Add baoyu-skills package
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baoyu-skills/skills/baoyu-image-gen/SKILL.md
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---
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name: baoyu-image-gen
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description: AI image generation with OpenAI GPT Image 2.5, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream, Replicate and Agnes APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
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version: 2.2.0
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metadata:
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openclaw:
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homepage: https://github.com/JimLiu/baoyu-skills#baoyu-image-gen
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requires:
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anyBins:
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- bun
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- npx
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---
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# Image Generation (AI SDK)
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Official API-based image generation. Supports OpenAI GPT Image 2.5, Azure OpenAI, Google, OpenRouter, DashScope (阿里通义万象), Z.AI GLM-Image, MiniMax, Jimeng (即梦), Seedream (豆包), Replicate and Agnes.
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## User Input Tools
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When this skill prompts the user, follow this tool-selection rule (priority order):
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1. **Prefer built-in user-input tools** exposed by the current agent runtime — e.g., `AskUserQuestion`, `request_user_input`, `clarify`, `ask_user`, or any equivalent.
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2. **Fallback**: if no such tool exists, emit a numbered plain-text message and ask the user to reply with the chosen number/answer for each question.
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3. **Batching**: if the tool supports multiple questions per call, combine all applicable questions into a single call; if only single-question, ask them one at a time in priority order.
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Concrete `AskUserQuestion` references below are examples — substitute the local equivalent in other runtimes.
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## Script Directory
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`{baseDir}` = this SKILL.md's directory. All `scripts/...` paths below are relative to `{baseDir}`. Main script: `{baseDir}/scripts/main.ts`. Batch payload helper: `{baseDir}/scripts/build-batch.ts`. Resolve `${BUN_X}`: prefer `bun`; else `npx -y bun`; else suggest `brew install oven-sh/bun/bun`.
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## Step 0: Load Preferences ⛔ BLOCKING
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This step MUST complete before any image generation — generation is blocked until EXTEND.md exists.
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Check these paths in order; first hit wins:
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| Path | Scope |
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|------|-------|
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| `.baoyu-skills/baoyu-image-gen/EXTEND.md` | Project |
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| `${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-image-gen/EXTEND.md` | XDG |
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| `$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md` | User home |
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- **Found** → load, parse, apply. If `default_model.[provider]` is null → ask model only.
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- **Not found** → run first-time setup (`references/config/first-time-setup.md`) using AskUserQuestion to collect provider + model + quality + save location. Save EXTEND.md, then continue. Do not generate images before this completes.
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Legacy compatibility: if `.baoyu-skills/baoyu-imagine/EXTEND.md` exists and the new path doesn't, the runtime renames it to `baoyu-image-gen`. If both exist, the runtime leaves them alone and uses the new path.
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**EXTEND.md keys**: default provider, default quality, default aspect ratio, default image size, OpenAI image API dialect, default models, batch worker cap, provider-specific batch limits. Schema: `references/config/preferences-schema.md`.
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## Usage
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Minimum working examples — see `references/usage-examples.md` for the full set including per-provider invocations and batch mode.
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### Identity-preserving reference prompts
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When the user wants a real person/character/object preserved from reference images, do **not** replace the reference with a long generic description. Prefer short, hard identity-preservation language:
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- "Use the person/object in the reference image(s) as the same identity. Do not redesign it or create a similar-looking new subject."
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- "Only change scene, clothing, pose, lighting, rendering style, and composition. Keep the face/proportions/hair/key accessories/overall identity from the references."
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- If using multiple references, state that they are the same subject and should jointly define identity.
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Pitfall: long descriptions like "young East Asian woman, oval face, clear eyes..." can cause the model to synthesize a new person matching the description instead of preserving the referenced person.
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```bash
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# Basic
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${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image cat.png
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# With aspect ratio and high quality
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${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9 --quality 2k
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# Prompt from files
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${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png
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# With reference image
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${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png
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# Specific provider
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${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider dashscope --model qwen-image-2.0-pro
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# OpenAI GPT Image 2
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${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openai --model gpt-image-2.5-flare
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# Codex CLI (uses logged-in Codex subscription — no OPENAI_API_KEY required; requires `codex` on PATH)
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${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider codex-cli --ar 16:9
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# Batch mode
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${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4
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# Build a batch file from outline.md + prompts/ (e.g. baoyu-article-illustrator output)
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${BUN_X} {baseDir}/scripts/build-batch.ts --outline outline.md --prompts prompts --output batch.json --images-dir attachments
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${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4
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```
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## Reference-Image Identity Preservation
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When the user wants a person/object preserved from reference images:
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- Prefer a small curated set of existing source references (usually 2–4) over many images; large multi-megabyte refs can destabilize streaming providers.
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- Make the prompt say the references are the same subject and the output must use that identity. Avoid long generic facial-feature descriptions that can cause the model to synthesize a new similar-looking person.
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- Do not use newly generated outputs as references unless the user explicitly asks; generated refs compound drift.
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- If results become too polished or influencer-like, reduce stylized refs and add explicit anti-beautification constraints (no face slimming, eye enlargement, heavy makeup, commercial travel shoot, over-smoothing).
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- If the subject should look younger/older, preserve the face and express age through clothing, posture, scene, and styling; do not ask the model to change facial identity.
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## Options
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| Option | Description |
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|--------|-------------|
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| `--prompt <text>`, `-p` | Prompt text |
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| `--promptfiles <files...>` | Read prompt from files (concatenated) |
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| `--image <path>` | Output image path (required in single-image mode) |
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| `--batchfile <path>` | JSON batch file for multi-image generation |
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| `--jobs <count>` | Worker count for batch mode (default: auto, max from config, built-in default 10) |
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| `--provider google\|openai\|azure\|openrouter\|dashscope\|zai\|minimax\|jimeng\|seedream\|replicate\|codex-cli\|agnes` | Force provider (default: auto-detect; `codex-cli` is never auto-selected — must be pinned via CLI or EXTEND.md) |
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| `--model <id>`, `-m` | Model ID — see provider references for defaults and allowed values |
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| `--ar <ratio>` | Aspect ratio (`16:9`, `1:1`, `4:3`, …) |
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| `--size <WxH>` | Explicit size (e.g., `1024x1024`; for `gpt-image-2.5-*` and `gpt-image-2`, width/height must be multiples of 16, max edge 3840px, ratio no wider than 3:1) |
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| `--quality normal\|2k` | Quality preset (default: `2k`) |
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| `--imageSize 1K\|2K\|4K` | Image size for Google/OpenRouter (default: from quality) |
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| `--imageApiDialect openai-native\|ratio-metadata` | OpenAI-compatible endpoint dialect — use `ratio-metadata` for gateways that expect aspect-ratio `size` plus `metadata.resolution` |
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| `--ref <files...>` | Reference images. Supported by Google multimodal, OpenAI GPT Image edits, Azure OpenAI edits (PNG/JPG only), OpenRouter multimodal models, Replicate supported families, MiniMax subject-reference, Seedream 5.0/4.5/4.0, DashScope `wan2.7-image-pro`/`wan2.7-image`. Not supported by Jimeng, Seedream 3.0, SeedEdit 3.0, or any DashScope model outside the `wan2.7-image*` family |
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| `--n <count>` | Number of images. Replicate requires `--n 1` (single-output save semantics) |
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| `--json` | JSON output |
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## Environment Variables
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| Variable | Description |
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|----------|-------------|
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| `OPENAI_API_KEY` | OpenAI API key |
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| `AZURE_OPENAI_API_KEY` | Azure OpenAI API key |
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| `OPENROUTER_API_KEY` | OpenRouter API key |
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| `GOOGLE_API_KEY` | Google API key |
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| `DASHSCOPE_API_KEY` | DashScope API key |
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| `ZAI_API_KEY` (alias `BIGMODEL_API_KEY`) | Z.AI API key |
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| `MINIMAX_API_KEY` | MiniMax API key |
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| `REPLICATE_API_TOKEN` | Replicate API token |
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| `JIMENG_ACCESS_KEY_ID`, `JIMENG_SECRET_ACCESS_KEY` | Jimeng (即梦) Volcengine credentials |
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| `ARK_API_KEY` | Seedream (豆包) Volcengine ARK API key |
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| `<PROVIDER>_IMAGE_MODEL` | Per-provider model override (`OPENAI_IMAGE_MODEL`, `GOOGLE_IMAGE_MODEL`, `DASHSCOPE_IMAGE_MODEL`, `ZAI_IMAGE_MODEL`/`BIGMODEL_IMAGE_MODEL`, `MINIMAX_IMAGE_MODEL`, `OPENROUTER_IMAGE_MODEL`, `REPLICATE_IMAGE_MODEL`, `JIMENG_IMAGE_MODEL`, `SEEDREAM_IMAGE_MODEL`, `AGNES_IMAGE_MODEL`) |
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| `AZURE_OPENAI_DEPLOYMENT` (alias `AZURE_OPENAI_IMAGE_MODEL`) | Azure default deployment |
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| `<PROVIDER>_BASE_URL` | Per-provider endpoint override |
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| `AZURE_API_VERSION` | Azure image API version (default `2025-04-01-preview`) |
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| `JIMENG_REGION` | Jimeng region (default `cn-north-1`) |
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| `OPENAI_IMAGE_API_DIALECT` | `openai-native` \| `ratio-metadata` |
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| `OPENROUTER_HTTP_REFERER`, `OPENROUTER_TITLE` | Optional OpenRouter attribution |
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| `BAOYU_IMAGE_GEN_MAX_WORKERS` | Override batch worker cap |
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| `BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY` | Per-provider concurrency (e.g., `BAOYU_IMAGE_GEN_REPLICATE_CONCURRENCY`; for codex-cli use `BAOYU_IMAGE_GEN_CODEX_CLI_CONCURRENCY`) |
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| `BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS` | Per-provider start-gap |
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| `BAOYU_CODEX_IMAGEGEN_BIN` | Override the codex-imagegen wrapper path for the `codex-cli` provider (default: bundled `scripts/codex-imagegen/main.ts`; accepts `.ts` or legacy `.sh`/binary) |
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| `BAOYU_CODEX_IMAGEGEN_CACHE_DIR` | Enable idempotency cache for the `codex-cli` provider (off by default) |
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| `BAOYU_CODEX_IMAGEGEN_TIMEOUT_MS` | Per-attempt `codex exec` timeout for the `codex-cli` provider (default: 300000 ms) |
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| `BAOYU_CODEX_IMAGEGEN_RETRIES` | Wrapper-side retry attempts on retryable errors for the `codex-cli` provider (default: 2) |
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| `BAOYU_CODEX_IMAGEGEN_LOG_FILE` | Append JSONL diagnostic log for the `codex-cli` provider |
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**Load priority**: CLI args > EXTEND.md > env vars > `<cwd>/.baoyu-skills/.env` > `~/.baoyu-skills/.env`
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### Codex/ChatGPT OAuth is not an OpenAI API key
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`--provider openai --model gpt-image-2.5-flare` uses the standard OpenAI Images API (`/v1/images/generations` or `/v1/images/edits`) and requires `OPENAI_API_KEY`. A Codex or ChatGPT desktop login is a different entitlement and is not a drop-in replacement for `OPENAI_API_KEY`; do not paste a Codex OAuth token into `OPENAI_API_KEY` or only set `OPENAI_BASE_URL` to a Codex backend.
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If the user wants to use their Codex subscription / GPT Image 2 entitlement without an OpenAI API key, route through a Codex-native backend instead of this skill's `openai` provider:
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- In Codex runtime: use the native `imagegen` skill/tool.
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- In non-Codex runtimes with `codex` CLI installed and logged in: use `baoyu-image-gen --provider codex-cli` (preferred — it gives you the same retry / cache / batch flow as every other provider). The provider spawns the bundled `scripts/codex-imagegen/main.ts`; the same code lives upstream at `packages/baoyu-codex-imagegen/src/main.ts` for standalone callers.
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- In Hermes runtimes with a native `image_generate` tool: use that tool as a fallback, and state whether reference images were passed directly or reconstructed from extracted traits.
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Do not modify the existing `openai` provider to silently consume Codex OAuth. The first-class Codex-CLI path is the dedicated `codex-cli` provider, which has its own auth (Codex login), route (`codex exec`), request shape, and tests. See `references/codex-oauth-vs-openai-api-key.md`.
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## Model Resolution
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Priority (highest → lowest) applies to every provider:
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1. CLI flag `--model <id>`
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2. EXTEND.md `default_model.[provider]`
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3. Env var `<PROVIDER>_IMAGE_MODEL`
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4. Built-in default
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For OpenAI, the built-in default is `gpt-image-2.5-flare` (fast, lowest latency). `gpt-image-2.5-sunburst` is the most capable variant for complex scenes and precise edits; `gpt-image-2`, `gpt-image-1.5`, `gpt-image-1`, and dated GPT Image snapshots (e.g. `gpt-image-2.5-flare-2026-09-08`, `gpt-image-2-2026-04-21`) remain selectable with `--model` or `OPENAI_IMAGE_MODEL`.
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For Google, the built-in default is `gemini-3-pro-image`. `gemini-3.1-flash-image` is the faster low-cost option, and `gemini-3.1-flash-lite-image` is the cheapest — it only produces 1K output, so `--quality 2k` / `--imageSize 2K|4K` is clamped to 1K with a warning.
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For DashScope, the built-in default is `qwen-image-2.0-pro`; `qwen-image-3.0-pro` is the newest flagship and uses the same sizing rules.
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For Azure, `--model` / `default_model.azure` is the Azure deployment name. `AZURE_OPENAI_DEPLOYMENT` is the preferred env var; `AZURE_OPENAI_IMAGE_MODEL` is kept as a backward-compatible alias. If your Azure deployment is named after the underlying model, use `gpt-image-2.5-flare`; otherwise use the exact custom deployment name.
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EXTEND.md overrides env vars: if EXTEND.md sets `default_model.google: "gemini-3-pro-image"` and the env var sets `GOOGLE_IMAGE_MODEL=gemini-3.1-flash-image`, EXTEND.md wins.
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**Display model info before each generation**:
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- `Using [provider] / [model]`
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- `Switch model: --model <id> | EXTEND.md default_model.[provider] | env <PROVIDER>_IMAGE_MODEL`
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## OpenAI-Compatible Gateway Dialects
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`provider=openai` means the auth and routing entrypoint is OpenAI-compatible. It does **not** guarantee the upstream image API uses OpenAI native semantics. When a gateway expects a different wire format, set `default_image_api_dialect` in EXTEND.md, `OPENAI_IMAGE_API_DIALECT`, or `--imageApiDialect`:
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- `openai-native`: pixel `size` (`1536x1024`) and native OpenAI quality fields
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- `ratio-metadata`: aspect-ratio `size` (`16:9`) plus `metadata.resolution` (`1K|2K|4K`) and `metadata.orientation`
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Use `openai-native` for the OpenAI native API or strict clones; try `ratio-metadata` for compatibility gateways in front of Gemini or similar models. Current limitation: `ratio-metadata` applies only to text-to-image; reference-image edits still need `openai-native` or a provider with first-class edit support.
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## Provider-Specific Guides
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Each provider has its own quirks (model families, size rules, ref support, limits). Read these when the user picks that provider or asks for non-default behavior:
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| Provider | Reference |
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|----------|-----------|
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| DashScope (Qwen-Image families, custom sizes) | `references/providers/dashscope.md` |
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| Z.AI (GLM-Image, cogview-4) | `references/providers/zai.md` |
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| MiniMax (image-01, subject-reference) | `references/providers/minimax.md` |
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| OpenRouter (multimodal models, `/chat/completions` flow) | `references/providers/openrouter.md` |
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| Replicate (nano-banana, Seedream, Wan) | `references/providers/replicate.md` |
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| Codex CLI (wraps bundled `scripts/codex-imagegen/`; Codex login, no `OPENAI_API_KEY`) | `references/providers/codex-cli.md` |
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| Agnes (agnes-image-2.5-flash, reference-image support) | `references/providers/agnes.md` |
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## Provider Selection
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1. `--ref` provided + no `--provider` → auto-select Google → OpenAI → Azure → OpenRouter → Replicate → Seedream → MiniMax → Agnes (MiniMax's subject reference is more specialized toward character/portrait consistency)
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2. `--provider` specified → use it (if `--ref`, must be google/openai/azure/openrouter/replicate/seedream/minimax/codex-cli/agnes)
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3. Only one API key present → use that provider
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4. Multiple keys → default priority: Google → OpenAI → Azure → OpenRouter → DashScope → Z.AI → MiniMax → Replicate → Jimeng → Seedream → Agnes
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5. `codex-cli` is **never auto-selected** — set `default_provider: codex-cli` in EXTEND.md or pass `--provider codex-cli`. It spawns `codex exec` via the bundled `scripts/codex-imagegen/main.ts` TS entrypoint (run with `bun`) and uses the user's Codex subscription (no `OPENAI_API_KEY`). Requires `codex` on `PATH` with an active `codex login`.
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## Quality Presets
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| Preset | Google imageSize | OpenAI size | OpenRouter size | Replicate resolution | Use case |
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|--------|------------------|-------------|-----------------|----------------------|----------|
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| `normal` | 1K | 1024px target | 1K | 1K | Quick previews |
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| `2k` (default) | 2K | 2048px target | 2K | 2K | Covers, illustrations, infographics |
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Google/OpenRouter `imageSize` can be overridden with `--imageSize 1K|2K|4K`.
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For OpenAI native `gpt-image-2.5-*` and `gpt-image-2`, `normal` maps to `quality=medium` and a low-latency valid size near the requested aspect ratio; `2k` maps to `quality=high` and 2048px-class sizes such as `2048x2048`, `2048x1152`, or `1152x2048`. Use explicit `--size` for valid custom or 4K outputs, e.g. `3840x2160`.
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## Aspect Ratios
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Supported: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `2.35:1`.
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- Google multimodal: `imageConfig.aspectRatio`
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- OpenAI: `gpt-image-2.5-*` and `gpt-image-2` use the closest valid custom size for the requested ratio; older GPT Image and DALL·E models use their closest supported fixed size
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- OpenRouter: `imageGenerationOptions.aspect_ratio`; if only `--size <WxH>` is given, the ratio is inferred
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- Replicate: behavior is model-specific — `google/nano-banana*` uses `aspect_ratio`, `bytedance/seedream-*` uses documented Replicate ratios, Wan 2.7 maps `--ar` to a concrete `size`
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- MiniMax: official `aspect_ratio` values; if `--size <WxH>` is given without `--ar`, sends `width`/`height` for `image-01`
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## Generation Mode
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**Default**: sequential. **Batch parallel**: enabled automatically when `--batchfile` contains 2+ pending tasks.
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| Situation | Prefer | Why |
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||||
|-----------|--------|-----|
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| One image, or 1-2 simple images | Sequential | Lower coordination overhead, easier debugging |
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| Multiple images with saved prompt files | Batch (`--batchfile`) | Reuses finalized prompts, applies shared throttling/retries, predictable throughput |
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| Each image still needs its own reasoning / prompt writing / style exploration | Subagents | Work is still exploratory, each needs independent analysis |
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| Input is `outline.md` + `prompts/` (e.g. from `baoyu-article-illustrator`) | Batch — use `{baseDir}/scripts/build-batch.ts` to assemble the payload | The outline + prompt files already contain everything needed |
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||||
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Rule of thumb: once prompt files are saved and the task is "generate all of these", prefer batch over subagents. Use subagents only when generation is coupled with per-image thinking or divergent creative exploration.
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**Parallel behavior**:
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||||
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- Default worker count is automatic, capped by config, built-in default 10
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- Provider-specific throttling applies only in batch mode; defaults are tuned for throughput while avoiding RPM bursts
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- Override with `--jobs <count>`
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- Each image retries up to 3 attempts
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- Final output includes success count, failure count, and per-image failure reasons
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## Error Handling
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||||
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- Missing API key → error with setup instructions
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- Generation failure → auto-retry up to 3 attempts per image
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||||
- Invalid aspect ratio → warning, proceed with default
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- Reference images with unsupported provider/model → error with fix hint
|
||||
|
||||
### Codex image2 fallback
|
||||
|
||||
If `--provider openai --model gpt-image-2.5-flare` fails because `OPENAI_API_KEY` is missing but the current runtime has a native image-generation backend or the repo-level `codex-imagegen` wrapper is available, use that path rather than leaving the user waiting. Be explicit about whether the fallback is true reference-image generation or only a text-prompt reconstruction from extracted visual traits. See `references/codex-image2-fallback.md`.
|
||||
|
||||
## References
|
||||
|
||||
| File | Content |
|
||||
|------|---------|
|
||||
| `references/usage-examples.md` | Extended CLI examples across providers and batch mode |
|
||||
| `references/codex-oauth-vs-openai-api-key.md` | Why Codex/ChatGPT OAuth image2 entitlement is not usable through baoyu-image-gen's standard OpenAI API-key provider |
|
||||
| `references/codex-image2-fallback.md` | Practical fallback behavior when OpenAI API credentials are absent but Codex/native image generation is available |
|
||||
| `references/providers/dashscope.md` | DashScope families, sizes, limits |
|
||||
| `references/providers/zai.md` | Z.AI GLM-image / cogview-4 |
|
||||
| `references/providers/minimax.md` | MiniMax image-01 + subject reference |
|
||||
| `references/providers/openrouter.md` | OpenRouter multimodal flow |
|
||||
| `references/providers/replicate.md` | Replicate supported families + guardrails |
|
||||
| `references/providers/agnes.md` | Agnes (agnes-image-2.5-flash) sizing, refs, and limits |
|
||||
| `references/config/preferences-schema.md` | EXTEND.md schema |
|
||||
| `references/config/first-time-setup.md` | First-time setup flow |
|
||||
|
||||
## Extension Support
|
||||
|
||||
Custom configurations via EXTEND.md. See Step 0 for paths and schema.
|
||||
@@ -0,0 +1,35 @@
|
||||
---
|
||||
name: codex-image2-fallback
|
||||
description: Fallback behavior when baoyu-image-gen lacks OpenAI API credentials but Codex/native image generation is available
|
||||
---
|
||||
|
||||
# Codex Image2 Fallback
|
||||
|
||||
When using `baoyu-image-gen` with `--provider openai --model gpt-image-2.5-flare`, the CLI can fail with:
|
||||
|
||||
```text
|
||||
OPENAI_API_KEY is required. Codex/ChatGPT desktop login does not automatically grant OpenAI Images API access to this script.
|
||||
```
|
||||
|
||||
This is expected. The `openai` provider uses the public OpenAI Images API and needs `OPENAI_API_KEY`. Codex / ChatGPT image2 entitlement is a separate runtime-native path.
|
||||
|
||||
## Practical fallback pattern
|
||||
|
||||
1. Try `baoyu-image-gen` when provider credentials are available.
|
||||
2. If it fails only because `OPENAI_API_KEY` is missing, do not leave the user waiting.
|
||||
3. Prefer a Codex/native raster backend in this order:
|
||||
- Codex runtime native `imagegen` skill/tool, if available.
|
||||
- `baoyu-image-gen --provider codex-cli` (preferred — wraps the bundled `scripts/codex-imagegen/main.ts`; the underlying repo-level package lives at `packages/baoyu-codex-imagegen/src/main.ts` for standalone callers), if `codex` CLI is installed/logged in.
|
||||
- Hermes native `image_generate`, if available.
|
||||
4. Be transparent about reference-image behavior:
|
||||
- If the fallback backend accepts references, pass the reference images.
|
||||
- If it does not, derive a concise identity-preserving prompt from the references and state that it is a text-description fallback, not strict reference-image editing.
|
||||
5. Return the generated media path or structured backend error promptly.
|
||||
|
||||
## User-facing wording
|
||||
|
||||
Use concise wording such as:
|
||||
|
||||
> The OpenAI API path needs `OPENAI_API_KEY`; Codex login is a separate image2 backend. I used the available Codex/native image backend instead. Reference images were [passed directly / reconstructed from visual traits].
|
||||
|
||||
Avoid implying that `baoyu-image-gen --provider openai` can use Codex OAuth without a dedicated provider implementation.
|
||||
@@ -0,0 +1,18 @@
|
||||
# Codex OAuth vs OpenAI API key for baoyu-image-gen
|
||||
|
||||
`baoyu-image-gen --provider openai` uses the standard OpenAI Images API and requires `OPENAI_API_KEY`. It calls OpenAI-compatible image endpoints such as `/images/generations` and `/images/edits`.
|
||||
|
||||
Codex / ChatGPT login is different. Codex image generation is driven by Codex OAuth and the Codex runtime's `image_gen` capability, not by the public OpenAI Images API key path. A Codex OAuth token is not a drop-in replacement for `OPENAI_API_KEY`, and setting `OPENAI_BASE_URL` to a Codex backend will not make baoyu-image-gen's existing `openai` provider work because the auth, route, and payload shape differ.
|
||||
|
||||
## What to use instead
|
||||
|
||||
- If running inside Codex and the native `imagegen` skill/tool is available, use it directly.
|
||||
- If running outside Codex but the `codex` CLI is installed and logged in, call `baoyu-image-gen --provider codex-cli` (preferred). It spawns the bundled `scripts/codex-imagegen/main.ts` and surfaces its retry/cache/log machinery through baoyu-image-gen's standard CLI + batch flow. Standalone callers outside this skill can run the same code at `packages/baoyu-codex-imagegen/src/main.ts`. Both invoke `codex exec` and the Codex `image_gen` tool; no `OPENAI_API_KEY` is required.
|
||||
- If running inside Hermes and a native `image_generate` tool is available, use that as a runtime-native fallback. Be explicit about whether reference images are passed directly or only reconstructed from extracted traits.
|
||||
- `baoyu-image-gen` already exposes a distinct `codex-cli` provider (wraps the bundled `scripts/codex-imagegen/`); do not modify the existing `openai` provider to add Codex OAuth.
|
||||
|
||||
## Reference-image prompting note
|
||||
|
||||
When using actual reference images for identity preservation, avoid long generic descriptions of the subject. Long descriptions can cause the model to synthesize a new similar-looking person/object. Prefer direct wording:
|
||||
|
||||
> Use the person/object in the reference image(s) as the same identity. Do not redesign it or create a similar-looking new subject. Only change scene, clothing, pose, lighting, rendering style, and composition.
|
||||
@@ -0,0 +1,392 @@
|
||||
---
|
||||
name: first-time-setup
|
||||
description: First-time setup and default model selection flow for baoyu-image-gen
|
||||
---
|
||||
|
||||
# First-Time Setup
|
||||
|
||||
## Overview
|
||||
|
||||
Triggered when:
|
||||
1. No EXTEND.md found → full setup (provider + model + preferences)
|
||||
2. EXTEND.md found but `default_model.[provider]` is null → model selection only
|
||||
|
||||
## Setup Flow
|
||||
|
||||
```
|
||||
No EXTEND.md found EXTEND.md found, model null
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────────┐ ┌──────────────────────┐
|
||||
│ AskUserQuestion │ │ AskUserQuestion │
|
||||
│ (full setup) │ │ (model only) │
|
||||
└─────────────────────┘ └──────────────────────┘
|
||||
│ │
|
||||
▼ ▼
|
||||
┌─────────────────────┐ ┌──────────────────────┐
|
||||
│ Create EXTEND.md │ │ Update EXTEND.md │
|
||||
└─────────────────────┘ └──────────────────────┘
|
||||
│ │
|
||||
▼ ▼
|
||||
Continue Continue
|
||||
```
|
||||
|
||||
## Flow 1: No EXTEND.md (Full Setup)
|
||||
|
||||
**Language**: Use user's input language or saved language preference.
|
||||
|
||||
Use AskUserQuestion with ALL questions in ONE call:
|
||||
|
||||
### Question 1: Default Provider
|
||||
|
||||
```yaml
|
||||
header: "Provider"
|
||||
question: "Default image generation provider?"
