Canary-ComfyUI

Canary-ComfyUI requires a minimum of 1536MB (1.5GB) of VRAM for base operation. For optimal performance, a GPU with at least 6GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.

✅ RTX 3060 (12GB): Yes, fully compatible with 9.3GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 9.3GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 12.9GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 20.1GB headroom

Canary-ComfyUI requires the following PyTorch-related packages: pytorch-lightning==2.5.4, pytorch-metric-learning==2.9.0, torch-audiomentations==0.12.0, torch-pitch-shift==1.2.5, torchmetrics==1.8.1. Ensure your ComfyUI environment has these installed. Ensure your PyTorch installation matches your CUDA version (use torch.version.cuda to check).

To run Canary-ComfyUI, you need to install: absl-py==2.3.1, accelerate==1.10.1, aiofiles==24.1.0, aiohappyeyeballs==2.6.1, aiohttp==3.12.15, aiosignal==1.4.0, alembic==1.16.5, annotated-types==0.7.0, antlr4-python3-runtime==4.9.3, anyio==4.10.0, asteroid-filterbanks==0.4.0, asttokens==3.0.0, async-timeout==5.0.1, attrs==25.3.0, audioread==3.0.1, backports-datetime-fromisoformat==2.0.3, braceexpand==0.1.7, brotli==1.1.0, certifi==2025.8.3, cffi==1.17.1, charset-normalizer==3.4.3, click==8.2.1, cloudpickle==3.1.1, colorama==0.4.6, coloredlogs==15.0.1, colorlog==6.9.0, contourpy==1.3.2, cycler==0.12.1, cytoolz==1.0.1, datasets==4.0.0, decorator==5.2.1, dill==0.3.8, docopt==0.6.2, editdistance==0.8.1, einops==0.8.1, exceptiongroup==1.3.0, executing==2.2.0, fastapi==0.116.1, ffmpy==0.6.1, fiddle==0.3.0, filelock==3.13.1, flatbuffers==25.2.10, fonttools==4.59.2, frozenlist==1.7.0, fsspec==2024.6.1, future==1.0.0, gitdb==4.0.12, gitpython==3.1.45, gradio==5.44.1, gradio-client==1.12.1, graphviz==0.21, greenlet==3.2.4, groovy==0.1.2, grpcio==1.74.0, h11==0.16.0, httpcore==1.0.9, httpx==0.28.1, huggingface-hub==0.34.4, humanfriendly==10.0, hydra-core==1.3.2, hyperpyyaml==1.2.2, idna==3.10, indic-numtowords==1.1.0, inflect==7.5.0, intervaltree==3.1.0, ipython==8.37.0, jedi==0.19.2, jinja2==3.1.4, jiwer==3.1.0, joblib==1.5.2, julius==0.2.7, kaldi-python-io==1.2.2, kiwisolver==1.4.9, lazy-loader==0.4, levenshtein==0.27.1, lhotse==1.30.3, libcst==1.8.2, librosa==0.11.0, lightning==2.4.0, lightning-utilities==0.15.2, lilcom==1.8.1, llvmlite==0.44.0, loguru==0.7.3, mako==1.3.10, markdown==3.8.2, markdown-it-py==4.0.0, markupsafe==2.1.5, marshmallow==4.0.1, matplotlib==3.10.6, matplotlib-inline==0.1.7, mdurl==0.1.2, mediapy==1.1.6, ml-dtypes==0.5.3, more-itertools==10.7.0, mpmath==1.3.0, msgpack==1.1.1, multidict==6.6.4, multiprocess==0.70.16, nemo-toolkit==2.4.0, networkx==3.3, num2words==0.5.14, numba==0.61.2, numpy==1.26.4, omegaconf==2.3.0, onnx==1.19.0, onnxruntime==1.22.1, optuna==4.5.0, orjson==3.11.3, packaging==24.2, pandas==2.3.2, parso==0.8.5, peft==0.17.1, pillow==11.3.0, pip==25.2, piper-tts==1.3.0, plac==1.4.5, platformdirs==4.4.0, pooch==1.8.2, primepy==1.3, prompt-toolkit==3.0.52, propcache==0.3.2, protobuf==5.29.5, psutil==7.0.0, pure-eval==0.2.3, pyannote-audio==3.3.2, pyannote-core==5.0.0, pyannote-database==5.1.3, pyannote-metrics==3.2.1, pyannote-pipeline==3.0.1, pyarrow==21.0.0, pybind11==3.0.1, pycparser==2.22, pydantic==2.11.7, pydantic-core==2.33.2, pydub==0.25.1, pygments==2.19.2, pyloudnorm==0.1.1, pyparsing==3.2.3, pyreadline3==3.5.4, python-dateutil==2.9.0.post0, python-multipart==0.0.20, pytorch-lightning==2.5.4, pytorch-metric-learning==2.9.0, pytz==2025.2, pyyaml==6.0.2, rapidfuzz==3.14.0, regex==2025.8.29, requests==2.32.5, resampy==0.4.3, rich==14.1.0, ruamel-yaml==0.18.15, ruamel-yaml-clib==0.2.12, ruff==0.12.11, sacremoses==0.1.1, safehttpx==0.1.6, safetensors==0.6.2, scikit-learn==1.7.1, scipy==1.15.3, semantic-version==2.10.0, semver==3.0.4, sentencepiece==0.2.1, sentry-sdk==2.35.1, setuptools==80.9.0, shellingham==1.5.4, six==1.17.0, smmap==5.0.2, sniffio==1.3.1, sortedcontainers==2.4.0, soundfile==0.13.1, sox==1.5.0, soxr==0.5.0.post1, speechbrain==1.0.3, sqlalchemy==2.0.43, stack-data==0.6.3, starlette==0.47.3, sympy==1.13.3, tabulate==0.9.0, tensorboard==2.20.0, tensorboard-data-server==0.7.2, tensorboardx==2.6.4, termcolor==3.1.0, text-unidecode==1.3, texterrors==1.0.9, threadpoolctl==3.6.0, tokenizers==0.21.4, tomli==2.2.1, tomlkit==0.13.3, toolz==1.0.0, torch-audiomentations==0.12.0, torch-pitch-shift==1.2.5, torchmetrics==1.8.1, tqdm==4.67.1, traitlets==5.14.3, transformers==4.51.3, typeguard==4.4.4, typer==0.17.3, typing-extensions==4.12.2, typing-inspection==0.4.1, tzdata==2025.2, urllib3==2.5.0, uv==0.8.14, uvicorn==0.35.0, wandb==0.21.3, wcwidth==0.2.13, webdataset==1.0.2, websockets==15.0.1, werkzeug==3.1.3, wget==3.2, whisper-normalizer==0.1.12, win32-setctime==1.2.0, wrapt==1.17.3, xxhash==3.5.0, yarl==1.20.1. You can install these using pip or add them to your requirements.txt file.

Canary-ComfyUI supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.

To install Canary-ComfyUI: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/Juste-Leo2/Canary-ComfyUI, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.