy as np 3 | import torch 4 | import subprocess 5 | import os 6 | import sys 7 | import time 8 | 9 | 10 | BASE_DIR = os.path.dirname(os.path.abspath(__file__)) 11 | DOCKER_COMPOSE_FILE = os.path.join(BASE_DIR, "docker-compose-lite.yml") 12 | CONTAINER_NAME = "heygem-gen-video" 13 | VOLUME_ENV_VAR = "HEGEM_FACE2FACE_DATA_PATH" 14 | 15 | os.environ['NO_PROXY'] = 'localhost,127.0.0.1,::1' 16 | os.environ['no_proxy'] = 'localhost,127.0.0.1,::1' 17 | 18 | def _run_docker_command(command_parts, check_result=False, capture_output=True, text_output=True, env=None): 19 | try: 20 | result = subprocess.run( 21 | command_parts, 22 | capture_output=capture_output, 23 | text=text_output, 24 | check=check_result, 25 | env=env 26 | ) 27 | return result 28 | except FileNotFoundError: 29 | print(f"Error: '{command_parts[0]}' command not found.", file=sys.stderr) 30 | print(f"Please ensure Docker or Docker Compose is installed and in your system's PATH.", file=sys.stderr) 31 | raise 32 | except subprocess.CalledProcessError as e: 33 | print(f"Error executing command: {' '.join(e.cmd)}", file=sys.stderr) 34 | print(f"Exit Code: {e.returncode}", file=sys.stderr) 35 | if e.stdout: print(f"Stdout:\n{e.stdout}", file=sys.stderr) 36 | if e.stderr: print(f"Stderr:\n{e.stderr}", file=sys.stderr) 37 | raise 38 | except Exception as e: 39 | print(f"An unexpected error occurred: {e}", file=sys.stderr) 40 | raise 41 | 42 | def is_docker_container_running(container_name): 43 | command = [ 44 | "docker", "ps", 45 | "-q", 46 | "--filter", f"name={container_name}" 47 | ] 48 | try: 49 | result = _run_docker_command(command) 50 | if result.stdout.strip(): 51 | print(f"Container '{container_name}' is running (ID: {result.stdout.strip()}).") 52 | return True 53 | else: 54 | return False 55 | except Exception: 56 | raise 57 | 58 | 59 | def docker_container_exists(container_name): 60 | command = [ 61 | "docker", "ps", 62 | "-aq", 63 | "--filter", f"name={container_name}" 64 | ] 65 | try: 66 | result = _run_docker_command(command) 67 | if result.stdout.strip(): 68 | print(f"Container '{container_name}' exists (ID: {result.stdout.strip()}).") 69 | return True 70 | else: 71 | print(f"Container '{container_name}' does not exist.") 72 | return False 73 | except Exception: 74 | return False 75 | 76 | def start_heygem_service(volume_host_path): 77 | if is_docker_container_running(CONTAINER_NAME): 78 | print(f"\nContainer '{CONTAINER_NAME}' is already running. No action needed.") 79 | return True 80 | 81 | if docker_container_exists(CONTAINER_NAME): 82 | start_command = ["docker", "start", CONTAINER_NAME] 83 | try: 84 | _run_docker_command(start_command, check_result=True) 85 | print(f"Successfully sent 'docker start {CONTAINER_NAME}' command.") 86 | 87 | time.sleep(5) 88 | if is_docker_container_running(CONTAINER_NAME): 89 | print(f"Container '{CONTAINER_NAME}' confirmed as running after direct start.") 90 | return True 91 | else: 92 | print(f"Container '{CONTAINER_NAME}' did not become ready after direct start.") 93 | return False 94 | 95 | except Exception: 96 | print(f"Failed to directly start container '{CONTAINER_NAME}'. Falling back to docker-compose.", file=sys.stderr) 97 | pass 98 | 99 | docker_daemon_running = False 100 | try: 101 | _run_docker_command(["docker", "info"], check_result=True) 102 | docker_daemon_running = True 103 | print("Docker daemon is running and reachable.") 