|
||||
options:
|
||||
- label: "Google (Recommended)"
|
||||
description: "Gemini multimodal - high quality, reference images, flexible sizes"
|
||||
- label: "OpenAI"
|
||||
description: "GPT Image 2 - latest OpenAI image model, reference-image workflows"
|
||||
- label: "Azure OpenAI"
|
||||
description: "Azure-hosted GPT Image deployments with resource-specific routing"
|
||||
- label: "OpenRouter"
|
||||
description: "Router for Gemini/FLUX/OpenAI-compatible image models"
|
||||
- label: "DashScope"
|
||||
description: "Alibaba Cloud - Qwen-Image, strong Chinese/English text rendering"
|
||||
- label: "Z.AI"
|
||||
description: "GLM-image, strong poster and text-heavy image generation"
|
||||
- label: "MiniMax"
|
||||
description: "MiniMax image generation with subject-reference character workflows"
|
||||
- label: "Replicate"
|
||||
description: "Curated Replicate image families - nano-banana-2, Seedream, and Wan image models"
|
||||
- label: "Agnes"
|
||||
description: "Sapiens AI Agnes - optimized for high information density, complex layouts, reference-image support"
|
||||
```
|
||||
|
||||
### Question 2: Default Google Model
|
||||
|
||||
Only show if user selected Google or auto-detect (no explicit provider).
|
||||
|
||||
```yaml
|
||||
header: "Google Model"
|
||||
question: "Default Google image generation model?"
|
||||
options:
|
||||
- label: "gemini-3-pro-image (Recommended)"
|
||||
description: "Highest quality, best for production use"
|
||||
- label: "gemini-3.1-flash-image"
|
||||
description: "Fast generation, good quality, lower cost"
|
||||
- label: "gemini-3.1-flash-lite-image"
|
||||
description: "Cheapest and fastest; 1K output only"
|
||||
- label: "gemini-3-flash-preview"
|
||||
description: "Fast generation, balanced quality and speed"
|
||||
```
|
||||
|
||||
### Question 2b: Default OpenRouter Model
|
||||
|
||||
Only show if user selected OpenRouter.
|
||||
|
||||
```yaml
|
||||
header: "OpenRouter Model"
|
||||
question: "Default OpenRouter image generation model?"
|
||||
options:
|
||||
- label: "google/gemini-3.1-flash-image (Recommended)"
|
||||
description: "Best general-purpose OpenRouter image model with reference-image workflows"
|
||||
- label: "google/gemini-2.5-flash-image-preview"
|
||||
description: "Fast Gemini preview model on OpenRouter"
|
||||
- label: "black-forest-labs/flux.2-pro"
|
||||
description: "Strong text-to-image quality through OpenRouter"
|
||||
```
|
||||
|
||||
### Question 2c: Default Azure Deployment
|
||||
|
||||
Only show if user selected Azure OpenAI.
|
||||
|
||||
```yaml
|
||||
header: "Azure Deploy"
|
||||
question: "Default Azure image deployment name?"
|
||||
options:
|
||||
- label: "gpt-image-2.5-flare (Recommended)"
|
||||
description: "Use if your Azure deployment uses the GPT Image 2.5 Flare model name"
|
||||
- label: "gpt-image-2.5-sunburst"
|
||||
description: "Use if your Azure deployment uses the GPT Image 2.5 Sunburst model name"
|
||||
- label: "gpt-image-2"
|
||||
description: "Use if your Azure deployment uses the GPT Image 2 model name"
|
||||
- label: "gpt-image-1.5"
|
||||
description: "Previous GPT Image deployment name"
|
||||
- label: "gpt-image-1"
|
||||
description: "Earlier GPT Image deployment name"
|
||||
```
|
||||
|
||||
### Question 2d: Default MiniMax Model
|
||||
|
||||
Only show if user selected MiniMax.
|
||||
|
||||
```yaml
|
||||
header: "MiniMax Model"
|
||||
question: "Default MiniMax image generation model?"
|
||||
options:
|
||||
- label: "image-01 (Recommended)"
|
||||
description: "Best default, supports aspect ratios and custom width/height"
|
||||
- label: "image-01-live"
|
||||
description: "Faster variant, use aspect ratio instead of custom size"
|
||||
```
|
||||
|
||||
### Question 2e: Default Z.AI Model
|
||||
|
||||
Only show if user selected Z.AI.
|
||||
|
||||
```yaml
|
||||
header: "Z.AI Model"
|
||||
question: "Default Z.AI image generation model?"
|
||||
options:
|
||||
- label: "glm-image (Recommended)"
|
||||
description: "Best default for posters, diagrams, and text-heavy images"
|
||||
- label: "cogview-4-250304"
|
||||
description: "Legacy Z.AI image model on the same endpoint"
|
||||
```
|
||||
|
||||
### Question 3: Default Quality
|
||||
|
||||
```yaml
|
||||
header: "Quality"
|
||||
question: "Default image quality?"
|
||||
options:
|
||||
- label: "2k (Recommended)"
|
||||
description: "2048px - covers, illustrations, infographics"
|
||||
- label: "normal"
|
||||
description: "1024px - quick previews, drafts"
|
||||
```
|
||||
|
||||
### Question 4: Save Location
|
||||
|
||||
```yaml
|
||||
header: "Save"
|
||||
question: "Where to save preferences?"
|
||||
options:
|
||||
- label: "Project (Recommended)"
|
||||
description: ".baoyu-skills/ (this project only)"
|
||||
- label: "User"
|
||||
description: "~/.baoyu-skills/ (all projects)"
|
||||
```
|
||||
|
||||
### Save Locations
|
||||
|
||||
| Choice | Path | Scope |
|
||||
|--------|------|-------|
|
||||
| Project | `.baoyu-skills/baoyu-image-gen/EXTEND.md` | Current project |
|
||||
| User | `$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md` | All projects |
|
||||
|
||||
### EXTEND.md Template
|
||||
|
||||
```yaml
|
||||
---
|
||||
version: 1
|
||||
default_provider: [selected provider or null]
|
||||
default_quality: [selected quality]
|
||||
default_aspect_ratio: null
|
||||
default_image_size: null
|
||||
default_image_api_dialect: null
|
||||
default_model:
|
||||
google: [selected google model or null]
|
||||
openai: null
|
||||
azure: [selected azure deployment or null]
|
||||
openrouter: [selected openrouter model or null]
|
||||
dashscope: null
|
||||
zai: [selected Z.AI model or null]
|
||||
minimax: [selected minimax model or null]
|
||||
replicate: null
|
||||
agnes: null
|
||||
---
|
||||
```
|
||||
|
||||
If the user selects `OpenAI` but says their endpoint is only OpenAI-compatible and fronts another image model family, save `default_image_api_dialect: ratio-metadata` when they explicitly confirm the gateway expects aspect-ratio `size` plus metadata-based resolution. Otherwise leave it `null` / `openai-native`.
|
||||
|
||||
## Flow 2: EXTEND.md Exists, Model Null
|
||||
|
||||
When EXTEND.md exists but `default_model.[current_provider]` is null, ask ONLY the model question for the current provider.
|
||||
|
||||
### Google Model Selection
|
||||
|
||||
```yaml
|
||||
header: "Google Model"
|
||||
question: "Choose a default Google image generation model?"
|
||||
options:
|
||||
- label: "gemini-3-pro-image (Recommended)"
|
||||
description: "Highest quality, best for production use"
|
||||
- label: "gemini-3.1-flash-image"
|
||||
description: "Fast generation, good quality, lower cost"
|
||||
- label: "gemini-3.1-flash-lite-image"
|
||||
description: "Cheapest and fastest; 1K output only"
|
||||
- label: "gemini-3-flash-preview"
|
||||
description: "Fast generation, balanced quality and speed"
|
||||
```
|
||||
|
||||
### OpenAI Model Selection
|
||||
|
||||
```yaml
|
||||
header: "OpenAI Model"
|
||||
question: "Choose a default OpenAI image generation model?"
|
||||
options:
|
||||
- label: "gpt-image-2.5-flare (Recommended)"
|
||||
description: "Latest GPT Image model, fastest; flexible sizes up to 4K, high-fidelity image inputs"
|
||||
- label: "gpt-image-2.5-sunburst"
|
||||
description: "Most capable GPT Image model for complex scenes and precise edits; slower"
|
||||
- label: "gpt-image-2"
|
||||
description: "Previous GPT Image generation, flexible sizes up to 4K"
|
||||
- label: "gpt-image-1.5"
|
||||
description: "Previous GPT Image model"
|
||||
- label: "gpt-image-1"
|
||||
description: "Earlier GPT Image model"
|
||||
```
|
||||
|
||||
### Azure Deployment Selection
|
||||
|
||||
```yaml
|
||||
header: "Azure Deploy"
|
||||
question: "Choose a default Azure image deployment name?"
|
||||
options:
|
||||
- label: "gpt-image-2.5-flare (Recommended)"
|
||||
description: "Use when your Azure deployment name matches the GPT Image 2.5 Flare model"
|
||||
- label: "gpt-image-2.5-sunburst"
|
||||
description: "Use when your Azure deployment name matches the GPT Image 2.5 Sunburst model"
|
||||
- label: "gpt-image-2"
|
||||
description: "Use when your Azure deployment name matches the GPT Image 2 model"
|
||||
- label: "gpt-image-1.5"
|
||||
description: "Use when your Azure deployment name matches the GPT Image 1.5 model"
|
||||
- label: "gpt-image-1"
|
||||
description: "Use when your Azure deployment name matches GPT-image-1"
|
||||
```
|
||||
|
||||
Notes for Azure setup:
|
||||
|
||||
- In `baoyu-image-gen`, Azure `--model` / `default_model.azure` should be the Azure deployment name, not just the underlying model family.
|
||||
- If the deployment name is custom, save that exact deployment name in `default_model.azure`.
|
||||
|
||||
### OpenRouter Model Selection
|
||||
|
||||
```yaml
|
||||
header: "OpenRouter Model"
|
||||
question: "Choose a default OpenRouter image generation model?"
|
||||
options:
|
||||
- label: "google/gemini-3.1-flash-image (Recommended)"
|
||||
description: "Recommended for image output and reference-image edits"
|
||||
- label: "google/gemini-2.5-flash-image-preview"
|
||||
description: "Fast preview-oriented image generation"
|
||||
- label: "black-forest-labs/flux.2-pro"
|
||||
description: "High-quality text-to-image through OpenRouter"
|
||||
```
|
||||
|
||||
### DashScope Model Selection
|
||||
|
||||
```yaml
|
||||
header: "DashScope Model"
|
||||
question: "Choose a default DashScope image generation model?"
|
||||
options:
|
||||
- label: "qwen-image-2.0-pro (Recommended)"
|
||||
description: "Best-verified DashScope model for text rendering and custom sizes"
|
||||
- label: "qwen-image-3.0-pro"
|
||||
description: "Newest Qwen flagship; same sizing rules, not yet the built-in default"
|
||||
- label: "qwen-image-2.0"
|
||||
description: "Faster 2.0 variant with flexible output size"
|
||||
- label: "qwen-image-max"
|
||||
description: "Legacy Qwen model with five fixed output sizes"
|
||||
- label: "qwen-image-plus"
|
||||
description: "Legacy Qwen model, same current capability as qwen-image"
|
||||
- label: "wan2.7-image-pro"
|
||||
description: "Wan 2.7 Pro — supports up to 4K text-to-image and reference-image editing"
|
||||
- label: "wan2.7-image"
|
||||
description: "Wan 2.7 base — faster generation, up to 2K, supports reference-image editing"
|
||||
- label: "z-image-turbo"
|
||||
description: "Legacy DashScope model for compatibility"
|
||||
- label: "z-image-ultra"
|
||||
description: "Legacy DashScope model, higher quality but slower"
|
||||
```
|
||||
|
||||
Notes for DashScope setup:
|
||||
|
||||
- Prefer `qwen-image-2.0-pro` (or the newer `qwen-image-3.0-pro`) when the user needs custom `--size`, uncommon ratios like `21:9`, or strong Chinese/English text rendering.
|
||||
- `qwen-image-max` / `qwen-image-plus` / `qwen-image` only support five fixed sizes: `1664*928`, `1472*1104`, `1328*1328`, `1104*1472`, `928*1664`.
|
||||
- `wan2.7-image-pro` and `wan2.7-image` are the only DashScope models that accept `--ref`. Pick one of these when the user wants reference-image editing or multi-image fusion via DashScope.
|
||||
- In `baoyu-image-gen`, `quality` is a compatibility preset. It is not a native DashScope parameter.
|
||||
|
||||
### Z.AI Model Selection
|
||||
|
||||
```yaml
|
||||
header: "Z.AI Model"
|
||||
question: "Choose a default Z.AI image generation model?"
|
||||
options:
|
||||
- label: "glm-image (Recommended)"
|
||||
description: "Current flagship image model with better text rendering and poster layouts"
|
||||
- label: "cogview-4-250304"
|
||||
description: "Legacy model on the sync image endpoint"
|
||||
```
|
||||
|
||||
Notes for Z.AI setup:
|
||||
|
||||
- Prefer `glm-image` for posters, diagrams, and Chinese/English text-heavy layouts.
|
||||
- In `baoyu-image-gen`, Z.AI currently exposes text-to-image only; reference images are not wired for this provider.
|
||||
- The sync Z.AI image API returns a downloadable image URL, which the runtime saves locally after download.
|
||||
|
||||
### Replicate Model Selection
|
||||
|
||||
```yaml
|
||||
header: "Replicate Model"
|
||||
question: "Choose a default Replicate image generation model?"
|
||||
options:
|
||||
- label: "google/nano-banana-2 (Recommended)"
|
||||
description: "Current default for general Replicate image generation in baoyu-image-gen"
|
||||
- label: "bytedance/seedream-4.5"
|
||||
description: "Replicate Seedream 4.5 with validated local size/ref guardrails"
|
||||
- label: "bytedance/seedream-5-lite"
|
||||
description: "Replicate Seedream 5 Lite with validated local size/ref guardrails"
|
||||
- label: "wan-video/wan-2.7-image-pro"
|
||||
description: "Replicate Wan 2.7 Image Pro with 4K text-to-image support"
|
||||
```
|
||||
|
||||
### MiniMax Model Selection
|
||||
|
||||
```yaml
|
||||
header: "MiniMax Model"
|
||||
question: "Choose a default MiniMax image generation model?"
|
||||
options:
|
||||
- label: "image-01 (Recommended)"
|
||||
description: "Best general-purpose MiniMax image model with custom width/height support"
|
||||
- label: "image-01-live"
|
||||
description: "Lower-latency MiniMax image model using aspect ratios"
|
||||
```
|
||||
|
||||
Notes for MiniMax setup:
|
||||
|
||||
- `image-01` is the safest default. It supports official `aspect_ratio` values and documented custom `width` / `height` output sizes.
|
||||
- `image-01-live` is useful when the user prefers faster generation and can work with aspect-ratio-based sizing.
|
||||
- MiniMax subject reference currently uses `subject_reference[].type = character`; docs recommend front-facing portrait references in JPG/JPEG/PNG under 10MB.
|
||||
|
||||
### Update EXTEND.md
|
||||
|
||||
After user selects a model:
|
||||
|
||||
1. Read existing EXTEND.md
|
||||
2. If `default_model:` section exists → update the provider-specific key
|
||||
3. If `default_model:` section missing → add the full section:
|
||||
|
||||
```yaml
|
||||
default_model:
|
||||
google: [value or null]
|
||||
openai: [value or null]
|
||||
azure: [value or null]
|
||||
openrouter: [value or null]
|
||||
dashscope: [value or null]
|
||||
zai: [value or null]
|
||||
minimax: [value or null]
|
||||
replicate: [value or null]
|
||||
agnes: [value or null]
|
||||
```
|
||||
|
||||
Only set the selected provider's model; leave others as their current value or null.
|
||||
|
||||
## After Setup
|
||||
|
||||
1. Create directory if needed
|
||||
2. Write/update EXTEND.md with frontmatter
|
||||
3. Confirm: "Preferences saved to [path]"
|
||||
4. Continue with image generation
|
||||
@@ -0,0 +1,149 @@
|
||||
---
|
||||
name: preferences-schema
|
||||
description: EXTEND.md YAML schema for baoyu-image-gen user preferences
|
||||
---
|
||||
|
||||
# Preferences Schema
|
||||
|
||||
## Full Schema
|
||||
|
||||
```yaml
|
||||
---
|
||||
version: 1
|
||||
|
||||
default_provider: null # google|openai|azure|openrouter|dashscope|zai|minimax|replicate|jimeng|seedream|codex-cli|agnes|null (null = auto-detect; codex-cli is never auto-detected — pin it here or via --provider)
|
||||
|
||||
default_quality: null # normal|2k|null (null = use default: 2k)
|
||||
|
||||
default_aspect_ratio: null # "16:9"|"1:1"|"4:3"|"3:4"|"2.35:1"|null
|
||||
|
||||
default_image_size: null # 1K|2K|4K|null (Google/OpenRouter, overrides quality)
|
||||
|
||||
default_image_api_dialect: null # openai-native|ratio-metadata|null (OpenAI-compatible gateways; null = use env/default)
|
||||
|
||||
default_model:
|
||||
google: null # e.g., "gemini-3-pro-image", "gemini-3.1-flash-image", "gemini-3.1-flash-lite-image"
|
||||
openai: null # e.g., "gpt-image-2.5-flare", "gpt-image-2.5-sunburst", "gpt-image-2", "gpt-image-1.5"
|
||||
azure: null # Azure deployment name, e.g., "gpt-image-2.5-flare" or "image-prod"
|
||||
openrouter: null # e.g., "google/gemini-3.1-flash-image"
|
||||
dashscope: null # e.g., "qwen-image-3.0-pro", "qwen-image-2.0-pro"
|
||||
zai: null # e.g., "glm-image"
|
||||
minimax: null # e.g., "image-01"
|
||||
replicate: null # e.g., "google/nano-banana-2"
|
||||
codex-cli: null # Logical label only — Codex image_gen has no user-selectable model. Default: "codex-image-gen"
|
||||
agnes: null # e.g., "agnes-image-2.5-flash"
|
||||
|
||||
batch:
|
||||
max_workers: 10
|
||||
provider_limits:
|
||||
replicate:
|
||||
concurrency: 5
|
||||
start_interval_ms: 700
|
||||
google:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
openai:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
azure:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
openrouter:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
dashscope:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
zai:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
minimax:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
codex-cli:
|
||||
concurrency: 1
|
||||
start_interval_ms: 2000
|
||||
agnes:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
---
|
||||
```
|
||||
|
||||
## Field Reference
|
||||
|
||||
| Field | Type | Default | Description |
|
||||
|-------|------|---------|-------------|
|
||||
| `version` | int | 1 | Schema version |
|
||||
| `default_provider` | string\|null | null | Default provider (null = auto-detect) |
|
||||
| `default_quality` | string\|null | null | Default quality (null = 2k) |
|
||||
| `default_aspect_ratio` | string\|null | null | Default aspect ratio |
|
||||
| `default_image_size` | string\|null | null | Google/OpenRouter image size (overrides quality) |
|
||||
| `default_image_api_dialect` | string\|null | null | OpenAI-compatible image dialect (`openai-native` or `ratio-metadata`) |
|
||||
| `default_model.google` | string\|null | null | Google default model |
|
||||
| `default_model.openai` | string\|null | null | OpenAI default model |
|
||||
| `default_model.azure` | string\|null | null | Azure default deployment name |
|
||||
| `default_model.openrouter` | string\|null | null | OpenRouter default model |
|
||||
| `default_model.dashscope` | string\|null | null | DashScope default model |
|
||||
| `default_model.zai` | string\|null | null | Z.AI default model |
|
||||
| `default_model.minimax` | string\|null | null | MiniMax default model |
|
||||
| `default_model.replicate` | string\|null | null | Replicate default model |
|
||||
| `default_model.codex-cli` | string\|null | null | Codex-CLI logical label (Codex image_gen has no user-selectable model) |
|
||||
| `default_model.agnes` | string\|null | null | Agnes default model |
|
||||
| `batch.max_workers` | int\|null | 10 | Batch worker cap |
|
||||
| `batch.provider_limits.<provider>.concurrency` | int\|null | provider default | Max simultaneous requests per provider |
|
||||
| `batch.provider_limits.<provider>.start_interval_ms` | int\|null | provider default | Minimum gap between request starts per provider |
|
||||
|
||||
## Examples
|
||||
|
||||
**Minimal**:
|
||||
```yaml
|
||||
---
|
||||
version: 1
|
||||
default_provider: google
|
||||
default_quality: 2k
|
||||
default_image_api_dialect: null
|
||||
---
|
||||
```
|
||||
|
||||
**Full**:
|
||||
```yaml
|
||||
---
|
||||
version: 1
|
||||
default_provider: google
|
||||
default_quality: 2k
|
||||
default_aspect_ratio: "16:9"
|
||||
default_image_size: 2K
|
||||
default_image_api_dialect: null
|
||||
default_model:
|
||||
google: "gemini-3-pro-image"
|
||||
openai: "gpt-image-2.5-flare"
|
||||
azure: "gpt-image-2.5-flare"
|
||||
openrouter: "google/gemini-3.1-flash-image"
|
||||
dashscope: "qwen-image-2.0-pro"
|
||||
zai: "glm-image"
|
||||
minimax: "image-01"
|
||||
replicate: "google/nano-banana-2"
|
||||
agnes: "agnes-image-2.5-flash"
|
||||
batch:
|
||||
max_workers: 10
|
||||
provider_limits:
|
||||
replicate:
|
||||
concurrency: 5
|
||||
start_interval_ms: 700
|
||||
azure:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
zai:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
openrouter:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
minimax:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
agnes:
|
||||
concurrency: 3
|
||||
start_interval_ms: 1100
|
||||
---
|
||||
```
|
||||
@@ -0,0 +1,55 @@
|
||||
# Sapiens AI Agnes Image
|
||||
|
||||
Read when the user picks `--provider agnes` or sets `default_model.agnes`. Default model is `agnes-image-2.5-flash`.
|
||||
|
||||
## Models
|
||||
|
||||
**`agnes-image-2.5-flash`** (only model)
|
||||
|
||||
- Text-to-image and image-to-image (with `--ref`) in a single `/images/generations` endpoint
|
||||
- Supports reference images as public URLs or Data URI (base64)
|
||||
- Optimized for high information density, complex layouts, and rich details
|
||||
- Size rules: both dimensions divisible by 32 (720px exception), long edge ≤ 2048, total pixels ≤ ~4M
|
||||
- Default size: `1024x1024`; custom `--size` supports arbitrary WxH within the above rules
|
||||
- `--ar` supported: computed as 2048-based size (long edge ≤ 2048, short edge proportional, both snapped to 32px); `1:1` special-cased to `1024x1024`
|
||||
|
||||
## Response Format
|
||||
|
||||
- The sync API always returns a URL
|
||||
- Default (`--response-format file`): downloads the image and saves as `.png`
|
||||
- Pass `--response-format url`: writes the URL string to `.txt` instead
|
||||
|
||||
## `--n` Behavior
|
||||
|
||||
The Agnes API returns a single image per request regardless of the `n` parameter. Passing `--n > 1` triggers a local error from `validateArgs` before any API call is made.
|
||||
|
||||
## Behavior Notes
|
||||
|
||||
- API key required: `AGNES_API_KEY`
|
||||
- Base URL: `https://apihub.agnes-ai.com/v1` (override with `AGNES_BASE_URL`)
|
||||
- Model override: `AGNES_IMAGE_MODEL` env
|
||||
- `response_format` is always embedded in `extra_body` (not at request top level)
|
||||
- Reference images: local files converted to Data URI base64 inline; remote URLs passed through
|
||||
- Rate limit defaults: concurrency=3, startIntervalMs=1100 (override via `BAOYU_IMAGE_GEN_AGNES_CONCURRENCY` / `BAOYU_IMAGE_GEN_AGNES_START_INTERVAL_MS`)
|
||||
- Timeout: 120s per request
|
||||
|
||||
## Size Resolution
|
||||
|
||||
- `--size <WxH>` wins over `--ar`
|
||||
- `--ar` maps to a concrete size using the algorithm: long edge ≤ 2048, short edge proportional, both dimensions snapped to 32px
|
||||
- `--ar 1:1` is special-cased to `1024x1024`
|
||||
|
||||
### Common `--ar` Results
|
||||
|
||||
| Aspect Ratio | Result |
|
||||
|--------------|--------|
|
||||
| `1:1` | `1024x1024` |
|
||||
| `16:9` | `2048x1152` |
|
||||
| `4:3` | `2048x1536` |
|
||||
| `3:2` | `2048x1376` |
|
||||
| `21:9` | `2048x896` |
|
||||
| Unlisted ratio | Computed on the fly (portrait mirror swaps width/height) |
|
||||
|
||||
## Official References
|
||||
|
||||
- [Agnes AIGC API Hub](https://apihub.agnes-ai.com)
|
||||
@@ -0,0 +1,81 @@
|
||||
# Codex CLI (`--provider codex-cli`)
|
||||
|
||||
Read when the user picks `--provider codex-cli`, sets `default_provider: codex-cli`, or asks for "Codex image generation without an OpenAI API key". This provider is a thin baoyu-image-gen wrapper around the bundled `scripts/codex-imagegen/main.ts` (synced from `packages/baoyu-codex-imagegen`), which spawns `codex exec --json --sandbox danger-full-access` and routes the request to Codex CLI's built-in `image_gen` tool. The Codex CLI uses the **user's Codex / ChatGPT subscription** — no `OPENAI_API_KEY` is read or sent.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
```bash
|
||||
npm install -g @openai/codex
|
||||
codex login # signs in with the user's OpenAI / Codex account
|
||||
codex --version # confirm >= 0.130
|
||||
```
|
||||
|
||||
`bun` is required for running the underlying wrapper (`scripts/codex-imagegen/main.ts`, carrying `#!/usr/bin/env bun`). If `bun` is missing from the runtime, `npx -y bun` works as a fallback.
|
||||
|
||||
## Selection
|
||||
|
||||
- **Never auto-selected.** `detectProvider` only picks `codex-cli` when it is pinned explicitly: pass `--provider codex-cli` or set `default_provider: codex-cli` in EXTEND.md.
|
||||
- Choose this provider when:
|
||||
- The user has a Codex subscription and explicitly does **not** want to manage an OpenAI API key.
|
||||
- You need Codex's specific `image_gen` behavior or quality.
|
||||
- Avoid this provider when latency matters — Codex CLI is typically 5–10× slower than direct OpenAI / Google API calls (except on cache hits).
