Customize your input with more control.
Customize your input with more control.
Hint: Drag and drop files from your computer, images from web pages, paste from clipboard (Ctrl/Cmd+V), or provide a URL.
Customize your input with more control.
The cost of training depends on the number of steps. The formula is: 0.01 * steps. With 1000 steps, your request will cost $10.00.
Fine-tune your training parameters and start right now.
/first-last-frame-to-video-audio)The /first-last-frame-to-video-audio endpoint trains a LoRA adapter for the MiniMax H3 (Hailuo-03) video+audio model with the full keyframe-signature mechanism: per training sample, the conditioning is drawn from {first frame, last frame, first+last frames, none} with configurable probabilities. This is the endpoint that teaches H3's first-to-last mode — generating a video that starts on one image and lands on another — while also covering first-frame-only, last-frame-only, and unconditioned generation.
Key features:
.txt train against the trigger phrase (or a blank prompt).<stem>.first_frame.png and <stem>.last_frame.png next to a clip replace the clip's own first/last frames as conditioning images.Provide a single .zip archive (linked via training_data_url) containing your clips and, optionally, captions and sidecar keyframes:
.mp4, .mov, .avi, .mkv.txt file with the same base name as each media file (e.g. clip01.mp4 + clip01.txt). Clips without a caption train on the trigger phrase, or a blank prompt if none is set.clip01.first_frame.png and/or clip01.last_frame.png (also .jpg/.jpeg, any case) next to clip01.mp4 replace the clip's own first/last frames as conditioning images. Either, both, or neither may be present per clip; missing ones fall back to the clip's actual frames.The archive must contain video clips only — image datasets are rejected (422), as are mixed image/video archives (sidecar keyframes are conditioning images, not dataset media, and stay allowed). Aim for at least 10 clips; more is generally better. Files in subfolders are fine — clips with the same name in different subfolders are kept distinct, and each sidecar binds to the clip in its own folder.
Minimum clip length: with auto_scale_input off (the default), each video must already have at least number_of_frames frames (default 73, ≈ 3.0 s at 24 fps). Shorter clips are silently skipped, and if every clip is too short the request fails (422). Turn on auto_scale_input to resample shorter clips to the target frame count instead.
training_data_url (required)Type: string
URL to the .zip archive of training clips (and optional captions/sidecars). See "Dataset Format" above.
trigger_phraseType: string
Default: ""
A phrase prepended to every caption during training (and used as the whole caption for clips without one). At inference, including this phrase activates the learned concept.
Per training sample, one conditioning signature is drawn: both frames with probability first_last_frame_conditioning_p, else last frame only with last_frame_conditioning_p, else first frame only with first_frame_conditioning_p, else no conditioning (pure text-to-video) with the remaining probability mass. The three probabilities must sum to at most 1.0.
first_frame_conditioning_pType: number
Default: 0.2 (range 0.0–1.0)
Probability of conditioning on the first frame only.
last_frame_conditioning_pType: number
Default: 0.2 (range 0.0–1.0)
Probability of conditioning on the last frame only.
first_last_frame_conditioning_pType: number
Default: 0.4 (range 0.0–1.0)
Probability of conditioning on BOTH the first and last frames — the API's first-to-last keyframe mode. With the defaults, 20% of samples train unconditioned.
rankType: integer (one of 8, 16, 32, 64, 128)
Default: 32
LoRA capacity. Higher values can capture more detail but use more memory and are more prone to overfitting on small datasets.
| Value | Use Case |
|---|---|
| 8–16 | Small datasets, subtle styles, lower risk of overfitting |
| 32 | Balanced default |
| 64–128 | Larger datasets or complex subjects with lots of variation |
number_of_stepsType: integer
Default: 2000 (range 1–6000)
How many optimization steps to run. More steps means more learning but also more time and a higher chance of overfitting. Note that billing has a 100-step floor (see Billing).
learning_rateType: number
Default: 0.0002
How aggressively the model updates each step. The default is a sensible starting point; raise it cautiously and lower it if results look unstable or degraded.
number_of_framesType: integer
Default: 73 (range 22–124)
Frames per training clip. H3's video VAE requires frames % 17 == 5 — valid counts are 22, 39, 56, 73, 90, 107, 124. Other values are adjusted with 17 * (value // 17) + 5 and clamped to the supported range rather than using nearest-value rounding; for example, 100 becomes 90 while 123 becomes 124.
frame_rateType: integer
Default: 24 (range 8–60)
Target frames per second for training clips. H3's native output rate is a fixed 24 fps, so keep the default unless you have a specific reason to deviate.
