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Vision — AI image generation

General-purpose image generation for tasks: product photos, marketplace listing images, infographics, banners, or an image the user just wants, via kie.ai's unified API. The agent proposes; the user confirms and can edit the prompt/model before anything is generated; the result renders inline in the task stream and is saved in the task workspace.

Model catalog (one key, many providers)

kie.ai is a unified aggregator — a single configured key reaches every image model through the same POST /api/v1/jobs/createTask endpoint. The selectable set is a curated, static catalog in app/vision.py (IMAGE_MODELS), one ImageModel per row: our stable id (the contract the agent/frontend/tool pass and we validate) → kie.ai's exact slug, plus the provider/label and a per-image usd price. The confirm card shows the price as $ (English) or ¥ (Chinese, fixed USD_CNY rate — an illustrative hint, not a bill). Default is Nano Banana Pro · 2K.

One selectable option per resolution/quality tier. The catalog is defined as per-family specs (_FAMILIES) and flattened to one ImageModel per tier, so the card shows the real price of each tier (GPT Image 2 at 1K/2K/4K = $0.03/$0.05/$0.08, etc.) instead of a single hand-picked number — the earlier bug that made cross-model prices look inconsistent (different models were shown at different, unlabeled resolutions). Each variant's extra injects its own kie param (resolution / quality / rendering_speed), so generation targets that tier; pinned by test_tier_variants_carry_their_param.

The other thing that differs per model is the reference-image input: field name and cardinality (image_input/input_urls/image_urls arrays, or a single image_url). generate_image() builds the input payload from each model's ref_field/ref_array + merged extra, so a non-nano model receives its references (and tier param) instead of silently dropping them. Prices/slugs/tier params verified against kie.ai's pricing API + docs (2026-07); refresh manually.

Providers/models (image-to-image, tiers in parens): Google — Nano Banana Pro (2K/4K), Nano Banana 2 (1K/2K/4K), Nano Banana Edit; OpenAI — GPT Image 2 (1K/2K/4K), GPT Image 1.5 (Medium — cheapest OpenAI; its High tier is omitted as it costs more than the newer, better GPT Image 2·4K); ByteDance — Seedream 5 Pro (Basic/High), Seedream 4.5; Black Forest — Flux-2 Pro (1K/2K), Flux 2 Flex (1K/2K); Qwen — Image Edit; Ideogram — V3 Remix (Turbo/Balanced/ Quality). Pure utilities (Recraft bg-removal, Topaz upscale) are intentionally excluded — they take no prompt and don't fit the card.

Layering: the MCP tool is platform-agnostic infrastructure. Platform knowledge lives in skills — amazon-image-studio (Amazon image requirements, gathering references from Amazon/1688, placeholder pitfalls) is the first; other marketplaces (noon, MercadoLibre, …) add their own skills on top of the same tool.

Pieces

Concern	Where
Config + kie.ai client + confirm registry	app/vision.py
HTTP endpoints	app/routers/vision.py
MCP tool vibe_seller_generate_image	app/mcp_tool_schemas.py + app/mcp_server.py
Settings UI	frontend/src/components/settings/VisionPanel.tsx (Settings → AI → Vision)
Confirm card + inline image	frontend/src/components/conversation/ImageRequestCard.tsx, GeneratedImageCard.tsx
Skill	app/skills_v2/amazon-image-studio/SKILL.md

Config / secret

The kie.ai key lives in ~/.vibe-seller/vision.json (mode 0600) — a secret, so not the DB, mirroring profiles.json. It is read back masked (last-4). Admin-only to set. The KIE_API_KEY env var overrides the file (for CI). See app/vision.py.

Not configured → tool hidden, agent guides the user

Image generation is conditionally registered: when no key is set (and not VISION_FAKE), mcp_server._visible_tools() drops vibe_seller_generate_image from tools/list, so the agent never sees (or dead-end-calls) a tool it can't use and the tool list stays clean — industry practice over advertise-then-error. The MCP process is per-task, so the set is read once at task start (no list_changed; a key added mid-task appears on the next task).

Discoverability is one line, in ONE place — not per-skill: VISION_SETUP_BREADCRUMB in app/task_runner.py, appended by _build_system_extra() only under the same unconfigured condition (configured tasks pay zero tokens). It tells the agent to guide the user to Settings → AI → Vision and to emit the link [Settings → AI → Vision](#vision-setup). The frontend (MessageBubble) renders that #vision-setup href as an inline CTA button that navigates to the Vision settings panel — the visible text may be translated, the href is the stable contract. Skills carry nothing about setup.

Tests: tests/unit/test_mcp_tool_visibility.py (hide/show/fake) + tests/unit/test_prompt_assembly.py::TestVisionSetupBreadcrumb.

Routes

Method	Path	Notes
GET	/api/vision/config	{kie_api_key_set, kie_api_key_masked, models, default_model} — never the raw key
PUT	/api/vision/config	Set the key (admin only)
POST	/api/tasks/{id}/image/generate	MCP-tool entry + confirm gate (see below)
POST	/api/tasks/{id}/image/confirm	User's approve/edit/cancel

Poll budget is per resolution tier (and a timeout is resumable)

kie.ai generation is async: createTask returns a taskId, then we poll recordInfo until success. Two rules, both learned from one live failure:

1. The budget scales with output pixels. It used to be a flat 60 × 4s = 240s with a comment sized for 2K ("Pro 2K ~ 60s"). Measured live against nano-banana-pro with the same prompt + reference: 2K lands in ~60s, 4K took 147s on one run and exceeded 240s on another — a real user generation, which the flat budget killed at 4m17s. So 4K's tail is what needs covering, not its median: poll_budget_s() in app/vision.py reads the tier from the model's own extra['resolution'] and gives 1K/2K 240s, 4K 600s. Quality- or speed-tier models (quality, rendering_speed) get the default. vibe_seller_generate_image's MCP call passes timeout=None (mcp_server.call_api), so nothing upstream caps this.

