Principle:Togethercomputer Together python Video Generation
| Knowledge Sources | |
|---|---|
| Domains | Video_Generation, Generative_AI |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
Principle for generating videos from text prompts and optional image conditioning using diffusion-based AI models.
Description
Video generation uses text-to-video diffusion models to create short video clips from natural language descriptions. The process is inherently asynchronous due to computational cost: a generation job is submitted and polled for completion. Generation can be conditioned on text prompts, keyframe images (start/end frames), and reference images for style guidance. Key parameters control the quality-speed tradeoff (denoising steps), prompt adherence (guidance scale), and output specifications (resolution, frame rate, duration).
Usage
Apply this principle when you need to programmatically generate video content from text descriptions. Suitable for content creation pipelines, creative tools, and automated media generation. Video generation is compute-intensive and asynchronous.
Theoretical Basis
Video generation follows an asynchronous job pattern with diffusion-based generation:
Pseudo-code Logic:
# Abstract video generation pipeline
job = submit_generation(
model=video_model,
prompt=text_description,
resolution=(width, height),
duration=seconds,
denoising_steps=steps,
guidance_scale=cfg_weight,
)
# Poll for completion
while job.status != "completed":
job = poll_status(job.id)
wait(interval)
video_url = job.outputs.video_url
Key considerations:
- Denoising Steps: More steps yield higher quality but longer generation time (10-50)
- Guidance Scale: Higher values follow prompt more closely; 6.0-10.0 is typical
- Resolution and FPS: Higher values increase compute cost and generation time
- Seed: Fixed seed enables reproducible generation