resolutionType: string (one of low, medium, high)
Default: medium
Training resolution bucket. Combined with aspect_ratio this picks the exact pixel size:
| Resolution | 21:9 | 16:9 | 4:3 | 1:1 | 3:4 | 9:16 |
|---|---|---|---|---|---|---|
| low | 672×288 | 512×288 | 512×384 | 512×512 | 384×512 | 288×512 |
| medium | 896×384 | 768×448 | 672×512 | 768×768 | 512×672 | 448×768 |
| high | 1120×480 | 960×544 | 896×672 | 960×960 | 672×896 | 544×960 |
H3's native output is 2K; LoRA training at these sub-native resolutions is standard practice across the video-trainer family and carries over to full-resolution inference.
aspect_ratioType: string (one of 21:9, 16:9, 4:3, 1:1, 3:4, 9:16)
Default: 16:9
Aspect ratio for training clips — the same set the H3 API supports. See the table above.
auto_scale_inputType: boolean
Default: false
When true, videos are automatically fit to the target frame count and frame rate.
split_input_into_scenesType: boolean
Default: true
When true, videos longer than the duration threshold are automatically split into separate scenes (shots) before training. Note: sidecar keyframes are dropped for clips that get split (the keyframes can't be matched to a specific scene) — disable this if your dataset uses sidecars on long clips. Splitting also weakens last-frame semantics: each scene's "last frame" is a cut point, not your intended endpoint.
split_input_duration_thresholdType: number
Default: 30.0 (range 1.0–60.0)
Videos longer than this many seconds are eligible for scene splitting.
lora_file — the trained LoRA weights (.safetensors). This is the main artifact.config_file — a small JSON containing the trigger phrase as instance_prompt and training_type set to fl2va, for setting up inference.debug_dataset — a downloadable archive of your preprocessed data, only present when debug_dataset is enabled.The endpoint does not accept validation samples and does not return preview inference; evaluate the LoRA by loading it into the inference endpoint.
debug_datasetType: boolean
Default: false
When enabled, returns an archive of the preprocessed training data so you can verify your videos, captions, and keyframes were processed correctly before committing to a longer run. The archive includes preprocessing_report.json, a bounded summary of any dataset fallbacks.
strict_datasetType: boolean
Default: false
Preprocessing always logs a summary when it uses a blank prompt, substitutes clip endpoints for missing first/last sidecars, or substitutes silence for an unreadable or absent video soundtrack. Enable this option to reject those fallbacks with a 422 instead. Captions longer than 4,096 tokenizer tokens are rejected with a 422 in either mode, before the GPU-heavy encoders load.
A successful run is billed max(100, number_of_steps) step units at $0.01000 per step (category: training). The default 2,000-step run bills 2,000 units = $20.00; the 6,000-step maximum bills $60.00; requests under 100 steps are floored to 100 units = $1.00. Requests that fail before training completes (input-validation errors / HTTP 422, or dataset-download failures) are billed 0 units. If the training node is interrupted, the run resumes from its latest checkpoint under the same request — you are billed once, on completion.
number_of_steps; per sample, a conditioning signature (first / last / both / none) is drawn with your configured probabilities. Resume checkpoints are written continuously.When a sample draws a conditioning signature, the corresponding keyframe images — sidecars if present, else the clip's actual first/last frames — are encoded and packed into the training sequence at a near-clean signal level. The model learns to generate the clip given those anchors:
Mixing signatures in one run (the default probabilities) produces a LoRA that works across all of H3's conditioning modes.
A LoRA is a small set of adapter weights layered on top of the frozen base model — here, H3's attention projections. Training only updates these adapters, so the result is a compact file you load alongside the base H3 model at inference. The base model's general capabilities are preserved; the LoRA nudges it toward your subject or style.
0.2 / 0.2 / 0.4, 20% unconditioned) give a balanced all-mode LoRA.first_last_frame_conditioning_p (e.g. 0.1 / 0.1 / 0.7).If split_input_into_scenes is on, one long video becomes several shorter clips that share the original caption — any sidecar keyframes for that video are dropped, and each scene's first/last frames become arbitrary cut points. For this endpoint especially, pre-split your clips and disable scene splitting so the endpoints of every training sample are intentional.
json{ "training_data_url": "https://example.com/my_dataset.zip", "trigger_phrase": "tronl0g0", "rank": 32, "number_of_steps": 2000, "learning_rate": 0.0002, "first_frame_conditioning_p": 0.2, "last_frame_conditioning_p": 0.2, "first_last_frame_conditioning_p": 0.4, "number_of_frames": 73, "frame_rate": 24, "resolution": "medium", "aspect_ratio": "16:9", "split_input_into_scenes": false }
first_last_frame_conditioning_p, verify your clips' last frames (or .last_frame sidecars) actually show the target state, and check that scene splitting is off.number_of_steps, lower rank, or add more varied data.number_of_steps, raise rank, or improve captions.split_input_into_scenes..first_frame.png / .last_frame.png sidecars.% 17 == 5 rule (they are snapped automatically — check the logs if durations look off).