DeepFuze is a state-of-the-art deep learning tool that seamlessly integrates with ComfyUI to revolutionize facial transformations, lipsyncing, video generation, voice cloning, face swapping, and lipsync translation. Leveraging advanced algorithms, DeepFuze enables users to combine audio and video with unparalleled realism, ensuring perfectly synchronized facial movements. This innovative solution is ideal for content creators, animators, developers, and anyone seeking to elevate their video editing projects with sophisticated AI-driven features.
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You must install Visual Studio, it works with the community version OR VS C++ Build Tools and select "Desktop Development with C++" under "Workloads -> Desktop & Mobile"
Install from the ComfyUI manager, select Install via git URL, and copy past:
https://github.com/SamKhoze/CompfyUI-DeepFuze.git
Restart your ComfyuUI
Video Tutorial How To Install on MAC YOUTUBE LINK
Install Pytorch
Here how to install and test your PyTorch
This method has been tested on a M1 and M3 Mac, You must run the below code on your terminal window for Mac Metal Performance Shaders (MPS) Apple's specialized solution for high-performance GPU programming on their devices. Integrating closely with the Metal framework, MPS provides a suite of highly optimized shaders for graphics and computing tasks, which is particularly beneficial in machine learning applications.
export PYTORCH_ENABLE_MPS_FALLBACK=1
pip install dlib
pip install TTS
After preparing the environmental variables navigate into your custom_nodes folder and git clone or manually download the code and extract it into the custom_nodes folder
cd custom_nodes
git clone https://github.com/SamKhoze/CompfyUI-DeepFuze.git
cd CompfyUI-DeepFuze
pip install -r requirements.txt
Below are the two ComfyUI repositories required to load video and audio. Install them into your custom_nodes folder:
Clone the repositories:
cd custom_nodes
git clone https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite.git
git clone https://github.com/a1lazydog/ComfyUI-AudioScheduler.git
If you get an error installing TTS, it is most likely because you have different versions of Python, make sure to install the correct version
If you get an error: ImportError: cannot import name 'get_full_repo_name' from 'huggingface_hub' Run the below codes on your terminal it will solve the issue
conda install chardet
pip install --upgrade transformers==4.39.2
if you get any error for any packages, open the requirements.txt file with any text editor remove the version from the front of the package name, and reinstall requirments.txt again
You can download models directly from GoogleDrive and place models into the PATH ./ComfyUI/models/deepfuze/ Ensure to manually download each model one by one and place them, due to the size of the models some of the models won't download if you download the folder preparing the environmental variables navigate into your custom_nodes folder and git clone or manually download the code and extract it into the custom_nodes folder.
You need an OpenAI API Key if you wish to use the "DeepFuze Openai LLM" node for generating dialogues for voice cloning
To use the "Openai LLM" node for voice cloning dialogues, you need an OpenAI API Key. You can get this key and set it up by following the instructions in the OpenAI Developer quickstart guide. Please note that the "Openai LLM" node does not save your API key. Every time you close the node, you will need to manually copy and paste your API key. You can also add the API key as an Environment Variable using the following commands: For Windows: setx OPENAI_API_KEY "your-api-key-here", and for Mac: export OPENAI_API_KEY='your-api-key-here'. The next time you need to copy and paste your API key into the LLM Node, you can type the following command in your terminal: echo $OPENAI_API_KEY, and it will print your API Key, allowing you to copy and paste it into your Openai LLM node.
