LTX 2

LTX-2 Model Card This model card focuses on the LTX-2 model, as presented in the paper . The codebase is available . LTX-2 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model. It brings together the core building blocks of modern video generation, with open weights and a focus on practical, local execution. # Model Checkpoints | Name | Notes | |--------------------------------|----------------------------------------------------------------------------------------------------------------| | ltx-2-19b-dev | The full model, flexible and trainable in bf16 | | ltx-2-19b-dev-fp8 | The full model in fp8 quantization | | ltx-2-19b-dev-fp4 | The full model in nvfp4 quantization | | ltx-2-19b-distilled | The distilled version of the full model, 8 steps, CFG=1 | | ltx-2-19b-distilled-lora-384 | A LoRA version of the distilled model applicable to the full model | | ltx-2-spatial-upscaler-x2-1.0 | An x2 spatial upscaler for the ltx-2 latents, used in multi stage (multiscale) pipelines for higher resolution | | ltx-2-temporal-upscaler-x2-1.0 | An x2 temporal upscaler for the ltx-2 latents, used in multi stage (multiscale) pipelines for higher FPS | ## Model Details - Lightricks - Diffusion-based audio-video foundation model - English # Online demo LTX-2 is accessible right away via the following links: - - # Run locally ## Direct use license You can use the models - full, distilled, upscalers and any derivatives of the models - for purposes under the . ## ComfyUI We recommend you use the built-in LTXVideo nodes that can be found in the ComfyUI Manager. For manual installation information, please refer to our . ## PyTorch codebase The is a monorepo with several packages. From model definition in 'ltx-core' to pipelines in 'ltx-pipelines' and training capabilities in 'ltx-trainer'. The codebase was tested with Python >=3.12, CUDA version >12.7, and supports PyTorch ~= 2.7. ### Installation ### Inference To use our model, please follow the instructions in our package. ## Diffusers 🧨 LTX-2 is supported in the for text & image-to-video generation. Read more on LTX-2 with diffusers . ### Use with diffusers To achieve production quality generation, it's recommended to use the two-stage generation pipeline. Example for 2-stage inference of text-to-video: For more inference examples, including generation with the distilled checkpoint, visit . ## General tips: * Width & height settings must be divisible by 32. Frame count must be divisible by 8 + 1. * In case the resolution or number of frames are not divisible by 32 or 8 + 1, the input should be padded with -1 and then cropped to the desired resolution and number of frames. * For tips on writing effective prompts, please visit our ### Limitations - This model is not intended or able to provide factual information. - As a statistical model this checkpoint might amplify existing societal biases. - The model may fail to generate videos that matches the prompts perfectly. - Prompt following is heavily influenced by the prompting-style. - The model may generate content that is inappropriate or offensive. - When generating audio without speech, the audio may be of lower quality. # Train the model The base (dev) model is fully trainable. It's extremely easy to reproduce the LoRAs and IC-LoRAs we publish with the model by following the instructions on the . Training for motion, style or likeness (sound+appearance) can take less than an hour in many settings. ## Citation