LTX2 video LoRA training

Train a Lightricks LTX video LoRA on a small set of source video clips using AI Toolkit. The output LoRA is usable in LTX2 video generation.

ecosystem	Base	Default price	Notes
ltx2	Lightricks/LTX-2 (19B)	2750 Buzz	Original LTX2.
ltx23	Lightricks/LTX-2.3 (22B)	2750 Buzz	Newer LTX 2.3.

The base checkpoint is fixed by ecosystem; there's no model field on the input.

Long-running step

Video training is the slowest training mode on the platform — video needs a longer run, so the LTX default is 3000 steps (vs 2000 for image ecosystems). Always use wait=0 and follow up via webhook or polling.

The request shape

{
  "$type": "training",
  "input": {
    "engine":    "ai-toolkit",
    "ecosystem": "ltx2"          // ltx2 | ltx23
  }
}

Prerequisites

• A Civitai orchestration token (Quick start → Prerequisites)
• A training-data zip containing source video clips
• An accurate count of clips in the zip

LTX2

Original 19B-parameter LTX video model. resolution: 768 is the typical training resolution.

POST https://orchestration.civitai.com/v2/consumer/workflows?wait=0
Authorization: Bearer <your-token>
Content-Type: application/json

{
  "tags": ["training", "video"],
  "steps": [{
    "$type": "training",
    "priority": "normal",
    "retries": 2,
    "input": {
      "engine": "ai-toolkit",
      "ecosystem": "ltx2",
      "steps": 3000,
      "resolution": 768,
      "lr": 0.0002,
      "trainTextEncoder": false,
      "lrScheduler": "cosine",
      "optimizerType": "adamw8bit",
      "networkDim": 32,
      "networkAlpha": 32,
      "trainingData": {
        "type": "zip",
        "sourceUrl": "https://civitai-delivery-worker-prod.5ac0637cfd0766c97916cefa3764fbdf.r2.cloudflarestorage.com/training-images/4470934/2725414TrainingData.nuB3.zip",
        "count": 4
      },
      "samples": { "prompts": ["a video of TOK", "TOK moving in a garden"] }
    }
  }]
}

POST/v2/consumer/workflows

Set your Civitai API token via the Token button in the navbar to enable Try It.

Request body — edit to customize (e.g. swap the image URL or prompt)

{ "tags": [ "training", "video" ], "steps": [ { "$type": "training", "priority": "normal", "retries": 2, "input": { "engine": "ai-toolkit", "ecosystem": "ltx2", "steps": 3000, "resolution": 768, "lr": 0.0002, "trainTextEncoder": false, "lrScheduler": "cosine", "optimizerType": "adamw8bit", "networkDim": 32, "networkAlpha": 32, "flipAugmentation": false, "trainingData": { "type": "zip", "sourceUrl": "https://civitai-delivery-worker-prod.5ac0637cfd0766c97916cefa3764fbdf.r2.cloudflarestorage.com/training-images/4470934/2725414TrainingData.nuB3.zip", "count": 4 }, "samples": { "prompts": [ "a video of TOK", "TOK moving in a garden" ] } } } ] }

Valid JSON

LTX 2.3

Newer 22B model. Same shape as LTX2; lr is typically lower. Both LTX ecosystems share the same rates and 3000-step default, so the default price matches (2750 Buzz).

POST https://orchestration.civitai.com/v2/consumer/workflows?wait=0
Authorization: Bearer <your-token>
Content-Type: application/json

{
  "tags": ["training", "video"],
  "steps": [{
    "$type": "training",
    "priority": "normal",
    "retries": 2,
    "input": {
      "engine": "ai-toolkit",
      "ecosystem": "ltx23",
      "steps": 3000,
      "lr": 0.0001,
      "trainTextEncoder": false,
      "lrScheduler": "cosine",
      "optimizerType": "adamw8bit",
      "networkDim": 32,
      "networkAlpha": 32,
      "trainingData": {
        "type": "zip",
        "sourceUrl": "https://civitai-delivery-worker-prod.5ac0637cfd0766c97916cefa3764fbdf.r2.cloudflarestorage.com/training-images/4470934/2725414TrainingData.nuB3.zip",
        "count": 4
      },
      "samples": { "prompts": ["a video of TOK", "TOK moving in a garden"] }
    }
  }]
}

POST/v2/consumer/workflows

Set your Civitai API token via the Token button in the navbar to enable Try It.

Request body — edit to customize (e.g. swap the image URL or prompt)

{ "tags": [ "training", "video" ], "steps": [ { "$type": "training", "priority": "normal", "retries": 2, "input": { "engine": "ai-toolkit", "ecosystem": "ltx23", "steps": 3000, "lr": 0.0001, "trainTextEncoder": false, "lrScheduler": "cosine", "optimizerType": "adamw8bit", "networkDim": 32, "networkAlpha": 32, "flipAugmentation": false, "trainingData": { "type": "zip", "sourceUrl": "https://civitai-delivery-worker-prod.5ac0637cfd0766c97916cefa3764fbdf.r2.cloudflarestorage.com/training-images/4470934/2725414TrainingData.nuB3.zip", "count": 4 }, "samples": { "prompts": [ "a video of TOK", "TOK moving in a garden" ] } } } ] }

Valid JSON

Common parameters

Defaults shown are the post-ApplyDefaults values for both LTX ecosystems.