ComfyUI-DeepFuze/
├── __init__.py
├── __pycache__/
│ ├── __init__.cpython-311.pyc
│ ├── audio_playback.cpython-311.pyc
│ ├── llm_node.cpython-311.pyc
│ ├── nodes.cpython-311.pyc
│ └── utils.cpython-311.pyc
├── audio_playback.py
├── deepfuze/
│ ├── __init__.py
│ ├── audio.py
│ ├── choices.py
│ ├── common_helper.py
│ ├── config.py
│ ├── content_analyser.py
│ ├── core.py
│ ├── download.py
│ ├── execution.py
│ ├── face_analyser.py
│ ├── face_helper.py
│ ├── face_masker.py
│ ├── face_store.py
│ ├── ffmpeg.py
│ ├── filesystem.py
│ ├── globals.py
│ ├── installer.py
│ ├── logger.py
│ ├── memory.py
│ ├── metadata.py
│ ├── normalizer.py
│ ├── process_manager.py
├── requirements.txt
├── images/
├── install.py
├── LICENSE.txt
├── llm_node.py
├── mypy.ini
├── nodes.py
├── README.md
├── requirements.txt
├── run.py
├── tests/
│ ├── __init__.py
│ ├── test_audio.py
│ ├── test_cli_face_debugger.py
│ ├── test_cli_face_enhancer.py
│ ├── test_cli_face_swapper.py
│ ├── test_cli_frame_colorizer.py
│ ├── test_cli_frame_enhancer.py
│ ├── test_cli_lip_syncer.py
│ ├── test_common_helper.py
│ ├── test_config.py
│ ├── test_download.py
│ ├── test_execution.py
│ ├── test_face_analyser.py
│ ├── test_ffmpeg.py
│ ├── test_filesystem.py
│ ├── test_memory.py
│ ├── test_normalizer.py
│ ├── test_process_manager.py
│ ├── test_vision.py
│ └── test_wording.py
├── tts_generation.py
└── utils.py
This node generates lipsyncing video from, video, image, and WAV audio files.
Input Types:
images: Extracted frame images as PyTorch tensors.audio: An instance of loaded audio data.mata_batch: Load batch numbers via the Meta Batch Manager node.
Output Types:
IMAGES: Extracted frame images as PyTorch tensors.frame_count: Output frame counts int.audio: Output audio.video_info: Output video metadata.
DeepFuze Lipsync Features:
enhancer: You can add a face enhancer to improve the quality of the generated video via the face restoration network.frame_enhancer: You can add an enhance the whole frame of the video.face_mask_padding_left: Padding to left of the face while lipsyncing.face_mask_padding_right: Padding to the right of the face while lipsyncing.face_mask_padding_bottom: Padding to the bottom of the face while lipsyncing.face_mask_padding_top: Padding to the top of the face while lipsyncing.device: [cpu,gpu]frame_rate: Set the frame rate.loop_count: How many additional times the video should repeat.filename_prefix: Prefix naming for the output video.pingpong: Causes the input to be played back in reverse to create a clean loop.save_output: Saving the output on output folder.
This node generates lipsyncing video from, video, image, and WAV audio files.
Input Types:
source_images: Extracted frame image as PyTorch tensors for swapping.target_images: Extracted frame images as PyTorch tensors to input the source video/image.mata_batch: Load batch numbers via the Meta Batch Manager node.
Output Types:
IMAGES: Extracted frame images as PyTorch tensors.frame_count: Output frame counts int.audio: Output audio.video_info: Output video metadata.
DeepFuze FaceSwap Features:
enhancer: You can add a face enhancer to improve the quality of the generated video via face restoration network.faceswap_model: You can select different models for swapping.frame_enhancer: You can add an enhance the whole frame of the video.face_detector_model: You can select different models for face detection.face_mask_padding_left: Padding to left on the face while lipsyncing.face_mask_padding_right: Padding to the right on the face while lipsyncing.face_mask_padding_bottom: Padding to the bottom on the face while lipsyncing.face_mask_padding_top: Padding to the top on the face while lipsyncing.device: [cpu,gpu]frame_rate: Set the frame rate.loop_count: How many additional times the video should repeat.filename_prefix: Prefix naming for the output video.pingpong: Causes the input to be played back in reverse to create a clean loop.save_output: Saving the output on output folder.
