Source asset
AI Models & Assistants / Knowledge library
LTX-2.5
image-to-video model from Lightricks. Animate a source image into a video clip.
Why learn LTX-2.5?
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-2.5: image-to-video model from Lightricks. Animate a source image into a video clip. License: other. Hugging Face downloads at snapshot: 1719766. 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-2.5 does.
- Identify a use case for LTX-2.5.
- Describe a limitation of LTX-2.5.
- Name a compatible tool for LTX-2.5.
- Compare LTX-2.5 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 Cinematic B-Roll Generation for Content Agencies
Digital marketing agencies producing high-volume social media video content.
The business problem
High production costs and slow turnaround times when manually animating static product photography into engaging video clips.
How this topic solves it
Utilizing LTX-2.5 to perform image-to-video inference, allowing agencies to animate static assets into cinematic clips using text-based motion prompts.
- Install the ltx-pipelines package and download the split model checkpoints.
- Prepare the static source image ensuring dimensions are divisible by 32.
- Execute the image-to-video inference command using the --image flag with the source path and frame index.
- Apply the latent spatial upscaler to enhance the output resolution for final delivery.
The flow from static input to rendered video output.
Transformer and VAE
Spatial enhancement
Final MP4
- Static Image → conditioning → LTX-2.5 Pipeline
- LTX-2.5 Pipeline → latent output → Latent Upscaler
- Latent Upscaler → decoding → Video Output
Implementation example
This maps to the business scenario by showing how an agency would programmatically pass a product image into the LTX-2.5 pipeline to generate a video clip.
# Example of triggering image-to-video inference
# --image path/to/image.jpg 0 1.0 sets the first frame as conditioning
# --prompt defines the cinematic motion
import subprocess
subprocess.run(["python", "-m", "ltx_pipelines.distilled", "--image", "input.jpg", "0", "1.0", "--prompt", "Wind blowing through trees", "--output-path", "output.mp4"])Expected business outcome
Rapid, scalable production of motion-rich video assets from existing static image libraries without requiring manual animation.
Limitations and checks
The model may fail to generate videos that match the prompt perfectly, requiring iterative prompt engineering to ensure visual adherence.