Field	Required	Default	Notes
engine	✅	—	Always ai-toolkit.
ecosystem	✅	—	ltx2 or ltx23.
steps		3000	1–10000. Total training steps. Primary driver of training length and pricing. Video needs a longer run, hence the higher default.
epochs		10	1–20. Number of saved checkpoints delivered, each separately downloadable. Each adds 50 Buzz — LTX preview samples are videos and costly to compute.
batchSize		1	Fixed at 1 for this ecosystem.
continueFrom		(none)	A previously-trained urn:air:ltx2:lora:... / urn:air:ltx23:lora:... AIR to resume from (see Continue training). Must be a LoRA of the same ecosystem.
lr		0.0001	LTX2 examples often use 0.0002; LTX 2.3 typically 0.0001.
trainTextEncoder		false	Leave off — LTX text encoder is not retrained by AI Toolkit.
lrScheduler		cosine	constant, constant_with_warmup, cosine, linear, step.
optimizerType		adamw8bit	See SDXL/SD1 page for full enum.
networkDim		32	1–256.
networkAlpha		matches networkDim	1–256.
noiseOffset		0	0–1.
flipAugmentation		false	Random horizontal flips.
shuffleTokens / keepTokens		false / 0	Caption-tag shuffling.
triggerWord		(none)	Activation token.
trainingData.{type, sourceUrl, count}	✅	—	type: "zip". Zip should contain video clips.
samples.prompts[]		[]	Preview videos rendered at each saved checkpoint.
samples.negativePrompt		(none)	—
samples.cfgScale		(ecosystem default)	Overrides the CFG / guidance scale used when rendering the preview samples.
samples.strength		1.0	Trained-LoRA weight applied in the preview samples.

Continue training / train further

To resume from an LTX LoRA you already trained instead of starting from the base checkpoint, set continueFrom to that LoRA's AIR. The new run starts from those weights and the new epochs build on top:

{
  "$type": "training",
  "input": {
    "engine": "ai-toolkit",
    "ecosystem": "ltx2",
    "continueFrom": "urn:air:ltx2:lora:civitai:<id>@<version>",
    "steps": 1500
  }
}

continueFrom must point at a LoRA of the same ecosystem as the model being trained (an ltx2 LoRA for ecosystem: "ltx2", an ltx23 LoRA for ecosystem: "ltx23") — a mismatched ecosystem is rejected.

Reading the result

Same envelope as the other training recipes — see SDXL/SD1 → Reading the result. Each saved checkpoint yields a video LoRA .safetensors blob plus any sample .mp4 files. Use the trained LoRA in LTX2 video generation by referencing it in the workflow's loras field.

Runtime

Wall time, default settings on a 4-clip dataset:

Ecosystem	Per 100 steps	Typical full run
ltx2	~1–2 min	30–60 min for 3000 steps
ltx23	~2–3 min	60–90 min for 3000 steps

Always use wait=0.

Cost

Training is billed per step plus a flat per-epoch storage surcharge, with a price floor. LTX defaults to 3000 steps (video needs a longer run):

price = steps × costPerStep + epochs × 50        (rounded)
costPerStep = 0.75   (ltx2 and ltx23)
floor: never less than 80% of the default-configuration price

epochs is the number of saved checkpoints delivered (default 10, range 1–20); each adds 50 Buzz — LTX preview samples are videos and costly to compute. The default run is 3000 steps / 10 epochs → 3000 × 0.75 + 10 × 50 = 2250 + 500 = 2750 Buzz. The floor is 80% of the default price (2200 Buzz).

Configuration	Buzz (training only)
LTX2 / LTX 2.3, default (steps: 3000, epochs: 10)	2750 + samples
LTX2 / LTX 2.3, steps: 3000, epochs: 20	3250 + samples (+500 for 10 more checkpoints)
LTX2 / LTX 2.3, steps: 2000, epochs: 10	2200 + samples (floor)

Sample-prompt rendering uses LTX2 video-generation rates and is billed separately. Run with whatif=true to see the exact pre-flight charge.

Troubleshooting

Symptom	Likely cause	Fix
400 with "trainingData.sourceUrl not reachable"	Signed URL expired, or zip behind auth	Regenerate the URL. R2 signed URLs default to 24h.
Step failed with VRAM-related error	Resolution × clip length too high	Lower resolution (e.g. to 512), shorten clips.
Training cost surprises you	Video defaults to 3000 steps, so the floor is higher than image ecosystems	Check whatif=true before submitting. Lowering steps/epochs saves at most 20% (the floor).
Trained LoRA produces no motion	Too few steps / static reference clips	Raise steps, ensure clips show the motion you want learned.
Step failed, moderationStatus: "Rejected"	Dataset failed content moderation	Replace flagged clips.

Related

• Wan video LoRA training — Wan video LoRA training (preview)
• LTX2 video generation — use a trained LoRA in LTX2 inference
• Flux 2 Klein LoRA training — image-side counterpart
• Results & webhooks
• SubmitWorkflow / GetWorkflow
• Endpoint OpenAPI spec