|
||||
|
||||
## Supported flags
|
||||
|
||||
| Flag | Behavior |
|
||||
|------|----------|
|
||||
| `--prompt <text>` / `--promptfiles <files>` | Required. Written to a temp file and passed to the wrapper as `--prompt-file`. |
|
||||
| `--image <path>` | Required. Final output PNG location. |
|
||||
| `--ar <ratio>` | Mapped to wrapper's `--aspect`. Supported by Codex: `1:1` (default), `16:9`, `9:16`, `4:3`, `2.35:1`. |
|
||||
| `--ref <files...>` | Mapped to wrapper's repeated `--ref`. Codex's `image_gen` accepts reference images for style/composition guidance. |
|
||||
| `--n` | Must be `1`. `validateArgs` throws if `n > 1` because Codex `image_gen` returns a single image per call. |
|
||||
| `--imageApiDialect` | Not applicable. Throws if set to a non-default value. |
|
||||
| `--size`, `--imageSize`, `--quality` | Silently ignored — Codex picks pixel dimensions from the aspect ratio. |
|
||||
| `--model`, `-m` | Logical label only. The wrapper does not forward a model selector to Codex; the underlying engine is whichever model Codex's `image_gen` currently uses. Default label: `codex-image-gen`. |
|
||||
|
||||
## Environment variables
|
||||
|
||||
| Variable | Effect |
|
||||
|----------|--------|
|
||||
| `BAOYU_CODEX_IMAGEGEN_BIN` | Override the wrapper path. Default: bundled `scripts/codex-imagegen/main.ts` resolved relative to this skill's installed location. Accepts a `.ts` file (spawned with `bun`) or a legacy `.sh`/binary (spawned directly). |
|
||||
| `BAOYU_CODEX_IMAGEGEN_CACHE_DIR` | Enable the wrapper's idempotency cache. Disabled by default; set to e.g. `~/.cache/baoyu-codex-imagegen` for high-value reuse. |
|
||||
| `BAOYU_CODEX_IMAGEGEN_TIMEOUT_MS` | Per-attempt `codex exec` timeout in ms. Default: `300000` (5 min). Raise for slow networks or large prompts. |
|
||||
| `BAOYU_CODEX_IMAGEGEN_RETRIES` | Wrapper-side retry attempts on retryable errors. Default: `2` (3 total attempts). |
|
||||
| `BAOYU_CODEX_IMAGEGEN_LOG_FILE` | Append a structured JSONL diagnostic log. Useful when triaging timeouts or `agent_refused` errors. |
|
||||
| `BAOYU_IMAGE_GEN_CODEX_CLI_CONCURRENCY` | Batch-mode concurrency for the `codex-cli` provider. Default: `1` — Codex exec is a heavy single-process workflow; raising this rarely helps. |
|
||||
| `BAOYU_IMAGE_GEN_CODEX_CLI_START_INTERVAL_MS` | Batch-mode minimum start-gap. Default: `2000` ms. |
|
||||
|
||||
## Error model
|
||||
|
||||
The wrapper emits a single JSON line on stdout. On failure:
|
||||
|
||||
```json
|
||||
{"status":"error","path":"...","bytes":0,"error":"...","error_kind":"..."}
|
||||
```
|
||||
|
||||
The provider re-throws each wrapper error as `Invalid codex-cli result (<error_kind>): <message>`. The `"Invalid "` prefix triggers `isRetryableGenerationError` to mark it **non-retryable** in baoyu-image-gen's outer retry loop — the wrapper has already retried internally per `BAOYU_CODEX_IMAGEGEN_RETRIES`, so re-spawning Codex from main.ts would only multiply latency without changing the outcome.
|
||||
|
||||
`error_kind` values to expect:
|
||||
|
||||
| Kind | Cause | Action |
|
||||
|------|-------|--------|
|
||||
| `codex_not_installed` | `codex` not on `PATH` or unreadable | `npm install -g @openai/codex`, then `codex login`. |
|
||||
| `invalid_args` | Programmer error in the spawn invocation | Inspect provider source; usually a path-injection guard fired. |
|
||||
| `prompt_file_missing` | Temp prompt file vanished mid-call | Retry once; check `$TMPDIR` permissions. |
|
||||
| `spawn_failed` | OS / process-launch failure | Verify `bun` or `npx` is installed; check filesystem permissions. |
|
||||
| `timeout` | `codex exec` exceeded `--timeout` | Raise `BAOYU_CODEX_IMAGEGEN_TIMEOUT_MS`; check network. |
|
||||
| `no_image_gen_tool_use` | Codex agent answered without calling `image_gen` | Often transient — retry. If persistent, refine the prompt. |
|
||||
| `output_missing` / `invalid_png` | Agent reported success but file is absent or not a valid PNG | Retry; check disk space. |
|
||||
| `agent_refused` | Codex agent refused (policy or content) | Adjust the prompt; surface the refusal to the user. |
|
||||
| `lock_busy` | Another `codex-imagegen` invocation holds the file lock | Wait or set a distinct `--cache-dir` per concurrent caller. |
|
||||
|
||||
## Trade-offs
|
||||
|
||||
- Slow: 5–10× direct OpenAI API latency (except cache hits).
|
||||
- Subject to the same TOS as interactive `codex exec` use — programmatic invocation from baoyu-image-gen is the same usage class.
|
||||
- Stateful: requires `codex login` to be live; an expired session manifests as `codex_not_installed` or `agent_refused`.
|
||||
|
||||
## See also
|
||||
|
||||
- `references/codex-oauth-vs-openai-api-key.md` — why Codex OAuth is not interchangeable with `OPENAI_API_KEY`.
|
||||
- `references/codex-image2-fallback.md` — when to fall back to `codex-cli` from a failed `openai` provider call.
|
||||
@@ -0,0 +1,71 @@
|
||||
# DashScope (阿里通义万象)
|
||||
|
||||
Read when the user picks `--provider dashscope`, sets `default_model.dashscope`, or asks for Qwen-Image behavior. The SKILL.md only names the default — this file covers model families, sizing rules, and limits.
|
||||
|
||||
## Model Families
|
||||
|
||||
**`qwen-image-3.0*` / `qwen-image-2.0*`** — recommended modern family (identical sizing envelope). Members: `qwen-image-3.0-pro`, `qwen-image-2.0-pro`, `qwen-image-2.0-pro-2026-04-22`, `qwen-image-2.0-pro-2026-03-03`, `qwen-image-2.0`, `qwen-image-2.0-2026-03-03`.
|
||||
|
||||
- `qwen-image-3.0-pro` is the newest flagship; `qwen-image-2.0-pro` remains the built-in default until the 3.0 request shape is verified end to end
|
||||
|
||||
- Free-form `size` in `宽*高` format
|
||||
- Total pixels must be between `512*512` and `2048*2048`
|
||||
- Default ≈ `1024*1024`
|
||||
- Best choice for custom ratios (e.g. `21:9`) and text-heavy Chinese/English layouts
|
||||
|
||||
**Fixed-size family** — `qwen-image-max`, `qwen-image-max-2025-12-30`, `qwen-image-plus`, `qwen-image-plus-2026-01-09`, `qwen-image`.
|
||||
|
||||
- Only five sizes allowed: `1664*928`, `1472*1104`, `1328*1328`, `1104*1472`, `928*1664`
|
||||
- Default is `1664*928`
|
||||
- `qwen-image` currently has the same capability as `qwen-image-plus`
|
||||
|
||||
**`wan2.7-image*`** — multimodal Wan 2.7 family. Members: `wan2.7-image-pro`, `wan2.7-image`.
|
||||
|
||||
- Free-form `size` in `宽*高` format, plus aspect-ratio inference
|
||||
- `wan2.7-image-pro` text-to-image (no `--ref`): total pixels in `[768*768, 4096*4096]`, ratio in `[1:8, 8:1]`
|
||||
- `wan2.7-image-pro` with reference images and `wan2.7-image` (all scenarios): total pixels in `[768*768, 2048*2048]`, ratio in `[1:8, 8:1]`
|
||||
- Default: `1024*1024` (`--quality normal`) or `2048*2048` (`--quality 2k`); 4K requires explicit `--size`
|
||||
- Supports up to 9 reference images in `--ref` (image editing / multi-image fusion)
|
||||
- Reference images are sent inline as base64 (or passed through if the path is an `http(s)://` URL)
|
||||
- API does NOT use `prompt_extend`; the skill omits it for this family
|
||||
- The Wan 2.7 API defaults `n` to **4** in non-collage mode and bills per generated image. baoyu-image-gen forces `n: 1` and rejects `--n > 1` to avoid silently paying for and discarding extra images.
|
||||
|
||||
**Legacy** — `z-image-turbo`, `z-image-ultra`, `wanx-v1`. Only use when the user explicitly asks for legacy behavior.
|
||||
|
||||
## Size Resolution
|
||||
|
||||
- `--size` wins over `--ar`
|
||||
- For `qwen-image-3.0*` / `qwen-image-2.0*`: prefer explicit `--size`; otherwise infer from `--ar` using the recommended table below
|
||||
- For `qwen-image-max/plus/image`: only use the five fixed sizes; if the requested ratio doesn't fit, switch to `qwen-image-2.0-pro` or `qwen-image-3.0-pro`
|
||||
- For `wan2.7-image*`: explicit `--size` is validated against the per-mode pixel/ratio limits; otherwise the size is derived from `--ar` and `--quality` (`normal` ≈ 1K, `2k` ≈ 2K). To request 4K with `wan2.7-image-pro` text-to-image, pass `--size` explicitly (e.g. `4096*4096`, `3840*2160`)
|
||||
- `--quality` is a baoyu-image-gen preset, not an official DashScope field. The mapping of `normal`/`2k` onto the `qwen-image-3.0*` / `qwen-image-2.0*` and `wan2.7-image*` tables is an implementation choice, not an API guarantee
|
||||
|
||||
### Recommended `qwen-image-3.0*` / `qwen-image-2.0*` sizes
|
||||
|
||||
| Ratio | `normal` | `2k` |
|
||||
|-------|----------|------|
|
||||
| `1:1` | `1024*1024` | `1536*1536` |
|
||||
| `2:3` | `768*1152` | `1024*1536` |
|
||||
| `3:2` | `1152*768` | `1536*1024` |
|
||||
| `3:4` | `960*1280` | `1080*1440` |
|
||||
| `4:3` | `1280*960` | `1440*1080` |
|
||||
| `9:16` | `720*1280` | `1080*1920` |
|
||||
| `16:9` | `1280*720` | `1920*1080` |
|
||||
| `21:9` | `1344*576` | `2048*872` |
|
||||
|
||||
## Reference Images
|
||||
|
||||
- Only `wan2.7-image-pro` and `wan2.7-image` accept `--ref`. Other DashScope models (qwen-image-3.0*, qwen-image-2.0*, qwen-image-max/plus/image, legacy) reject `--ref` and the user is steered to a different provider/model.
|
||||
- Up to 9 reference images per request. Local files are inlined as base64 data URLs; `http(s)://` URLs are forwarded as-is.
|
||||
- Supplying any `--ref` automatically clamps the wan2.7-image-pro pixel ceiling from 4K to 2K (the API only supports 4K for pure text-to-image with no image input).
|
||||
|
||||
## Not Exposed
|
||||
|
||||
DashScope APIs also support `negative_prompt`, `prompt_extend`, `watermark`, `thinking_mode`, `seed`, `bbox_list`, `enable_sequential`, and `color_palette`. `baoyu-image-gen` does not expose them as CLI flags today; the wan2.7 family relies on the API defaults (e.g. `thinking_mode=true`). The skill always sends `n=1` for wan2.7 — if you want grid/collage mode you currently need to call the API directly.
|
||||
|
||||
## Official References
|
||||
|
||||
- [Qwen-Image API](https://help.aliyun.com/zh/model-studio/qwen-image-api)
|
||||
- [Text-to-image guide](https://help.aliyun.com/zh/model-studio/text-to-image)
|
||||
- [Qwen-Image Edit API](https://help.aliyun.com/zh/model-studio/qwen-image-edit-api)
|
||||
- [Wan 2.7 image generation & editing API](https://help.aliyun.com/zh/model-studio/wan-image-generation-and-editing-api-reference)
|
||||
@@ -0,0 +1,29 @@
|
||||
# MiniMax
|
||||
|
||||
Read when the user picks `--provider minimax` or sets `default_model.minimax`. Default model is `image-01`.
|
||||
|
||||
## Models
|
||||
|
||||
**`image-01`** (recommended default)
|
||||
|
||||
- Supports text-to-image and subject-reference image generation
|
||||
- Supports official `aspect_ratio` values: `1:1`, `16:9`, `4:3`, `3:2`, `2:3`, `3:4`, `9:16`, `21:9`
|
||||
- Supports documented custom `width` / `height` via `--size <WxH>`
|
||||
- Both width and height must be in `[512, 2048]` and divisible by `8`
|
||||
|
||||
**`image-01-live`** — lower-latency variant
|
||||
|
||||
- Use `--ar` for sizing; MiniMax documents custom `width`/`height` only for `image-01`
|
||||
|
||||
## Subject Reference
|
||||
|
||||
- `--ref` files are sent as MiniMax `subject_reference`
|
||||
- `subject_reference[].type` is currently `character`
|
||||
- Official docs say `image_file` supports public URLs or Base64 Data URLs; baoyu-image-gen sends local refs as Data URLs
|
||||
- Recommended refs: front-facing portraits, JPG/JPEG/PNG, under 10MB
|
||||
|
||||
## Official References
|
||||
|
||||
- [Image Generation Guide](https://platform.minimaxi.com/docs/guides/image-generation)
|
||||
- [Text-to-Image API](https://platform.minimaxi.com/docs/api-reference/image-generation-t2i)
|
||||
- [Image-to-Image API](https://platform.minimaxi.com/docs/api-reference/image-generation-i2i)
|
||||
@@ -0,0 +1,19 @@
|
||||
# OpenRouter
|
||||
|
||||
Read when the user picks `--provider openrouter`. Default model is `google/gemini-3.1-flash-image`.
|
||||
|
||||
## Common Models
|
||||
|
||||
Use full OpenRouter model IDs:
|
||||
|
||||
- `google/gemini-3.1-flash-image` (recommended — supports image output and reference-image workflows)
|
||||
- `google/gemini-2.5-flash-image-preview`
|
||||
- `black-forest-labs/flux.2-pro`
|
||||
- Any other OpenRouter image-capable model ID
|
||||
|
||||
## Behavior Notes
|
||||
|
||||
- OpenRouter image generation uses `/chat/completions`, not the OpenAI `/images` endpoints
|
||||
- `--ref` requires a multimodal model that supports both image input and image output
|
||||
- `--imageSize` maps to `imageGenerationOptions.size`
|
||||
- `--size <WxH>` is converted to the nearest supported OpenRouter size, and the aspect ratio is inferred when possible
|
||||
@@ -0,0 +1,50 @@
|
||||
# Replicate
|
||||
|
||||
Read when the user picks `--provider replicate`. Replicate support is intentionally scoped to model families baoyu-image-gen can validate locally and save without dropping outputs.
|
||||
|
||||
## Supported Families
|
||||
|
||||
**`google/nano-banana*`** (default: `google/nano-banana-2`)
|
||||
|
||||
- Supports prompt-only and reference-image generation
|
||||
- Uses Replicate `aspect_ratio`, `resolution`, and `output_format`
|
||||
- `--size <WxH>` is accepted only as a shorthand for a documented `aspect_ratio` plus `1K` / `2K`
|
||||
|
||||
**`bytedance/seedream-4.5`**
|
||||
|
||||
- Supports prompt-only and reference-image generation
|
||||
- Uses Replicate `size`, `aspect_ratio`, and `image_input`
|
||||
- Local validation blocks unsupported `1K` requests before the API call
|
||||
|
||||
**`bytedance/seedream-5-lite`**
|
||||
|
||||
- Supports prompt-only and reference-image generation
|
||||
- Uses Replicate `size`, `aspect_ratio`, and `image_input`
|
||||
- Local validation currently accepts `2K` / `3K` only
|
||||
|
||||
**`wan-video/wan-2.7-image`**
|
||||
|
||||
- Supports prompt-only and reference-image generation
|
||||
- Uses Replicate `size` and `images`
|
||||
- Max output is 2K
|
||||
|
||||
**`wan-video/wan-2.7-image-pro`**
|
||||
|
||||
- Supports prompt-only and reference-image generation
|
||||
- Uses Replicate `size` and `images`
|
||||
- 4K is allowed only for text-to-image; local validation blocks `4K + --ref`
|
||||
|
||||
## Guardrails
|
||||
|
||||
- Replicate currently supports only single-output save semantics in this tool — keep `--n 1`
|
||||
- If a model is outside the compatibility list above, baoyu-image-gen treats it as prompt-only and rejects advanced local options instead of guessing a nano-banana-style schema
|
||||
|
||||
## Examples
|
||||
|
||||
```bash
|
||||
# Default model
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
|
||||
|
||||
# Explicit model
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate --model google/nano-banana
|
||||
```
|
||||
@@ -0,0 +1,27 @@
|
||||
# Z.AI GLM-Image
|
||||
|
||||
Read when the user picks `--provider zai` or sets `default_model.zai`. Default model is `glm-image`.
|
||||
|
||||
## Models
|
||||
|
||||
**`glm-image`** (recommended default)
|
||||
|
||||
- Text-to-image only in baoyu-image-gen (no `--ref` support yet)
|
||||
- Native `quality` options are `hd` and `standard`; this skill maps `2k → hd` and `normal → standard`
|
||||
- Recommended sizes: `1280x1280`, `1568x1056`, `1056x1568`, `1472x1088`, `1088x1472`, `1728x960`, `960x1728`
|
||||
- Custom `--size` requires width/height in `[1024, 2048]`, divisible by `32`, total pixels ≤ `2^22`
|
||||
|
||||
**`cogview-4-250304`** (legacy family, same endpoint)
|
||||
|
||||
- Custom `--size` requires width/height in `[512, 2048]`, divisible by `16`, total pixels ≤ `2^21`
|
||||
|
||||
## Behavior Notes
|
||||
|
||||
- The sync API returns a temporary URL; baoyu-image-gen downloads it and writes locally
|
||||
- `--ref` is not supported for Z.AI in this skill yet
|
||||
- The sync API returns a single image, so `--n > 1` is rejected
|
||||
|
||||
## Official References
|
||||
|
||||
- [GLM-Image Guide](https://docs.z.ai/guides/image/glm-image)
|
||||
- [Generate Image API](https://docs.z.ai/api-reference/image/generate-image)
|
||||
137
baoyu-skills/skills/baoyu-image-gen/references/usage-examples.md
Normal file
137
baoyu-skills/skills/baoyu-image-gen/references/usage-examples.md
Normal file
@@ -0,0 +1,137 @@
|
||||
# Usage Examples
|
||||
|
||||
Extended CLI examples. SKILL.md shows the minimum set; read this file when the user asks about provider-specific invocation, batch generation, or less-common flags.
|
||||
|
||||
## Core Patterns
|
||||
|
||||
```bash
|
||||
# Basic text-to-image
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image cat.png
|
||||
|
||||
# With aspect ratio
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9
|
||||
|
||||
# High quality
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --quality 2k
|
||||
|
||||
# Prompt from files
|
||||
${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png
|
||||
|
||||
# With reference images (any provider family that supports refs)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png
|
||||
```
|
||||
|
||||
## Per-Provider
|
||||
|
||||
```bash
|
||||
# OpenAI
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openai --model gpt-image-2.5-flare
|
||||
|
||||
# Azure OpenAI (model = deployment name)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider azure --model gpt-image-2.5-flare
|
||||
|
||||
# OpenAI GPT Image 2 custom 4K size
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic landscape" --image out.png --provider openai --model gpt-image-2.5-sunburst --size 3840x2160
|
||||
|
||||
# Google with explicit model
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider google --model gemini-3-pro-image --ref source.png
|
||||
|
||||
# OpenRouter (recommended default)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openrouter
|
||||
|
||||
# OpenRouter with reference
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider openrouter --model google/gemini-3.1-flash-image --ref source.png
|
||||
|
||||
# DashScope (default model)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "一只可爱的猫" --image out.png --provider dashscope
|
||||
|
||||
# DashScope Qwen-Image 2.0 Pro (custom size, Chinese text)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "为咖啡品牌设计一张 21:9 横幅海报，包含清晰中文标题" --image out.png --provider dashscope --model qwen-image-2.0-pro --size 2048x872
|
||||
|
||||
# DashScope legacy fixed-size
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "一张电影感海报" --image out.png --provider dashscope --model qwen-image-max --size 1664x928
|
||||
|
||||
# DashScope Wan 2.7 Image Pro (4K text-to-image)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "一间有着精致窗户的花店" --image out.png --provider dashscope --model wan2.7-image-pro --size 4096x4096
|
||||
|
||||
# DashScope Wan 2.7 Image with reference image (multi-image fusion)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "把图2的涂鸦喷绘在图1的汽车上" --image out.png --provider dashscope --model wan2.7-image-pro --ref car.webp paint.webp
|
||||
|
||||
# Z.AI GLM-image
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "一张带清晰中文标题的科技海报" --image out.png --provider zai
|
||||
|
||||
# Z.AI with custom size
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A science illustration with labels" --image out.png --provider zai --model glm-image --size 1472x1088
|
||||
|
||||
# MiniMax
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A fashion editorial portrait" --image out.jpg --provider minimax
|
||||
|
||||
# MiniMax with subject reference (character/portrait consistency)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A girl by the library window" --image out.jpg --provider minimax --model image-01 --ref portrait.png --ar 16:9
|
||||
|
||||
# Replicate (default: google/nano-banana-2)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
|
||||
|
||||
# Replicate Seedream 4.5
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic portrait" --image out.png --provider replicate --model bytedance/seedream-4.5 --ar 3:2
|
||||
|
||||
# Replicate Wan 2.7 Image Pro
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A concept frame" --image out.png --provider replicate --model wan-video/wan-2.7-image-pro --size 2048x1152
|
||||
|
||||
# Codex CLI (uses Codex / ChatGPT subscription — no OPENAI_API_KEY; requires `codex` on PATH and `codex login`)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic portrait" --image out.png --provider codex-cli --ar 16:9
|
||||
|
||||
# Codex CLI with reference images (style/composition guidance)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "Match this color palette" --image out.png --provider codex-cli --ref source.png --ar 1:1
|
||||
|
||||
# Agnes (default model)
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A detailed infographic" --image out.png --provider agnes
|
||||
|
||||
# Agnes with aspect ratio and URL output
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic scene" --image out.txt --provider agnes --ar 16:9 --response-format url
|
||||
|
||||
# Agnes with reference image
|
||||
${BUN_X} {baseDir}/scripts/main.ts --prompt "Apply this style" --image out.png --provider agnes --ref source.png
|
||||
```
|
||||
|
||||
Notes on `codex-cli`:
|
||||
- Never auto-selected — pin via `--provider codex-cli` or `default_provider: codex-cli` in EXTEND.md.
|
||||
- Only `n=1` supported (Codex `image_gen` returns one image per call); `--size`, `--imageSize`, `--quality`, and `--imageApiDialect` are ignored or rejected.
|
||||
- Typically 5–10× slower than direct OpenAI / Google API calls (except on cache hits). Tune via `BAOYU_CODEX_IMAGEGEN_TIMEOUT_MS`, `BAOYU_CODEX_IMAGEGEN_RETRIES`, and `BAOYU_CODEX_IMAGEGEN_CACHE_DIR`.
|
||||
|
||||
## Batch Mode
|
||||
|
||||
```bash
|
||||
# Batch from saved prompt files
|
||||
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json
|
||||
|
||||
# Batch with explicit worker count
|
||||
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4 --json
|
||||
```
|
||||
|
||||
### Batch File Format
|
||||
|
||||
```json
|
||||
{
|
||||
"jobs": 4,
|
||||
"tasks": [
|
||||
{
|
||||
"id": "hero",
|
||||
"promptFiles": ["prompts/hero.md"],
|
||||
"image": "out/hero.png",
|
||||
"provider": "replicate",
|
||||
"model": "google/nano-banana-2",
|
||||
"ar": "16:9",
|
||||
"quality": "2k"
|
||||
},
|
||||
{
|
||||
"id": "diagram",
|
||||
"promptFiles": ["prompts/diagram.md"],
|
||||
"image": "out/diagram.png",
|
||||
"ref": ["references/original.png"]
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
Paths in `promptFiles`, `image`, and `ref` are resolved relative to the batch file's directory. `jobs` is optional (overridden by CLI `--jobs`). A top-level array without the `jobs` wrapper is also accepted.