2. Giving up does not cancel the job — it finishes and is billed. The old code raised a bare RuntimeError('timed out') and dropped the taskId, orphaning an image the user had already paid for. Now exhaustion raises vision.GenerationTimeout, which carries kie_task_id; the router stores it in _INFLIGHT[task_id] = (kie_task_id, model) and returns 504 whose message tells the agent to retry the same model. That retry resumes the existing job via generate_image(resume_task_id=…) — skipping both the reference upload and createTask, so the already-billed image is collected instead of bought twice. The handle is keyed by model as well, because resuming another model's job would return the wrong image.

Why the message insists on the same model. The model on the confirm card is the user's choice. On the live failure the agent read timed out, reasoned "let me try a different model", and silently dropped from the 4K the user had selected to gpt-image-2-2k — a different image, a second charge, and the 4K job abandoned mid-flight. The 504 text and the tool description now both say: retry the same model, never substitute.

Confirm-gate flow

The confirmation is a server-side block, not a permission hook — so it works regardless of the agent's permission mode (auto/bypass or plan):

1. The agent calls vibe_seller_generate_image. The MCP proxy forwards it to POST /api/tasks/{id}/image/generate with a long httpx timeout.
2. The endpoint fails immediately with 400 if no key is configured.
3. Otherwise it registers a per-request asyncio.Future, emits an image_request SSE event {task_id, request_id, prompt, model, models, reference_images, ...}, and awaits the future.
4. The frontend renders ImageRequestCard — an editable prompt textarea
5. model dropdown + Confirm/Cancel — and on submit calls POST /api/tasks/{id}/image/confirm {request_id, action, prompt, model}, which resolves the future.
6. On confirm the endpoint calls kie.ai (create → poll → download), saves the PNG to ~/.vibe-seller/tasks/{id}/generated_images/<name>.png, emits image_generated {task_id, request_id, path, url}, and returns the workspace path to the agent. On cancel it returns a cancelled status and writes nothing.

The user's edited prompt/model win over the agent's proposal. The saved image is served by the existing GET /api/tasks/{id}/files/{path} endpoint and shown inline via GeneratedImageCard (distinct from the finished-task file explorer).

Prompt-generality contract (baked into the tool + skill)

The product's appearance comes only from the reference images, never from prompt words — so the same prompt skeleton works for any product (a phone charger, a kettle, a lantern). The prompt sets only layout, background, on-image text (infographics, spelled exactly, in the user's language), and compliance. The one negative constraint is generic: "do not invent elements not in the references." The agent writes the prompt in the user's language and self-audits each result against the original supplier photo, regenerating with a specific correction if it differs.

generated_images/ is model-owned (enforced)

The directory has exactly one writer: the vision router, after the user confirms a generation. Every file there carries provenance — a generated_image task message recording the prompt and the model — and that message is the claim GeneratedImageCard renders those bytes under. So an image deliverable must come from the model, never from local pixel editing, no matter how the request is phrased ("just remove the background", "only crop it", "make it pure white" are all generation jobs).

Two PreToolUse guards in app/ai/image_guards.py hold the invariant (a third, below the table, is about reading):

Guard	Denies
check_local_image_edit (Bash, registered in the first_bash_deny chain)	rembg/backgroundremover/carvekit in any form (including pip install); an inline snippet that both uses an imaging library and calls a produce/mutate function (.save(, imwrite(, Image.new(, .paste(, …); ImageMagick/ffmpeg against an image file; any write landing in generated_images/ (redirect, cp/mv/rm/tee, inline save)
check_generated_image_write (Write/Edit/MultiEdit/NotebookEdit tool args)	the file-tool hop around the above — any generated_images/** target

Reading an image is not blocked (file, stat, Image.open + numpy): only producing one is. The one exception is the built-in Read tool on a model the catalog labels text-only (profiles.model_sees_images is False, e.g. MiniMax M2.x): check_image_read_without_vision refuses it and points the agent at the page's DOM text. That model's image block is dropped silently, so whatever it "saw" is invented — in CI one reported campaigns that were not on the page. Unknown models are left alone. Guards inspect the inline command text only, so skill scripts that legitimately touch images (amazon-listing/scripts/ocr_1688.py reads them for OCR) are unaffected. The deny message routes the agent to the correct fix — regenerate with the previous generated image as a reference_image and a prompt naming only the change — and states that the rule is platform policy, so a reflection pass does not record a local-processing workaround as knowledge.

Why it is a guard and not just prose. Task 2fde3b9c ("keep the content, only remove the background") generated the image with the tool, then could not see the result: the task ran on a text-only profile, so Read returned an image block the model discarded. It substituted a numpy pixel audit, judged the background off-white (254,254,253) and the product over-filled, and rebuilt the deliverable with rembg + Pillow over the tool's own PNG at the same path. The user's inline card still read model: gpt-image-2-2k while displaying a locally composited image, and the reflection wrote "prefer rembg over vibe_seller_generate_image" into store knowledge — which would have made every later image task do the same. Hence the companion rule in the tool description and amazon-image-studio §3: if you cannot actually see the image, hand it to the user to judge (it renders inline) — never substitute a pixel-measurement proxy, and never act on what one measured.

Testing / VISION_FAKE

VISION_FAKE=1 short-circuits the kie.ai network and returns a deterministic placeholder PNG, so the whole confirm→save→display path is exercised offline and for free. Used by the workflow tests (tests/workflow/test_wf_vision_image.py) and the Playwright e2e (tests/e2e/test_vision_image_ui.py). Unit tests: tests/unit/test_vision.py.