A high-rank LoRA adapter for LTX-Video 2 that substantially improves image-to-video generation quality. No complex workflows, no image preprocessing, no compression tricks -- just a direct image embedding pipeline that works.
What This Is
Out of the box, getting LTX-2 to reliably infer motion from a single image requires heavy workflow engineering -- ControlNet stacking, image preprocessing, latent manipulation, and careful node routing. The purpose of this LoRA is to eliminate that complexity entirely. It teaches the model to produce solid image-to-video results from a straightforward image embedding, no elaborate pipelines needed.
Trained on 30,000 generated videos spanning a wide range of subjects, styles, and motion types, the result is a highly generalized adapter that strengthens LTX-2's image-to-video capabilities without any of the typical workflow overhead.
Key Specs
Parameter
Value
Base Model
LTX-Video 2
LoRA Rank
256
Training Set
~30,000 generated videos
Training Scope
Visual only (no explicit audio training)
What It Does
Improved image fidelity -- the generated video maintains stronger adherence to the source image with less drift or distortion across frames.
Better motion coherence -- subjects move more naturally and consistently throughout the clip.
Broader generalization -- performs well across diverse subjects and scenes without needing per-category tuning.
A Note on Audio
Audio was not explicitly trained into this LoRA. However, due to the nature of how LTX-2 handles its latent space, there are subtle shifts in audio output compared to the base model. This is a side effect of the training process, not an intentional feature.
Usage (ComfyUI)
Place the LoRA file in your ComfyUI/models/loras/ directory.
Add an LTX-2 model loader node and load the base LTX-2 checkpoint.
Add a Load LoRA node and select this adapter.
Connect an image embedding node with your source image.
Add your text prompt and generate.
Workflow
ComfyUI Workflow
Examples
Reference videos demonstrating the adapter's output quality:
Model Details
Architecture: LoRA (Low-Rank Adaptation) applied to LTX-Video 2's transformer layers
Rank 256 provides a high-capacity adaptation while remaining efficient to load and merge
Training data was intentionally diverse to avoid overfitting to any single domain, producing a general-purpose image-to-video adapter rather than a style-specific fine-tune
