LTX 2.3 Trainer — Video-to-Video (/v2v)
Overview
The /v2v endpoint trains a LoRA for the LTX 2.3 video model. The LoRA learns a transformation: it is conditioned on a reference (control) video and learns to produce a corresponding target video. You provide pairs of clips — a "before" reference and an "after" target — and the LoRA learns the mapping between them.
This is the right endpoint when you want a control-driven video transformation: pose/depth/sketch/edge control to full-resolution video, restyling, colorization, a recurring edit applied to arbitrary footage, and similar reference-to-video tasks.
Key features:
- Trains a LoRA from paired reference→target video clips.
- Optional reference downscaling so the LoRA can be driven by a coarse/low-resolution control proxy (e.g. a small pose or depth map) yet output full resolution.
- Optional reference temporal downsampling so the LoRA can be driven by a low-FPS reference.
- Video-only (no audio is learned).
Provide a single .zip archive (linked via training_data_url) of paired clips:
<name>_start.<ext> — the reference / control video (the "input" to transform).<name>_end.<ext> — the target video (the desired "output").<name>.txt — caption for the pair (optional only if a trigger_phrase is set; otherwise required).
Each _start must have a matching _end with the same base <name>. Video formats: .mp4, .mov, .avi, .mkv. The _start and _end clips of a pair must have matching frame counts. File names must be unique across the archive. Aim for at least 10 pairs.
Minimum clip length: with auto_scale_input off (the default), both clips in each pair (_start and _end) must already have at least number_of_frames frames (default 89, ≈ 3.7 s at 24 fps). A pair with either side too short is skipped, and if no pair qualifies the request fails (422). Turn on auto_scale_input to resample shorter clips instead.
Example layout:
sample01_start.mp4 sample01_end.mp4 sample01.txt
sample02_start.mp4 sample02_end.mp4 sample02.txt
Within each pair, the _start and _end must have the same frame count. If any reference/target pair differs in length, the entire request is rejected (HTTP 422); the mismatched pair is not silently skipped. Pre-trim your clips so each pair matches.
Dataset
training_data_url (required)
Type: string
URL to the .zip archive of _start/_end pairs (and optional captions).
trigger_phrase
Type: string
Default: ""
Phrase prepended to captions during training; include it at inference to activate the transformation.
Training Parameters
rank
Type: integer (8, 16, 32, 64, 128)
Default: 32
LoRA capacity. Higher values capture more detail at the cost of memory and overfitting risk.
number_of_steps
Type: integer
Default: 3000 (range 100–20000)
Number of optimization steps. Video-to-video transformations typically benefit from a somewhat higher step count than plain text-to-video, hence the higher default.
learning_rate
Type: number
Default: 0.0002
Optimization step size.
first_frame_conditioning_p
Type: number
Default: 0.1 (range 0.0–1.0)
Probability of conditioning on the first frame during training. Lower values work better for video-to-video transformation (the default is intentionally low).
reference_downscale_factor
Type: integer
Default: 1 (range 1–8)
Spatially downscale the reference (control) video by this factor before it is encoded, so the LoRA learns to drive a full-resolution output from a coarse/low-resolution reference (e.g. a small pose, depth, or sketch proxy). 1 means no downscaling.
| Value | Use Case |
|---|
| 1 | Reference and target at the same resolution (default) |
| 2–8 | Reference is a smaller/coarser control proxy than the desired output |
Note: both width and height must be divisible by the factor, and width ÷ factor and height ÷ factor must each be divisible by 32 (checked against both the training and validation resolutions); an incompatible value fails the request with a 422.
reference_temporal_scale_factor
Type: integer
Default: 1 (range 1–8)
Temporally downsample the reference video (lower FPS) by this factor before encoding, so the LoRA can be driven by a low-FPS reference. 1 means no change.
(number_of_frames − 1) must be divisible by the factor, and after subsampling (frames − 1) must remain a multiple of 8 — checked against both number_of_frames and validation_number_of_frames. An incompatible factor/frame-count combination fails the request with a 422. (Example: with the default 89 frames, a factor of 2 is invalid because (89 − 1) ÷ 2 = 44, which is not a multiple of 8.)
Video Configuration
number_of_frames
Type: integer
Default: 89 (range 9–121)
Frames per training clip. Must satisfy frames % 8 == 1; other values are snapped down to the nearest valid count.
frame_rate
Type: integer
Default: 24 (range 8–60)
Target frames per second. LTX 2.3's native rate is 24.
resolution
Type: string (low, medium, high)
Default: medium
| Resolution | 16:9 | 1:1 | 9:16 |
|---|
| low | 512×288 | 512×512 | 288×512 |
| medium | 768×448 | 768×768 | 448×768 |
| high | 960×544 | 960×960 | 544×960 |
aspect_ratio
Type: string (16:9, 1:1, 9:16)
Default: 1:1
Type: boolean
Default: false
Fit videos to the target frame count and frame rate.
Type: boolean
Default: true
Split long clips into scenes (kept in sync across the reference/target pair).
Type: number
Default: 30.0 (range 1.0–60.0)
Duration above which a clip is eligible for scene splitting.
