Direct answer
Android bitmap reuse local AI requires containing decoded images and working buffers without holding on to stale user content. This focus centers on scoping rather than inference: the concern is the boundary between images that are actively in use and any content that could persist beyond its necessary lifetime, ensuring retention does not extend past the point of functional need.
Evidence to collect
The claim becomes reviewable only when the following evidence is attached to the same artifact and test run:
- Source and working dimensions
- Buffer ownership
- Repeated-capture memory trace
A device badge must come from an exact device, OS, artifact, runtime, backend, and workload record, not parameter-count intuition.
Implementation workflow
Evidence items to record and compare include the source and working dimensions of images, the ownership status of buffers involved in processing, and a memory trace taken across repeated captures. These observations should be captured consistently so that patterns and changes can be examined side by side rather than assumed.
- Record the exact artifact and format.
- Measure the app before model load.
- Measure after model initialization.
- Run a representative product task.
- Repeat the fixed workload without restarting.
- Store failures, fallback, and cleanup behavior.
Failure patterns to prevent
- Keeping every preview
- Reusing a bitmap still owned by UI
Pitfalls to avoid include keeping every preview generated during operation and reusing a bitmap that is still owned by the UI. Wording about any release of previously held material should be limited to what the recorded evidence shows, rather than extended to conclusions about behavior not directly documented.
Minimum reproducibility record
| Layer | Record |
|---|---|
| Device | Manufacturer, model, chipset, RAM class, operating-system build |
| Software | Application version and git commit |
| Model | Family, variant, revision, format, file length, hash, quantization |
| Runtime | Name, revision, requested backend, observed backend evidence |
| Workload | Fixture revision, input hash, prompt hash, output policy |
| Outcome | Completed, failed, cancelled, fallback, and privacy-safe diagnostics |
Release checklist
- ☐ The primary query is answered without an unsupported number.
- ☐ Every artifact and runtime is pinned.
- ☐ The representative task and failure policy are explicit.
- ☐ Lifecycle, cancellation, cleanup, and fallback are tested.
- ☐ User-content and network boundaries are documented.
- ☐ Result wording applies only to the recorded configuration.
- ☐ The page links to raw method or evidence when results are added.
Sources and related evidence
This page relies on the Cove fact ledger and benchmark policy. Results remain blocked until raw records exist.
Chinese deployment and troubleshooting content is organized in the .奇连 AI 端侧专题.