|
||||
190
baoyu-skills/skills/baoyu-image-gen/scripts/build-batch.test.ts
Normal file
190
baoyu-skills/skills/baoyu-image-gen/scripts/build-batch.test.ts
Normal file
@@ -0,0 +1,190 @@
|
||||
import assert from "node:assert/strict";
|
||||
import fs from "node:fs/promises";
|
||||
import os from "node:os";
|
||||
import path from "node:path";
|
||||
import { execFile } from "node:child_process";
|
||||
import { promisify } from "node:util";
|
||||
import test from "node:test";
|
||||
|
||||
const execFileAsync = promisify(execFile);
|
||||
const repoRoot = path.resolve(import.meta.dirname, "..", "..", "..");
|
||||
const scriptPath = path.join(repoRoot, "skills", "baoyu-image-gen", "scripts", "build-batch.ts");
|
||||
|
||||
async function makeFixture(): Promise<{
|
||||
root: string;
|
||||
outlinePath: string;
|
||||
promptsDir: string;
|
||||
outputPath: string;
|
||||
}> {
|
||||
const root = await fs.mkdtemp(path.join(os.tmpdir(), "baoyu-image-gen-build-batch-"));
|
||||
const outlinePath = path.join(root, "outline.md");
|
||||
const promptsDir = path.join(root, "prompts");
|
||||
const outputPath = path.join(root, "batch.json");
|
||||
|
||||
await fs.mkdir(promptsDir, { recursive: true });
|
||||
await fs.writeFile(
|
||||
outlinePath,
|
||||
`## Illustration 1
|
||||
**Position**: demo
|
||||
**Purpose**: demo
|
||||
**Visual Content**: demo
|
||||
**Filename**: 01-demo.png
|
||||
`,
|
||||
);
|
||||
await fs.writeFile(path.join(promptsDir, "01-demo.md"), "A demo prompt\n");
|
||||
|
||||
return { root, outlinePath, promptsDir, outputPath };
|
||||
}
|
||||
|
||||
async function runBuildBatch(args: string[]): Promise<void> {
|
||||
await execFileAsync(process.execPath, ["--import", "tsx", scriptPath, ...args], {
|
||||
cwd: repoRoot,
|
||||
});
|
||||
}
|
||||
|
||||
test("build-batch omits default model so baoyu-image-gen can resolve env or EXTEND defaults", async () => {
|
||||
const fixture = await makeFixture();
|
||||
|
||||
await runBuildBatch([
|
||||
"--outline",
|
||||
fixture.outlinePath,
|
||||
"--prompts",
|
||||
fixture.promptsDir,
|
||||
"--output",
|
||||
fixture.outputPath,
|
||||
]);
|
||||
|
||||
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
|
||||
tasks: Array<Record<string, unknown>>;
|
||||
};
|
||||
|
||||
assert.equal(batch.tasks.length, 1);
|
||||
assert.equal(batch.tasks[0]?.provider, "replicate");
|
||||
assert.equal(Object.hasOwn(batch.tasks[0]!, "model"), false);
|
||||
});
|
||||
|
||||
test("build-batch preserves explicit model overrides", async () => {
|
||||
const fixture = await makeFixture();
|
||||
|
||||
await runBuildBatch([
|
||||
"--outline",
|
||||
fixture.outlinePath,
|
||||
"--prompts",
|
||||
fixture.promptsDir,
|
||||
"--output",
|
||||
fixture.outputPath,
|
||||
"--model",
|
||||
"acme/custom-model",
|
||||
]);
|
||||
|
||||
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
|
||||
tasks: Array<Record<string, unknown>>;
|
||||
};
|
||||
|
||||
assert.equal(batch.tasks[0]?.model, "acme/custom-model");
|
||||
});
|
||||
|
||||
test("build-batch propagates direct-usage references from prompt frontmatter", async () => {
|
||||
const fixture = await makeFixture();
|
||||
|
||||
await fs.writeFile(
|
||||
path.join(fixture.promptsDir, "01-demo.md"),
|
||||
`---
|
||||
illustration_id: 01
|
||||
type: infographic
|
||||
references:
|
||||
- ref_id: 01
|
||||
filename: 01-ref-brand.png
|
||||
usage: direct
|
||||
- ref_id: 02
|
||||
filename: 02-ref-style.png
|
||||
usage: style
|
||||
---
|
||||
|
||||
A demo prompt
|
||||
`,
|
||||
);
|
||||
|
||||
await runBuildBatch([
|
||||
"--outline",
|
||||
fixture.outlinePath,
|
||||
"--prompts",
|
||||
fixture.promptsDir,
|
||||
"--output",
|
||||
fixture.outputPath,
|
||||
]);
|
||||
|
||||
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
|
||||
tasks: Array<Record<string, unknown>>;
|
||||
};
|
||||
|
||||
assert.deepEqual(batch.tasks[0]?.ref, ["references/01-ref-brand.png"]);
|
||||
});
|
||||
|
||||
test("build-batch omits ref field when no direct references exist", async () => {
|
||||
const fixture = await makeFixture();
|
||||
|
||||
await fs.writeFile(
|
||||
path.join(fixture.promptsDir, "01-demo.md"),
|
||||
`---
|
||||
illustration_id: 01
|
||||
references:
|
||||
- ref_id: 01
|
||||
filename: 01-ref-palette.png
|
||||
usage: palette
|
||||
---
|
||||
|
||||
A demo prompt
|
||||
`,
|
||||
);
|
||||
|
||||
await runBuildBatch([
|
||||
"--outline",
|
||||
fixture.outlinePath,
|
||||
"--prompts",
|
||||
fixture.promptsDir,
|
||||
"--output",
|
||||
fixture.outputPath,
|
||||
]);
|
||||
|
||||
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
|
||||
tasks: Array<Record<string, unknown>>;
|
||||
};
|
||||
|
||||
assert.equal(Object.hasOwn(batch.tasks[0]!, "ref"), false);
|
||||
});
|
||||
|
||||
test("build-batch honors --refs-dir override", async () => {
|
||||
const fixture = await makeFixture();
|
||||
|
||||
await fs.writeFile(
|
||||
path.join(fixture.promptsDir, "01-demo.md"),
|
||||
`---
|
||||
illustration_id: 01
|
||||
references:
|
||||
- ref_id: 01
|
||||
filename: brand.png
|
||||
usage: direct
|
||||
---
|
||||
|
||||
A demo prompt
|
||||
`,
|
||||
);
|
||||
|
||||
await runBuildBatch([
|
||||
"--outline",
|
||||
fixture.outlinePath,
|
||||
"--prompts",
|
||||
fixture.promptsDir,
|
||||
"--output",
|
||||
fixture.outputPath,
|
||||
"--refs-dir",
|
||||
"refs",
|
||||
]);
|
||||
|
||||
const batch = JSON.parse(await fs.readFile(fixture.outputPath, "utf8")) as {
|
||||
tasks: Array<Record<string, unknown>>;
|
||||
};
|
||||
|
||||
assert.deepEqual(batch.tasks[0]?.ref, ["refs/brand.png"]);
|
||||
});
|
||||
239
baoyu-skills/skills/baoyu-image-gen/scripts/build-batch.ts
Normal file
239
baoyu-skills/skills/baoyu-image-gen/scripts/build-batch.ts
Normal file
@@ -0,0 +1,239 @@
|
||||
import path from "node:path";
|
||||
import process from "node:process";
|
||||
import { readdir, readFile, writeFile } from "node:fs/promises";
|
||||
|
||||
type CliArgs = {
|
||||
outlinePath: string | null;
|
||||
promptsDir: string | null;
|
||||
outputPath: string | null;
|
||||
imagesDir: string | null;
|
||||
refsDir: string;
|
||||
provider: string;
|
||||
model: string | null;
|
||||
aspectRatio: string;
|
||||
quality: string;
|
||||
jobs: number | null;
|
||||
help: boolean;
|
||||
};
|
||||
|
||||
type OutlineEntry = {
|
||||
index: number;
|
||||
filename: string;
|
||||
};
|
||||
|
||||
type PromptReference = {
|
||||
filename: string;
|
||||
usage: "direct" | "style" | "palette";
|
||||
};
|
||||
|
||||
function printUsage(): void {
|
||||
console.log(`Usage:
|
||||
bun <baseDir>/scripts/build-batch.ts --outline outline.md --prompts prompts --output batch.json --images-dir attachments
|
||||
npx -y tsx <baseDir>/scripts/build-batch.ts --outline outline.md --prompts prompts --output batch.json --images-dir attachments
|
||||
|
||||
Options:
|
||||
--outline <path> Path to outline.md
|
||||
--prompts <path> Path to prompts directory
|
||||
--output <path> Path to output batch.json
|
||||
--images-dir <path> Directory for generated images
|
||||
--refs-dir <path> Directory holding reference images, relative to batch file (default: references)
|
||||
--provider <name> Provider for baoyu-image-gen batch tasks (default: replicate)
|
||||
--model <id> Explicit model for baoyu-image-gen batch tasks (default: resolved by baoyu-image-gen config/env)
|
||||
--ar <ratio> Aspect ratio for all tasks (default: 16:9)
|
||||
--quality <level> Quality for all tasks (default: 2k)
|
||||
--jobs <count> Recommended worker count metadata (optional)
|
||||
-h, --help Show help`);
|
||||
}
|
||||
|
||||
function parseArgs(argv: string[]): CliArgs {
|
||||
const args: CliArgs = {
|
||||
outlinePath: null,
|
||||
promptsDir: null,
|
||||
outputPath: null,
|
||||
imagesDir: null,
|
||||
refsDir: "references",
|
||||
provider: "replicate",
|
||||
model: null,
|
||||
aspectRatio: "16:9",
|
||||
quality: "2k",
|
||||
jobs: null,
|
||||
help: false,
|
||||
};
|
||||
|
||||
for (let i = 0; i < argv.length; i++) {
|
||||
const current = argv[i]!;
|
||||
if (current === "--outline") args.outlinePath = argv[++i] ?? null;
|
||||
else if (current === "--prompts") args.promptsDir = argv[++i] ?? null;
|
||||
else if (current === "--output") args.outputPath = argv[++i] ?? null;
|
||||
else if (current === "--images-dir") args.imagesDir = argv[++i] ?? null;
|
||||
else if (current === "--refs-dir") args.refsDir = argv[++i] ?? args.refsDir;
|
||||
else if (current === "--provider") args.provider = argv[++i] ?? args.provider;
|
||||
else if (current === "--model") args.model = argv[++i] ?? args.model;
|
||||
else if (current === "--ar") args.aspectRatio = argv[++i] ?? args.aspectRatio;
|
||||
else if (current === "--quality") args.quality = argv[++i] ?? args.quality;
|
||||
else if (current === "--jobs") {
|
||||
const value = argv[++i];
|
||||
args.jobs = value ? parseInt(value, 10) : null;
|
||||
} else if (current === "--help" || current === "-h") {
|
||||
args.help = true;
|
||||
}
|
||||
}
|
||||
return args;
|
||||
}
|
||||
|
||||
function parsePromptReferences(content: string): PromptReference[] {
|
||||
const fmMatch = content.match(/^---\s*\n([\s\S]*?)\n---\s*(?:\n|$)/);
|
||||
if (!fmMatch) return [];
|
||||
const lines = fmMatch[1]!.split(/\r?\n/);
|
||||
|
||||
const refs: PromptReference[] = [];
|
||||
let current: Partial<PromptReference> | null = null;
|
||||
let inReferences = false;
|
||||
let listIndent = 0;
|
||||
|
||||
const flush = () => {
|
||||
if (current?.filename) {
|
||||
refs.push({
|
||||
filename: current.filename,
|
||||
usage: (current.usage ?? "direct") as PromptReference["usage"],
|
||||
});
|
||||
}
|
||||
current = null;
|
||||
};
|
||||
|
||||
const unquote = (raw: string): string => raw.trim().replace(/^["']|["']$/g, "");
|
||||
|
||||
for (const line of lines) {
|
||||
if (!line.trim() || line.trim().startsWith("#")) continue;
|
||||
|
||||
const keyMatch = line.match(/^(\S[^:]*):\s*(.*)$/);
|
||||
if (keyMatch) {
|
||||
flush();
|
||||
if (keyMatch[1] === "references") {
|
||||
inReferences = true;
|
||||
listIndent = 0;
|
||||
continue;
|
||||
}
|
||||
inReferences = false;
|
||||
continue;
|
||||
}
|
||||
|
||||
if (!inReferences) continue;
|
||||
|
||||
const itemMatch = line.match(/^(\s*)-\s*(.*)$/);
|
||||
if (itemMatch) {
|
||||
flush();
|
||||
listIndent = itemMatch[1]!.length;
|
||||
current = {};
|
||||
const rest = itemMatch[2]!.trim();
|
||||
if (rest) {
|
||||
const kv = rest.match(/^(\w+)\s*:\s*(.*)$/);
|
||||
if (kv && (kv[1] === "filename" || kv[1] === "usage")) {
|
||||
(current as Record<string, string>)[kv[1]] = unquote(kv[2]!);
|
||||
}
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
const kvMatch = line.match(/^(\s+)(\w+)\s*:\s*(.*)$/);
|
||||
if (kvMatch && kvMatch[1]!.length > listIndent && current) {
|
||||
if (kvMatch[2] === "filename" || kvMatch[2] === "usage") {
|
||||
(current as Record<string, string>)[kvMatch[2]!] = unquote(kvMatch[3]!);
|
||||
}
|
||||
}
|
||||
}
|
||||
flush();
|
||||
|
||||
return refs;
|
||||
}
|
||||
|
||||
function parseOutline(content: string): OutlineEntry[] {
|
||||
const entries: OutlineEntry[] = [];
|
||||
const blocks = content.split(/^## Illustration\s+/m).slice(1);
|
||||
|
||||
for (const block of blocks) {
|
||||
const indexMatch = block.match(/^(\d+)/);
|
||||
const filenameMatch = block.match(/\*\*Filename\*\*:\s*(.+)/);
|
||||
if (indexMatch && filenameMatch) {
|
||||
entries.push({
|
||||
index: parseInt(indexMatch[1]!, 10),
|
||||
filename: filenameMatch[1]!.trim(),
|
||||
});
|
||||
}
|
||||
}
|
||||
return entries;
|
||||
}
|
||||
|
||||
async function findPromptFile(promptsDir: string, entry: OutlineEntry): Promise<string | null> {
|
||||
const files = await readdir(promptsDir);
|
||||
const prefix = String(entry.index).padStart(2, "0");
|
||||
const match = files.find((f) => f.startsWith(prefix) && f.endsWith(".md"));
|
||||
return match ? path.join(promptsDir, match) : null;
|
||||
}
|
||||
|
||||
async function main(): Promise<void> {
|
||||
const args = parseArgs(process.argv.slice(2));
|
||||
if (args.help) {
|
||||
printUsage();
|
||||
return;
|
||||
}
|
||||
|
||||
if (!args.outlinePath) {
|
||||
console.error("Error: --outline is required");
|
||||
process.exit(1);
|
||||
}
|
||||
if (!args.promptsDir) {
|
||||
console.error("Error: --prompts is required");
|
||||
process.exit(1);
|
||||
}
|
||||
if (!args.outputPath) {
|
||||
console.error("Error: --output is required");
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
const outlineContent = await readFile(args.outlinePath, "utf8");
|
||||
const entries = parseOutline(outlineContent);
|
||||
|
||||
if (entries.length === 0) {
|
||||
console.error("No illustration entries found in outline.");
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
const tasks = [];
|
||||
for (const entry of entries) {
|
||||
const promptFile = await findPromptFile(args.promptsDir, entry);
|
||||
if (!promptFile) {
|
||||
console.error(`Warning: No prompt file found for illustration ${entry.index}, skipping.`);
|
||||
continue;
|
||||
}
|
||||
|
||||
const imageDir = args.imagesDir ?? path.dirname(args.outputPath);
|
||||
const promptContent = await readFile(promptFile, "utf8");
|
||||
const refs = parsePromptReferences(promptContent)
|
||||
.filter((r) => r.usage === "direct")
|
||||
.map((r) => path.posix.join(args.refsDir, r.filename));
|
||||
|
||||
const task: Record<string, unknown> = {
|
||||
id: `illustration-${String(entry.index).padStart(2, "0")}`,
|
||||
promptFiles: [promptFile],
|
||||
image: path.join(imageDir, entry.filename),
|
||||
provider: args.provider,
|
||||
ar: args.aspectRatio,
|
||||
quality: args.quality,
|
||||
};
|
||||
if (args.model) task.model = args.model;
|
||||
if (refs.length > 0) task.ref = refs;
|
||||
tasks.push(task);
|
||||
}
|
||||
|
||||
const output: Record<string, unknown> = { tasks };
|
||||
if (args.jobs) output.jobs = args.jobs;
|
||||
|
||||
await writeFile(args.outputPath, JSON.stringify(output, null, 2) + "\n");
|
||||
console.log(`Batch file written: ${args.outputPath} (${tasks.length} tasks)`);
|
||||
}
|
||||
|
||||
main().catch((error) => {
|
||||
console.error(error instanceof Error ? error.message : String(error));
|
||||
process.exit(1);
|
||||
});
|
||||
@@ -0,0 +1,80 @@
|
||||
import { createHash } from "node:crypto";
|
||||
import { mkdir, readFile, writeFile, copyFile, stat } from "node:fs/promises";
|
||||
import { existsSync, openSync, closeSync } from "node:fs";
|
||||
import path from "node:path";
|
||||
import { setTimeout as delay } from "node:timers/promises";
|
||||
|
||||
export function cacheKey(prompt: string, aspect: string, refs: string[]): string {
|
||||
const h = createHash("sha256");
|
||||
h.update(prompt);
|
||||
h.update("|");
|
||||
h.update(aspect);
|
||||
h.update("|");
|
||||
for (const r of [...refs].sort()) h.update(r);
|
||||
return h.digest("hex").slice(0, 16);
|
||||
}
|
||||
|
||||
export async function lookupCache(cacheDir: string, key: string): Promise<string | null> {
|
||||
const entry = path.join(cacheDir, `${key}.png`);
|
||||
try {
|
||||
const s = await stat(entry);
|
||||
if (s.size > 1000) return entry;
|
||||
} catch {}
|
||||
return null;
|
||||
}
|
||||
|
||||
export async function storeCache(cacheDir: string, key: string, sourcePath: string): Promise<void> {
|
||||
await mkdir(cacheDir, { recursive: true });
|
||||
const entry = path.join(cacheDir, `${key}.png`);
|
||||
await copyFile(sourcePath, entry);
|
||||
}
|
||||
|
||||
export class FileLock {
|
||||
private fd: number | null = null;
|
||||
constructor(private lockPath: string) {}
|
||||
|
||||
async acquire(timeoutMs = 30_000): Promise<void> {
|
||||
const start = Date.now();
|
||||
await mkdir(path.dirname(this.lockPath), { recursive: true });
|
||||
while (Date.now() - start < timeoutMs) {
|
||||
try {
|
||||
this.fd = openSync(this.lockPath, "wx");
|
||||
return;
|
||||
} catch (e: any) {
|
||||
if (e.code !== "EEXIST") throw e;
|
||||
if (await this.isStale()) {
|
||||
try {
|
||||
await this.release(true);
|
||||
} catch {}
|
||||
continue;
|
||||
}
|
||||
await delay(200);
|
||||
}
|
||||
}
|
||||
throw new Error(`Failed to acquire lock at ${this.lockPath} within ${timeoutMs}ms`);
|
||||
}
|
||||
|
||||
private async isStale(): Promise<boolean> {
|
||||
try {
|
||||
const s = await stat(this.lockPath);
|
||||
return Date.now() - s.mtimeMs > 10 * 60 * 1000;
|
||||
} catch {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
async release(force = false): Promise<void> {
|
||||
if (this.fd != null) {
|
||||
try {
|
||||
closeSync(this.fd);
|
||||
} catch {}
|
||||
this.fd = null;
|
||||
}
|
||||
if (existsSync(this.lockPath) || force) {
|
||||
const { unlink } = await import("node:fs/promises");
|
||||
try {
|
||||
await unlink(this.lockPath);
|
||||
} catch {}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
import { appendFile, mkdir } from "node:fs/promises";
|
||||
import path from "node:path";
|
||||
|
||||
export interface LogEntry {
|
||||
ts: string;
|
||||
level: "info" | "warn" | "error";
|
||||
event: string;
|
||||
[k: string]: unknown;
|
||||
}
|
||||
|
||||
export class JsonLogger {
|
||||
constructor(private logFile: string | null, public verbose: boolean) {}
|
||||
|
||||
async log(level: LogEntry["level"], event: string, extra: Record<string, unknown> = {}): Promise<void> {
|
||||
const entry: LogEntry = { ts: new Date().toISOString(), level, event, ...extra };
|
||||
const line = JSON.stringify(entry);
|
||||
if (this.verbose) process.stderr.write(`[${level}] ${event} ${jsonExtras(extra)}\n`);
|
||||
if (this.logFile) {
|
||||
await mkdir(path.dirname(this.logFile), { recursive: true });
|
||||
await appendFile(this.logFile, line + "\n", "utf-8");
|
||||
}
|
||||
}
|
||||
|
||||
info(event: string, extra?: Record<string, unknown>) {
|
||||
return this.log("info", event, extra);
|
||||
}
|
||||
warn(event: string, extra?: Record<string, unknown>) {
|
||||
return this.log("warn", event, extra);
|
||||
}
|
||||
error(event: string, extra?: Record<string, unknown>) {
|
||||
return this.log("error", event, extra);
|
||||
}
|
||||
}
|
||||
|
||||
function jsonExtras(extra: Record<string, unknown>): string {
|
||||
const entries = Object.entries(extra);
|
||||
if (entries.length === 0) return "";
|
||||
return entries.map(([k, v]) => `${k}=${typeof v === "string" ? v : JSON.stringify(v)}`).join(" ");
|
||||
}
|
||||
@@ -0,0 +1,332 @@
|
||||
#!/usr/bin/env bun
|
||||
import { readFile, mkdir, copyFile, stat } from "node:fs/promises";
|
||||
import { homedir } from "node:os";
|
||||
import path from "node:path";
|
||||
import process from "node:process";
|
||||
import { setTimeout as delay } from "node:timers/promises";
|
||||
import { GenError, type CliOptions, type GenerateResult } from "./types.ts";
|
||||
import { runCodexExec } from "./spawn.ts";
|
||||
import { hasImageGenEvidence, verifyImageGenWasInvoked, verifyOutput } from "./validator.ts";
|
||||
import { cacheKey, lookupCache, storeCache, FileLock } from "./cache.ts";
|
||||
import { JsonLogger } from "./logger.ts";
|
||||
|
||||
const HELP = `codex-imagegen — generate images via Codex CLI's image_gen tool
|
||||
|
||||
Usage:
|
||||
codex-imagegen --image <output.png> [--prompt <text> | --prompt-file <path>] [options]
|
||||
|
||||
Required:
|
||||
--image <path> Output PNG path
|
||||
--prompt <text> Prompt text (or use --prompt-file)
|
||||
--prompt-file <path> Read prompt from file
|
||||
|
||||
Options:
|
||||
--aspect <ratio> Aspect ratio (1:1, 16:9, 9:16, 4:3, 2.35:1). Default: 1:1
|
||||
--ref <file> Reference image (repeatable)
|
||||
--timeout <ms> Codex exec timeout in ms. Default: 300000
|
||||
--retries <n> Retry attempts on retryable errors. Default: 2
|
||||
--retry-delay <ms> Base retry delay (exponential). Default: 1500
|
||||
--cache-dir <path> Enable idempotency cache. Disabled by default.
|
||||
--log-file <path> Append JSONL log
|
||||
-v, --verbose Verbose stderr logging
|
||||
-h, --help Show this help
|
||||
|
||||
Stdout: single JSON line on success or failure.
|
||||
`;
|
||||
|
||||
const SHELL_METACHAR = /[;|&`$<>\n\r()'"]/;
|
||||
|
||||
function assertSafePath(label: string, value: string): void {
|
||||
if (SHELL_METACHAR.test(value)) {
|
||||
throw new GenError(
|
||||
"invalid_args",
|
||||
`${label} contains shell metacharacters and would be unsafe to interpolate into the codex instruction: ${value}`,
|
||||
false,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
function parseArgs(argv: string[]): CliOptions {
|
||||
const opts: CliOptions = {
|
||||
prompt: "",
|
||||
promptFile: null,
|
||||
outputPath: "",
|
||||
aspect: "1:1",
|
||||
refImages: [],
|
||||
timeoutMs: 300_000,
|
||||
retries: 2,
|
||||
retryDelayMs: 1500,
|
||||
cacheDir: null,
|
||||
logFile: null,
|
||||
verbose: false,
|
||||
};
|
||||
for (let i = 0; i < argv.length; i++) {
|
||||
const a = argv[i];
|
||||
const next = () => argv[++i];
|
||||
switch (a) {
|
||||
case "--prompt": opts.prompt = next(); break;
|
||||
case "--prompt-file": opts.promptFile = next(); break;
|
||||
case "--image": opts.outputPath = next(); break;
|
||||
case "--aspect": opts.aspect = next(); break;
|
||||
case "--ref": opts.refImages.push(next()); break;
|
||||
case "--timeout": opts.timeoutMs = Number(next()); break;
|
||||
case "--retries": opts.retries = Number(next()); break;
|
||||
case "--retry-delay": opts.retryDelayMs = Number(next()); break;
|
||||
case "--cache-dir": opts.cacheDir = next(); break;
|
||||
case "--log-file": opts.logFile = next(); break;
|
||||
case "-v":
|
||||
case "--verbose": opts.verbose = true; break;
|
||||
case "-h":
|
||||
case "--help": process.stdout.write(HELP); process.exit(0);
|
||||
default: throw new GenError("invalid_args", `Unknown argument: ${a}`, false);
|
||||
}
|
||||
}
|
||||
if (!opts.outputPath) throw new GenError("invalid_args", "--image is required", false);
|
||||
if (opts.prompt && opts.promptFile) {
|
||||
throw new GenError("invalid_args", "--prompt and --prompt-file are mutually exclusive", false);
|
||||
}
|
||||
if (!opts.prompt && !opts.promptFile) {
|
||||
throw new GenError("invalid_args", "--prompt or --prompt-file required", false);
|
||||
}
|
||||
|
||||
// Resolve every filesystem path to absolute up front, so behavior is
|
||||
// independent of the caller's cwd. This matters when the wrapper is
|
||||
// invoked from a skill running in an arbitrary working directory.
|
||||
const cwd = process.cwd();
|
||||
const toAbs = (p: string) => (path.isAbsolute(p) ? p : path.resolve(cwd, p));
|
||||
|
||||
opts.outputPath = toAbs(opts.outputPath);
|
||||
if (opts.promptFile) opts.promptFile = toAbs(opts.promptFile);
|
||||
opts.refImages = opts.refImages.map(toAbs);
|
||||
if (opts.cacheDir) opts.cacheDir = toAbs(opts.cacheDir);
|
||||
if (opts.logFile) opts.logFile = toAbs(opts.logFile);
|
||||
|
||||
// The output and ref paths are interpolated raw into the agent instruction
|
||||
// sent to `codex exec --sandbox danger-full-access`. A path containing shell
|
||||
// metacharacters could be misread by the agent's shell when it cp's the
|
||||
// result into place. Reject upfront rather than trusting the agent to quote.
|
||||
assertSafePath("--image path", opts.outputPath);
|
||||
for (const ref of opts.refImages) assertSafePath("--ref path", ref);
|
||||
|
||||
return opts;
|
||||
}
|
||||
|
||||
async function loadPrompt(opts: CliOptions): Promise<string> {
|
||||
if (opts.prompt) return opts.prompt;
|
||||
const file = opts.promptFile!;
|
||||
try {
|
||||
return await readFile(file, "utf-8");
|
||||
} catch {
|
||||
throw new GenError("prompt_file_missing", `Prompt file not found: ${file}`, false);
|
||||
}
|
||||
}
|
||||
|
||||
function buildInstruction(prompt: string, opts: CliOptions): string {
|
||||
const refHint = opts.refImages.length > 0
|
||||
? `\nREFERENCE IMAGES (attached above): ${opts.refImages.length} image(s) provided for style/composition guidance.\n`
|
||||
: "";
|
||||
return `You have an internal tool called image_gen for image generation. You MUST call it before doing anything else.
|
||||
|
||||
TASK: Generate an image with the spec below, then save to disk.
|
||||
|
||||
PROMPT:
|
||||
${prompt}
|
||||
|
||||
ASPECT RATIO: ${opts.aspect}
|
||||
OUTPUT PATH: ${opts.outputPath}
|
||||
${refHint}
|
||||
STEPS:
|
||||
1. Call image_gen with the prompt and aspect ratio above${opts.refImages.length > 0 ? " (using the attached reference images for guidance)" : ""}.
|
||||
2. Move or copy ONLY the image produced by that image_gen call from Codex default location ($CODEX_HOME/generated_images/...) to: ${opts.outputPath}
|
||||
3. Verify with: ls -la ${opts.outputPath}
|
||||
4. Reply with ONLY this JSON line (no markdown fences, no other text):
|
||||
{"status":"ok","path":"${opts.outputPath}","bytes":<file_size_in_bytes>}
|
||||
|
||||
HARD CONSTRAINTS:
|
||||
- Do NOT search for, find, inspect, reuse, or copy any pre-existing files from $CODEX_HOME/generated_images/ or any other directory.
|
||||
- Do NOT run ls/find/rg/grep/glob over $CODEX_HOME/generated_images/ before image_gen has been called.
|
||||
- You MUST call image_gen first. Only after image_gen completes may you copy the newly created file from this turn.
|
||||
- Do NOT use curl, wget, Python, or any external API.
|
||||
- Do NOT use bash to fabricate an image; only image_gen produces real pixels.
|
||||
- Use ONLY the image_gen internal tool.`;
|
||||
}
|
||||
|
||||
async function attemptGenerate(
|
||||
opts: CliOptions,
|
||||
instruction: string,
|
||||
attempt: number,
|
||||
log: JsonLogger,
|
||||
): Promise<{ bytes: number; threadId: string | null; usage: any; toolCalls: any[] }> {
|
||||
await log.info("attempt.start", { attempt, output: opts.outputPath, aspect: opts.aspect });
|
||||
|
||||
const run = await runCodexExec({
|
||||
instruction,
|
||||
timeoutMs: opts.timeoutMs,
|
||||
refImages: opts.refImages,
|
||||
});
|
||||
|
||||
await log.info("codex.completed", {
|
||||
duration_ms: run.durationMs,
|
||||
thread_id: run.threadId,
|
||||
tool_calls: run.toolCalls.length,
|
||||
usage: run.usage,
|
||||
raw_log: run.rawLogPath,
|
||||
});
|
||||
|
||||
// verify: thread id must be present
|
||||
if (!run.threadId) {
|
||||
throw new GenError("agent_refused", "No thread id in event stream");
|
||||
}
|
||||
|
||||
// verify image_gen ran in THIS thread. A PNG in this thread's
|
||||
// generated_images dir is the real signal (image_gen does not surface as a
|
||||
// stream item); the stream check is a forward-compatible fallback. The #185
|
||||
// shortcut (copying an unrelated history image) yields neither.