Validation
validation
Type: array
Default: [] (max 2 entries)
Validation samples, each an object with:
prompt (string) — the text prompt.reference_video_url (string, required) — the reference/control video to transform.
validation_negative_prompt
Type: string
Default: a built-in quality negative prompt.
validation_number_of_frames
Type: integer
Default: 89 (range 9–121)
validation_frame_rate
Type: integer
Default: 24 (range 8–60)
validation_resolution
Type: string
Default: high
validation_aspect_ratio
Type: string
Default: 1:1
stg_scale
Type: number
Default: 1.0 (range 0.0–3.0)
debug_dataset
Type: boolean
Default: false
Return an archive of the preprocessed data for inspection.
Outputs
lora_file — the trained LoRA weights (.safetensors).config_file — JSON describing the trigger phrase and training type.video — combined validation reel (when validation samples were provided).debug_dataset — preprocessed-data archive, only when debug_dataset is enabled.
Billing
A successful run is billed max(100, number_of_steps) billable units. Requests that fail before training completes (input-validation errors / HTTP 422, or dataset-download failures) are billed 0 units.
How the Training Works
Pipeline Overview
- Preprocessing — the archive is extracted,
_start/_end pairs and captions are matched, each clip is resized/cropped to the resolution bucket (optionally scene-split in sync), and the reference is optionally downscaled/temporally downsampled.
- Training — the LoRA trains for
number_of_steps, conditioned on the reference clip, learning to produce the target. Validation previews run at intervals.
- Output — the LoRA, config, and validation reel are uploaded.
What Happens to Your Data
- Archive extraction: the
.zip is unpacked; macOS __MACOSX metadata folders are ignored.
- Pair matching: each
<name>_start is paired with its <name>_end and the optional <name>.txt caption. Targets missing a reference (or vice versa) are reported as errors.
- Video fitting: both clips in a pair are resized to fill the resolution bucket and center-cropped; with
auto_scale_input they are resampled to the target frame rate/count. Reference and target stay aligned.
- Reference scaling: when
reference_downscale_factor or reference_temporal_scale_factor is above 1, the reference is physically downscaled/temporally downsampled before encoding, so the LoRA learns to drive full-resolution output from a coarse reference.
- Scene splitting: when on, pairs are split using synchronized boundaries so the reference and target never desync.
- Captions: the trigger phrase (if set) is prepended.
How It Works
This trains a LoRA to perform a reference-conditioned transformation: instead of generating from text alone, it conditions on a reference video supplied at inference and produces a transformed result. The trained file specializes the base model at your reference→target mapping. At inference you supply a reference video (matching how you scaled it during training) plus a prompt.
Tips for Getting Good Results
Dataset Quality
- Use at least 10 well-aligned reference→target pairs; more variety yields a more general transformation.
- The reference and target of each pair must describe the same scene/motion differing only by the transformation you want learned.
- Keep frame counts matched within a pair.
Caption Best Practices
- Describe the target content plainly, optionally with a trigger phrase.
- Keep captions consistent in style across pairs so the LoRA associates the transformation, not the wording.
Good caption: tron1ze a neon-outlined city street at night
Weak caption: street
Trigger Phrases
- A distinctive trigger phrase helps cleanly invoke the transformation at inference; include it in every caption and at inference.
Reference Scaling
- If you want to drive generation from a small/coarse control map (pose, depth, edges), set
reference_downscale_factor above 1 and supply matching coarse references at inference.
- Use the same scaling at inference as you did at training; the validation localizer mirrors the training-time scaling for you.
Scene Splitting and Captions
Scene splitting keeps reference/target in sync, but each split inherits the pair's single caption. For precise captions, pre-split your pairs and disable scene splitting.
At inference, supply a reference video, use the same trigger phrase, and apply the same reference scaling you trained with.
Recommended Starting Configuration
{
"training_data_url": "https://example.com/v2v_pairs.zip",
"trigger_phrase": "tron1ze",
"rank": 32,
"number_of_steps": 3000,
"learning_rate": 0.0002,
"first_frame_conditioning_p": 0.1,
"reference_downscale_factor": 1,
"reference_temporal_scale_factor": 1,
"number_of_frames": 89,
"frame_rate": 24,
"resolution": "medium",
"aspect_ratio": "1:1",
"validation": [
{ "prompt": "tron1ze a busy city street", "reference_video_url": "https://example.com/ref.mp4" }
]
}
Diagnosing Issues
- Transformation too weak: more steps, higher
rank, or more consistent pairs.
- Output ignores the reference structure: ensure your pairs are well aligned; consider lowering
first_frame_conditioning_p (already low by default) and check reference scaling matches training.
- Overfitting (previews copy training targets): fewer steps, lower
rank, more varied pairs.
- Dataset errors: every
_start needs a matching _end; frame counts within a pair must match; names must be unique.
Validation Prompt Tips
- Use a reference video that the LoRA has not seen, so previews reveal generalization.
- Match the caption style and trigger phrase you trained with.
Common Pitfalls
- Mismatched or missing
_start/_end pairs.
- Reference and target that differ by more than the intended transformation.
- Using different reference scaling at inference than at training.
fal-ai/ltx23-trainer-v2/v2v