104 | except (subprocess.CalledProcessError, FileNotFoundError) as e: 105 | print("\n--- Docker Daemon Not Running ---", file=sys.stderr) 106 | print("Error: Could not connect to the Docker daemon.", file=sys.stderr) 107 | print("Cannot proceed with docker-compose without a running Docker daemon.", file=sys.stderr) 108 | print("\nPlease ensure the Docker daemon is running:", file=sys.stderr) 109 | 110 | if sys.platform.startswith('linux'): 111 | print(" - On Linux, try: 'sudo systemctl start docker' or 'sudo service docker start'", file=sys.stderr) 112 | elif sys.platform == 'win32': 113 | print(" - On Windows with Docker Desktop: Ensure Docker Desktop application is running.", file=sys.stderr) 114 | print(" - If running within WSL: Ensure Docker Desktop on Windows is running and WSL integration is enabled.", file=sys.stderr) 115 | elif sys.platform == 'darwin': 116 | print(" - On macOS: Ensure Docker Desktop application is running.", file=sys.stderr) 117 | else: 118 | print(" - Please consult your operating system documentation for starting the Docker service.", file=sys.stderr) 119 | 120 | print("-----------------------------------", file=sys.stderr) 121 | raise 122 | 123 | if docker_daemon_running: 124 | env_for_subprocess = os.environ.copy() 125 | env_for_subprocess[VOLUME_ENV_VAR] = volume_host_path 126 | env_for_subprocess['PWD'] = os.getcwd() 127 | 128 | compose_up_command = [ 129 | "docker-compose", 130 | "-f", 131 | DOCKER_COMPOSE_FILE, 132 | "up", 133 | "-d" 134 | ] 135 | 136 | try: 137 | subprocess.run( 138 | compose_up_command, 139 | check=True, 140 | env=env_for_subprocess, 141 | stdout=sys.stdout, 142 | stderr=sys.stderr 143 | ) 144 | print(f"\nSuccessfully executed 'docker-compose up -d' for '{CONTAINER_NAME}'.") 145 | 146 | wait_sec = 0 147 | max_wait_sec = 60 148 | wait_interval_sec = 3 149 | 150 | while wait_sec < max_wait_sec: 151 | if is_docker_container_running(CONTAINER_NAME): 152 | print(f"Container '{CONTAINER_NAME}' confirmed as running after docker-compose up.") 153 | return True 154 | 155 | try: 156 | status_command = ["docker", "inspect", "-f", "{{.State.Status}}", CONTAINER_NAME] 157 | status_result = _run_docker_command(status_command) 158 | status = status_result.stdout.strip() 159 | except Exception: 160 | pass 161 | 162 | time.sleep(wait_interval_sec) 163 | wait_sec += wait_interval_sec 164 | 165 | print(f"\nError: Timed out waiting for container '{CONTAINER_NAME}' to start after docker-compose up ({max_wait_sec}s).", file=sys.stderr) 166 | print("Please check container logs for details (e.g., 'docker logs {}').".format(CONTAINER_NAME), file=sys.stderr) 167 | return False 168 | 169 | except Exception: 170 | print(f"\nFailed to start '{CONTAINER_NAME}' using docker-compose. The command output above should provide details.", file=sys.stderr) 171 | return False 172 | 173 | else: 174 | print("Skipping docker-compose up because Docker daemon is not running.", file=sys.stderr) 175 | return False 176 | 177 | def _get_container_id(container_name): 178 | command = [ 179 | "docker", "ps", 180 | "-q", 181 | "--filter", f"name={container_name}" 182 | ] 183 | try: 184 | result = _run_docker_command(command) 185 | container_id = result.stdout.strip() 186 | if container_id: 187 | return container_id 188 | return None 189 | except Exception as e: 190 | print(f"Error getting container ID for '{container_name}': {e}", file=sys.stderr) 191 | return None 192 | 193 | def stop_heygem_service(): 194 | current_container_id = _get_container_id(CONTAINER_NAME) 195 | if not current_container_id: 196 | print(f"\nContainer '{CONTAINER_NAME}' is not running. No need to stop.") 197 | return True 198 | 199 | stop_command = ["docker", "stop", current_container_id] 200 | 201 | try: 202 | _run_docker_command(stop_command, check_result=True) 203 | print(f"\nSuccessfully sent 'docker stop {CONTAINER_NAME}' command.") 