# How to Choose the Right LTX-2 Pipeline for Your Specific Use Case

> Discover how to select the ideal LTX-2 pipeline by evaluating speed quality conditioning and HDR support for your unique video editing audio generation or HDR output needs.

- Repository: [Lightricks/LTX-2](https://github.com/Lightricks/LTX-2)
- Tags: how-to-guide
- Published: 2026-08-20

---

**LTX-2 provides multiple specialized pipelines that trade off speed, quality, conditioning options, and HDR support—your choice depends on whether you need video editing, audio-driven generation, HDR output, or maximum fidelity.**

Selecting the right LTX-2 pipeline requires matching your project's technical constraints against the library's available generation modes. Lightricks designed each pipeline for distinct scenarios: single-stage speed, two-stage quality, mask-based editing, or specialized conditioning like audio or keyframe interpolation. This guide maps concrete use cases to their optimal pipeline implementations based on the source code in `packages/ltx-pipelines/src/ltx_pipelines/`.

## Editing Existing Video: RetakePipeline

If you need to regenerate a specific time region while preserving the rest of your video, **RetakePipeline** is purpose-built for this workflow.

Implemented in [`packages/ltx-pipelines/src/ltx_pipelines/retake.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/src/ltx_pipelines/retake.py), this pipeline performs single-stage, mask-based regeneration. It accepts both video and audio inputs, making it suitable for replacing visual content without re-rendering unaffected segments.

```bash
uv run python -m ltx_pipelines.retake \
    --checkpoint-path models/ltx-2.5/diffusion_models/ltx-2.5-22b-dev.safetensors \
    --video-path source.mp4 \
    --start-time 5.0 \
    --end-time 8.0 \
    --prompt "Replace the middle scene with a flying dragon" \
    --output-path edited.mp4

```

## Audio-Driven Video Generation: A2VidPipelineTwoStage

For synchronizing video to audio tracks, **A2VidPipelineTwoStage** provides dedicated audio conditioning. Located in [`packages/ltx-pipelines/src/ltx_pipelines/a2vid_two_stage.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/src/ltx_pipelines/a2vid_two_stage.py), this pipeline runs two stages: Stage 1 generates low-resolution video conditioned on encoded audio features, and Stage 2 upsamples to final resolution.

This architecture ensures temporal coherence with the audio waveform rather than treating sound as an afterthought.

```bash
uv run python -m ltx_pipelines.a2vid_two_stage \
    --checkpoint-path models/ltx-2.5/diffusion_models/ltx-2.5-22b-dev.safetensors \
    --distilled-lora models/ltx-2.5/loras/ltx-2.5-22b-distilled-lora.safetensors \
    --spatial-upsampler-path models/ltx-2.5/latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2.safetensors \
    --audio-path mytrack.wav \
    --prompt "A serene forest animated to the music" \
    --output-path audio_to_video.mp4

```

## HDR and Professional Output Workflows

LTX-2 supports multiple HDR pipelines depending on your color space requirements. The documentation in [`packages/ltx-pipelines/docs/hdr.md`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/docs/hdr.md) specifies native `--hdr` support with options for `SRGB_LINEAR`, `ACESCG`, and `ACESCCT`.

### Standard HDR Pipelines

If you have EXR plates or need native HDR output, use **DistilledPipeline**, **TI2V**, **RetakePipeline**, or **ICLoraPipeline** with the `--hdr` flag:

```bash
--hdr SRGB_LINEAR  # or ACESCG, ACESCCT

```

### Advanced HDR with Custom Tonemapping

For workflows requiring linear HDR float data for custom color grading, **HDRICLoraPipeline** in [`packages/ltx-pipelines/src/ltx_pipelines/hdr_ic_lora.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/src/ltx_pipelines/hdr_ic_lora.py) outputs linear EXR files directly. This bypasses built-in tonemapping for compositing pipelines.

## Video-to-Video and Keyframe Workflows

Two pipelines handle reference-based generation with distinct input modalities.

### Reference Video Transformations: ICLoraPipeline

**ICLoraPipeline** ([`packages/ltx_pipelines/src/ltx_pipelines/ic_lora.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx_pipelines/src/ltx_pipelines/ic_lora.py)) applies video-to-video transformations using IC-LoRA conditioning. Provide a source video and prompt to restyle or modify content while maintaining structural consistency.

### Image Keyframe Interpolation: KeyframeInterpolationPipeline

For generating video between static keyframe images, **KeyframeInterpolationPipeline** in [`packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py) accepts multiple reference frames and interpolates smooth motion between them.

