ComfyUI-UniverSR

ComfyUI nodes for UniverSR — Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching (ICASSP 2026, arXiv:2510.00771).

A single model upscales 8 / 12 / 16 / 24 kHz effective bandwidth → 48 kHz across speech, music and sound effects. It works directly in the complex‑STFT domain with flow matching — no neural vocoder — and regenerates the missing high‑frequency band rather than just interpolating.

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Nodes

Node	Output	Purpose
UniverSR Model Loader	UNIVERSR_MODEL	Loads + caches a checkpoint. Auto-downloads the presets to models/universr/.
UniverSR Super-Resolution	AUDIO, IMAGE	Runs the SR. Chunks long audio (click-free overlap-add). Optional before/after spectrogram.

Wire it up:

LoadAudio ─────────────┐
                       ▼
UniverSR Model Loader ─► UniverSR Super-Resolution ─► SaveAudio
                                          └─ spectrogram ─► PreviewImage

Model Loader

• model — universr-audio (general; music/SFX/mixed, recommended) or universr-speech (voice). Each downloads ~230 MB to models/universr/<name> on first use. Local checkpoint folders placed in models/universr/ also appear in this list.
• device — auto / cuda / cpu.
• local_path (optional) — override with a folder (config.yaml + pytorch_model.bin) or a raw .pth/.ckpt training checkpoint.
• config_path (optional) — config.yaml for a raw checkpoint. Empty → the bundled default config.

Super-Resolution

• input_sr — the effective bandwidth of your content in Hz. The model treats everything up to input_sr/2 as valid and regenerates above it.
  • 8000 → genuine low-rate audio (8 kHz → 48 kHz; the strongest, best-trained case).
  • 16000 → brighten muffled but full-rate audio by regenerating only above 8 kHz (most natural).
• ode_method — euler (fastest) → midpoint (balanced) → rk4 (best).
• ode_steps — flow-matching steps. 4 is fast and validated; 4–10 is a good range.
• guidance_scale — classifier-free guidance. Speech 1.0–1.5, music 1.5–2.0, SFX ~1.5. Higher = denser highs but less faithful. 0 disables CFG.
• seed — noise seed (0 = random each run).
• chunk_seconds / overlap_seconds — long-audio handling (see below). chunk_seconds=0 processes the whole clip at once.
• blend — wet/dry mix. 1.0 = full SR. Lower keeps more of the original (handy for bandwidth extension of already-48 kHz audio).
• unload_model — free VRAM after the run.
• show_spectrogram — also output a before/after spectrogram comparison IMAGE.

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Long audio & chunking

UniverSR runs the whole clip through a flow-matching ODE in one shot, which OOMs on long files (the upstream notebook added chunking specifically to survive clips > 2 min). This node chunks in the time domain and stitches the results with overlap-add + linear crossfade (weight-normalised), so seams are click-free — an improvement over the upstream GUI's naive concatenation. Drop chunk_seconds if you hit VRAM limits; raise overlap_seconds if you ever hear a seam. Stereo is processed per-channel and preserved.

Compared to the FoleyTune BWE node (which brightens short foley clips and processes the whole clip at once), this node adds the chunking needed for arbitrarily long sequences.

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Installation

cd ComfyUI/custom_nodes
git clone <this repo> ComfyUI-UniverSR
pip install -r ComfyUI-UniverSR/requirements.txt

The universr model code is vendored under vendor/ (an installed pip copy is preferred if present), so the only dependency beyond ComfyUI's stack is torchdiffeq (plus einops, timm, huggingface_hub, pyyaml, which ComfyUI usually already has). Weights download automatically on first use.

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How it works (implementation note)

ComfyUI audio arrives at an arbitrary real sample rate. UniverSR's file path relies on torchaudio.load (fragile torchcodec backend), and its tensor path assumes the tensor is already at input_sr. So this node does the band-limit itself: resample to 48 kHz → downsample each chunk to input_sr (pure DSP, no codec) → hand UniverSR a genuine low-rate tensor to super-resolve. This exactly reproduces the model's training-time degradation.

Credits & license

UniverSR © Woongjib Choi et al., DSPAI Lab, Yonsei University — released under the MIT License (see LICENSE). This node wrapper vendors the UniverSR inference code unmodified under vendor/.

@inproceedings{choi2026universr,
  title     = {{UniverSR}: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching},
  author    = {Choi, Woongjib and Lee, Sangmin and Lim, Hyungseob and Kang, Hong-Goo},
  booktitle = {IEEE ICASSP},
  year      = {2026}
}