LTX 2.3 Pro: Text to Video

Try LTX 2.3 Pro: Text to Video in the Workbench

Run this model interactively, tune parameters, and compare outputs.

Model ID: ltx-2-3-pro-text-to-video High-fidelity text-to-video generation from Lightricks LTX-2.3 Pro. Creates video from text prompts up to 4K at 50 FPS with optional synchronized audio, camera motion effects, and flexible resolution (1080p, 2K, 4K).

Example request

Use the Workbench as a request builder: configure parameters for this model in the UI, then open the API tab to copy the exact cURL or Python call.

Sync
Async
Async with SSE

This blocks until the video is ready (typically 5-15 minutes). Prefer Async or Async with SSE for anything beyond quick experimentation.See the video generation reference for more details.

Minimal
All parameters

curl -X POST https://hub.oxen.ai/api/ai/videos/generate \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "ltx-2-3-pro-text-to-video",
  "prompt": "<prompt>"
}'

curl -X POST https://hub.oxen.ai/api/ai/videos/generate \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "ltx-2-3-pro-text-to-video",
  "prompt": "<prompt>",
  "task": "text_to_video",
  "resolution": "1080p",
  "duration": 6,
  "aspect_ratio": "16:9",
  "fps": 25,
  "camera_motion": "none",
  "generate_audio": true
}'

See the async queue reference for more details.

Minimal
All parameters

# Enqueue, capture the generation id.
GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "ltx-2-3-pro-text-to-video",
  "prompt": "<prompt>"
}' | jq -r '.generations[0].generation_id')

# Poll the single generation until it 404s (terminal state).
while curl -s -o /dev/null -w "%{http_code}" \
    -H "Authorization: Bearer $OXEN_API_KEY" \
    "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | grep -q "^200$"; do
  sleep 5
done
echo "Done. See the 'Async with SSE' tab to receive the result URL."

# Enqueue, capture the generation id.
GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "ltx-2-3-pro-text-to-video",
  "prompt": "<prompt>",
  "task": "text_to_video",
  "resolution": "1080p",
  "duration": 6,
  "aspect_ratio": "16:9",
  "fps": 25,
  "camera_motion": "none",
  "generate_audio": true
}' | jq -r '.generations[0].generation_id')

# Poll the single generation until it 404s (terminal state).
while curl -s -o /dev/null -w "%{http_code}" \
    -H "Authorization: Bearer $OXEN_API_KEY" \
    "https://hub.oxen.ai/api/ai/queue/$GEN_ID" | grep -q "^200$"; do
  sleep 5
done
echo "Done. See the 'Async with SSE' tab to receive the result URL."

See the async queue reference for more details.

Minimal
All parameters

# Enqueue, capture the generation id.
GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "ltx-2-3-pro-text-to-video",
  "prompt": "<prompt>"
}' | jq -r '.generations[0].generation_id')

# Stream the SSE channel, grab the data line that follows a
# media_generation_completed event for our id, and pretty-print it.
curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \
  | awk -v id="$GEN_ID" '
    /^event: media_generation_completed$/ { expect=1; next }
    /^data: / && expect {
      payload = substr($0, 7)
      if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit }
      expect = 0
    }
  ' | jq .

# Enqueue, capture the generation id.
GEN_ID=$(curl -s -X POST https://hub.oxen.ai/api/ai/queue \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OXEN_API_KEY" \
  -d '{
  "model": "ltx-2-3-pro-text-to-video",
  "prompt": "<prompt>",
  "task": "text_to_video",
  "resolution": "1080p",
  "duration": 6,
  "aspect_ratio": "16:9",
  "fps": 25,
  "camera_motion": "none",
  "generate_audio": true
}' | jq -r '.generations[0].generation_id')

# Stream the SSE channel, grab the data line that follows a
# media_generation_completed event for our id, and pretty-print it.
curl -sN -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/events \
  | awk -v id="$GEN_ID" '
    /^event: media_generation_completed$/ { expect=1; next }
    /^data: / && expect {
      payload = substr($0, 7)
      if (index(payload, "\"generation_id\":\"" id "\"")) { print payload; exit }
      expect = 0
    }
  ' | jq .

Fetch model details

The models endpoint returns the full model object, including its json_request_schema.

curl -H "Authorization: Bearer $OXEN_API_KEY" https://hub.oxen.ai/api/ai/models/ltx-2-3-pro-text-to-video

Request parameters

Required parameters

Field	Type	Default	Description
`prompt`	`string`	—	Text prompt describing the video to generate

Optional parameters

Field	Type	Default	Description
`task`	`string`	`"text_to_video"`	The generation task (fixed for this model) One of: text_to_video.
`resolution`	`string`	`"1080p"`	Resolution quality of the generated video One of: 1080p, 2k, 4k.
`duration`	`integer`	`6`	Duration of the video in seconds One of: 6, 8, 10.
`aspect_ratio`	`string`	`"16:9"`	Aspect ratio of the generated video One of: 16:9, 9:16.
`fps`	`integer`	`25`	Frame rate in frames per second One of: 24, 25, 48, 50.
`camera_motion`	`string`	`"none"`	Camera motion effect to apply to the generated video. Use ‘none’ for no camera motion. One of: none, dolly_in, dolly_out, dolly_left, dolly_right, jib_up, jib_down, static, focus_shift.
`generate_audio`	`boolean`	`true`	Generate audio for the video

Inference API

Try LTX 2.3 Pro: Text to Video in the Workbench

​Example request

​Fetch model details

​Request parameters

​Required parameters

​Optional parameters

Example request

Fetch model details

Request parameters

Required parameters

Optional parameters