ComfyUI-LTXVideo

ComfyUI-LTXVideo

A collection of powerful custom nodes that extend ComfyUI's capabilities for the LTX-2 video generation model.

LTX-2 is built into ComfyUI core (see it here), making it readily accessible to all ComfyUI users. This repository hosts additional nodes and workflows to help you get the most out of LTX-2's advanced features.

To learn more about LTX-2 See the main LTX-2 repository for model details and additional resources.

Prerequisites

Before you begin using an LTX-2 workflow in ComfyUI, make sure you have:

• ComfyUI installed (Download here](https://www.comfy.org/download)
• CUDA-compatible GPU with 32GB+ VRAM
• 100GB+ free disk space for models and cache

Quick Start 🚀

We recommend using the LTX-2 workflows available in Comfy Manager.

1. Open ComfyUI
2. Click the Manager button (or press Ctrl+M)
3. Select Install Custom Nodes
4. Search for “LTXVideo”
5. Click Install
6. Wait for installation to complete
7. Restart ComfyUI

The nodes will appear in your node menu under the “LTXVideo” category. Required models will be downloaded on first use.

Example Workflows

The ComfyUI-LTXVideo installation includes several example workflows. You can see them all at:

ComfyUI/custom_nodes/ComfyUI-LTXVideo/example_workflows/

LTX-2.3 Workflows:

• Text/image to video full/distilled model; single stage
• Text/image to video distilled model; two stages (with upsampling)
• IC-LoRA distilled model depth + human pose + edges
• IC-LoRA distilled model I2V motion tracking
• IC-LoRA distilled model HDR
• IC-LoRA distilled model Dub-It; two stages (with upsampling)
• IC-LoRA distilled model pixel spatial upscaling
• Text to audio distilled model; single stage

Older Workflows (LTX-2.0):

• Text to video full model
• Text to video distilled model (Fast)
• Image to video full model
• Image to video distilled model (Fast)
• Video to video detailer
• IC-LoRA distilled model (depth + human pose + edges)
• IC-LoRA distilled model with downscaled reference latents

Union IC-LoRA Model

We introduce a new Union IC-LoRA model that combines depth and edge (canny) control conditions into a single unified LoRA.

Key Features

• Unified Control: A single LoRA that supports multiple control conditions (depth or edges).
• Downsampled Latent Processing: The union LoRA operates on a downsampled latent size, which reduces memory usage and significantly speeds up inference while maintaining quality.

How It Works

The union LoRA is trained to understand and respond to both control signals (depth maps and edge maps) within a single model. The model learns to:

1. Parse multiple conditions: Identify which control signals are present in the input
2. Process at reduced resolution: Work on downsampled latents to improve efficiency

HDR IC-LoRA

We provide an HDR IC-LoRA that upgrades SDR video into ACEScct HDR (same working space as pipelines hdr_ic_lora), suitable for grading, EXR export, and HLG masters. Use the tiled 2.5 graph LTX-2.5_V2V_TiledFusion_SDR_to_HDR.json, or the 2.3 single-stage LTX-2.3_ICLoRA_HDR_Distilled.json.

Key Features

• Linear HDR output: The LoRA produces frames in LogC3-compressed space; the LTXVHDRDecodePostprocess node decodes these back to linear HDR values.
• SDR preview + raw HDR: The node outputs both a Reinhard-tonemapped SDR preview and the raw linear HDR tensor for downstream use.
• EXR export: Optionally writes the linear HDR frames as a 16/32-bit EXR image sequence via OpenImageIO (pip install openimageio, also listed in requirements.txt). The exported EXR sequence is best viewed in DJV (or DJV for macOS).

Dub-It IC-LoRA

We provide a Dub-It IC-LoRA that dubs or rephrases speech in video. Given a source video and a text prompt containing the desired dialogue, it generates new lip movements and audio that match the target text while preserving the speaker's identity.

Key Features

• Multilingual dubbing: Translate speech into another language - the model regenerates lips and audio to match.
• Same-language rephrasing: Change what the speaker says while keeping the original language.
• Two-stage pipeline: Stage 1 generates the video and audio at base resolution; Stage 2 upscales while freezing the audio.
• Speaker identity preservation: Reference audio tokens provide speaker context so the generated voice stays consistent.

Pixel Spatial Upscaler IC-LoRA

We provide Pixel Spatial Upscaler IC-LoRAs that creatively upscale low-resolution video by synthesizing fine detail rather than simply interpolating pixels. Given a low-resolution reference clip, the model re-renders it at 2× or 4× resolution with generative spatial detail — making it a creative upsampler, not a pixel-accurate refiner.

Key Features

• 2× and 4× variants: Choose the 2× upscaler for moderate upscaling or the 4× upscaler for larger resolution jumps.
• Generative detail synthesis: The model synthesizes texture and structure from the reference rather than faithfully preserving every pixel.
• Draft-then-upscale workflow: Generate at a low base resolution (e.g. ~280p) to lock in composition and motion, then run the upscaler for the final high-resolution output.
• Tunable fidelity: LoRA strength, guidance, and step count control how closely the output follows the reference — lower values stay closer to the source; higher values allow more creative detail.

Text-to-Audio (T2A)

LTX-2 is a single joint audio/video transformer, but it can generate audio on its own. The LTXVAudioOnlyModel node puts the model into audio-only mode for text-to-audio, with no video output.