|
||||
const ver = await verifyImageGenWasInvoked(run.threadId);
|
||||
if (!hasImageGenEvidence(run.toolCalls, ver.ok)) {
|
||||
throw new GenError(
|
||||
"no_image_gen_tool_use",
|
||||
`image_gen was not invoked (no image_gen event in stream; ${ver.reason})`,
|
||||
);
|
||||
}
|
||||
|
||||
// verify output
|
||||
const { bytes } = await verifyOutput(opts.outputPath);
|
||||
|
||||
return {
|
||||
bytes,
|
||||
threadId: run.threadId,
|
||||
usage: run.usage,
|
||||
toolCalls: run.toolCalls.map((tc) => ({ tool: tc.tool, status: tc.status })),
|
||||
};
|
||||
}
|
||||
|
||||
async function generate(opts: CliOptions, log: JsonLogger): Promise<GenerateResult> {
|
||||
const startEpoch = Date.now();
|
||||
const prompt = await loadPrompt(opts);
|
||||
|
||||
// Cache lookup
|
||||
if (opts.cacheDir) {
|
||||
const key = cacheKey(prompt, opts.aspect, opts.refImages);
|
||||
const cached = await lookupCache(opts.cacheDir, key);
|
||||
if (cached) {
|
||||
await mkdir(path.dirname(opts.outputPath), { recursive: true });
|
||||
await copyFile(cached, opts.outputPath);
|
||||
const s = await stat(opts.outputPath);
|
||||
await log.info("cache.hit", { key, source: cached });
|
||||
return {
|
||||
status: "ok",
|
||||
path: opts.outputPath,
|
||||
bytes: s.size,
|
||||
elapsed_seconds: 0,
|
||||
thread_id: null,
|
||||
attempts: 0,
|
||||
cached: true,
|
||||
usage: null,
|
||||
tool_calls: [],
|
||||
};
|
||||
}
|
||||
await log.info("cache.miss", { key });
|
||||
}
|
||||
|
||||
// lock to prevent concurrent codex exec
|
||||
const lockDir = opts.cacheDir ?? path.join(homedir(), ".cache", "baoyu-codex-imagegen");
|
||||
const lock = new FileLock(path.join(lockDir, "codex-exec.lock"));
|
||||
try {
|
||||
await lock.acquire(60_000);
|
||||
} catch (e) {
|
||||
throw new GenError("lock_busy", String(e), false);
|
||||
}
|
||||
|
||||
await mkdir(path.dirname(opts.outputPath), { recursive: true });
|
||||
const instruction = buildInstruction(prompt, opts);
|
||||
|
||||
let lastErr: GenError | null = null;
|
||||
let lastAttempt = 0;
|
||||
try {
|
||||
for (let attempt = 1; attempt <= opts.retries + 1; attempt++) {
|
||||
lastAttempt = attempt;
|
||||
try {
|
||||
const result = await attemptGenerate(opts, instruction, attempt, log);
|
||||
|
||||
// write to cache
|
||||
if (opts.cacheDir) {
|
||||
const key = cacheKey(prompt, opts.aspect, opts.refImages);
|
||||
await storeCache(opts.cacheDir, key, opts.outputPath);
|
||||
await log.info("cache.stored", { key });
|
||||
}
|
||||
|
||||
return {
|
||||
status: "ok",
|
||||
path: opts.outputPath,
|
||||
bytes: result.bytes,
|
||||
elapsed_seconds: Math.round((Date.now() - startEpoch) / 1000),
|
||||
thread_id: result.threadId,
|
||||
attempts: attempt,
|
||||
cached: false,
|
||||
usage: result.usage,
|
||||
tool_calls: result.toolCalls,
|
||||
};
|
||||
} catch (e) {
|
||||
lastErr = e instanceof GenError ? e : new GenError("spawn_failed", String(e));
|
||||
await log.warn("attempt.failed", {
|
||||
attempt,
|
||||
kind: lastErr.kind,
|
||||
retryable: lastErr.retryable,
|
||||
error: lastErr.message,
|
||||
});
|
||||
if (!lastErr.retryable || attempt > opts.retries) break;
|
||||
const wait = opts.retryDelayMs * Math.pow(2, attempt - 1);
|
||||
await log.info("retry.wait", { wait_ms: wait, next_attempt: attempt + 1 });
|
||||
await delay(wait);
|
||||
}
|
||||
}
|
||||
} finally {
|
||||
await lock.release();
|
||||
}
|
||||
|
||||
const err = lastErr ?? new GenError("spawn_failed", "Unknown failure");
|
||||
err.attempts = lastAttempt;
|
||||
throw err;
|
||||
}
|
||||
|
||||
async function main() {
|
||||
let opts: CliOptions;
|
||||
try {
|
||||
opts = parseArgs(process.argv.slice(2));
|
||||
} catch (e) {
|
||||
const err = e instanceof GenError ? e : new GenError("invalid_args", String(e), false);
|
||||
process.stderr.write(`Error: ${err.message}\n`);
|
||||
process.exit(2);
|
||||
}
|
||||
|
||||
const log = new JsonLogger(opts.logFile, opts.verbose);
|
||||
await log.info("start", { output: opts.outputPath, aspect: opts.aspect, refs: opts.refImages.length });
|
||||
|
||||
try {
|
||||
const result = await generate(opts, log);
|
||||
await log.info("done", { bytes: result.bytes, attempts: result.attempts, cached: result.cached });
|
||||
process.stdout.write(JSON.stringify(result) + "\n");
|
||||
process.exit(0);
|
||||
} catch (e) {
|
||||
const err = e instanceof GenError ? e : new GenError("spawn_failed", String(e));
|
||||
await log.error("failed", { kind: err.kind, error: err.message, attempts: err.attempts ?? 0 });
|
||||
const out: GenerateResult = {
|
||||
status: "error",
|
||||
path: opts.outputPath,
|
||||
bytes: 0,
|
||||
elapsed_seconds: 0,
|
||||
thread_id: null,
|
||||
attempts: err.attempts ?? 0,
|
||||
cached: false,
|
||||
usage: null,
|
||||
tool_calls: [],
|
||||
error: err.message,
|
||||
error_kind: err.kind,
|
||||
};
|
||||
process.stdout.write(JSON.stringify(out) + "\n");
|
||||
process.exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
@@ -0,0 +1,64 @@
|
||||
import type { CodexRunResult, ToolCall, TokenUsage } from "./types.ts";
|
||||
|
||||
export function parseEventStream(raw: string): Omit<CodexRunResult, "rawLogPath" | "durationMs"> {
|
||||
const lines = raw.split("\n").filter((l) => l.trim().length > 0);
|
||||
let threadId: string | null = null;
|
||||
let agentMessage: string | null = null;
|
||||
let usage: TokenUsage | null = null;
|
||||
const toolCallsById = new Map<string, ToolCall>();
|
||||
|
||||
for (const line of lines) {
|
||||
let event: any;
|
||||
try {
|
||||
event = JSON.parse(line);
|
||||
} catch {
|
||||
continue;
|
||||
}
|
||||
const type = event?.type;
|
||||
if (type === "thread.started") {
|
||||
threadId = event.thread_id ?? null;
|
||||
} else if (type === "item.started" || type === "item.completed") {
|
||||
const item = event.item;
|
||||
if (!item?.id) continue;
|
||||
const tc: ToolCall = {
|
||||
id: item.id,
|
||||
tool: deriveToolName(item),
|
||||
status: item.status ?? (type === "item.completed" ? "completed" : "in_progress"),
|
||||
command: item.command,
|
||||
};
|
||||
toolCallsById.set(item.id, tc);
|
||||
if (item.type === "agent_message" && type === "item.completed") {
|
||||
agentMessage = String(item.text ?? "");
|
||||
}
|
||||
} else if (type === "turn.completed") {
|
||||
const u = event.usage;
|
||||
if (u) {
|
||||
usage = {
|
||||
input: u.input_tokens ?? 0,
|
||||
cached_input: u.cached_input_tokens ?? 0,
|
||||
output: u.output_tokens ?? 0,
|
||||
reasoning: u.reasoning_output_tokens ?? 0,
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
threadId,
|
||||
toolCalls: Array.from(toolCallsById.values()),
|
||||
agentMessage,
|
||||
usage,
|
||||
};
|
||||
}
|
||||
|
||||
function deriveToolName(item: any): string {
|
||||
if (item.type === "command_execution") return "shell";
|
||||
if (item.type === "agent_message") return "agent_message";
|
||||
if (item.type === "image_gen" || item.type === "image_generation") return "image_gen";
|
||||
if (typeof item.tool === "string") return item.tool;
|
||||
return item.type ?? "unknown";
|
||||
}
|
||||
|
||||
export function hasImageGenInvocation(toolCalls: ToolCall[]): boolean {
|
||||
return toolCalls.some((tc) => tc.tool === "image_gen");
|
||||
}
|
||||
@@ -0,0 +1,81 @@
|
||||
import { spawn } from "node:child_process";
|
||||
import { writeFile, mkdtemp } from "node:fs/promises";
|
||||
import { tmpdir } from "node:os";
|
||||
import path from "node:path";
|
||||
import { GenError, type CodexRunResult } from "./types.ts";
|
||||
import { parseEventStream } from "./parser.ts";
|
||||
|
||||
export interface SpawnInput {
|
||||
instruction: string;
|
||||
timeoutMs: number;
|
||||
refImages?: string[];
|
||||
}
|
||||
|
||||
export async function runCodexExec(input: SpawnInput): Promise<CodexRunResult> {
|
||||
const start = Date.now();
|
||||
const logDir = await mkdtemp(path.join(tmpdir(), "codex-imggen-"));
|
||||
const rawLogPath = path.join(logDir, "stream.jsonl");
|
||||
|
||||
// --skip-git-repo-check: lets the wrapper run from non-git cwds
|
||||
// (e.g. /tmp, or a skill installed under ~/.claude/plugins/...).
|
||||
// Without it, codex refuses with "Not inside a trusted directory".
|
||||
const args = [
|
||||
"exec",
|
||||
"--json",
|
||||
"--sandbox",
|
||||
"danger-full-access",
|
||||
"--skip-git-repo-check",
|
||||
];
|
||||
for (const img of input.refImages ?? []) {
|
||||
args.push("--image", img);
|
||||
}
|
||||
args.push("-");
|
||||
|
||||
let timedOut = false;
|
||||
const child = spawn("codex", args, { stdio: ["pipe", "pipe", "pipe"] });
|
||||
|
||||
let stdout = "";
|
||||
let stderr = "";
|
||||
child.stdout.on("data", (chunk) => {
|
||||
stdout += chunk.toString();
|
||||
});
|
||||
child.stderr.on("data", (chunk) => {
|
||||
stderr += chunk.toString();
|
||||
});
|
||||
|
||||
child.stdin.write(input.instruction);
|
||||
child.stdin.end();
|
||||
|
||||
const timer = setTimeout(() => {
|
||||
timedOut = true;
|
||||
child.kill("SIGTERM");
|
||||
setTimeout(() => child.kill("SIGKILL"), 2000);
|
||||
}, input.timeoutMs);
|
||||
|
||||
const exit = await new Promise<{ code: number | null; signal: NodeJS.Signals | null }>((resolve) => {
|
||||
child.on("close", (code, signal) => resolve({ code, signal }));
|
||||
});
|
||||
clearTimeout(timer);
|
||||
|
||||
await writeFile(rawLogPath, stdout + (stderr ? `\n--- stderr ---\n${stderr}` : ""));
|
||||
|
||||
if (timedOut) {
|
||||
throw new GenError("timeout", `codex exec exceeded ${input.timeoutMs}ms (log: ${rawLogPath})`);
|
||||
}
|
||||
if (exit.code !== 0) {
|
||||
if (stderr.includes("command not found") || stderr.includes("not found: codex")) {
|
||||
throw new GenError("codex_not_installed", "codex CLI not installed", false);
|
||||
}
|
||||
throw new GenError(
|
||||
"spawn_failed",
|
||||
`codex exec exited ${exit.code} signal=${exit.signal} (log: ${rawLogPath})`,
|
||||
);
|
||||
}
|
||||
|
||||
const parsed = parseEventStream(stdout);
|
||||
return {
|
||||
...parsed,
|
||||
rawLogPath,
|
||||
durationMs: Date.now() - start,
|
||||
};
|
||||
}
|
||||
@@ -0,0 +1,79 @@
|
||||
export interface CliOptions {
|
||||
prompt: string;
|
||||
promptFile: string | null;
|
||||
outputPath: string;
|
||||
aspect: string;
|
||||
refImages: string[];
|
||||
timeoutMs: number;
|
||||
retries: number;
|
||||
retryDelayMs: number;
|
||||
cacheDir: string | null;
|
||||
logFile: string | null;
|
||||
verbose: boolean;
|
||||
}
|
||||
|
||||
export interface ToolCall {
|
||||
id: string;
|
||||
tool: string;
|
||||
status: string;
|
||||
command?: string;
|
||||
}
|
||||
|
||||
export interface TokenUsage {
|
||||
input: number;
|
||||
cached_input: number;
|
||||
output: number;
|
||||
reasoning: number;
|
||||
}
|
||||
|
||||
export interface CodexRunResult {
|
||||
threadId: string | null;
|
||||
toolCalls: ToolCall[];
|
||||
agentMessage: string | null;
|
||||
usage: TokenUsage | null;
|
||||
rawLogPath: string;
|
||||
durationMs: number;
|
||||
}
|
||||
|
||||
export interface GenerateResult {
|
||||
status: "ok" | "error";
|
||||
path: string;
|
||||
bytes: number;
|
||||
elapsed_seconds: number;
|
||||
thread_id: string | null;
|
||||
attempts: number;
|
||||
cached: boolean;
|
||||
usage: TokenUsage | null;
|
||||
tool_calls: { tool: string; status: string }[];
|
||||
error?: string;
|
||||
error_kind?: ErrorKind;
|
||||
}
|
||||
|
||||
export type ErrorKind =
|
||||
| "codex_not_installed"
|
||||
| "invalid_args"
|
||||
| "prompt_file_missing"
|
||||
| "spawn_failed"
|
||||
| "timeout"
|
||||
| "no_image_gen_tool_use"
|
||||
| "output_missing"
|
||||
| "invalid_png"
|
||||
| "agent_refused"
|
||||
| "lock_busy";
|
||||
|
||||
export const RETRYABLE: ReadonlySet<ErrorKind> = new Set([
|
||||
"spawn_failed",
|
||||
"timeout",
|
||||
"no_image_gen_tool_use",
|
||||
"output_missing",
|
||||
"invalid_png",
|
||||
"agent_refused",
|
||||
]);
|
||||
|
||||
export class GenError extends Error {
|
||||
attempts?: number;
|
||||
constructor(public kind: ErrorKind, message: string, public retryable?: boolean) {
|
||||
super(message);
|
||||
this.retryable = retryable ?? RETRYABLE.has(kind);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,56 @@
|
||||
import { stat, readdir } from "node:fs/promises";
|
||||
import { homedir } from "node:os";
|
||||
import path from "node:path";
|
||||
import { GenError } from "./types.ts";
|
||||
import type { ToolCall } from "./types.ts";
|
||||
import { hasImageGenInvocation } from "./parser.ts";
|
||||
|
||||
const PNG_MAGIC = Buffer.from([0x89, 0x50, 0x4e, 0x47, 0x0d, 0x0a, 0x1a, 0x0a]);
|
||||
|
||||
export function codexHome(): string {
|
||||
return process.env.CODEX_HOME ?? path.join(homedir(), ".codex");
|
||||
}
|
||||
|
||||
export async function verifyImageGenWasInvoked(threadId: string | null): Promise<{ ok: boolean; reason?: string }> {
|
||||
if (!threadId) return { ok: false, reason: "no thread id" };
|
||||
const dir = path.join(codexHome(), "generated_images", threadId);
|
||||
try {
|
||||
const entries = await readdir(dir);
|
||||
const pngs = entries.filter((e) => e.toLowerCase().endsWith(".png"));
|
||||
if (pngs.length === 0) return { ok: false, reason: `no PNG in ${dir}` };
|
||||
return { ok: true };
|
||||
} catch (e: any) {
|
||||
return { ok: false, reason: `cannot read ${dir}: ${e?.code ?? e?.message}` };
|
||||
}
|
||||
}
|
||||
|
||||
// Real evidence that image_gen ran in THIS thread. Codex's image_gen tool does
|
||||
// not surface as a stream item, so a successful run shows only reasoning/shell/
|
||||
// agent_message — `dirHasImage` (a PNG in this thread's generated_images dir) is
|
||||
// what proves it. The stream check is kept as a forward-compatible signal in
|
||||
// case a future Codex version emits the item. The #185 shortcut (copying an
|
||||
// unrelated history image, which lives under a different thread id) yields
|
||||
// neither, so it is correctly rejected.
|
||||
export function hasImageGenEvidence(toolCalls: ToolCall[], dirHasImage: boolean): boolean {
|
||||
return dirHasImage || hasImageGenInvocation(toolCalls);
|
||||
}
|
||||
|
||||
export async function verifyOutput(outputPath: string): Promise<{ bytes: number }> {
|
||||
let s;
|
||||
try {
|
||||
s = await stat(outputPath);
|
||||
} catch {
|
||||
throw new GenError("output_missing", `Output file not created: ${outputPath}`);
|
||||
}
|
||||
if (s.size < 1000) {
|
||||
throw new GenError("invalid_png", `Output file too small (${s.size} bytes)`);
|
||||
}
|
||||
const file = Bun.file(outputPath);
|
||||
const head = new Uint8Array(await file.slice(0, 8).arrayBuffer());
|
||||
for (let i = 0; i < PNG_MAGIC.length; i++) {
|
||||
if (head[i] !== PNG_MAGIC[i]) {
|
||||
throw new GenError("invalid_png", `Output is not a valid PNG (magic mismatch)`);
|
||||
}
|
||||
}
|
||||
return { bytes: s.size };
|
||||
}
|
||||
644
baoyu-skills/skills/baoyu-image-gen/scripts/main.test.ts
Normal file
644
baoyu-skills/skills/baoyu-image-gen/scripts/main.test.ts
Normal file
@@ -0,0 +1,644 @@
|
||||
import assert from "node:assert/strict";
|
||||
import fs from "node:fs/promises";
|
||||
import os from "node:os";
|
||||
import path from "node:path";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import type { CliArgs, ExtendConfig } from "./types.ts";
|
||||
import {
|
||||
createTaskArgs,
|
||||
detectProvider,
|
||||
ensureDir,
|
||||
getConfiguredMaxWorkers,
|
||||
getConfiguredProviderRateLimits,
|
||||
getWorkerCount,
|
||||
isRetryableGenerationError,
|
||||
loadBatchTasks,
|
||||
loadExtendConfig,
|
||||
mergeConfig,
|
||||
normalizeOutputImagePath,
|
||||
parseArgs,
|
||||
parseOpenAIImageApiDialect,
|
||||
parseSimpleYaml,
|
||||
validateReferenceImages,
|
||||
} from "./main.ts";
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
aspectRatioSource: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageSizeSource: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
async function makeTempDir(prefix: string): Promise<string> {
|
||||
return fs.mkdtemp(path.join(os.tmpdir(), prefix));
|
||||
}
|
||||
|
||||
test("parseArgs parses the main baoyu-image-gen CLI flags", () => {
|
||||
const args = parseArgs([
|
||||
"--promptfiles",
|
||||
"prompts/system.md",
|
||||
"prompts/content.md",
|
||||
"--image",
|
||||
"out/hero",
|
||||
"--provider",
|
||||
"zai",
|
||||
"--quality",
|
||||
"2k",
|
||||
"--imageSize",
|
||||
"4k",
|
||||
"--imageApiDialect",
|
||||
"ratio-metadata",
|
||||
"--ref",
|
||||
"ref/one.png",
|
||||
"ref/two.jpg",
|
||||
"--n",
|
||||
"3",
|
||||
"--jobs",
|
||||
"5",
|
||||
"--json",
|
||||
]);
|
||||
|
||||
assert.deepEqual(args.promptFiles, ["prompts/system.md", "prompts/content.md"]);
|
||||
assert.equal(args.imagePath, "out/hero");
|
||||
assert.equal(args.provider, "zai");
|
||||
assert.equal(args.quality, "2k");
|
||||
assert.equal(args.aspectRatioSource, null);
|
||||
assert.equal(args.imageSize, "4K");
|
||||
assert.equal(args.imageSizeSource, "cli");
|
||||
assert.equal(args.imageApiDialect, "ratio-metadata");
|
||||
assert.deepEqual(args.referenceImages, ["ref/one.png", "ref/two.jpg"]);
|
||||
assert.equal(args.n, 3);
|
||||
assert.equal(args.jobs, 5);
|
||||
assert.equal(args.json, true);
|
||||
});
|
||||
|
||||
test("parseArgs falls back to positional prompt and rejects invalid provider", () => {
|
||||
const positional = parseArgs(["draw", "a", "cat"]);
|
||||
assert.equal(positional.prompt, "draw a cat");
|
||||
|
||||
assert.throws(
|
||||
() => parseArgs(["--provider", "stability"]),
|
||||
/Invalid provider/,
|
||||
);
|
||||
});
|
||||
|
||||
test("validateReferenceImages can skip remote URLs for providers that support them", async () => {
|
||||
await validateReferenceImages(["https://example.com/ref.png"], { allowRemoteUrls: true });
|
||||
|
||||
await assert.rejects(
|
||||
() => validateReferenceImages(["https://example.com/ref.png"]),
|
||||
/Reference image not found/,
|
||||
);
|
||||
});
|
||||
|
||||
test("parseSimpleYaml parses nested defaults and provider limits", () => {
|
||||
const yaml = `
|
||||
version: 2
|
||||
default_provider: openrouter
|
||||
default_quality: normal
|
||||
default_aspect_ratio: '16:9'
|
||||
default_image_size: 2K
|
||||
default_image_api_dialect: ratio-metadata
|
||||
default_model:
|
||||
google: gemini-3-pro-image
|
||||
openai: gpt-image-2.5-flare
|
||||
zai: glm-image
|
||||
azure: image-prod
|
||||
minimax: image-01
|
||||
batch:
|
||||
max_workers: 8
|
||||
provider_limits:
|
||||
google:
|
||||
concurrency: 2
|
||||
start_interval_ms: 900
|
||||
openai:
|
||||
concurrency: 4
|
||||
zai:
|
||||
concurrency: 2
|
||||
start_interval_ms: 1000
|
||||
minimax:
|
||||
concurrency: 2
|
||||
start_interval_ms: 1400
|
||||
azure:
|
||||
concurrency: 1
|
||||
start_interval_ms: 1500
|
||||
`;
|
||||
|
||||
const config = parseSimpleYaml(yaml);
|
||||
|
||||
assert.equal(config.version, 2);
|
||||
assert.equal(config.default_provider, "openrouter");
|
||||
assert.equal(config.default_quality, "normal");
|
||||
assert.equal(config.default_aspect_ratio, "16:9");
|
||||
assert.equal(config.default_image_size, "2K");
|
||||
assert.equal(config.default_image_api_dialect, "ratio-metadata");
|
||||
assert.equal(config.default_model?.google, "gemini-3-pro-image");
|
||||
assert.equal(config.default_model?.openai, "gpt-image-2.5-flare");
|
||||
assert.equal(config.default_model?.zai, "glm-image");
|
||||
assert.equal(config.default_model?.azure, "image-prod");
|
||||
assert.equal(config.default_model?.minimax, "image-01");
|
||||
assert.equal(config.batch?.max_workers, 8);
|
||||
assert.deepEqual(config.batch?.provider_limits?.google, {
|
||||
concurrency: 2,
|
||||
start_interval_ms: 900,
|
||||
});
|
||||
assert.deepEqual(config.batch?.provider_limits?.openai, {
|
||||
concurrency: 4,
|
||||
});
|
||||
assert.deepEqual(config.batch?.provider_limits?.zai, {
|
||||
concurrency: 2,
|
||||
start_interval_ms: 1000,
|
||||
});
|
||||
assert.deepEqual(config.batch?.provider_limits?.minimax, {
|
||||
concurrency: 2,
|
||||
start_interval_ms: 1400,
|
||||
});
|
||||
assert.deepEqual(config.batch?.provider_limits?.azure, {
|
||||
concurrency: 1,
|
||||
start_interval_ms: 1500,
|
||||
});
|
||||
});
|
||||
|
||||
test("ensureDir creates nested dirs, is idempotent on an existing dir, and rethrows for a non-directory", async (t: TestContext) => {
|
||||
const root = await makeTempDir("ensure-dir-");
|
||||
t.after(() => fs.rm(root, { recursive: true, force: true }));
|
||||
|
||||
const nested = path.join(root, "a", "b", "c");
|
||||
await ensureDir(nested);
|
||||
assert.equal((await fs.stat(nested)).isDirectory(), true);
|
||||
|
||||
// Idempotent: a second call on an existing directory must not throw. This is the
|
||||
// Bun-on-Windows regression the helper guards against (Bun wrongly throws EEXIST
|
||||
// for mkdir(existingDir, { recursive: true })).
|
||||
await ensureDir(nested);
|
||||
|
||||
// Rethrows when the path exists but is a file rather than a directory, so a real
|
||||
// EEXIST against a non-directory is not silently swallowed.
