What is LTX Workflow?
LTX Workflow is an AI video generation tool that lets creators and developers produce text-to-video and image-to-video content using the LTX 2.3 model inside ComfyUI or directly in the browser for those without a local GPU.
Built on Lightricks' LTX 2.3 architecture, the platform supports T2V, I2V, LoRA fine-tuning, V2V composition, and motion-track workflows. A built-in VRAM Adapter lets users select their GPU memory (16 GB, 24 GB, or 32 GB+) and instantly receive a matched model recommendation — for example, the FP8-quantized distilled checkpoint for 16 GB cards such as an RTX 4080 — along with the correct ComfyUI workflow JSON to download. Optional spatial (x1.5 or x2) and temporal (x2) upscalers extend output resolution and frame count without re-running the full model.
For users who prefer not to manage local hardware, LTX Workflow offers a browser-based online generation mode. The ComfyUI Workflow Configurator walks through GPU VRAM, generation type, quality preference, video length (up to roughly 8 seconds), orientation, resolution tier, and frame rate, then outputs a ready-to-use workflow JSON. This makes LTX Workflow practical both for hobbyists exploring AI video generation and for technical teams integrating Lightricks models into custom ComfyUI pipelines.
What is LTX Workflow used for?
LTX Workflow is primarily used for generating AI videos from text prompts or reference images using the LTX 2.3 model inside ComfyUI or a browser. It is also used by technical teams to configure and extend custom ComfyUI pipelines with LoRA fine-tuning, upscaling, and motion-track workflows.
How much does LTX Workflow cost?
Pros and cons
Pros
- VRAM Adapter matches your GPU to the correct LTX 2.3 model checkpoint automatically
- Supports T2V, I2V, V2V composition, LoRA fine-tuning, and motion-track workflows in one platform
- ComfyUI Workflow Configurator outputs a ready-to-use JSON based on VRAM, resolution, and frame-rate settings
- Spatial (x1.5/x2) and temporal (x2) upscalers let you extend resolution and frame count without re-running generation
- Online mode runs LTX 2.3 generation in the browser with no local GPU required
Cons
- Requires ComfyUI setup and technical familiarity for local use
- FP8 transformer models require RTX 40xx or newer — RTX 30xx users must use an alternative variant
- Pricing tiers are not publicly listed on the website
Questions about LTX Workflow
What is LTX Workflow used for?
LTX Workflow is used for AI video generation, letting users create text-to-video and image-to-video outputs using Lightricks' LTX 2.3 model. It supports local ComfyUI pipelines as well as browser-based online generation. Use cases include AI filmmaking, motion graphics prototyping, and custom video model integration.
Is LTX Workflow free?
LTX Workflow offers a free online generation mode you can try without a local GPU. Pricing tiers for additional features or higher usage limits are not publicly published on the website, so you would need to contact the team for details.
How does LTX Workflow work with ComfyUI?
LTX Workflow provides official ComfyUI workflow JSON files — including T2V, I2V, two-stage upscaler, and ICLoRA motion-tracking templates — that you drag directly into ComfyUI to load. The VRAM Adapter on the site recommends the correct model checkpoint and node settings for your GPU, reducing manual configuration.
What GPU VRAM do I need to run LTX Workflow locally?
LTX Workflow supports GPUs with 16 GB, 24 GB, and 32 GB or more VRAM. The 16 GB path uses an FP8-quantized distilled checkpoint and requires an RTX 40xx card for optimal fp8 matmuls; RTX 30xx users should select the mxfp8_block32 variant or a BF16 model instead.
What makes LTX Workflow different from other AI video generators?
LTX Workflow is built around Lightricks' open LTX 2.3 model and integrates directly with ComfyUI, giving users full control over model weights, LoRA adapters, and workflow JSON rather than a black-box API. It also includes spatial and temporal upscalers that can double frame count or resolution as post-processing steps without re-running the base model.
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