Design GenAI System: Text-to-Video Generation
Design a text-to-video generation system: temporal diffusion models, consistency controls, and large-scale video synthesis.
What is a text-to-video generation system?
A text-to-video system converts a prompt and optional reference media into a playable sequence of frames. Unlike text-to-image generation, it must preserve the same subjects, scene state, camera intent, and safety decision as time advances.
The core invariant is timeline coherence: each shot may change because the prompt asks it to, but it must not drift accidentally. This lesson designs a production service for 5-60 second clips, from request admission through generation, validation, and delivery.
- Prerequisite: Review text-to-image generation if diffusion, latent representations, or conditioning are unfamiliar.
- Design target: Serve 10,000 jobs per day, deliver an initial preview quickly, and protect quality and safety even when accelerators are saturated.
Control
Plan before rendering
Turn the prompt into shots, entities, motion, and camera constraints before spending most of the compute budget.
Coherence
Anchor across time
Carry identity and scene state through overlapping temporal windows instead of generating unrelated frames.
Release
Validate the sequence
Inspect motion, prompt adherence, identity, and policy across transitions before publishing the encoded asset.
Define the first product narrowly
Start with creator-generated clips rather than a complete video editor. A request contains a prompt, an aspect ratio, a duration, and optional reference images. The service returns a low-resolution preview followed by a final encoded clip after the user approves the direction.
Functional requirements
- Generate 5-60 second clips from text prompts.
- Accept reference images for subject identity and visual style.
- Expose bounded controls for aspect ratio, camera motion, seed, and shot duration.
- Let users cancel, retry, or approve a preview for final rendering.
- Preserve job history and downloadable assets for a defined retention period.
Non-functional requirements
- Keep preview queue time below 30 seconds at p95 for paid users.
- Finish a 10-second 720p preview within 90 seconds at p95.
- Keep accepted final jobs durable across worker loss and regional failover.
- Isolate uploaded references and generated assets by tenant.
- Fail closed when a prompt, frame, transition, or identity policy is uncertain.
State the exclusions
- No frame-level timeline editor in the first release.
- No photorealistic public-figure generation or unverified face cloning.
- No promise that a 60-second clip is one uninterrupted generative pass.
- No synchronous wait on the API connection; generation is an asynchronous job.
Ask questions that change the design
- Is the clip one shot or several? Multi-shot narratives require a storyboard and shared entity memory.
- How exact must motion be? Cinematic presets are cheaper to validate than arbitrary trajectories.
- Which references are permitted? Identity use, copyright, retention, and deletion policies affect every storage boundary.
- What is the failure contract? The system should retry a bounded temporal window, not silently restart a paid job or publish a partial asset.
Generation pattern for the text-to-video service
Users submit a prompt and get back a short generated video. Each render takes minutes of GPU time. Which generation pattern should the service use?
Constraint: Video generation is extremely compute-heavy (minutes of GPU per render); cost control is paramount.