|
||||
const filePath = path.join(root, "not-a-dir");
|
||||
await fs.writeFile(filePath, "x");
|
||||
await assert.rejects(() => ensureDir(filePath));
|
||||
});
|
||||
|
||||
test("loadExtendConfig renames legacy EXTEND.md when the new path is missing", async () => {
|
||||
const root = await makeTempDir("baoyu-image-gen-extend-");
|
||||
const cwd = path.join(root, "project");
|
||||
const home = path.join(root, "home");
|
||||
const legacyPath = path.join(cwd, ".baoyu-skills", "baoyu-imagine", "EXTEND.md");
|
||||
const currentPath = path.join(cwd, ".baoyu-skills", "baoyu-image-gen", "EXTEND.md");
|
||||
|
||||
await fs.mkdir(path.dirname(legacyPath), { recursive: true });
|
||||
await fs.mkdir(home, { recursive: true });
|
||||
await fs.writeFile(legacyPath, `---
|
||||
default_provider: google
|
||||
default_quality: 2k
|
||||
---
|
||||
`);
|
||||
|
||||
const config = await loadExtendConfig(cwd, home);
|
||||
|
||||
assert.equal(config.default_provider, "google");
|
||||
assert.equal(config.default_quality, "2k");
|
||||
await fs.access(currentPath);
|
||||
await assert.rejects(() => fs.access(legacyPath));
|
||||
});
|
||||
|
||||
test("loadExtendConfig leaves legacy EXTEND.md untouched when both paths exist", async () => {
|
||||
const root = await makeTempDir("baoyu-image-gen-extend-dual-");
|
||||
const cwd = path.join(root, "project");
|
||||
const home = path.join(root, "home");
|
||||
const legacyPath = path.join(cwd, ".baoyu-skills", "baoyu-imagine", "EXTEND.md");
|
||||
const currentPath = path.join(cwd, ".baoyu-skills", "baoyu-image-gen", "EXTEND.md");
|
||||
|
||||
await fs.mkdir(path.dirname(legacyPath), { recursive: true });
|
||||
await fs.mkdir(path.dirname(currentPath), { recursive: true });
|
||||
await fs.mkdir(home, { recursive: true });
|
||||
await fs.writeFile(legacyPath, `---
|
||||
default_provider: google
|
||||
---
|
||||
`);
|
||||
await fs.writeFile(currentPath, `---
|
||||
default_provider: openai
|
||||
---
|
||||
`);
|
||||
|
||||
const config = await loadExtendConfig(cwd, home);
|
||||
|
||||
assert.equal(config.default_provider, "openai");
|
||||
assert.equal(await fs.readFile(legacyPath, "utf8"), `---
|
||||
default_provider: google
|
||||
---
|
||||
`);
|
||||
assert.equal(await fs.readFile(currentPath, "utf8"), `---
|
||||
default_provider: openai
|
||||
---
|
||||
`);
|
||||
});
|
||||
|
||||
test("mergeConfig only fills values missing from CLI args", () => {
|
||||
const merged = mergeConfig(
|
||||
makeArgs({
|
||||
provider: "openai",
|
||||
quality: null,
|
||||
aspectRatio: null,
|
||||
imageSize: "4K",
|
||||
}),
|
||||
{
|
||||
default_provider: "google",
|
||||
default_quality: "2k",
|
||||
default_aspect_ratio: "3:2",
|
||||
default_image_size: "2K",
|
||||
default_image_api_dialect: "ratio-metadata",
|
||||
} satisfies Partial<ExtendConfig>,
|
||||
);
|
||||
|
||||
assert.equal(merged.provider, "openai");
|
||||
assert.equal(merged.quality, "2k");
|
||||
assert.equal(merged.aspectRatio, "3:2");
|
||||
assert.equal(merged.aspectRatioSource, "config");
|
||||
assert.equal(merged.imageSize, "4K");
|
||||
assert.equal(merged.imageSizeSource, "cli");
|
||||
assert.equal(merged.imageApiDialect, "ratio-metadata");
|
||||
});
|
||||
|
||||
test("mergeConfig tags inherited imageSize defaults so providers can ignore incompatible config", () => {
|
||||
const merged = mergeConfig(
|
||||
makeArgs(),
|
||||
{
|
||||
default_image_size: "2K",
|
||||
} satisfies Partial<ExtendConfig>,
|
||||
);
|
||||
|
||||
assert.equal(merged.imageSize, "2K");
|
||||
assert.equal(merged.imageSizeSource, "config");
|
||||
});
|
||||
|
||||
test("mergeConfig falls back to OPENAI_IMAGE_API_DIALECT when CLI and EXTEND are unset", (t) => {
|
||||
useEnv(t, {
|
||||
OPENAI_IMAGE_API_DIALECT: "ratio-metadata",
|
||||
});
|
||||
|
||||
const merged = mergeConfig(makeArgs(), {});
|
||||
assert.equal(merged.imageApiDialect, "ratio-metadata");
|
||||
});
|
||||
|
||||
test("parseOpenAIImageApiDialect validates supported values", () => {
|
||||
assert.equal(parseOpenAIImageApiDialect("openai-native"), "openai-native");
|
||||
assert.equal(parseOpenAIImageApiDialect("ratio-metadata"), "ratio-metadata");
|
||||
assert.equal(parseOpenAIImageApiDialect(null), null);
|
||||
assert.throws(
|
||||
() => parseOpenAIImageApiDialect("gateway-magic"),
|
||||
/Invalid OpenAI image API dialect/,
|
||||
);
|
||||
});
|
||||
|
||||
test("detectProvider rejects non-ref-capable providers and prefers Google first when multiple keys exist", (t) => {
|
||||
assert.throws(
|
||||
() =>
|
||||
detectProvider(
|
||||
makeArgs({
|
||||
provider: "zai",
|
||||
referenceImages: ["ref.png"],
|
||||
}),
|
||||
),
|
||||
/Reference images require a ref-capable provider/,
|
||||
);
|
||||
|
||||
useEnv(t, {
|
||||
GOOGLE_API_KEY: "google-key",
|
||||
OPENAI_API_KEY: "openai-key",
|
||||
OPENROUTER_API_KEY: null,
|
||||
DASHSCOPE_API_KEY: null,
|
||||
MINIMAX_API_KEY: null,
|
||||
REPLICATE_API_TOKEN: null,
|
||||
JIMENG_ACCESS_KEY_ID: null,
|
||||
JIMENG_SECRET_ACCESS_KEY: null,
|
||||
ARK_API_KEY: null,
|
||||
});
|
||||
assert.equal(detectProvider(makeArgs()), "google");
|
||||
});
|
||||
|
||||
test("detectProvider selects an available ref-capable provider for reference-image tasks", (t) => {
|
||||
useEnv(t, {
|
||||
GOOGLE_API_KEY: null,
|
||||
OPENAI_API_KEY: "openai-key",
|
||||
AZURE_OPENAI_API_KEY: null,
|
||||
AZURE_OPENAI_BASE_URL: null,
|
||||
OPENROUTER_API_KEY: null,
|
||||
DASHSCOPE_API_KEY: null,
|
||||
MINIMAX_API_KEY: null,
|
||||
REPLICATE_API_TOKEN: null,
|
||||
JIMENG_ACCESS_KEY_ID: null,
|
||||
JIMENG_SECRET_ACCESS_KEY: null,
|
||||
ARK_API_KEY: null,
|
||||
});
|
||||
assert.equal(
|
||||
detectProvider(makeArgs({ referenceImages: ["ref.png"] })),
|
||||
"openai",
|
||||
);
|
||||
});
|
||||
|
||||
test("detectProvider selects Azure when only Azure credentials are configured", (t) => {
|
||||
useEnv(t, {
|
||||
GOOGLE_API_KEY: null,
|
||||
OPENAI_API_KEY: null,
|
||||
AZURE_OPENAI_API_KEY: "azure-key",
|
||||
AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com",
|
||||
OPENROUTER_API_KEY: null,
|
||||
DASHSCOPE_API_KEY: null,
|
||||
MINIMAX_API_KEY: null,
|
||||
REPLICATE_API_TOKEN: null,
|
||||
JIMENG_ACCESS_KEY_ID: null,
|
||||
JIMENG_SECRET_ACCESS_KEY: null,
|
||||
ARK_API_KEY: null,
|
||||
});
|
||||
|
||||
assert.equal(detectProvider(makeArgs()), "azure");
|
||||
assert.equal(
|
||||
detectProvider(makeArgs({ referenceImages: ["ref.png"] })),
|
||||
"azure",
|
||||
);
|
||||
});
|
||||
|
||||
test("detectProvider selects Z.AI when credentials are present or the model id matches", (t) => {
|
||||
useEnv(t, {
|
||||
GOOGLE_API_KEY: null,
|
||||
OPENAI_API_KEY: null,
|
||||
AZURE_OPENAI_API_KEY: null,
|
||||
AZURE_OPENAI_BASE_URL: null,
|
||||
OPENROUTER_API_KEY: null,
|
||||
DASHSCOPE_API_KEY: null,
|
||||
ZAI_API_KEY: "zai-key",
|
||||
BIGMODEL_API_KEY: null,
|
||||
MINIMAX_API_KEY: null,
|
||||
REPLICATE_API_TOKEN: null,
|
||||
JIMENG_ACCESS_KEY_ID: null,
|
||||
JIMENG_SECRET_ACCESS_KEY: null,
|
||||
ARK_API_KEY: null,
|
||||
});
|
||||
|
||||
assert.equal(detectProvider(makeArgs()), "zai");
|
||||
assert.equal(detectProvider(makeArgs({ model: "glm-image" })), "zai");
|
||||
});
|
||||
|
||||
test("detectProvider infers Seedream from model id and allows Seedream reference-image workflows", (t) => {
|
||||
useEnv(t, {
|
||||
GOOGLE_API_KEY: null,
|
||||
OPENAI_API_KEY: null,
|
||||
OPENROUTER_API_KEY: null,
|
||||
DASHSCOPE_API_KEY: null,
|
||||
MINIMAX_API_KEY: null,
|
||||
REPLICATE_API_TOKEN: null,
|
||||
JIMENG_ACCESS_KEY_ID: null,
|
||||
JIMENG_SECRET_ACCESS_KEY: null,
|
||||
ARK_API_KEY: "ark-key",
|
||||
});
|
||||
|
||||
assert.equal(
|
||||
detectProvider(
|
||||
makeArgs({
|
||||
model: "doubao-seedream-4-5-251128",
|
||||
referenceImages: ["ref.png"],
|
||||
}),
|
||||
),
|
||||
"seedream",
|
||||
);
|
||||
|
||||
assert.equal(
|
||||
detectProvider(
|
||||
makeArgs({
|
||||
provider: "seedream",
|
||||
referenceImages: ["ref.png"],
|
||||
}),
|
||||
),
|
||||
"seedream",
|
||||
);
|
||||
});
|
||||
|
||||
test("detectProvider allows DashScope reference-image workflows when explicitly chosen for wan2.7 models", (t) => {
|
||||
useEnv(t, {
|
||||
GOOGLE_API_KEY: null,
|
||||
OPENAI_API_KEY: null,
|
||||
AZURE_OPENAI_API_KEY: null,
|
||||
AZURE_OPENAI_BASE_URL: null,
|
||||
OPENROUTER_API_KEY: null,
|
||||
DASHSCOPE_API_KEY: "dashscope-key",
|
||||
MINIMAX_API_KEY: null,
|
||||
REPLICATE_API_TOKEN: null,
|
||||
JIMENG_ACCESS_KEY_ID: null,
|
||||
JIMENG_SECRET_ACCESS_KEY: null,
|
||||
ARK_API_KEY: null,
|
||||
});
|
||||
|
||||
assert.equal(
|
||||
detectProvider(
|
||||
makeArgs({
|
||||
provider: "dashscope",
|
||||
model: "wan2.7-image-pro",
|
||||
referenceImages: ["ref.png"],
|
||||
}),
|
||||
),
|
||||
"dashscope",
|
||||
);
|
||||
});
|
||||
|
||||
test("detectProvider selects MiniMax when only MiniMax credentials are configured or the model id matches", (t) => {
|
||||
useEnv(t, {
|
||||
GOOGLE_API_KEY: null,
|
||||
OPENAI_API_KEY: null,
|
||||
AZURE_OPENAI_API_KEY: null,
|
||||
AZURE_OPENAI_BASE_URL: null,
|
||||
OPENROUTER_API_KEY: null,
|
||||
DASHSCOPE_API_KEY: null,
|
||||
MINIMAX_API_KEY: "minimax-key",
|
||||
REPLICATE_API_TOKEN: null,
|
||||
JIMENG_ACCESS_KEY_ID: null,
|
||||
JIMENG_SECRET_ACCESS_KEY: null,
|
||||
ARK_API_KEY: null,
|
||||
});
|
||||
|
||||
assert.equal(detectProvider(makeArgs()), "minimax");
|
||||
assert.equal(detectProvider(makeArgs({ referenceImages: ["ref.png"] })), "minimax");
|
||||
assert.equal(detectProvider(makeArgs({ model: "image-01-live" })), "minimax");
|
||||
});
|
||||
|
||||
test("batch worker and provider-rate-limit configuration prefer env over EXTEND config", (t) => {
|
||||
useEnv(t, {
|
||||
BAOYU_IMAGE_GEN_MAX_WORKERS: "12",
|
||||
BAOYU_IMAGE_GEN_GOOGLE_CONCURRENCY: "5",
|
||||
BAOYU_IMAGE_GEN_GOOGLE_START_INTERVAL_MS: "450",
|
||||
BAOYU_IMAGE_GEN_ZAI_CONCURRENCY: "4",
|
||||
});
|
||||
|
||||
const extendConfig: Partial<ExtendConfig> = {
|
||||
batch: {
|
||||
max_workers: 7,
|
||||
provider_limits: {
|
||||
google: {
|
||||
concurrency: 2,
|
||||
start_interval_ms: 900,
|
||||
},
|
||||
zai: {
|
||||
concurrency: 1,
|
||||
start_interval_ms: 1200,
|
||||
},
|
||||
minimax: {
|
||||
concurrency: 1,
|
||||
start_interval_ms: 1500,
|
||||
},
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
assert.equal(getConfiguredMaxWorkers(extendConfig), 12);
|
||||
assert.deepEqual(getConfiguredProviderRateLimits(extendConfig).google, {
|
||||
concurrency: 5,
|
||||
startIntervalMs: 450,
|
||||
});
|
||||
assert.deepEqual(getConfiguredProviderRateLimits(extendConfig).zai, {
|
||||
concurrency: 4,
|
||||
startIntervalMs: 1200,
|
||||
});
|
||||
assert.deepEqual(getConfiguredProviderRateLimits(extendConfig).minimax, {
|
||||
concurrency: 1,
|
||||
startIntervalMs: 1500,
|
||||
});
|
||||
});
|
||||
|
||||
test("loadBatchTasks and createTaskArgs resolve batch-relative paths", async (t) => {
|
||||
const root = await makeTempDir("baoyu-image-gen-batch-");
|
||||
t.after(() => fs.rm(root, { recursive: true, force: true }));
|
||||
|
||||
const batchFile = path.join(root, "jobs", "batch.json");
|
||||
await fs.mkdir(path.dirname(batchFile), { recursive: true });
|
||||
await fs.writeFile(
|
||||
batchFile,
|
||||
JSON.stringify({
|
||||
jobs: 2,
|
||||
tasks: [
|
||||
{
|
||||
id: "hero",
|
||||
promptFiles: ["prompts/hero.md"],
|
||||
image: "out/hero",
|
||||
ref: ["refs/hero.png", "https://example.com/ref.png"],
|
||||
ar: "16:9",
|
||||
},
|
||||
],
|
||||
}),
|
||||
);
|
||||
|
||||
const loaded = await loadBatchTasks(batchFile);
|
||||
assert.equal(loaded.jobs, 2);
|
||||
assert.equal(loaded.batchDir, path.dirname(batchFile));
|
||||
assert.equal(loaded.tasks[0]?.id, "hero");
|
||||
|
||||
const taskArgs = createTaskArgs(
|
||||
makeArgs({
|
||||
provider: "replicate",
|
||||
quality: "2k",
|
||||
imageApiDialect: "ratio-metadata",
|
||||
json: true,
|
||||
}),
|
||||
loaded.tasks[0]!,
|
||||
loaded.batchDir,
|
||||
);
|
||||
|
||||
assert.deepEqual(taskArgs.promptFiles, [
|
||||
path.join(loaded.batchDir, "prompts/hero.md"),
|
||||
]);
|
||||
assert.equal(taskArgs.imagePath, path.join(loaded.batchDir, "out/hero"));
|
||||
assert.deepEqual(taskArgs.referenceImages, [
|
||||
path.join(loaded.batchDir, "refs/hero.png"),
|
||||
"https://example.com/ref.png",
|
||||
]);
|
||||
assert.equal(taskArgs.provider, "replicate");
|
||||
assert.equal(taskArgs.aspectRatio, "16:9");
|
||||
assert.equal(taskArgs.quality, "2k");
|
||||
assert.equal(taskArgs.imageApiDialect, "ratio-metadata");
|
||||
assert.equal(taskArgs.json, true);
|
||||
});
|
||||
|
||||
test("path normalization, worker count, and retry classification follow expected rules", () => {
|
||||
assert.match(normalizeOutputImagePath("out/sample"), /out[\\/]+sample\.png$/);
|
||||
assert.match(normalizeOutputImagePath("out/sample", ".jpg"), /out[\\/]+sample\.jpg$/);
|
||||
assert.match(normalizeOutputImagePath("out/sample.webp"), /out[\\/]+sample\.webp$/);
|
||||
|
||||
assert.equal(getWorkerCount(8, null, 3), 3);
|
||||
assert.equal(getWorkerCount(2, 6, 5), 2);
|
||||
assert.equal(getWorkerCount(5, 0, 4), 1);
|
||||
|
||||
assert.equal(isRetryableGenerationError(new Error("API error (401): denied")), false);
|
||||
assert.equal(
|
||||
isRetryableGenerationError(
|
||||
new Error("Replicate returned 2 outputs, but baoyu-image-gen currently supports saving exactly one image per request."),
|
||||
),
|
||||
false,
|
||||
);
|
||||
assert.equal(
|
||||
isRetryableGenerationError(
|
||||
new Error("DashScope wan2.7 image models accept at most 9 reference images. Received 10."),
|
||||
),
|
||||
false,
|
||||
);
|
||||
assert.equal(
|
||||
isRetryableGenerationError(
|
||||
new Error("DashScope wan2.7 image models in baoyu-image-gen support exactly one output image per request."),
|
||||
),
|
||||
false,
|
||||
);
|
||||
assert.equal(
|
||||
isRetryableGenerationError(
|
||||
new Error("DashScope wan2.7 image models support aspect ratios in [1:8, 8:1]."),
|
||||
),
|
||||
false,
|
||||
);
|
||||
assert.equal(
|
||||
isRetryableGenerationError(
|
||||
new Error("DashScope wan2.7-image requires total pixels between 768*768 and 2048*2048."),
|
||||
),
|
||||
false,
|
||||
);
|
||||
assert.equal(isRetryableGenerationError(new Error("socket hang up")), true);
|
||||
});
|
||||
1293
baoyu-skills/skills/baoyu-image-gen/scripts/main.ts
Normal file
1293
baoyu-skills/skills/baoyu-image-gen/scripts/main.ts
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,213 @@
|
||||
import assert from "node:assert/strict";
|
||||
import fs from "node:fs/promises";
|
||||
import os from "node:os";
|
||||
import path from "node:path";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
buildRequestBody,
|
||||
extractImageFromResponse,
|
||||
parseAspectRatio,
|
||||
resolveReferenceImages,
|
||||
resolveSize,
|
||||
snapDim,
|
||||
validateArgs,
|
||||
} from "./agnes.ts";
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
responseFormat: null,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
test("snapDim rounds to the nearest multiple of 32", () => {
|
||||
assert.equal(snapDim(767), 768);
|
||||
assert.equal(snapDim(1023), 1024);
|
||||
assert.equal(snapDim(1024), 1024);
|
||||
assert.equal(snapDim(32), 32);
|
||||
assert.equal(snapDim(0), 32);
|
||||
assert.equal(snapDim(16), 32);
|
||||
assert.equal(snapDim(48), 64);
|
||||
});
|
||||
|
||||
test("parseAspectRatio parses valid ratios and rejects invalid inputs", () => {
|
||||
assert.deepEqual(parseAspectRatio("3:4"), { width: 3, height: 4 });
|
||||
assert.deepEqual(parseAspectRatio("16:9"), { width: 16, height: 9 });
|
||||
assert.deepEqual(parseAspectRatio("1:1"), { width: 1, height: 1 });
|
||||
assert.deepEqual(parseAspectRatio("1.5:1"), { width: 1.5, height: 1 });
|
||||
|
||||
assert.equal(parseAspectRatio(""), null);
|
||||
assert.equal(parseAspectRatio("invalid"), null);
|
||||
assert.equal(parseAspectRatio("3x4"), null);
|
||||
assert.equal(parseAspectRatio("0:1"), null);
|
||||
assert.equal(parseAspectRatio("1:0"), null);
|
||||
});
|
||||
|
||||
test("resolveSize returns explicit --size directly", () => {
|
||||
assert.equal(resolveSize({ size: "1024x1024" }), "1024x1024");
|
||||
assert.equal(resolveSize({ size: "768x1024", aspectRatio: "16:9" }), "768x1024");
|
||||
});
|
||||
|
||||
test("resolveSize returns default 1024x1024 when no size or ratio given", () => {
|
||||
assert.equal(resolveSize({}), "1024x1024");
|
||||
assert.equal(resolveSize({ size: null, aspectRatio: null }), "1024x1024");
|
||||
});
|
||||
|
||||
test("resolveSize computes 32-aligned size within 2048 max edge", () => {
|
||||
assert.equal(resolveSize({ aspectRatio: "1:1" }), "1024x1024");
|
||||
assert.equal(resolveSize({ aspectRatio: "16:9" }), "2048x1152");
|
||||
assert.equal(resolveSize({ aspectRatio: "4:3" }), "2048x1536");
|
||||
assert.equal(resolveSize({ aspectRatio: "3:4" }), "1536x2048");
|
||||
assert.equal(resolveSize({ aspectRatio: "9:16" }), "1152x2048");
|
||||
});
|
||||
|
||||
test("resolveSize aligns to 32 and respects max edge", () => {
|
||||
assert.equal(resolveSize({ aspectRatio: "3:1" }), "2048x672");
|
||||
assert.equal(resolveSize({ aspectRatio: "1:3" }), "672x2048");
|
||||
});
|
||||
|
||||
test("validateArgs rejects --n > 1", () => {
|
||||
assert.throws(
|
||||
() => validateArgs("agnes-image-2.5-flash", makeArgs({ n: 2 })),
|
||||
/returns a single image per request/,
|
||||
);
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs("agnes-image-2.5-flash", makeArgs({ n: 1 })),
|
||||
);
|
||||
});
|
||||
|
||||
test("buildRequestBody maps prompt, model, size, and reference images", () => {
|
||||
const body = buildRequestBody("a cat", "agnes-image-2.5-flash", {
|
||||
size: "1024x1024",
|
||||
aspectRatio: null,
|
||||
referenceImages: [],
|
||||
});
|
||||
assert.equal(body.model, "agnes-image-2.5-flash");
|
||||
assert.equal(body.prompt, "a cat");
|
||||
assert.equal(body.size, "1024x1024");
|
||||
assert.deepEqual(body.extra_body, { response_format: "url" });
|
||||
|
||||
const bodyWithRef = buildRequestBody("a cat", "agnes-image-2.5-flash", {
|
||||
size: null,
|
||||
aspectRatio: "3:4",
|
||||
referenceImages: ["https://example.com/ref.jpg"],
|
||||
});
|
||||
assert.equal(bodyWithRef.size, "1536x2048");
|
||||
assert.deepEqual(bodyWithRef.image, ["https://example.com/ref.jpg"]);
|
||||
});
|
||||
|
||||
test("extractImageFromResponse decodes b64_json payloads", async () => {
|
||||
const fromBase64 = await extractImageFromResponse({
|
||||
data: [{ b64_json: Buffer.from("hello").toString("base64") }],
|
||||
});
|
||||
assert.equal(Buffer.from(fromBase64).toString("utf8"), "hello");
|
||||
});
|
||||
|
||||
test("extractImageFromResponse downloads URL payloads", async (t) => {
|
||||
const originalFetch = globalThis.fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
globalThis.fetch = async () =>
|
||||
new Response(Uint8Array.from([1, 2, 3]), {
|
||||
status: 200,
|
||||
headers: { "Content-Type": "image/png" },
|
||||
});
|
||||
|
||||
const fromUrl = await extractImageFromResponse({
|
||||
data: [{ url: "https://example.com/output.png" }],
|
||||
});
|
||||
assert.deepEqual([...fromUrl], [1, 2, 3]);
|
||||
});
|
||||
|
||||
test("extractImageFromResponse throws on empty data", async () => {
|
||||
await assert.rejects(
|
||||
() => extractImageFromResponse({ data: [] }),
|
||||
/No image/,
|
||||
);
|
||||
await assert.rejects(
|
||||
() => extractImageFromResponse({ data: [{}] }),
|
||||
/No image/,
|
||||
);
|
||||
});
|
||||
|
||||
test("resolveReferenceImages converts local files to data URIs and passes URLs through", async (t) => {
|
||||
const dir = await fs.mkdtemp(path.join(os.tmpdir(), "agnes-ref-"));
|
||||
t.after(() => fs.rm(dir, { recursive: true, force: true }));
|
||||
|
||||
const localPath = path.join(dir, "ref.png");
|
||||
const localBytes = Buffer.from([0x89, 0x50, 0x4e, 0x47]);
|
||||
await fs.writeFile(localPath, localBytes);
|
||||
|
||||
const jpegPath = path.join(dir, "photo.jpeg");
|
||||
await fs.writeFile(jpegPath, Buffer.from([0xff, 0xd8]));
|
||||
|
||||
const results = await resolveReferenceImages([
|
||||
localPath,
|
||||
"https://example.com/remote.jpg",
|
||||
jpegPath,
|
||||
]);
|
||||
|
||||
assert.equal(results.length, 3);
|
||||
assert.match(results[0]!, /^data:image\/png;base64,/);
|
||||
assert.match(results[1]!, /^https:\/\/example.com\/remote.jpg$/);
|
||||
assert.match(results[2]!, /^data:image\/jpeg;base64,/);
|
||||
});
|
||||
|
||||
test("resolveReferenceImages detects gif and webp mime types", async (t) => {
|
||||
const dir = await fs.mkdtemp(path.join(os.tmpdir(), "agnes-mime-"));
|
||||
t.after(() => fs.rm(dir, { recursive: true, force: true }));
|
||||
|
||||
const webpPath = path.join(dir, "ref.webp");
|
||||
const gifPath = path.join(dir, "ref.gif");
|
||||
await fs.writeFile(webpPath, Buffer.from([0x00]));
|
||||
await fs.writeFile(gifPath, Buffer.from([0x00]));
|
||||
|
||||
const results = await resolveReferenceImages([webpPath, gifPath]);
|
||||
assert.match(results[0]!, /^data:image\/webp;base64,/);
|
||||
assert.match(results[1]!, /^data:image\/gif;base64,/);
|
||||
});
|
||||
175
baoyu-skills/skills/baoyu-image-gen/scripts/providers/agnes.ts
Normal file
175
baoyu-skills/skills/baoyu-image-gen/scripts/providers/agnes.ts
Normal file
@@ -0,0 +1,175 @@
|
||||
import { readFile } from "node:fs/promises";
|
||||
import path from "node:path";
|
||||
import type { CliArgs } from "../types";
|
||||
|
||||
const DEFAULT_MODEL = "agnes-image-2.5-flash";
|
||||
const DEFAULT_BASE_URL = "https://apihub.agnes-ai.com/v1";
|
||||
const DEFAULT_SIZE = "1024x1024";
|
||||
|
||||
type AgnesResponse = {
|
||||
created?: number;
|
||||
data: Array<{ url?: string; b64_json?: string }>;
|
||||
};
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.AGNES_IMAGE_MODEL || DEFAULT_MODEL;
|
||||
}
|
||||
|
||||
function getApiKey(): string {
|
||||
const key = process.env.AGNES_API_KEY;
|
||||
if (!key) {
|
||||
throw new Error("AGNES_API_KEY is required. Get one from https://apihub.agnes-ai.com.");
|
||||
}
|
||||
return key;
|
||||
}
|
||||
|
||||
function getBaseUrl(): string {
|
||||
return (process.env.AGNES_BASE_URL || DEFAULT_BASE_URL).replace(/\/+$/, "");
|
||||
}
|
||||
|
||||
export function parseAspectRatio(ar: string): { width: number; height: number } | null {
|
||||
const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
|
||||
if (!match) return null;
|
||||
const w = parseFloat(match[1]!);
|
||||
const h = parseFloat(match[2]!);
|
||||
if (w <= 0 || h <= 0) return null;
|
||||
return { width: w, height: h };
|
||||
}
|
||||
|
||||
export function snapDim(n: number): number {
|
||||
return Math.max(32, Math.round(n / 32) * 32);
|
||||
}
|
||||
|
||||
export function resolveSize(args: Pick<CliArgs, "size" | "aspectRatio">): string {
|
||||
if (args.size) return args.size;
|
||||
|
||||
if (args.aspectRatio) {
|
||||
const parsed = parseAspectRatio(args.aspectRatio);
|
||||
if (parsed) {
|
||||
if (parsed.width === 1 && parsed.height === 1) return "1024x1024";
|
||||
const maxEdge = 2048;
|
||||
const scale = Math.max(1, Math.floor(maxEdge / Math.max(parsed.width, parsed.height)));
|
||||
const width = parsed.width * scale;
|
||||
const height = parsed.height * scale;
|
||||
return `${snapDim(width)}x${snapDim(height)}`;
|
||||
}
|
||||
}
|
||||
|
||||
return DEFAULT_SIZE;
|
||||
}
|
||||
|
||||
function isRemoteUrl(refPath: string): boolean {
|
||||
return /^https?:\/\//i.test(refPath);
|
||||
}
|
||||
|
||||
export async function resolveReferenceImages(
|
||||
referenceImages: string[]
|
||||
): Promise<string[]> {
|
||||
const result: string[] = [];
|
||||
for (const refPath of referenceImages) {
|
||||
if (isRemoteUrl(refPath)) {
|
||||
result.push(refPath);
|
||||
continue;
|
||||
}
|
||||
const bytes = await readFile(refPath);
|
||||
const ext = path.extname(refPath).toLowerCase();
|
||||
let mime = "image/png";
|
||||
if (ext === ".jpg" || ext === ".jpeg") mime = "image/jpeg";
|
||||
else if (ext === ".webp") mime = "image/webp";
|
||||
else if (ext === ".gif") mime = "image/gif";
|
||||
