Lab 05: Conditioning Flow
Annotated code reading lab. Running code is optional.
Conditioning Flow
This lab maps directly to the handbook section. Read the related handbook section first, then use the lab page and starter file to connect the concept to concrete variables, shapes, APIs, and interview-ready explanations.
Conditioning Flow
Read text/image/video conditions as separate encoder paths that meet inside the denoiser.
Mechanism to keep in mind
- `text` becomes embeddings.
- `image/video reference` may become latent or encoder features.
- `control` may be injected through architecture-specific branches or residual paths.
Annotated Code Preview
Open starter fileStarter preview
Excerpt from code/lab-05-conditioning-flow/conditioning_flow.md. The linked starter file is the source of truth.
A Transformer block turns token ids into vectors, mixes context with attention, applies per-token nonlinear transformations, and uses residual and normalization layers to keep deep training stable.What each block is doing
- Setup / contract
- `text` becomes embeddings.
- Main transition
- `image/video reference` may become latent or encoder features.
- Interview hook
- `control` injection is architecture- and implementation-dependent.
Reading checkpoints
- Different conditions have different shapes.
- Condition weights and timing matter.
- Conditioning is the main source of pipeline variation.
What this lab prevents
- Do not reduce every condition to a prompt.
- Do not ignore where the condition enters the model.
How to say it out loud
Read text/image/video conditions as separate encoder paths that meet inside the denoiser. Then explain the code by naming the state being transformed, the axis or shape that matters, and the tradeoff that would appear in a real system.
Additional intuition
- Use official docs and papers for API behavior and factual claims; use blogs only to improve the mental picture.
- If support matrices, performance behavior or backend choices are version-sensitive, check current docs before repeating them.
- A strong interview answer names the state object, the shape or axis it changes, and the tradeoff it creates.