Source product photo
AI Models & Assistants / Knowledge library
LTX-2.3
image-to-video model from Lightricks. Animate a source image into a video clip.
Why learn LTX-2.3?
Provides downloadable weights for image-to-video experimentation, subject to the model card and license.
The problem it solves
Animate a source image into a video clip.
Where it came from
LTX-2.3: image-to-video model from Lightricks. Animate a source image into a video clip. License: other. Hugging Face downloads at snapshot: 970701. This is a task-stratified popularity sample, not a performance ranking. Runtime compatibility must be checked for the specific architecture.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what LTX-2.3 does.
- Identify a use case for LTX-2.3.
- Describe a limitation of LTX-2.3.
- Name a compatible tool for LTX-2.3.
- Compare LTX-2.3 against an alternative.
Documentation and sources
- Primary source / model card (huggingface.co)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Automated Product Catalog Animation for E-commerce
E-commerce retailers managing large inventories of static product photography.
The business problem
High production costs and long lead times associated with hiring videographers to create short, engaging product showcase clips for social media marketing.
How this topic solves it
Utilizing the LTX-2.3 DiT-based foundation model to programmatically animate existing static product images into synchronized audio-visual video clips, significantly reducing the manual effort required for content production.
- Pre-process static product images to ensure dimensions are divisible by 32 for model compatibility.
- Select the appropriate LTX-2.3 distilled checkpoint to balance inference speed with visual fidelity.
- Define text prompts describing the desired motion or environment for the product.
- Execute the inference pipeline using the ltx-pipelines package to generate the video.
- Apply the spatial upscaler if higher resolution output is required for specific marketing channels.
The flow from static asset to final animated video.
Padding/Resizing
DiT Inference
Resolution enhancement
Animated output
- Static Image → input → Preprocessing
- Preprocessing → processed_latents → LTX-2.3 Model
- LTX-2.3 Model → raw_output → Spatial Upscaler
- Spatial Upscaler → high_res_video → Final Video
Expected business outcome
A scalable, automated workflow that transforms static catalogs into dynamic video assets, enhancing customer engagement without increasing headcount.
Limitations and checks
The model may fail to generate videos that match the prompts perfectly, requiring iterative prompt engineering or validation steps to ensure the generated motion aligns with brand guidelines.