Overview

This cycle pushed the image-generation demo from a “one-shot generator” toward an “editable workstation.” I added a rectangle mask tool to the inpaint editor, taught the LLM to detect people the user only implied (not named) and auto-inject the right model reference, and introduced allowlist-based login gating so the demo could be opened to external users. These 54 commits group into six threads: inpaint, model injection, block prompts, auth, library UX, and infra.

Previous: #21 — hybrid-image-search-demo dev21

graph TD
    U["User prompt"] --> LLM["LLM intent analysis (intent_person)"]
    LLM -->|"implied person detected"| INJ["auto-inject model reference"]
    INJ --> GEN["generation engine (OpenAI / Gemini)"]
    GEN --> NORM["PNG normalization + byte-format filenames"]
    NORM --> GAL["library / gallery"]
    GAL --> EDIT["inpaint editor (rectangle mask)"]
    EDIT --> GEN
    AUTH["account allowlist gating"] -.->|"only if login passes"| GAL

Inpaint Editor: A Mask Tool for Partial Regeneration

The biggest feature was the inpaint editor. Redrawing only part of a generated image requires a mask that says where to redraw, so this cycle added a rectangle mask tool. I also placed an inpaint entry button in the action bar, so you can pick an image from the gallery and jump straight into the partial-edit flow.

Debugging

Wiring up the inpaint entry button also surfaced a re-upload bug — re-uploading an image didn’t cleanly reset the previous state. At one point an unbalanced JSX block in InpaintEditor broke the build (fix(frontend): close unbalanced InpaintEditor JSX block), a side effect of the component growing more conditional render branches. I then refined the inpaint comparison view and gallery controls so the original and the inpainted result sit side by side as an A/B pair.

Implied-Person Model Injection: Trusting LLM Intent

The most “AI-native” work this cycle was implied-person model injection. When a user doesn’t explicitly name a model — saying “sitting in a café” rather than “place this person in…” — the LLM reads that intent (intent_person) and automatically injects the appropriate model reference into the prompt.

Implementation

It evolved in three steps. First, the base logic to auto-inject a model reference for implied-person prompts (feat: auto-inject model ref for implied-person prompts). Then I changed it to trust the LLM’s intent_person verdict directly (fix: trust LLM intent_person for model injection) — an early heuristic re-check was occasionally overriding the LLM, so I leaned on trust instead. Finally I broadened the intent_person definition itself so more phrasings of an implied person get recognized (fix: broaden LLM intent_person definition).

The lesson was the trade-off between trusting an LLM’s structured output versus filtering it again with rules. Trusting the model’s intent verdict — while widening the definition — produced the more natural UX.

Block-Based Prompts and Dual-Engine Preview

Prompt authoring changed substantially too. Block-based prompts treat a prompt not as one long string but as a composition of reusable blocks (feat(model-gen): block-based prompts + dual-engine preview/select), with simultaneous OpenAI and Gemini previews the user chooses between.

Follow-ups added a block-as-template UX, Korean localization, and a background generation lifecycle (block-as-template UX + KO localization + bg lifecycle), surfaced the fully resolved prompt on the preview wait screen (show resolved prompt on preview wait screen), grouped resumed previews into per-pair summary blocks, and made each pair’s resolved blocks a collapsible summary.

Login Gating: Opening the Demo to Outsiders

To open the demo to external users, I reworked auth. The core is allowlist-based login gating: Google login itself is allowed, but only accounts on an explicit allowlist actually enter the app (feat(auth): gate Google login behind an explicit account allowlist). Adding a specific designer account (joonghodesign) to the allowlist came along with it.

Alongside, I collapsed a two-account structure (get rid of two accounts), added a confirmed logout flow (feat(ui): add confirmed logout flow), and removed a default Korean race directive baked into the UI (chore: remove default Korean race directive) so the demo no longer assumes a particular region/race as the default.

graph LR
    A["Google login attempt"] --> B{"account on allowlist?"}
    B -->|Yes| C["enter app"]
    B -->|No| D["access blocked"]

Library / Gallery UX and Infra Cleanup

The library moved out of a separate panel into a tab switcher inside General mode (merge library panel into General mode with tab switcher), letting model and product references coexist in one General tab. I added prompt search over generated images and a flow to regenerate failed model generations in place from the library.

On infra, I made the S3 reference-key cache build in the background at startup so the app no longer hangs on launch (build S3 ref-key cache in background so startup doesn't hang), and added telemetry instrumenting the Gemini semaphore’s acquire-wait time. Generated images are now named by their real byte format rather than a hardcoded extension, and all are normalized to PNG. On cost, I lowered OpenAI image quality from high to medium to tune cost/latency.

Commit Log

Insights

Two currents run through this cycle. One is editability: inpaint masks, block prompts, in-place regeneration of failures — all moving away from “generate once, done” toward “grab the result and keep refining it.” That mirrors how the value of generative tools shifts from single-shot quality to the smoothness of the iterative edit loop.

The other is robustness for going public: allowlist gating, confirmed logout, non-blocking background S3 caching, and semaphore-wait instrumentation are exactly what you need crossing from “a demo I use” to “a demo I show others.” The decision to trust the LLM’s intent_person — accepting the model’s structured verdict instead of re-filtering with a heuristic — is a clean example of a trade-off you keep meeting in modern agent design.