KoshiMazaki/akuspace-ltx25

AKUSPACE — LTX-2.5 Audio LoRA v0.5

AKUSPACE is a spatially aware Audio LoRA for LTX-2.5. It gives generated or reference audio the prompted acoustic character of a room, outdoor environment or experimental sound treatment while preserving—or generating—audio in sync with the picture. AKUSPACE supports two synchronized-video paths. In the audio-to-audio finishing route, a video’s generated soundtrack is re-rendered through AKUSPACE and remuxed onto the same frames. In the native joint workflows, LTX generates the picture and reverberant synchronized audio together, using either a one-pass graph or a two-stage HQ graph.

Explore the interactive audiovisual study → · ComfyUI nodes

Trigger word: AKUSPACE

Controls

Mode	Options	Levels
Space — rooms a sound sits in	small room, medium room, empty club, cathedral	gentle · moderate · heavy
Place — environments a sound sits among	outdoor day, outdoor night	gentle · heavy
Sound effects — processing a sound goes through	Dual Delay	gentle · heavy

Outdoor places have two levels rather than three: an ambience bed is a separate recording rather than a reverb tail, so it scales down but not up. moderate is the showcase level; heavy is the edge of the trained range and ships as experimental.

Use

Supply the dry audio as the audio-to-audio reference and use the complete trained caption. The level word sits between the space and its character:

AKUSPACE female spoken voice through synthetic cathedral reverb, moderate wide diffuse reflections and a long decaying tail, no background ambience

AKUSPACE electronic rhythm loop in a small bathroom-like room, gentle bright close reflections and a short 0.67-second reverb decay, no background ambience

Partial captions work less well than complete ones — the trailing clauses were present in every training caption. Room captions carry a decay time where applicable; cathedral, the granular effect and both outdoor places have no numeric decay.

Suggested settings: 24 steps, CFG 4.

Turn prompt enhancement OFF. The rewriter paraphrases away the trigger word and the level word, which are exactly the tokens the control surface depends on.

This adapter transforms audio it is given. Text-to-audio with the LoRA loaded and no reference present produces near-silence — there is nothing to transform.

Video. The proven route is dry voice → audio-to-audio space pass → image+audio-to-video, with the treated audio held fixed while the image conditions the first frame. The adapter targets the audio branches; video generation remains handled by the base model.

Evaluation

Envelope cross-correlation of the six published examples against their own dry references, measured on this checkpoint at 2 ms resolution, gives a 16–24 ms offset — a small consistent lead rather than per-clip drift, inside lip-sync tolerance.

Full timing table, and how to read it

Example	Offset	Peak r
Voice · small room	−20 ms	0.96
Voice · cathedral	−22 ms	0.97
Beat · medium room	−16 ms	0.34
Beat · empty club	−20 ms	0.30
Beat · outdoor day	−24 ms	0.32
Beat · dual delay	−22 ms	0.34

These are the six published examples, not an exhaustive sweep of every configuration.

Read the correlation column with care: it measures how much the amplitude envelope changed, and reverb changes it by design — most of all on transient-dense material, where the tail fills the gaps between hits. The low figures on percussion are the effect working, not a defect. It is a timing measure, not a quality measure; judge quality by ear.

Training

Base Lightricks/LTX-2.5 (22B dev transformer, bf16), trained with the official LTX Trainer on 266 paired clips. Trained to 12,300 steps; this release ships step 11,500, chosen by ear from test renders.

Every item is the same performance twice — once dry, once through a real acoustic treatment — so the model learns a transformation rather than an association. All training material is owned and was recorded or produced over several years: multiple speaking voices, beats and electronic music, percussion, and acoustic instruments. Treatments come from digital reverbs, custom presets, original Eurorack modular patches and original field recordings used for the outdoor beds. Nothing was scraped, and no third-party licensed audio was used.

Training configuration and dataset figures

Rank / alpha	32 / 32, dropout 0
Target modules	audio_attn1, audio_attn2, audio_ff
Optimiser	AdamW, 2e-4, batch 1, bf16, seed 42
Paired clips	266 — 228 train / 38 validation
Distinct audio files	364 — 98 dry / 266 processed
Sources	14, across 7 source types
Treatment cells	19, every cell carrying all 14 sources
Training runs	5 (v1→v5); v5 ships
Clip format	6.000 s, 48 kHz, −3 dBFS ceiling
Held out	one beat and one male voice source

Sources were rendered as a deliberate grid so that no space is defined by one voice and no voice is defined by one space. The full config is in config/a2a_v5_ltx25.yaml.

Limitations

• Requires an audio reference; it will not generate a space from text alone.
• This is generative transformation, not physically accurate acoustic simulation.
• Audio is regenerated rather than filtered, so wording, timing, pitch or timbre can shift.
• Outdoor ambience can mask a quiet source.
• The level axis is a learned caption control, not a calibrated wet/dry percentage.

Files

Path	What it is
akuspace-ltx25-v0.5.safetensors	The adapter — step 11500, bf16, rank 32
config/a2a_v5_ltx25.yaml	The training config this run used
examples/	The six A/B pairs, 320k MP3

dry-voice.mp3 pairs with the two voice examples; dry-beat.mp3 with the four beat examples. Rooms are at moderate, outdoor at gentle, dual delay at heavy. No gain or normalisation was applied on export, so the level relationship between dry and processed is the one the model produced. Hear them crossfaded at akuspace.pages.dev.

The published examples use a synthetic TTS voice and an AI-generated beat as dry sources — deliberately, because a replacement voice that needs seating in a scene is the realistic use case. The training grid itself is my own recordings.

Licence

Released under the LTX-2.x Community License terms that govern the base model. Confirm the current terms at https://github.com/Lightricks/LTX-2/blob/main/LICENSE.md before commercial use — the community licence covers entities under $10M annual revenue, with separate commercial agreements above that threshold.