Languages:
DeepFuze_TTS voice cloning supports 17 languages: English (en), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Polish (pl), Turkish (tr), Russian (ru), Dutch (nl), Czech (cs), Arabic (ar), Chinese (zh-cn), Japanese (ja), Hungarian (hu), Korean (ko) Hindi (hi).
This node is used to clone any voice from typed input. The audio file should be 10-15 seconds long for better results and should not have much noise.
Input Types:
audio: An instance of loaded audio data.text: Text to generate the cloned voice audio.
Output Types:
audio: An instance of loaded audio data.
This node is used to integrate LLM into the voice cloning node. You can type your dialogue and set up parameters; the AI-generated texts will be used for voice cloning.
Input Types:
user_query: Type your dialogues.
Output Types:
LLM_RESPONSE: Outputs AI Generated texts.
DeepFuze Openai LLM Features:
model_name: You can select from the available openai models.api_key: Add your API Key. (Your API Key will not be saved, each time you use this node you must manually enter it.max_tokens: is a parameter that limits the number of tokens in a model's response in OpenAI GPT APIs. It's used in requests made through GPT for Sheets and Docs, and in the ChatOpenAI() class. The default value for max_tokens is 4096 tokens, which is roughly equivalent to 3,000 words.temperature: controls the level of randomness and creativity in its responses. It's a hyper-parameter in Large Language Models (LLMs) that balances creativity and coherence in generated text. The temperature setting is always a number between 0 and 1, with the default being 0.7: 0: Produces very straightforward, almost deterministic responses 1: Results in wildly varying responses 0.7: The default temperature for ChatGPT.timeout: set up time if request takes too long to complete and the server closes the connection.
Input Types:
image: Provides a preview of the padding for the face mask.
DeepFuze Padding Features:
face_mask_padding_left: Padding to left on the face while lipsyncing.face_mask_padding_right: Padding to the right on the face while lipsyncing.face_mask_padding_bottom: Padding to the bottom on the face while lipsyncing.face_mask_padding_top: Padding to the top on the face while lipsyncing.
Voice Cloning + Lipsync Generation
Voice Cloning + Lipsync Generation + FaceSwap
from deepfuze import DeepFuze
# Initialize the DeepFuze instance
deepfuze = DeepFuze()
# Load video and audio files
deepfuze.load_video('path/to/video.mp4')
deepfuze.load_audio('path/to/audio.mp3')
deepfuze.load_checkpoint('path/to/checkpoint_path')
# Set parameters (optional)
deepfuze.set_parameters(sync_level=5, transform_intensity=3)
# Generate lipsynced video
output_path = deepfuze.generate(output='path/to/output.mp4')
print(f"Lipsynced video saved at {output_path}")This repository could not have been completed without the contributions from FaceFusion, InsightFace,SadTalker, Facexlib, GFPGAN, GPEN, Real-ESRGAN, TTS, SSD, and wav2lip,
- Please carefully read and comply with the open-source license applicable to this code and models before using it.
- Please carefully read and comply with the intellectual property declaration applicable to this code and models before using it.
- This open-source code runs completely offline and does not collect any personal information or other data. If you use this code to provide services to end-users and collect related data, please take necessary compliance measures according to applicable laws and regulations (such as publishing privacy policies, adopting necessary data security strategies, etc.). If the collected data involves personal information, user consent must be obtained (if applicable).
- It is prohibited to use this open-source code for activities that harm the legitimate rights and interests of others (including but not limited to fraud, deception, infringement of others' portrait rights, reputation rights, etc.), or other behaviors that violate applicable laws and regulations or go against social ethics and good customs (including providing incorrect or false information, terrorist, child/minors pornography and violent information, etc.). Otherwise, you may be liable for legal responsibilities.
The DeepFuze code is developed by Dr. Sam Khoze and his team. Feel free to use the DeepFuze code for personal, research, academic, and non-commercial purposes. You can create videos with this tool, but please make sure to follow local laws and use it responsibly. The developers will not be responsible for any misuse of the tool by users. For commercial use, please contact us at [email protected].