DeepFuze
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.
Quick Technical Summary: DeepFuze
- Base VRAM Footprint:
- 4096 MB (8 GB Tier)
- Primary Dependencies:
- accelerate, aiohttp>=3.8.1, anyascii>=0.3.0, bangla, bnnumerizer, bnunicodenormalizer, coqpit>=0.0.16, cython>=0.29.30, einops>=0.6.0, encodec>=0.1.1, filetype==1.2.0, flask>=2.0.1, fsspec>=2023.6.0 # <= 2023.9.1 makes aux tests fail, g2pkk>=0.1.1, gruut[de,es,fr]==2.2.3, hangul_romanize, imageio_ffmpeg, inflect>=5.6.0, jamo, jieba, kornia, librosa, matplotlib>=3.7.0, mutagen==1.47.0, nltk, num2words, numba>=0.57.0, numpy==1.26.4, onnx==1.16.0, onnxruntime-gpu, openai, opencv-python==4.9.0.80, opencv-python-headless==4.8.0.76, packaging>=23.1, pandas>=1.4,<2.0, psutil==5.9.8, pydub, pypinyin, pysbd>=0.3.4, pyyaml>=6.0, scikit-learn>=1.3.0, scipy==1.13.0, sounddevice, soundfile>=0.12.0, spacy>=3, spandrel, torch>=2.1, torchaudio, torchsde, tqdm>=4.64.1, trainer>=0.0.36, transformers>=4.33.0, umap-learn>=0.5.1, unidecode>=1.3.2
- Min PyTorch / CUDA:
- PyTorch 2.1 | CUDA 12.1+
- GitHub Repository:
- https://github.com/SamKhoze/ComfyUI-DeepFuze
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does DeepFuze require?
Direct Answer: The ComfyUI node DeepFuze requires a minimum base VRAM of 4096MB and is optimized for GPUs with at least 8GB of VRAM. Low VRAM mode is not supported for this node.
- Base VRAM:
- 4096MB (4.0GB)
- Recommended GPU:
- 8GB+ VRAM
- Low VRAM Mode:
- ✗ Not supported
- Estimation Confidence:
- MEDIUM
Cheapest VRAM Upgrade Paths (Live Market Prices):
- GeForce RTX 3060 12GB (Ultimate Budget VRAM King)──► Used: $209.62View eBay ↗
- GeForce RTX 4060 8GB (Modern Entry-Level)──► New: $303.50View Amazon ↗
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Verify Compatibility for Your Specific GPU VRAM
Select your graphics card's VRAM capacity to view optimized batch sizes, suggested resolutions, and custom performance tips for DeepFuze:
Buy NVIDIA GeForce RTX 3060 (12GB VRAM)
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What Python packages are required for DeepFuze?
Direct Answer: Running DeepFuze requires installing the following Python package dependencies: accelerate, aiohttp>=3.8.1, anyascii>=0.3.0, bangla, bnnumerizer, bnunicodenormalizer, coqpit>=0.0.16, cython>=0.29.30, einops>=0.6.0, encodec>=0.1.1, filetype==1.2.0, flask>=2.0.1, fsspec>=2023.6.0 # <= 2023.9.1 makes aux tests fail, g2pkk>=0.1.1, gruut[de,es,fr]==2.2.3, hangul_romanize, imageio_ffmpeg, inflect>=5.6.0, jamo, jieba, kornia, librosa, matplotlib>=3.7.0, mutagen==1.47.0, nltk, num2words, numba>=0.57.0, numpy==1.26.4, onnx==1.16.0, onnxruntime-gpu, openai, opencv-python==4.9.0.80, opencv-python-headless==4.8.0.76, packaging>=23.1, pandas>=1.4,<2.0, psutil==5.9.8, pydub, pypinyin, pysbd>=0.3.4, pyyaml>=6.0, scikit-learn>=1.3.0, scipy==1.13.0, sounddevice, soundfile>=0.12.0, spacy>=3, spandrel, torch>=2.1, torchaudio, torchsde, tqdm>=4.64.1, trainer>=0.0.36, transformers>=4.33.0, umap-learn>=0.5.1, unidecode>=1.3.2. This node specifically requires PyTorch version 2.1 or newer.