Key Features

• Audio-only sampling: The node sets the model's run_vx, a2v_cross_attn and v2a_cross_attn flags off, so the audio is denoised with no dependence on the video latent and the video stream is skipped. This matches the reference single-stage T2A pipeline's video=None behavior.
• Minimal dummy video latent: The model splits its input positionally into [video, audio], so the sampler still needs a video latent at index 0. Use the LTXVAudioOnlyEmptyVideoLatent node (a fixed 64x64 single-frame placeholder, no params to tweak) joined with the audio latent via LTXVConcatAVLatent; with LTXVAudioOnlyModel active it is never attended to and adds negligible cost.
• Audio decode: LTXVAudioVAEDecode extracts the audio directly from the joint latent, then save it with a standard built-in audio node (for example Save Audio (FLAC)).

Required Models

Download the following models:

LTX-2.3 Model Checkpoint - Choose and download one of the models to COMFYUI_ROOT_FOLDER/models/checkpoints folder.

• ltx-2.3-22b-dev.safetensors
• ltx-2.3-22b-distilled-1.1.safetensors

Spatial Upscaler - Required for current two-stage pipeline implementations in this repository. Download to COMFYUI_ROOT_FOLDER/models/latent_upscale_models folder.

• ltx-2.3-spatial-upscaler-x2-1.1.safetensors
• ltx-2.3-spatial-upscaler-x1.5-1.0.safetensors

Temporal Upscaler - Required for current two-stage pipeline implementations in this repository. Download to COMFYUI_ROOT_FOLDER/models/latent_upscale_models folder.

• ltx-2.3-temporal-upscaler-x2-1.0.safetensors

Distilled LoRA - Required for current two-stage pipeline implementations in this repository (except DistilledPipeline and ICLoraPipeline). Download to COMFYUI_ROOT_FOLDER/models/loras folder.

• ltx-2.3-22b-distilled-lora-384-1.1.safetensors

Gemma Text Encoder Download all files from the repository to COMFYUI_ROOT_FOLDER/models/text_encoders/gemma-3-12b-it-qat-q4_0-unquantized.

• Gemma 3

LoRAs Choose and download to COMFYUI_ROOT_FOLDER/models/loras folder.

• ltx-2.3-22b-ic-lora-union-control-ref0.5.safetensors
• ltx-2.3-22b-ic-lora-motion-track-control-ref0.5.safetensors
• ltx-2.3-22b-ic-lora-hdr-0.9.safetensors
• ltx-2.3-22b-ic-lora-dubit-0.9.safetensors
• ltx-2-19b-ic-lora-detailer.safetensors
• ltx-2-19b-ic-lora-pose-control.safetensors
• ltx-2-19b-lora-camera-control-dolly-in.safetensors
• ltx-2-19b-lora-camera-control-dolly-left.safetensors
• ltx-2-19b-lora-camera-control-dolly-out.safetensors
• ltx-2-19b-lora-camera-control-dolly-right.safetensors
• ltx-2-19b-lora-camera-control-jib-down.safetensors
• ltx-2-19b-lora-camera-control-jib-up.safetensors
• ltx-2-19b-lora-camera-control-static.safetensors
• ltx-2.3-22b-ic-lora-instant-shave-0.9.safetensors
• ltx-2.3-22b-ic-lora-colorization-0.9.safetensors
• ltx-2.3-22b-ic-lora-cross-eyed-0.9.safetensors
• ltx-2.3-22b-ic-lora-day-to-night-0.9.safetensors
• ltx-2.3-22b-ic-lora-deblur-0.9.safetensors
• ltx-2.3-22b-ic-lora-decompression-0.9.safetensors
• ltx-2.3-22b-ic-lora-in-outpainting-0.9.safetensors
• ltx-2.3-22b-ic-lora-water-simulation-0.9.safetensors
• ltx-2.3-22b-ic-lora-ingredients-0.9.safetensors
• ltx-2.3-22b-ic-lora-pixel-spatial-upscaler-x4-0.9.safetensors
• ltx-2.3-22b-ic-lora-pixel-spatial-upscaler-x2-0.9.safetensors

Advanced Techniques

Tiled Fusion Sampler

LTXVTiledFusionSampler (🅛🅣🅧 Tiled Fusion Sampler) denoises a canvas larger than the model's trained window by running each denoise step on overlapping spatial tiles that share one latent canvas and one noise field. It is meant for IC-LoRA video-to-video where LTXAddVideoICLoRAGuide has already attached the guide. Pair it with LTXVGetTilingSizes (🅛🅣🅧 Get Tiling Sizes) for legal canvas / tile / tile_frames values. See tiled_fusion_sampler.md. Do not confuse it with LTXVTiledSampler, which samples tiles independently.

Example graphs (2.5 distilled):

• V2V upscale — detail-refine at FullHD / 4K / 8K
• V2V native 4K/8K — FullHD stage 1, then 4K or 8K
• V2V SDR→HDR — same ladder with the HDR IC-LoRA

Prompt those graphs for look and style only (lighting, palette, sharpness, grade). Do not name specific objects in the scene: after upscale, named details can appear in every spatial tile.

Low VRAM

• For systems with low VRAM you can use the model loader nodes from low_vram_loaders.py. Those nodes ensure the correct order of execution and perform the model offloading such that generation fits in 32 GB VRAM.
• Use --reserve-vram ComfyUI parameter: python -m main --reserve-vram 5 (or other number in GB).
• For complete information about using LTX-2 models, workflows, and nodes in ComfyUI, please visit our Open Source documentation.