const b64 = Buffer.from(bytes).toString("base64");
|
||||
result.push(`data:${mime};base64,${b64}`);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
export function validateArgs(_model: string, args: CliArgs): void {
|
||||
if (args.n > 1) {
|
||||
throw new Error("Agnes image generation currently returns a single image per request. Set --n 1 or omit --n.");
|
||||
}
|
||||
}
|
||||
|
||||
export function getDefaultOutputExtension(_model: string, args: CliArgs): string {
|
||||
return args.responseFormat === "url" ? ".txt" : ".png";
|
||||
}
|
||||
|
||||
export function buildRequestBody(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: Pick<CliArgs, "size" | "aspectRatio" | "referenceImages">
|
||||
): Record<string, unknown> {
|
||||
const body: Record<string, unknown> = {
|
||||
model,
|
||||
prompt,
|
||||
size: resolveSize(args),
|
||||
};
|
||||
|
||||
if (args.referenceImages.length > 0) {
|
||||
body.image = args.referenceImages;
|
||||
}
|
||||
|
||||
body.extra_body = { response_format: "url" };
|
||||
|
||||
return body;
|
||||
}
|
||||
|
||||
export async function extractImageFromResponse(result: AgnesResponse): Promise<Uint8Array> {
|
||||
const img = result.data[0];
|
||||
|
||||
if (img?.b64_json) {
|
||||
return Uint8Array.from(Buffer.from(img.b64_json, "base64"));
|
||||
}
|
||||
|
||||
if (img?.url) {
|
||||
const imgRes = await fetch(img.url);
|
||||
if (!imgRes.ok) throw new Error(`Failed to download image from Agnes: ${imgRes.status}`);
|
||||
return new Uint8Array(await imgRes.arrayBuffer());
|
||||
}
|
||||
|
||||
throw new Error("No image in Agnes response");
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
const apiKey = getApiKey();
|
||||
const baseUrl = getBaseUrl();
|
||||
|
||||
const referenceImages = await resolveReferenceImages(args.referenceImages);
|
||||
|
||||
const body = buildRequestBody(prompt, model, { ...args, referenceImages });
|
||||
|
||||
const controller = new AbortController();
|
||||
const timeout = setTimeout(() => controller.abort(), 120_000);
|
||||
|
||||
try {
|
||||
const res = await fetch(`${baseUrl}/images/generations`, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
},
|
||||
body: JSON.stringify(body),
|
||||
signal: controller.signal,
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`Agnes API error (${res.status}): ${err}`);
|
||||
}
|
||||
|
||||
const result = (await res.json()) as AgnesResponse;
|
||||
|
||||
if (args.responseFormat === "url") {
|
||||
const url = result.data[0]?.url;
|
||||
if (!url) throw new Error("No URL in Agnes response");
|
||||
return new Uint8Array(Buffer.from(url, "utf-8"));
|
||||
}
|
||||
|
||||
return extractImageFromResponse(result);
|
||||
} finally {
|
||||
clearTimeout(timeout);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,189 @@
|
||||
import assert from "node:assert/strict";
|
||||
import fs from "node:fs/promises";
|
||||
import os from "node:os";
|
||||
import path from "node:path";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
generateImage,
|
||||
getDefaultModel,
|
||||
parseAzureBaseURL,
|
||||
validateArgs,
|
||||
} from "./azure.ts";
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
async function makeTempDir(prefix: string): Promise<string> {
|
||||
return fs.mkdtemp(path.join(os.tmpdir(), prefix));
|
||||
}
|
||||
|
||||
test("Azure endpoint parsing and default deployment selection follow env precedence", (t) => {
|
||||
assert.deepEqual(parseAzureBaseURL("https://example.openai.azure.com"), {
|
||||
resourceBaseURL: "https://example.openai.azure.com/openai",
|
||||
deployment: null,
|
||||
});
|
||||
assert.deepEqual(
|
||||
parseAzureBaseURL("https://example.openai.azure.com/openai/deployments/from-url"),
|
||||
{
|
||||
resourceBaseURL: "https://example.openai.azure.com/openai",
|
||||
deployment: "from-url",
|
||||
},
|
||||
);
|
||||
|
||||
useEnv(t, {
|
||||
AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com/openai/deployments/from-url",
|
||||
AZURE_OPENAI_DEPLOYMENT: "explicit-deploy",
|
||||
AZURE_OPENAI_IMAGE_MODEL: "env-fallback",
|
||||
});
|
||||
assert.equal(getDefaultModel(), "explicit-deploy");
|
||||
});
|
||||
|
||||
test("Azure validateArgs rejects unsupported edit input formats before the API call", () => {
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs("demo-deployment", makeArgs({ referenceImages: ["hero.png", "photo.jpeg"] })),
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs("demo-deployment", makeArgs({ referenceImages: ["hero.webp"] })),
|
||||
/PNG or JPG\/JPEG/,
|
||||
);
|
||||
});
|
||||
|
||||
test("Azure image generation routes model to deployment and sends mapped quality", async (t) => {
|
||||
useEnv(t, {
|
||||
AZURE_OPENAI_API_KEY: "azure-key",
|
||||
AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com/openai/deployments/default-deploy",
|
||||
AZURE_API_VERSION: null,
|
||||
AZURE_OPENAI_DEPLOYMENT: null,
|
||||
AZURE_OPENAI_IMAGE_MODEL: null,
|
||||
});
|
||||
|
||||
const originalFetch = globalThis.fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
const calls: Array<{ url: string; body: string }> = [];
|
||||
globalThis.fetch = async (input, init) => {
|
||||
calls.push({
|
||||
url: String(input),
|
||||
body: String(init?.body ?? ""),
|
||||
});
|
||||
return Response.json({
|
||||
data: [{ b64_json: Buffer.from("azure-image").toString("base64") }],
|
||||
});
|
||||
};
|
||||
|
||||
const bytes = await generateImage(
|
||||
"A calm lake at sunset",
|
||||
"custom-deploy",
|
||||
makeArgs({ quality: "normal" }),
|
||||
);
|
||||
|
||||
assert.equal(Buffer.from(bytes).toString("utf8"), "azure-image");
|
||||
assert.equal(
|
||||
calls[0]?.url,
|
||||
"https://example.openai.azure.com/openai/deployments/custom-deploy/images/generations?api-version=2025-04-01-preview",
|
||||
);
|
||||
|
||||
const body = JSON.parse(calls[0]!.body) as Record<string, string>;
|
||||
assert.equal(body.quality, "medium");
|
||||
assert.equal(body.size, "1024x1024");
|
||||
});
|
||||
|
||||
test("Azure image edits include quality in multipart requests", async (t) => {
|
||||
const root = await makeTempDir("baoyu-image-gen-azure-");
|
||||
t.after(() => fs.rm(root, { recursive: true, force: true }));
|
||||
|
||||
const pngPath = path.join(root, "ref.png");
|
||||
const jpgPath = path.join(root, "ref.jpg");
|
||||
await fs.writeFile(pngPath, "png-bytes");
|
||||
await fs.writeFile(jpgPath, "jpg-bytes");
|
||||
|
||||
useEnv(t, {
|
||||
AZURE_OPENAI_API_KEY: "azure-key",
|
||||
AZURE_OPENAI_BASE_URL: "https://example.openai.azure.com",
|
||||
AZURE_API_VERSION: "2025-04-01-preview",
|
||||
AZURE_OPENAI_DEPLOYMENT: null,
|
||||
AZURE_OPENAI_IMAGE_MODEL: null,
|
||||
});
|
||||
|
||||
const originalFetch = globalThis.fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
const calls: Array<{ url: string; form: FormData }> = [];
|
||||
globalThis.fetch = async (input, init) => {
|
||||
calls.push({
|
||||
url: String(input),
|
||||
form: init?.body as FormData,
|
||||
});
|
||||
return Response.json({
|
||||
data: [{ b64_json: Buffer.from("edited-image").toString("base64") }],
|
||||
});
|
||||
};
|
||||
|
||||
const bytes = await generateImage(
|
||||
"Add warm lighting",
|
||||
"edit-deploy",
|
||||
makeArgs({
|
||||
quality: "2k",
|
||||
referenceImages: [pngPath, jpgPath],
|
||||
}),
|
||||
);
|
||||
|
||||
assert.equal(Buffer.from(bytes).toString("utf8"), "edited-image");
|
||||
assert.equal(
|
||||
calls[0]?.url,
|
||||
"https://example.openai.azure.com/openai/deployments/edit-deploy/images/edits?api-version=2025-04-01-preview",
|
||||
);
|
||||
assert.equal(calls[0]?.form.get("quality"), "high");
|
||||
assert.equal(calls[0]?.form.get("size"), "1024x1024");
|
||||
assert.equal(calls[0]?.form.getAll("image[]").length, 2);
|
||||
});
|
||||
192
baoyu-skills/skills/baoyu-image-gen/scripts/providers/azure.ts
Normal file
192
baoyu-skills/skills/baoyu-image-gen/scripts/providers/azure.ts
Normal file
@@ -0,0 +1,192 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
import type { CliArgs } from "../types";
|
||||
import { getOpenAISize, extractImageFromResponse } from "./openai.ts";
|
||||
|
||||
type OpenAIImageResponse = { data: Array<{ url?: string; b64_json?: string }> };
|
||||
type AzureEndpoint = {
|
||||
resourceBaseURL: string;
|
||||
deployment: string | null;
|
||||
};
|
||||
|
||||
const DEFAULT_AZURE_API_VERSION = "2025-04-01-preview";
|
||||
const AZURE_EDIT_IMAGE_EXTENSIONS = new Set([".png", ".jpg", ".jpeg"]);
|
||||
|
||||
export function parseAzureBaseURL(url: string): AzureEndpoint {
|
||||
const parsed = new URL(url);
|
||||
const trimmedPath = parsed.pathname.replace(/\/+$/, "");
|
||||
const deploymentMatch = trimmedPath.match(/^(.*?)(?:\/openai)?\/deployments\/([^/]+)$/);
|
||||
|
||||
if (deploymentMatch) {
|
||||
parsed.pathname = `${deploymentMatch[1] || ""}/openai`;
|
||||
return {
|
||||
resourceBaseURL: parsed.toString().replace(/\/+$/, ""),
|
||||
deployment: decodeURIComponent(deploymentMatch[2]!),
|
||||
};
|
||||
}
|
||||
|
||||
parsed.pathname = trimmedPath.endsWith("/openai") ? trimmedPath : `${trimmedPath}/openai`;
|
||||
return {
|
||||
resourceBaseURL: parsed.toString().replace(/\/+$/, ""),
|
||||
deployment: null,
|
||||
};
|
||||
}
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
const explicitDeployment = process.env.AZURE_OPENAI_DEPLOYMENT?.trim();
|
||||
if (explicitDeployment) return explicitDeployment;
|
||||
|
||||
const baseURL = process.env.AZURE_OPENAI_BASE_URL;
|
||||
if (baseURL) {
|
||||
try {
|
||||
const { deployment } = parseAzureBaseURL(baseURL);
|
||||
if (deployment) return deployment;
|
||||
} catch {
|
||||
// Ignore invalid URLs here so the required-env check can raise the user-facing error later.
|
||||
}
|
||||
}
|
||||
|
||||
return process.env.AZURE_OPENAI_IMAGE_MODEL || "gpt-image-2.5-flare";
|
||||
}
|
||||
|
||||
function getEndpoint(): AzureEndpoint {
|
||||
const url = process.env.AZURE_OPENAI_BASE_URL;
|
||||
if (!url) {
|
||||
throw new Error(
|
||||
"AZURE_OPENAI_BASE_URL is required. Set it to your Azure resource or deployment endpoint, e.g.: https://your-resource.openai.azure.com or https://your-resource.openai.azure.com/openai/deployments/your-deployment"
|
||||
);
|
||||
}
|
||||
return parseAzureBaseURL(url);
|
||||
}
|
||||
|
||||
function getApiKey(): string {
|
||||
const key = process.env.AZURE_OPENAI_API_KEY;
|
||||
if (!key) {
|
||||
throw new Error(
|
||||
"AZURE_OPENAI_API_KEY is required. Get it from Azure Portal → your OpenAI resource → Keys and Endpoint."
|
||||
);
|
||||
}
|
||||
return key;
|
||||
}
|
||||
|
||||
function getApiVersion(): string {
|
||||
return process.env.AZURE_API_VERSION || DEFAULT_AZURE_API_VERSION;
|
||||
}
|
||||
|
||||
function getDeployment(model: string): string {
|
||||
const deployment = model.trim();
|
||||
if (!deployment) {
|
||||
throw new Error(
|
||||
"Azure deployment name is required. Use --model <deployment>, AZURE_OPENAI_DEPLOYMENT, AZURE_OPENAI_IMAGE_MODEL, or embed the deployment in AZURE_OPENAI_BASE_URL."
|
||||
);
|
||||
}
|
||||
return deployment;
|
||||
}
|
||||
|
||||
function buildURL(deployment: string, pathSuffix: string): string {
|
||||
const { resourceBaseURL } = getEndpoint();
|
||||
return `${resourceBaseURL}/deployments/${encodeURIComponent(deployment)}${pathSuffix}?api-version=${getApiVersion()}`;
|
||||
}
|
||||
|
||||
function authHeaders(): Record<string, string> {
|
||||
return { "api-key": getApiKey() };
|
||||
}
|
||||
|
||||
function getAzureQuality(quality: CliArgs["quality"]): "medium" | "high" {
|
||||
return quality === "2k" ? "high" : "medium";
|
||||
}
|
||||
|
||||
export function validateArgs(_model: string, args: CliArgs): void {
|
||||
for (const refPath of args.referenceImages) {
|
||||
const ext = path.extname(refPath).toLowerCase();
|
||||
if (!AZURE_EDIT_IMAGE_EXTENSIONS.has(ext)) {
|
||||
throw new Error(
|
||||
`Azure OpenAI reference images must be PNG or JPG/JPEG. Unsupported file: ${refPath}`
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
const deployment = getDeployment(model);
|
||||
const size = args.size || getOpenAISize(model, args.aspectRatio, args.quality);
|
||||
|
||||
if (args.referenceImages.length > 0) {
|
||||
return generateWithAzureEdits(prompt, deployment, size, args.referenceImages, args.quality);
|
||||
}
|
||||
|
||||
return generateWithAzureGenerations(prompt, deployment, size, args.quality);
|
||||
}
|
||||
|
||||
async function generateWithAzureGenerations(
|
||||
prompt: string,
|
||||
deployment: string,
|
||||
size: string,
|
||||
quality: CliArgs["quality"]
|
||||
): Promise<Uint8Array> {
|
||||
const body: Record<string, any> = {
|
||||
prompt,
|
||||
size,
|
||||
n: 1,
|
||||
quality: getAzureQuality(quality),
|
||||
};
|
||||
|
||||
const res = await fetch(buildURL(deployment, "/images/generations"), {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
...authHeaders(),
|
||||
},
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`Azure OpenAI API error: ${err}`);
|
||||
}
|
||||
|
||||
const result = (await res.json()) as OpenAIImageResponse;
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
|
||||
async function generateWithAzureEdits(
|
||||
prompt: string,
|
||||
deployment: string,
|
||||
size: string,
|
||||
referenceImages: string[],
|
||||
quality: CliArgs["quality"]
|
||||
): Promise<Uint8Array> {
|
||||
const form = new FormData();
|
||||
form.append("prompt", prompt);
|
||||
form.append("size", size);
|
||||
form.append("n", "1");
|
||||
form.append("quality", getAzureQuality(quality));
|
||||
|
||||
for (const refPath of referenceImages) {
|
||||
const bytes = await readFile(refPath);
|
||||
const filename = path.basename(refPath);
|
||||
const mimeType = path.extname(filename).toLowerCase() === ".png" ? "image/png" : "image/jpeg";
|
||||
const blob = new Blob([bytes], { type: mimeType });
|
||||
form.append("image[]", blob, filename);
|
||||
}
|
||||
|
||||
const res = await fetch(buildURL(deployment, "/images/edits"), {
|
||||
method: "POST",
|
||||
headers: {
|
||||
...authHeaders(),
|
||||
},
|
||||
body: form,
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`Azure OpenAI edits API error: ${err}`);
|
||||
}
|
||||
|
||||
const result = (await res.json()) as OpenAIImageResponse;
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
@@ -0,0 +1,62 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
getDefaultModel,
|
||||
getDefaultOutputExtension,
|
||||
validateArgs,
|
||||
} from "./codex-cli.ts";
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: "codex-cli",
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
aspectRatioSource: null,
|
||||
size: null,
|
||||
quality: "2k",
|
||||
imageSize: null,
|
||||
imageSizeSource: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
test("codex-cli defaults to codex-image-gen model and PNG output", () => {
|
||||
assert.equal(getDefaultModel(), "codex-image-gen");
|
||||
assert.equal(getDefaultOutputExtension(), ".png");
|
||||
});
|
||||
|
||||
test("codex-cli validateArgs rejects n>1 with a non-retryable message", () => {
|
||||
assert.throws(
|
||||
() => validateArgs("codex-image-gen", makeArgs({ n: 2 })),
|
||||
/supports only n=1/,
|
||||
);
|
||||
});
|
||||
|
||||
test("codex-cli validateArgs rejects ratio-metadata dialect", () => {
|
||||
assert.throws(
|
||||
() => validateArgs("codex-image-gen", makeArgs({ imageApiDialect: "ratio-metadata" })),
|
||||
/Invalid imageApiDialect/,
|
||||
);
|
||||
});
|
||||
|
||||
test("codex-cli validateArgs accepts default n=1 with no dialect", () => {
|
||||
assert.doesNotThrow(() => validateArgs("codex-image-gen", makeArgs()));
|
||||
});
|
||||
|
||||
test("codex-cli validateArgs accepts reference images (Codex image_gen supports refs)", () => {
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs("codex-image-gen", makeArgs({ referenceImages: ["/tmp/a.png", "/tmp/b.png"] })),
|
||||
);
|
||||
});
|
||||
@@ -0,0 +1,197 @@
|
||||
import path from "node:path";
|
||||
import { spawn } from "node:child_process";
|
||||
import { fileURLToPath } from "node:url";
|
||||
import { tmpdir } from "node:os";
|
||||
import { mkdir, readFile, rm, writeFile, access } from "node:fs/promises";
|
||||
import { randomBytes } from "node:crypto";
|
||||
import type { CliArgs } from "../types";
|
||||
|
||||
const PROVIDER_FILE = fileURLToPath(import.meta.url);
|
||||
const SCRIPTS_DIR = path.resolve(path.dirname(PROVIDER_FILE), "..");
|
||||
const BUNDLED_WRAPPER = path.join(SCRIPTS_DIR, "codex-imagegen", "main.ts");
|
||||
|
||||
type WrapperOkResult = {
|
||||
status: "ok";
|
||||
path: string;
|
||||
bytes: number;
|
||||
elapsed_seconds: number;
|
||||
thread_id: string | null;
|
||||
attempts: number;
|
||||
cached: boolean;
|
||||
};
|
||||
|
||||
type WrapperErrorResult = {
|
||||
status: "error";
|
||||
path: string;
|
||||
bytes: number;
|
||||
error: string;
|
||||
error_kind: string;
|
||||
};
|
||||
|
||||
type WrapperResult = WrapperOkResult | WrapperErrorResult;
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return "codex-image-gen";
|
||||
}
|
||||
|
||||
export function getDefaultOutputExtension(): string {
|
||||
return ".png";
|
||||
}
|
||||
|
||||
export function validateArgs(_model: string, args: CliArgs): void {
|
||||
if (args.n > 1) {
|
||||
throw new Error(
|
||||
"codex-cli provider supports only n=1 (Codex image_gen returns a single image per call).",
|
||||
);
|
||||
}
|
||||
if (args.imageApiDialect && args.imageApiDialect !== "openai-native") {
|
||||
throw new Error(
|
||||
`Invalid imageApiDialect for codex-cli: ${args.imageApiDialect}. codex-cli does not use OpenAI Images API dialects.`,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
async function exists(filePath: string): Promise<boolean> {
|
||||
try {
|
||||
await access(filePath);
|
||||
return true;
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
async function resolveWrapperPath(): Promise<string> {
|
||||
const override = process.env.BAOYU_CODEX_IMAGEGEN_BIN;
|
||||
if (override) {
|
||||
if (!(await exists(override))) {
|
||||
throw new Error(
|
||||
`Invalid BAOYU_CODEX_IMAGEGEN_BIN: ${override} does not exist.`,
|
||||
);
|
||||
}
|
||||
return override;
|
||||
}
|
||||
if (await exists(BUNDLED_WRAPPER)) return BUNDLED_WRAPPER;
|
||||
throw new Error(
|
||||
`codex-cli wrapper not found at ${BUNDLED_WRAPPER}. ` +
|
||||
`Reinstall baoyu-image-gen, or set BAOYU_CODEX_IMAGEGEN_BIN to a codex-imagegen main.ts (or .sh) path.`,
|
||||
);
|
||||
}
|
||||
|
||||
type SpawnResult = {
|
||||
stdout: string;
|
||||
stderr: string;
|
||||
code: number;
|
||||
};
|
||||
|
||||
async function spawnWrapper(wrapperPath: string, cliArgs: string[]): Promise<SpawnResult> {
|
||||
return new Promise((resolve, reject) => {
|
||||
const isTs = wrapperPath.endsWith(".ts");
|
||||
const command = isTs ? "bun" : wrapperPath;
|
||||
const args = isTs ? [wrapperPath, ...cliArgs] : cliArgs;
|
||||
const child = spawn(command, args, { stdio: ["ignore", "pipe", "pipe"] });
|
||||
let stdout = "";
|
||||
let stderr = "";
|
||||
child.stdout.on("data", (chunk: Buffer) => {
|
||||
stdout += chunk.toString("utf8");
|
||||
});
|
||||
child.stderr.on("data", (chunk: Buffer) => {
|
||||
const text = chunk.toString("utf8");
|
||||
stderr += text;
|
||||
process.stderr.write(text);
|
||||
});
|
||||
child.on("error", (err) => reject(err));
|
||||
child.on("close", (code) => resolve({ stdout, stderr, code: code ?? 1 }));
|
||||
});
|
||||
}
|
||||
|
||||
function parseWrapperJson(stdout: string): WrapperResult {
|
||||
const trimmed = stdout.trim();
|
||||
if (!trimmed) {
|
||||
throw new Error("Invalid codex-cli response: empty stdout from wrapper.");
|
||||
}
|
||||
const lastLine = trimmed.split(/\r?\n/).pop() ?? trimmed;
|
||||
try {
|
||||
return JSON.parse(lastLine) as WrapperResult;
|
||||
} catch (parseErr) {
|
||||
throw new Error(
|
||||
`Invalid codex-cli response: could not parse JSON from wrapper stdout (${(parseErr as Error).message}).`,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
function parsePositiveInt(value: string | undefined): number | null {
|
||||
if (!value) return null;
|
||||
const parsed = parseInt(value, 10);
|
||||
return Number.isFinite(parsed) && parsed > 0 ? parsed : null;
|
||||
}
|
||||
|
||||
function getEnvOverride(name: string): string | null {
|
||||
const value = process.env[name];
|
||||
return value && value.length > 0 ? value : null;
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
_model: string,
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
const wrapperPath = await resolveWrapperPath();
|
||||
|
||||
const sessionDir = path.join(tmpdir(), "baoyu-image-gen-codex-cli");
|
||||
await mkdir(sessionDir, { recursive: true });
|
||||
const token = randomBytes(8).toString("hex");
|
||||
const tmpOutput = path.join(sessionDir, `out-${token}.png`);
|
||||
const tmpPrompt = path.join(sessionDir, `prompt-${token}.md`);
|
||||
await writeFile(tmpPrompt, prompt, "utf8");
|
||||
|
||||
const aspect = args.aspectRatio ?? "1:1";
|
||||
const cliArgs: string[] = [
|
||||
"--image",
|
||||
tmpOutput,
|
||||
"--prompt-file",
|
||||
tmpPrompt,
|
||||
"--aspect",
|
||||
aspect,
|
||||
];
|
||||
|
||||
for (const ref of args.referenceImages) {
|
||||
cliArgs.push("--ref", path.resolve(ref));
|
||||
}
|
||||
|
||||
const cacheDir = getEnvOverride("BAOYU_CODEX_IMAGEGEN_CACHE_DIR");
|
||||
if (cacheDir) cliArgs.push("--cache-dir", cacheDir);
|
||||
|
||||
const timeoutMs = parsePositiveInt(process.env.BAOYU_CODEX_IMAGEGEN_TIMEOUT_MS);
|
||||
if (timeoutMs) cliArgs.push("--timeout", String(timeoutMs));
|
||||
|
||||
const retries = parsePositiveInt(process.env.BAOYU_CODEX_IMAGEGEN_RETRIES);
|
||||
if (retries !== null) cliArgs.push("--retries", String(retries));
|
||||
|
||||
const logFile = getEnvOverride("BAOYU_CODEX_IMAGEGEN_LOG_FILE");
|
||||
if (logFile) cliArgs.push("--log-file", logFile);
|
||||
|
||||
try {
|
||||
const spawnResult = await spawnWrapper(wrapperPath, cliArgs);
|
||||
const parsed = parseWrapperJson(spawnResult.stdout);
|
||||
|
||||
if (parsed.status === "error") {
|
||||
throw new Error(
|
||||
`Invalid codex-cli result (${parsed.error_kind}): ${parsed.error}`,
|
||||
);
|
||||
}
|
||||
|
||||
if (spawnResult.code !== 0) {
|
||||
throw new Error(
|
||||
`Invalid codex-cli result: wrapper exited with code ${spawnResult.code} despite reporting status=ok.`,
|
||||
);
|
||||
}
|
||||
|
||||
const bytes = await readFile(parsed.path ?? tmpOutput);
|
||||
return new Uint8Array(bytes);
|
||||
} finally {
|
||||
await Promise.allSettled([
|
||||
rm(tmpOutput, { force: true }),
|
||||
rm(tmpPrompt, { force: true }),
|
||||
]);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,394 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import {
|
||||
generateImage,
|
||||
getDefaultModel,
|
||||
getModelFamily,
|
||||
getQwen2SizeFromAspectRatio,
|
||||
getSizeFromAspectRatio,
|
||||
getWan27SizeFromAspectRatio,
|
||||
normalizeSize,
|
||||
parseAspectRatio,
|
||||
parseSize,
|
||||
resolveSizeForModel,
|
||||
} from "./dashscope.ts";
|
||||
import type { CliArgs } from "../types.ts";
|
||||
|
||||
function makeCliArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: "dashscope",
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
aspectRatioSource: null,
|
||||
size: null,
|
||||
quality: "2k",
|
||||
imageSize: null,
|
||||
imageSizeSource: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
test("DashScope default model prefers env override and otherwise uses qwen-image-2.0-pro", (t) => {
|
||||
useEnv(t, { DASHSCOPE_IMAGE_MODEL: null });
|
||||
assert.equal(getDefaultModel(), "qwen-image-2.0-pro");
|
||||
|
||||
process.env.DASHSCOPE_IMAGE_MODEL = "qwen-image-max";
|
||||
assert.equal(getDefaultModel(), "qwen-image-max");
|
||||
});
|
||||
|
||||
test("DashScope aspect-ratio parsing accepts numeric ratios only", () => {
|
||||
assert.deepEqual(parseAspectRatio("3:2"), { width: 3, height: 2 });
|
||||
assert.equal(parseAspectRatio("square"), null);
|
||||
assert.equal(parseAspectRatio("-1:2"), null);
|
||||
});
|
||||
|
||||
test("DashScope model family routing distinguishes qwen-2.0, fixed-size qwen, wan2.7, and legacy models", () => {
