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.
The AKUSPACE control surface: a scene volume showing source, listener and the
selected acoustic space, with the trained decay time read out
beneath.
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.
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 1-2 adjust to CFG4 for higher volume and detail.
Supported workflows
Input
Output
Audio
Audio-to-audio treatment for an existing recording
Text + audio
Text-to-video with AKUSPACE-treated synchronized audio
Image + audio
Image-to-video with AKUSPACE-treated synchronized audio
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.