vllm-omni/recipes/LTX/LTX-2.5.md

LTX-2.5

Text-to-video and first-frame image-to-video generation with synchronized audio

Overview

vLLM-Omni supports the gated Lightricks/LTX-2.5-Diffusers checkpoint for T2V and first-frame I2V. Every output includes synchronized 48 kHz stereo audio.

The raw Lightricks/LTX-2.5 repository is not directly loadable with --model. It supplies the official upsampler and LoRA sidecars used by the Full/SFT two-stage pipeline, so accept both model licenses and authenticate before first use.

Choose a pipeline

Pipeline	Mode	Default size	Schedule
LTX2Pipeline	Full/SFT one-stage	960x544	30 steps
LTX2TwoStagePipeline	Full/SFT two-stage	1920x1088	30 + 3 steps
LTX2DistilledOneStagePipeline	Distilled one-stage	960x544	8 steps
LTX2DistilledTwoStagePipeline	Distilled two-stage	1920x1088	8 + 3 steps

Both two-stage pipelines generate at half resolution, apply the official x2 latent upsampler, and run a three-step refinement stage. Select the class with --model-class-name; no --task-type flag is required. Supplying one initial image selects I2V, while omitting it selects T2V.

Prerequisites

hf auth login
export MODEL=Lightricks/LTX-2.5-Diffusers

Install matching vLLM and vLLM-Omni versions, and ensure ffmpeg and ffprobe are on PATH. I2V requires PyAV backed by an FFmpeg build with the libx264 encoder.

Hardware

GPU	Status	Recommended scope
NVIDIA B300	Verified	All four canonical pipelines
NVIDIA B200 or H200	Capacity-based recommendation; not yet verified	All four canonical pipelines
NVIDIA GB200 or GB300	Capacity-based recommendation; not yet verified	All four canonical pipelines
NVIDIA H100 80 GB	FP8 recipe	Distilled one-stage at 960x544

The 1920x1088 two-stage examples require about 114 GB of peak GPU memory. 80 GB GPUs do not have enough safety margin for the canonical two-stage configuration; use a larger GPU or reduce the output configuration.

H100 80 GB FP8

Use the distilled one-stage pipeline with FP8 and cuDNN attention:

vllm serve Lightricks/LTX-2.5-Diffusers \
  --omni \
  --model-class-name LTX2DistilledOneStagePipeline \
  --quantization fp8 \
  --diffusion-attention-backend CUDNN_ATTN \
  --host 0.0.0.0 \
  --port 8000

Use the T2V or I2V request below after the server is ready.

Offline inference

Choose values from the pipeline table. For example, the distilled two-stage path uses:

export PIPELINE=LTX2DistilledTwoStagePipeline
export WIDTH=1920
export HEIGHT=1088
export STEPS=8

Text-to-video

python examples/offline_inference/text_to_video/text_to_video.py \
  --model "${MODEL}" \
  --model-class-name "${PIPELINE}" \
  --prompt "A cinematic shot of a red fox walking through a snowy forest at dawn, the camera tracking alongside, snow crunching underfoot." \
  --width "${WIDTH}" \
  --height "${HEIGHT}" \
  --num-frames 121 \
  --num-inference-steps "${STEPS}" \
  --frame-rate 24 \
  --fps 24 \
  --seed 42 \
  --output ltx25-t2v.mp4

First-frame image-to-video

python examples/offline_inference/image_to_video/image_to_video.py \
  --model "${MODEL}" \
  --model-class-name "${PIPELINE}" \
  --image /absolute/path/to/first-frame.png \
  --prompt "The red fox walks forward while the camera tracks alongside." \
  --width "${WIDTH}" \
  --height "${HEIGHT}" \
  --num-frames 121 \
  --num-inference-steps "${STEPS}" \
  --frame-rate 24 \
  --fps 24 \
  --seed 42 \
  --output ltx25-i2v.mp4

LTX-2.5 uses the official CRF-18 first-frame conditioning path by default.

Online serving

Start one server for the selected pipeline:

vllm serve "${MODEL}" \
  --omni \
  --model-class-name "${PIPELINE}" \
  --host 0.0.0.0 \
  --port 8000 \
  --stage-init-timeout 900

T2V request:

curl -sS --fail-with-body \
  -X POST http://127.0.0.1:8000/v1/videos/sync \
  -F 'prompt=A cinematic shot of a red fox walking through a snowy forest at dawn, the camera tracking alongside, snow crunching underfoot.' \
  -o ltx25-online-t2v.mp4

For I2V, add exactly one first frame:

export FIRST_FRAME=/absolute/path/to/first-frame.png

curl -sS --fail-with-body \
  -X POST http://127.0.0.1:8000/v1/videos/sync \
  -F "input_reference=@${FIRST_FRAME};type=image/png" \
  -F 'prompt=The red fox walks forward while the camera tracks alongside.' \
  -o ltx25-online-i2v.mp4

Restart the server after changing PIPELINE because each class selects a different weight layout and execution topology.

Constraints

• Online width, height, num_frames, fps, and num_inference_steps are optional and use the selected pipeline defaults when omitted. seed is optional and random when omitted.
• num_frames must be 8k+1 when overridden.
• One-stage width and height must be divisible by 32; two-stage final dimensions must be divisible by 64.
• Full/SFT pipelines accept negative prompts. Distilled pipelines are positive-only and reject negative prompts.
• I2V accepts exactly one initial image per prompt.
• The supported --model value is Lightricks/LTX-2.5-Diffusers.

References

• LTX-2.5-Diffusers model card
• Official LTX-2 repository
• Generic T2V example
• Generic I2V example