204 | 205 | except Exception as e: 206 | print(f"\nFailed to stop '{CONTAINER_NAME}' using 'docker stop': {e}. Please check the logs above.", file=sys.stderr) 207 | 208 | def process_tensor_by_duration(image_tensor, duration, mode='normal', fps=24): 209 | original_frames_count = image_tensor.shape[0] 210 | target_frames_count = int(duration * fps) 211 | 212 | if mode == 'normal': 213 | if target_frames_count <= original_frames_count: 214 | return image_tensor[:target_frames_count] 215 | else: 216 | return image_tensor 217 | 218 | frame_indices_to_write = [] 219 | original_indices = np.arange(original_frames_count) 220 | 221 | if mode == 'repeat': 222 | if target_frames_count <= original_frames_count: 223 | return image_tensor[:target_frames_count] 224 | 225 | while len(frame_indices_to_write) < target_frames_count: 226 | frame_indices_to_write.extend(original_indices) 227 | frame_indices_to_write = frame_indices_to_write[:target_frames_count] 228 | 229 | elif mode == 'pingpong': 230 | if target_frames_count <= original_frames_count: 231 | return image_tensor[:target_frames_count] 232 | 233 | forward_indices = original_indices 234 | backward_indices = original_indices[::-1] 235 | 236 | while len(frame_indices_to_write) < target_frames_count: 237 | frame_indices_to_write.extend(forward_indices) 238 | if len(frame_indices_to_write) < target_frames_count: 239 | frame_indices_to_write.extend(backward_indices) 240 | frame_indices_to_write = frame_indices_to_write[:target_frames_count] 241 | 242 | return image_tensor[frame_indices_to_write] 243 | 244 | def save_tensor_as_video(image_tensor, output_path, fps=24): 245 | if image_tensor.is_cuda: 246 | tensor_np = image_tensor.cpu().numpy() 247 | else: 248 | tensor_np = image_tensor.numpy() 249 | 250 | tensor_np = np.clip(np.round(tensor_np * 255), 0, 255).astype(np.uint8) 251 | 252 | try: 253 | writer_params = {'codec': 'ffv1'} 254 | with imageio.get_writer(output_path, fps=fps, **writer_params) as writer: 255 | for frame in tensor_np: 256 | writer.append_data(frame) 257 | print(f"Successfully saved video to {output_path} using FFV1 (lossless).") 258 | 259 | except Exception as e: 260 | print(f"Failed to use FFV1 codec: {e}. Trying libx264 with crf=0 (lossless, but might have YUV conversion).") 261 | try: 262 | ffmpeg_params = ['-crf', '0', '-pix_fmt', 'yuv444p'] 263 | 264 | with imageio.get_writer(output_path, fps=fps, codec='libx264', ffmpeg_params=ffmpeg_params) as writer: 265 | for frame in tensor_np: 266 | writer.append_data(frame) 267 | 268 | print(f"Successfully saved video to {output_path} using libx264 (crf=0, lossless).") 269 | 270 | except Exception as e_h264: 271 | print(f"Failed to use libx264 crf=0: {e_h264}. Falling back to default (potentially lossy).") 272 | try: 273 | with imageio.get_writer(output_path, fps=fps) as writer: 274 | for frame in tensor_np: 275 | writer.append_data(frame) 276 | print(f"Saved video to {output_path} using default imageio settings (potentially lossy).") 277 | except Exception as e_default: 278 | print(f"Failed even with default settings: {e_default}") 279 | print("Video saving failed.") 280 | 281 | def video_to_tensor(video_path): 282 | if not os.path.exists(video_path): 283 | raise FileNotFoundError(f"Video file not found: {video_path}") 284 | 285 | reader = None 286 | try: 287 | reader = imageio.get_reader(video_path) 288 | meta = reader.get_meta_data() 289 | 290 | width, height = meta['size'] 291 | channels = 3 292 | 293 | def frame_processor_generator(imgio_reader, expected_shape): 294 | for i, frame in enumerate(imgio_reader): 295 | if frame.shape[-1] == 4 and expected_shape[-1] == 3: 296 | frame = frame[:, :, :3] 297 | if frame.shape != expected_shape: 298 | print(f"Warning: Frame {i} has unexpected shape {frame.shape}. Expected {expected_shape}. Skipping.") 