accelerate
aiohttp>=3.8.1
anyascii>=0.3.0
bangla
bnnumerizer
bnunicodenormalizer
coqpit>=0.0.16
cython>=0.29.30
einops>=0.6.0
encodec>=0.1.1
filetype==1.2.0
flask>=2.0.1
fsspec>=2023.6.0 # <= 2023.9.1 makes aux tests fail
g2pkk>=0.1.1
gruut[de,es,fr]==2.2.3
hangul_romanize
imageio_ffmpeg
inflect>=5.6.0
jamo
jieba
kornia
librosa
matplotlib>=3.7.0
mutagen==1.47.0
nltk
num2words
numba>=0.57.0
numpy==1.26.4
onnx==1.16.0
onnxruntime-gpu
openai
opencv-python==4.9.0.80
opencv-python-headless==4.8.0.76
packaging>=23.1
pandas>=1.4,<2.0
psutil==5.9.8
pydub
pypinyin
pysbd>=0.3.4
pyyaml>=6.0
scikit-learn>=1.3.0
scipy==1.13.0
sounddevice
soundfile>=0.12.0
spacy>=3
spandrel
torch>=2.1
torchaudio
torchsde
tqdm>=4.64.1
trainer>=0.0.36
transformers>=4.33.0
umap-learn>=0.5.1
unidecode>=1.3.2Interactive Setup & Dependency Resolver
# Loading command...Special Environment Requirements:
Requires PyTorch version 2.1+.
# Loading PyTorch command...Frequently Asked Questions
How much VRAM does DeepFuze require?
DeepFuze requires a minimum of 4096MB (4.0GB) of VRAM for base operation. For optimal performance, a GPU with at least 8GB of VRAM is recommended. Low VRAM mode is not supported for this node.
Can I run DeepFuze on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 6.8GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 6.8GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 10.4GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 17.6GB headroom
How much VRAM does DeepFuze take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), DeepFuze runs smoothly on an RTX 3060 (12GB) with 6.8GB of headroom. This is sufficient to run the node alongside standard SD 1.5 and SDXL workflows in full precision. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 17.6GB of dedicated headroom. This allows you to combine the node with massive models (like FLUX.1 Dev, Schnell, or Hunyuan Video) in full precision (FP16) without any offload flags.
What PyTorch version does DeepFuze need?
DeepFuze requires the following PyTorch-related packages: torch>=2.1, torchaudio, torchsde. Ensure your ComfyUI environment has these installed. Ensure your PyTorch installation matches your CUDA version (use torch.version.cuda to check).
What Python packages are required for DeepFuze?
To run DeepFuze, you need to install: accelerate, aiohttp>=3.8.1, anyascii>=0.3.0, bangla, bnnumerizer, bnunicodenormalizer, coqpit>=0.0.16, cython>=0.29.30, einops>=0.6.0, encodec>=0.1.1, filetype==1.2.0, flask>=2.0.1, fsspec>=2023.6.0 # <= 2023.9.1 makes aux tests fail, g2pkk>=0.1.1, gruut[de,es,fr]==2.2.3, hangul_romanize, imageio_ffmpeg, inflect>=5.6.0, jamo, jieba, kornia, librosa, matplotlib>=3.7.0, mutagen==1.47.0, nltk, num2words, numba>=0.57.0, numpy==1.26.4, onnx==1.16.0, onnxruntime-gpu, openai, opencv-python==4.9.0.80, opencv-python-headless==4.8.0.76, packaging>=23.1, pandas>=1.4,<2.0, psutil==5.9.8, pydub, pypinyin, pysbd>=0.3.4, pyyaml>=6.0, scikit-learn>=1.3.0, scipy==1.13.0, sounddevice, soundfile>=0.12.0, spacy>=3, spandrel, torch>=2.1, torchaudio, torchsde, tqdm>=4.64.1, trainer>=0.0.36, transformers>=4.33.0, umap-learn>=0.5.1, unidecode>=1.3.2. You can install these using pip or add them to your requirements.txt file.
How do I install DeepFuze in ComfyUI?
To install DeepFuze: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/SamKhoze/ComfyUI-DeepFuze, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.