## Speed vs. Quality Trade-offs

### Maximum Fidelity: TI2VidTwoStagesPipeline

When production quality is paramount and inference time is secondary, **TI2VidTwoStagesPipeline** ([`packages/ltx_pipelines/src/ltx_pipelines/ti2vid_two_stages.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx_pipelines/src/ltx_pipelines/ti2vid_two_stages.py)) provides multimodal guidance with dedicated upsampling. The HQ variant, **TI2VidTwoStagesHQPipeline**, adds further refinement layers.

```bash
uv run python -m ltx_pipelines.ti2vid_two_stages_hq \
    --checkpoint-path models/ltx-2.5/diffusion_models/ltx-2.5-22b-dev.safetensors \
    --distilled-lora models/ltx-2.5/loras/ltx-2.5-22b-distilled-lora.safetensors \
    --spatial-upsampler-path models/ltx-2.5/latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2.safetensors \
    --prompt "A photorealistic underwater city" \
    --output-path hq_output.mp4

```

### Fastest Inference: DistilledPipeline

For batch workloads where speed matters, **DistilledPipeline** ([`packages/ltx_pipelines/src/ltx_pipelines/distilled.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx_pipelines/src/ltx_pipelines/distilled.py)) uses a distilled model with a fixed 8-sigma noise schedule. This is the default recommendation for most production deployments requiring throughput over absolute quality.

```bash
uv run python -m ltx_pipelines.distilled \
    --checkpoint-path models/ltx-2.5/diffusion_models/ltx-2.5-22b-distilled.safetensors \
    --distilled-lora models/ltx-2.5/loras/ltx-2.5-22b-distilled-lora.safetensors \
    --spatial-upsampler-path models/ltx-2.5/latent_upscale_models/ltx-2.5-latent-spatial-upscaler-x2.safetensors \
    --prompt "A sunrise over a futuristic cityscape" \
    --output-path output.mp4

```

## Detail Enhancement and Temporal Upsampling

**DFRPipeline** in [`packages/ltx_pipelines/src/ltx_pipelines/dfr_pipeline.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx_pipelines/src/ltx_pipelines/dfr_pipeline.py) runs a Diffusion Fidelity Rendering LoRA for maximum detail recovery. It optionally performs temporal upsampling at 2× or 4× frame rates, useful when the base model's framerate is insufficient for your delivery requirements.

## Specialized Single-Purpose Pipelines

### Rapid Prototyping: TI2VidOneStagePipeline

**TI2VidOneStagePipeline** ([`packages/ltx_pipelines/src/ltx_pipelines/ti2vid_one_stage.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx_pipelines/src/ltx_pipelines/ti2vid_one_stage.py)) skips the upsampling stage for fastest iteration during development. Quality is reduced but generation completes in roughly half the time of two-stage variants.

### Audio-Only Generation: T2AOneStagePipeline

When you need text-to-audio without video, **T2AOneStagePipeline** ([`packages/ltx_pipelines/src/ltx_pipelines/t2a_one_stage.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx_pipelines/src/ltx_pipelines/t2a_one_stage.py)) runs the audio generation branch in isolation.

### Lip-Sync Dubbing: DubItPipeline

For replacing dialogue while preserving mouth movements, **DubItPipeline** ([`packages/ltx_pipelines/src/ltx_pipelines/dubit.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx_pipelines/src/ltx_pipelines/dubit.py)) applies Dub-It IC-LoRA conditioning across both generation stages.

## Pipeline Selection Decision Framework

The definitive decision tree lives in [`packages/ltx-pipelines/docs/pipeline-selection.md`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/docs/pipeline-selection.md). This document codifies the mapping between technical requirements and pipeline selection.

For complete API reference including stage configurations, conditioning parameters, and model compatibility, consult [`packages/ltx-pipelines/docs/pipelines.md`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-pipelines/docs/pipelines.md).

## Summary

- **RetakePipeline** — Edit existing video segments without full regeneration
- **A2VidPipelineTwoStage** — Synchronize video generation to audio input
- **HDRICLoraPipeline** — Linear EXR output for professional color workflows
- **ICLoraPipeline** — Video-to-video transformation with reference conditioning
- **KeyframeInterpolationPipeline** — Animate between static keyframe images
- **TI2VidTwoStagesPipeline** — Maximum quality for production delivery
- **DistilledPipeline** — Fastest inference for batch and real-time workflows
- **DFRPipeline** — Enhanced detail with optional temporal upsampling
- **TI2VidOneStagePipeline** — Rapid prototyping without quality upsampling
- **T2AOneStagePipeline** — Audio-only generation when video is not required
- **DubItPipeline** — Dialogue replacement with preserved lip synchronization

## Frequently Asked Questions

### What is the fastest LTX-2 pipeline for batch processing?

**DistilledPipeline** provides the fastest inference by using a distilled model with a fixed 8-sigma schedule. According to the LTX-2 source code, this pipeline is explicitly recommended as the default for most batch workloads where throughput outweighs absolute visual fidelity.

### How do I generate HDR video with LTX-2?

For standard HDR output, use `DistilledPipeline`, `TI2V`, `RetakePipeline`, or `ICLoraPipeline` with the `--hdr` flag set to `SRGB_LINEAR`, `ACESCG`, or `ACESCCT`. For linear EXR float data requiring custom tonemapping, use **HDRICLoraPipeline** which outputs directly to linear color space without built-in curve application.

### Can LTX-2 edit a specific portion of an existing video?

Yes. **RetakePipeline** in [`packages/ltx_pipelines/src/ltx_pipelines/retake.py`](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx_pipelines/src/ltx_pipelines/retake.py) performs mask-based regeneration on specified time regions (via `--start-time` and `--end-time`) while preserving unselected video segments. It handles both video and audio channels in the edited region.

### Which pipeline should I choose for audio-reactive video generation?

Use **A2VidPipelineTwoStage**. This pipeline encodes audio features in Stage 1 to condition low-resolution generation, then upsamples in Stage 2 with audio-guided temporal coherence. The conditioning is integrated rather than post-processed, ensuring genuine audio-visual synchronization.