299 | continue 300 | 301 | frame_processed = (frame.astype(np.float32) / 255.0) 302 | 303 | yield frame_processed 304 | 305 | frame_shape = (height, width, channels) 306 | gen_instance = frame_processor_generator(reader, frame_shape) 307 | 308 | frames_np_flat = np.fromiter( 309 | gen_instance, 310 | np.dtype((np.float32, frame_shape)) 311 | ) 312 | 313 | num_loaded_frames = len(frames_np_flat) 314 | 315 | if num_loaded_frames == 0: 316 | reader.close() 317 | raise RuntimeError(f"Failed to load any frames from video '{video_path}'. Check video file.") 318 | 319 | frame_shape = (height, width, channels) 320 | gen_instance = frame_processor_generator(reader, frame_shape) 321 | 322 | frames_structured_np = np.fromiter( 323 | gen_instance, 324 | dtype=np.dtype((np.float32, frame_shape)) 325 | ) 326 | print(f"Finished reading frames. Structured numpy array shape: {frames_structured_np.shape}, dtype: {frames_structured_np.dtype}") 327 | 328 | num_loaded_frames = len(frames_structured_np) 329 | 330 | if num_loaded_frames == 0: 331 | reader.close() 332 | raise RuntimeError(f"Failed to load any frames from video '{video_path}'. Check video file or its content.") 333 | 334 | total_scalars = num_loaded_frames * height * width * channels 335 | try: 336 | if frames_structured_np.size * frames_structured_np.dtype.itemsize != total_scalars * np.dtype(np.float32).itemsize: 337 | pass 338 | 339 | frames_np = frames_structured_np.view(np.float32).reshape(-1, height, width, channels) 340 | print(f"Reshaped numpy array shape: {frames_np.shape}, dtype: {frames_np.dtype}") 341 | 342 | except Exception as reshape_e: 343 | print(f"Error reshaping numpy array after fromiter: {reshape_e}") 344 | reader.close() 345 | raise RuntimeError(f"Failed to reshape frame data loaded from '{video_path}'. Likely mismatch in expected frame dimensions or data.") from reshape_e 346 | 347 | try: 348 | tensor = torch.from_numpy(frames_np) 349 | print("Tensor conversion successful.") 350 | except Exception as tensor_e: 351 | print(f"Error converting numpy array to torch tensor: {tensor_e}") 352 | reader.close() 353 | raise RuntimeError(f"Failed to convert numpy array to torch tensor for '{video_path}'. Likely out of memory.") from tensor_e 354 | 355 | reader.close() 356 | 357 | print(f"Successfully loaded video '{video_path}' into tensor with shape {tensor.shape}.") 358 | return tensor 359 | 360 | except Exception as e: 361 | print(f"An error occurred during video loading for '{video_path}': {e}") 362 | if reader is not None: 363 | try: 364 | reader.close() 365 | except Exception: 366 | pass 367 | raise -------------------------------------------------------------------------------- /images/2025-05-22_22-41-52.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/billwuhao/Comfyui_HeyGem/95a9e534babd68984bb70297ce67355d49642f1d/images/2025-05-22_22-41-52.png -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "heygem-mw" 3 | description = "HeyGem AI avatar." 4 | version = "1.0.3" 5 | license = {file = "LICENSE"} 6 | dependencies = [] 7 | 8 | [project.urls] 9 | Repository = "https://github.com/billwuhao/Comfyui_HeyGem" 10 | # Used by Comfy Registry https://comfyregistry.org 11 | 12 | [tool.comfy] 13 | PublisherId = "mw" 14 | DisplayName = "Comfyui_HeyGem" 15 | Icon = "" 16 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/billwuhao/Comfyui_HeyGem/95a9e534babd68984bb70297ce67355d49642f1d/requirements.txt --------------------------------------------------------------------------------