|
||||
assert.equal(getModelFamily("qwen-image-3.0-pro"), "qwen2");
|
||||
assert.equal(getModelFamily("qwen-image-2.0-pro"), "qwen2");
|
||||
assert.equal(getModelFamily("qwen-image-2.0-pro-2026-04-22"), "qwen2");
|
||||
assert.equal(getModelFamily("qwen-image"), "qwenFixed");
|
||||
assert.equal(getModelFamily("wan2.7-image"), "wan27");
|
||||
assert.equal(getModelFamily("wan2.7-image-pro"), "wan27");
|
||||
assert.equal(getModelFamily("z-image-turbo"), "legacy");
|
||||
assert.equal(getModelFamily("wanx-v1"), "legacy");
|
||||
});
|
||||
|
||||
test("Legacy DashScope size selection keeps the previous quality-based heuristic", () => {
|
||||
assert.equal(getSizeFromAspectRatio(null, "normal"), "1024*1024");
|
||||
assert.equal(getSizeFromAspectRatio("16:9", "normal"), "1280*720");
|
||||
assert.equal(getSizeFromAspectRatio("16:9", "2k"), "2048*1152");
|
||||
assert.equal(getSizeFromAspectRatio("invalid", "2k"), "1536*1536");
|
||||
});
|
||||
|
||||
test("Qwen 2.0 recommended sizes follow the official common-ratio table", () => {
|
||||
assert.equal(getQwen2SizeFromAspectRatio(null, "normal"), "1024*1024");
|
||||
assert.equal(getQwen2SizeFromAspectRatio(null, "2k"), "1536*1536");
|
||||
assert.equal(getQwen2SizeFromAspectRatio("16:9", "normal"), "1280*720");
|
||||
assert.equal(getQwen2SizeFromAspectRatio("21:9", "2k"), "2048*872");
|
||||
});
|
||||
|
||||
test("Qwen 2.0 derives free-form sizes within pixel budget for uncommon ratios", () => {
|
||||
const size = getQwen2SizeFromAspectRatio("5:2", "normal");
|
||||
const parsed = parseSize(size);
|
||||
assert.ok(parsed);
|
||||
assert.ok(parsed.width * parsed.height >= 512 * 512);
|
||||
assert.ok(parsed.width * parsed.height <= 2048 * 2048);
|
||||
assert.ok(Math.abs(parsed.width / parsed.height - 2.5) < 0.08);
|
||||
});
|
||||
|
||||
test("resolveSizeForModel validates explicit qwen-image-2.0 sizes by total pixels", () => {
|
||||
assert.equal(
|
||||
resolveSizeForModel("qwen-image-2.0-pro", {
|
||||
size: "2048x872",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
}),
|
||||
"2048*872",
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSizeForModel("qwen-image-2.0-pro", {
|
||||
size: "4096x4096",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
}),
|
||||
/total pixels between/,
|
||||
);
|
||||
});
|
||||
|
||||
test("resolveSizeForModel enforces fixed sizes for qwen-image-max/plus/image", () => {
|
||||
assert.equal(
|
||||
resolveSizeForModel("qwen-image-max", {
|
||||
size: null,
|
||||
aspectRatio: "1:1",
|
||||
quality: "2k",
|
||||
}),
|
||||
"1328*1328",
|
||||
);
|
||||
|
||||
assert.equal(
|
||||
resolveSizeForModel("qwen-image", {
|
||||
size: "1664x928",
|
||||
aspectRatio: "9:16",
|
||||
quality: "normal",
|
||||
}),
|
||||
"1664*928",
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSizeForModel("qwen-image-max", {
|
||||
size: null,
|
||||
aspectRatio: "21:9",
|
||||
quality: "2k",
|
||||
}),
|
||||
/supports only fixed ratios/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSizeForModel("qwen-image-plus", {
|
||||
size: "1024x1024",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
}),
|
||||
/support only these sizes/,
|
||||
);
|
||||
});
|
||||
|
||||
test("DashScope size normalization converts WxH into provider format", () => {
|
||||
assert.equal(normalizeSize("1024x1024"), "1024*1024");
|
||||
assert.equal(normalizeSize("2048*1152"), "2048*1152");
|
||||
});
|
||||
|
||||
test("Wan 2.7 derives sizes that match the requested ratio at the chosen pixel budget", () => {
|
||||
const square2k = getWan27SizeFromAspectRatio(null, "2k", 2048 * 2048);
|
||||
const parsedSquare = parseSize(square2k);
|
||||
assert.ok(parsedSquare);
|
||||
assert.equal(parsedSquare.width, parsedSquare.height);
|
||||
assert.ok(parsedSquare.width * parsedSquare.height <= 2048 * 2048);
|
||||
|
||||
const widescreen = getWan27SizeFromAspectRatio("16:9", "2k", 2048 * 2048);
|
||||
const parsedWide = parseSize(widescreen);
|
||||
assert.ok(parsedWide);
|
||||
assert.ok(Math.abs(parsedWide.width / parsedWide.height - 16 / 9) < 0.05);
|
||||
assert.ok(parsedWide.width * parsedWide.height <= 2048 * 2048);
|
||||
|
||||
const pro4k = getWan27SizeFromAspectRatio("16:9", "2k", 4096 * 4096);
|
||||
const parsed4k = parseSize(pro4k);
|
||||
assert.ok(parsed4k);
|
||||
assert.ok(parsed4k.width * parsed4k.height > 2048 * 2048);
|
||||
assert.ok(parsed4k.width * parsed4k.height <= 4096 * 4096);
|
||||
});
|
||||
|
||||
test("Wan 2.7 rejects aspect ratios outside the [1:8, 8:1] range", () => {
|
||||
assert.throws(
|
||||
() => getWan27SizeFromAspectRatio("9:1", "2k", 2048 * 2048),
|
||||
/1:8, 8:1/,
|
||||
);
|
||||
assert.throws(
|
||||
() => getWan27SizeFromAspectRatio("1:9", "normal", 2048 * 2048),
|
||||
/1:8, 8:1/,
|
||||
);
|
||||
});
|
||||
|
||||
test("Wan 2.7 derived sizes stay inside the boundary ratio limits after rounding", () => {
|
||||
for (const ar of ["8:1", "1:8"]) {
|
||||
const size = getWan27SizeFromAspectRatio(ar, "2k", 2048 * 2048);
|
||||
const parsed = parseSize(size);
|
||||
assert.ok(parsed);
|
||||
const ratio = parsed.width / parsed.height;
|
||||
assert.ok(ratio >= 1 / 8);
|
||||
assert.ok(ratio <= 8);
|
||||
assert.ok(parsed.width * parsed.height <= 2048 * 2048);
|
||||
}
|
||||
});
|
||||
|
||||
test("resolveSizeForModel routes wan2.7-image to the 2K-capped derivation", () => {
|
||||
const size = resolveSizeForModel("wan2.7-image", {
|
||||
size: null,
|
||||
aspectRatio: "16:9",
|
||||
quality: "2k",
|
||||
});
|
||||
const parsed = parseSize(size);
|
||||
assert.ok(parsed);
|
||||
assert.ok(parsed.width * parsed.height <= 2048 * 2048);
|
||||
assert.ok(Math.abs(parsed.width / parsed.height - 16 / 9) < 0.05);
|
||||
});
|
||||
|
||||
test("resolveSizeForModel allows wan2.7-image-pro 4K only when there are no reference images", () => {
|
||||
assert.equal(
|
||||
resolveSizeForModel("wan2.7-image-pro", {
|
||||
size: "4096*4096",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
}),
|
||||
"4096*4096",
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSizeForModel("wan2.7-image-pro", {
|
||||
size: "4096*4096",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
referenceImages: ["a.png"],
|
||||
}),
|
||||
/total pixels between 768\*768 and 2048\*2048/,
|
||||
);
|
||||
|
||||
const proWithRef = resolveSizeForModel("wan2.7-image-pro", {
|
||||
size: null,
|
||||
aspectRatio: "1:1",
|
||||
quality: "2k",
|
||||
referenceImages: ["a.png"],
|
||||
});
|
||||
const parsedRef = parseSize(proWithRef);
|
||||
assert.ok(parsedRef);
|
||||
assert.ok(parsedRef.width * parsedRef.height <= 2048 * 2048);
|
||||
});
|
||||
|
||||
test("Wan 2.7 request body forces n=1 and omits prompt_extend / negative_prompt", async (t) => {
|
||||
useEnv(t, { DASHSCOPE_API_KEY: "fake-key" });
|
||||
|
||||
const originalFetch = globalThis.fetch;
|
||||
let capturedBody: any = null;
|
||||
globalThis.fetch = (async (_url: string, init?: RequestInit) => {
|
||||
capturedBody = JSON.parse(String(init?.body));
|
||||
return new Response(
|
||||
JSON.stringify({
|
||||
output: {
|
||||
choices: [
|
||||
{
|
||||
message: {
|
||||
content: [{ image: "data:image/png;base64,iVBORw0KGgo=" }],
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
}),
|
||||
{ status: 200, headers: { "content-type": "application/json" } },
|
||||
);
|
||||
}) as typeof fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
await generateImage("hello", "wan2.7-image-pro", makeCliArgs({ aspectRatio: "1:1" }));
|
||||
|
||||
assert.equal(capturedBody.model, "wan2.7-image-pro");
|
||||
assert.deepEqual(Object.keys(capturedBody.parameters).sort(), ["n", "size", "watermark"]);
|
||||
assert.equal(capturedBody.parameters.n, 1);
|
||||
assert.equal(capturedBody.parameters.watermark, false);
|
||||
assert.equal(typeof capturedBody.parameters.size, "string");
|
||||
assert.ok(!("prompt_extend" in capturedBody.parameters));
|
||||
assert.ok(!("negative_prompt" in capturedBody.parameters));
|
||||
|
||||
assert.deepEqual(capturedBody.input.messages[0].content, [{ text: "hello" }]);
|
||||
});
|
||||
|
||||
test("Wan 2.7 request body forwards remote reference image URLs", async (t) => {
|
||||
useEnv(t, { DASHSCOPE_API_KEY: "fake-key" });
|
||||
|
||||
const originalFetch = globalThis.fetch;
|
||||
let capturedBody: any = null;
|
||||
globalThis.fetch = (async (_url: string, init?: RequestInit) => {
|
||||
capturedBody = JSON.parse(String(init?.body));
|
||||
return new Response(
|
||||
JSON.stringify({
|
||||
output: {
|
||||
choices: [
|
||||
{
|
||||
message: {
|
||||
content: [{ image: "data:image/png;base64,iVBORw0KGgo=" }],
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
}),
|
||||
{ status: 200, headers: { "content-type": "application/json" } },
|
||||
);
|
||||
}) as typeof fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
await generateImage(
|
||||
"combine these",
|
||||
"wan2.7-image-pro",
|
||||
makeCliArgs({ referenceImages: ["https://example.com/ref.png"] }),
|
||||
);
|
||||
|
||||
assert.deepEqual(capturedBody.input.messages[0].content, [
|
||||
{ image: "https://example.com/ref.png" },
|
||||
{ text: "combine these" },
|
||||
]);
|
||||
});
|
||||
|
||||
test("Wan 2.7 rejects --n > 1 to prevent silent multi-image billing", async (t) => {
|
||||
useEnv(t, { DASHSCOPE_API_KEY: "fake-key" });
|
||||
|
||||
await assert.rejects(
|
||||
() => generateImage("hi", "wan2.7-image-pro", makeCliArgs({ n: 2 })),
|
||||
/support exactly one output image/,
|
||||
);
|
||||
});
|
||||
|
||||
test("resolveSizeForModel validates explicit wan2.7 sizes by pixel budget and ratio", () => {
|
||||
assert.equal(
|
||||
resolveSizeForModel("wan2.7-image-pro", {
|
||||
size: "3840x2160",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
}),
|
||||
"3840*2160",
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSizeForModel("wan2.7-image-pro", {
|
||||
size: "3840x2160",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
referenceImages: ["a.png"],
|
||||
}),
|
||||
/total pixels between 768\*768 and 2048\*2048/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSizeForModel("wan2.7-image", {
|
||||
size: "4096x4096",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
}),
|
||||
/total pixels between 768\*768 and 2048\*2048/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSizeForModel("wan2.7-image-pro", {
|
||||
size: "3072*256",
|
||||
aspectRatio: null,
|
||||
quality: "2k",
|
||||
}),
|
||||
/1:8, 8:1/,
|
||||
);
|
||||
});
|
||||
@@ -0,0 +1,627 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
import type { CliArgs, Quality } from "../types";
|
||||
|
||||
type DashScopeModelFamily = "qwen2" | "qwenFixed" | "wan27" | "legacy";
|
||||
|
||||
type DashScopeModelSpec = {
|
||||
family: DashScopeModelFamily;
|
||||
defaultSize: string;
|
||||
};
|
||||
|
||||
const DEFAULT_MODEL = "qwen-image-2.0-pro";
|
||||
const MIN_QWEN_2_TOTAL_PIXELS = 512 * 512;
|
||||
const MAX_QWEN_2_TOTAL_PIXELS = 2048 * 2048;
|
||||
const SIZE_STEP = 16;
|
||||
const QWEN_NEGATIVE_PROMPT =
|
||||
"低分辨率，低画质，肢体畸形，手指畸形，画面过饱和，蜡像感，人脸无细节，过度光滑，画面具有AI感，构图混乱，文字模糊，扭曲";
|
||||
|
||||
const QWEN_2_TARGET_PIXELS: Record<Quality, number> = {
|
||||
normal: 1024 * 1024,
|
||||
"2k": 1536 * 1536,
|
||||
};
|
||||
|
||||
const MIN_WAN27_TOTAL_PIXELS = 768 * 768;
|
||||
const MAX_WAN27_PRO_T2I_PIXELS = 4096 * 4096;
|
||||
const MAX_WAN27_GENERAL_PIXELS = 2048 * 2048;
|
||||
const WAN27_MAX_REFERENCE_IMAGES = 9;
|
||||
|
||||
const WAN27_TARGET_PIXELS: Record<Quality, number> = {
|
||||
normal: 1024 * 1024,
|
||||
"2k": 2048 * 2048,
|
||||
};
|
||||
|
||||
const QWEN_2_RECOMMENDED: Record<string, Record<Quality, string>> = {
|
||||
"1:1": { normal: "1024*1024", "2k": "1536*1536" },
|
||||
"2:3": { normal: "768*1152", "2k": "1024*1536" },
|
||||
"3:2": { normal: "1152*768", "2k": "1536*1024" },
|
||||
"3:4": { normal: "960*1280", "2k": "1080*1440" },
|
||||
"4:3": { normal: "1280*960", "2k": "1440*1080" },
|
||||
"9:16": { normal: "720*1280", "2k": "1080*1920" },
|
||||
"16:9": { normal: "1280*720", "2k": "1920*1080" },
|
||||
"21:9": { normal: "1344*576", "2k": "2048*872" },
|
||||
};
|
||||
|
||||
const QWEN_FIXED_SIZES_BY_RATIO: Record<string, string> = {
|
||||
"16:9": "1664*928",
|
||||
"4:3": "1472*1104",
|
||||
"1:1": "1328*1328",
|
||||
"3:4": "1104*1472",
|
||||
"9:16": "928*1664",
|
||||
};
|
||||
|
||||
const QWEN_FIXED_SIZES = Object.values(QWEN_FIXED_SIZES_BY_RATIO);
|
||||
|
||||
const LEGACY_STANDARD_SIZES: [number, number][] = [
|
||||
[1024, 1024],
|
||||
[1280, 720],
|
||||
[720, 1280],
|
||||
[1024, 768],
|
||||
[768, 1024],
|
||||
[1536, 1024],
|
||||
[1024, 1536],
|
||||
[1536, 864],
|
||||
[864, 1536],
|
||||
];
|
||||
|
||||
const LEGACY_STANDARD_SIZES_2K: [number, number][] = [
|
||||
[1536, 1536],
|
||||
[2048, 1152],
|
||||
[1152, 2048],
|
||||
[1536, 1024],
|
||||
[1024, 1536],
|
||||
[1536, 864],
|
||||
[864, 1536],
|
||||
[2048, 2048],
|
||||
];
|
||||
|
||||
const QWEN_2_SPEC: DashScopeModelSpec = {
|
||||
family: "qwen2",
|
||||
defaultSize: "1024*1024",
|
||||
};
|
||||
|
||||
const QWEN_FIXED_SPEC: DashScopeModelSpec = {
|
||||
family: "qwenFixed",
|
||||
defaultSize: QWEN_FIXED_SIZES_BY_RATIO["16:9"],
|
||||
};
|
||||
|
||||
const WAN27_SPEC: DashScopeModelSpec = {
|
||||
family: "wan27",
|
||||
defaultSize: "2048*2048",
|
||||
};
|
||||
|
||||
const LEGACY_SPEC: DashScopeModelSpec = {
|
||||
family: "legacy",
|
||||
defaultSize: "1536*1536",
|
||||
};
|
||||
|
||||
const MODEL_SPEC_ALIASES: Record<string, DashScopeModelSpec> = {
|
||||
"qwen-image-3.0-pro": QWEN_2_SPEC,
|
||||
"qwen-image-2.0-pro": QWEN_2_SPEC,
|
||||
"qwen-image-2.0-pro-2026-04-22": QWEN_2_SPEC,
|
||||
"qwen-image-2.0-pro-2026-03-03": QWEN_2_SPEC,
|
||||
"qwen-image-2.0": QWEN_2_SPEC,
|
||||
"qwen-image-2.0-2026-03-03": QWEN_2_SPEC,
|
||||
"qwen-image-max": QWEN_FIXED_SPEC,
|
||||
"qwen-image-max-2025-12-30": QWEN_FIXED_SPEC,
|
||||
"qwen-image-plus": QWEN_FIXED_SPEC,
|
||||
"qwen-image-plus-2026-01-09": QWEN_FIXED_SPEC,
|
||||
"qwen-image": QWEN_FIXED_SPEC,
|
||||
"wan2.7-image-pro": WAN27_SPEC,
|
||||
"wan2.7-image": WAN27_SPEC,
|
||||
};
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.DASHSCOPE_IMAGE_MODEL || DEFAULT_MODEL;
|
||||
}
|
||||
|
||||
function getReferenceImageMime(filePath: string): string {
|
||||
const ext = path.extname(filePath).toLowerCase();
|
||||
if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
|
||||
if (ext === ".webp") return "image/webp";
|
||||
if (ext === ".bmp") return "image/bmp";
|
||||
return "image/png";
|
||||
}
|
||||
|
||||
async function loadReferenceImage(refPath: string): Promise<string> {
|
||||
if (/^https?:\/\//i.test(refPath)) {
|
||||
return refPath;
|
||||
}
|
||||
const fullPath = path.resolve(refPath);
|
||||
const bytes = await readFile(fullPath);
|
||||
return `data:${getReferenceImageMime(fullPath)};base64,${bytes.toString("base64")}`;
|
||||
}
|
||||
|
||||
function getApiKey(): string | null {
|
||||
return process.env.DASHSCOPE_API_KEY || null;
|
||||
}
|
||||
|
||||
function getBaseUrl(): string {
|
||||
const base = process.env.DASHSCOPE_BASE_URL || "https://dashscope.aliyuncs.com";
|
||||
return base.replace(/\/+$/g, "");
|
||||
}
|
||||
|
||||
function getModelSpec(model: string): DashScopeModelSpec {
|
||||
return MODEL_SPEC_ALIASES[model.trim().toLowerCase()] || LEGACY_SPEC;
|
||||
}
|
||||
|
||||
export function getModelFamily(model: string): DashScopeModelFamily {
|
||||
return getModelSpec(model).family;
|
||||
}
|
||||
|
||||
function normalizeQuality(quality: CliArgs["quality"]): Quality {
|
||||
return quality === "normal" ? "normal" : "2k";
|
||||
}
|
||||
|
||||
export function parseAspectRatio(ar: string): { width: number; height: number } | null {
|
||||
const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
|
||||
if (!match) return null;
|
||||
const w = parseFloat(match[1]!);
|
||||
const h = parseFloat(match[2]!);
|
||||
if (w <= 0 || h <= 0) return null;
|
||||
return { width: w, height: h };
|
||||
}
|
||||
|
||||
export function normalizeSize(size: string): string {
|
||||
return size.replace("x", "*");
|
||||
}
|
||||
|
||||
export function parseSize(size: string): { width: number; height: number } | null {
|
||||
const match = normalizeSize(size).match(/^(\d+)\*(\d+)$/);
|
||||
if (!match) return null;
|
||||
const width = Number(match[1]);
|
||||
const height = Number(match[2]);
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
function formatSize(width: number, height: number): string {
|
||||
return `${width}*${height}`;
|
||||
}
|
||||
|
||||
function getRatioValue(ar: string): number | null {
|
||||
const parsed = parseAspectRatio(ar);
|
||||
if (!parsed) return null;
|
||||
return parsed.width / parsed.height;
|
||||
}
|
||||
|
||||
function findKnownRatioKey(ar: string, candidates: string[], tolerance = 0.02): string | null {
|
||||
const targetRatio = getRatioValue(ar);
|
||||
if (targetRatio == null) return null;
|
||||
|
||||
let bestKey: string | null = null;
|
||||
let bestDiff = Infinity;
|
||||
|
||||
for (const candidate of candidates) {
|
||||
const candidateRatio = getRatioValue(candidate);
|
||||
if (candidateRatio == null) continue;
|
||||
const diff = Math.abs(candidateRatio - targetRatio);
|
||||
if (diff < bestDiff) {
|
||||
bestDiff = diff;
|
||||
bestKey = candidate;
|
||||
}
|
||||
}
|
||||
|
||||
return bestDiff <= tolerance ? bestKey : null;
|
||||
}
|
||||
|
||||
function roundToStep(value: number): number {
|
||||
return Math.max(SIZE_STEP, Math.round(value / SIZE_STEP) * SIZE_STEP);
|
||||
}
|
||||
|
||||
function floorToStep(value: number): number {
|
||||
return Math.max(SIZE_STEP, Math.floor(value / SIZE_STEP) * SIZE_STEP);
|
||||
}
|
||||
|
||||
function fitToPixelBudget(
|
||||
width: number,
|
||||
height: number,
|
||||
minPixels: number,
|
||||
maxPixels: number,
|
||||
): { width: number; height: number } {
|
||||
let nextWidth = width;
|
||||
let nextHeight = height;
|
||||
let pixels = nextWidth * nextHeight;
|
||||
|
||||
if (pixels > maxPixels) {
|
||||
const scale = Math.sqrt(maxPixels / pixels);
|
||||
nextWidth *= scale;
|
||||
nextHeight *= scale;
|
||||
} else if (pixels < minPixels) {
|
||||
const scale = Math.sqrt(minPixels / pixels);
|
||||
nextWidth *= scale;
|
||||
nextHeight *= scale;
|
||||
}
|
||||
|
||||
let roundedWidth = roundToStep(nextWidth);
|
||||
let roundedHeight = roundToStep(nextHeight);
|
||||
pixels = roundedWidth * roundedHeight;
|
||||
|
||||
while (pixels > maxPixels && (roundedWidth > SIZE_STEP || roundedHeight > SIZE_STEP)) {
|
||||
if (roundedWidth >= roundedHeight && roundedWidth > SIZE_STEP) {
|
||||
roundedWidth -= SIZE_STEP;
|
||||
} else if (roundedHeight > SIZE_STEP) {
|
||||
roundedHeight -= SIZE_STEP;
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
pixels = roundedWidth * roundedHeight;
|
||||
}
|
||||
|
||||
while (pixels < minPixels) {
|
||||
if (roundedWidth <= roundedHeight) {
|
||||
roundedWidth += SIZE_STEP;
|
||||
} else {
|
||||
roundedHeight += SIZE_STEP;
|
||||
}
|
||||
pixels = roundedWidth * roundedHeight;
|
||||
}
|
||||
|
||||
return { width: roundedWidth, height: roundedHeight };
|
||||
}
|
||||
|
||||
function clampWan27DerivedSizeToRatioBounds(
|
||||
size: { width: number; height: number },
|
||||
): { width: number; height: number } {
|
||||
let { width, height } = size;
|
||||
const ratio = width / height;
|
||||
|
||||
if (ratio > 8) {
|
||||
width = floorToStep(height * 8);
|
||||
} else if (ratio < 1 / 8) {
|
||||
height = floorToStep(width * 8);
|
||||
}
|
||||
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
export function getSizeFromAspectRatio(ar: string | null, quality: CliArgs["quality"]): string {
|
||||
const normalizedQuality = normalizeQuality(quality);
|
||||
const sizes = normalizedQuality === "2k" ? LEGACY_STANDARD_SIZES_2K : LEGACY_STANDARD_SIZES;
|
||||
const defaultSize = normalizedQuality === "2k" ? "1536*1536" : "1024*1024";
|
||||
|
||||
if (!ar) return defaultSize;
|
||||
|
||||
const parsed = parseAspectRatio(ar);
|
||||
if (!parsed) return defaultSize;
|
||||
|
||||
const targetRatio = parsed.width / parsed.height;
|
||||
let best = defaultSize;
|
||||
let bestDiff = Infinity;
|
||||
|
||||
for (const [width, height] of sizes) {
|
||||
const diff = Math.abs(width / height - targetRatio);
|
||||
if (diff < bestDiff) {
|
||||
bestDiff = diff;
|
||||
best = formatSize(width, height);
|
||||
}
|
||||
}
|
||||
|
||||
return best;
|
||||
}
|
||||
|
||||
export function getQwen2SizeFromAspectRatio(ar: string | null, quality: CliArgs["quality"]): string {
|
||||
const normalizedQuality = normalizeQuality(quality);
|
||||
|
||||
if (!ar) {
|
||||
return QWEN_2_RECOMMENDED["1:1"][normalizedQuality];
|
||||
}
|
||||
|
||||
const recommendedRatio = findKnownRatioKey(ar, Object.keys(QWEN_2_RECOMMENDED));
|
||||
if (recommendedRatio) {
|
||||
return QWEN_2_RECOMMENDED[recommendedRatio][normalizedQuality];
|
||||
}
|
||||
|
||||
const parsed = parseAspectRatio(ar);
|
||||
if (!parsed) {
|
||||
return QWEN_2_RECOMMENDED["1:1"][normalizedQuality];
|
||||
}
|
||||
|
||||
const targetRatio = parsed.width / parsed.height;
|
||||
const targetPixels = QWEN_2_TARGET_PIXELS[normalizedQuality];
|
||||
const rawWidth = Math.sqrt(targetPixels * targetRatio);
|
||||
const rawHeight = Math.sqrt(targetPixels / targetRatio);
|
||||
const fitted = fitToPixelBudget(
|
||||
rawWidth,
|
||||
rawHeight,
|
||||
MIN_QWEN_2_TOTAL_PIXELS,
|
||||
MAX_QWEN_2_TOTAL_PIXELS,
|
||||
);
|
||||
|
||||
return formatSize(fitted.width, fitted.height);
|
||||
}
|
||||
|
||||
function isWan27ProModel(model: string): boolean {
|
||||
return model.trim().toLowerCase() === "wan2.7-image-pro";
|
||||
}
|
||||
|
||||
function getWan27MaxPixels(model: string, hasReferenceImages: boolean): number {
|
||||
if (isWan27ProModel(model) && !hasReferenceImages) {
|
||||
return MAX_WAN27_PRO_T2I_PIXELS;
|
||||
}
|
||||
return MAX_WAN27_GENERAL_PIXELS;
|
||||
}
|
||||
|
||||
export function getWan27SizeFromAspectRatio(
|
||||
ar: string | null,
|
||||
quality: CliArgs["quality"],
|
||||
maxPixels: number,
|
||||
): string {
|
||||
const normalizedQuality = normalizeQuality(quality);
|
||||
const targetPixels = Math.min(WAN27_TARGET_PIXELS[normalizedQuality], maxPixels);
|
||||
|
||||
if (!ar) {
|
||||
const side = roundToStep(Math.sqrt(targetPixels));
|
||||
return formatSize(side, side);
|
||||
}
|
||||
|
||||
const parsed = parseAspectRatio(ar);
|
||||
if (!parsed) {
|
||||
const side = roundToStep(Math.sqrt(targetPixels));
|
||||
return formatSize(side, side);
|
||||
}
|
||||
|
||||
const ratio = parsed.width / parsed.height;
|
||||
if (ratio < 1 / 8 || ratio > 8) {
|
||||
throw new Error(
|
||||
`DashScope wan2.7 image models support aspect ratios in [1:8, 8:1]. Received "${ar}".`
|
||||
);
|
||||
}
|
||||
|
||||
const rawWidth = Math.sqrt(targetPixels * ratio);
|
||||
const rawHeight = Math.sqrt(targetPixels / ratio);
|
||||
const fitted = fitToPixelBudget(
|
||||
rawWidth,
|
||||
rawHeight,
|
||||
MIN_WAN27_TOTAL_PIXELS,
|
||||
maxPixels,
|
||||
);
|
||||
const bounded = clampWan27DerivedSizeToRatioBounds(fitted);
|
||||
|
||||
return formatSize(bounded.width, bounded.height);
|
||||
}
|
||||
|
||||
function validateWan27Size(size: string, maxPixels: number, model: string): string {
|
||||
const normalized = normalizeSize(size);
|
||||
const parsed = validateSizeFormat(normalized);
|
||||
const totalPixels = parsed.width * parsed.height;
|
||||
if (totalPixels < MIN_WAN27_TOTAL_PIXELS || totalPixels > maxPixels) {
|
||||
const limit = maxPixels === MAX_WAN27_PRO_T2I_PIXELS ? "4096*4096" : "2048*2048";
|
||||
throw new Error(
|
||||
`DashScope ${model} requires total pixels between 768*768 and ${limit} ` +
|
||||
`for the current request. Received ${normalized} (${totalPixels} pixels).`
|
||||
);
|
||||
}
|
||||
const ratio = parsed.width / parsed.height;
|
||||
if (ratio < 1 / 8 || ratio > 8) {
|
||||
throw new Error(
|
||||
`DashScope wan2.7 image models support aspect ratios in [1:8, 8:1]. ` +
|
||||
`Received ${normalized} (ratio ${ratio.toFixed(3)}).`
|
||||
);
|
||||
}
|
||||
return normalized;
|
||||
}
|
||||
|
||||
function getQwenFixedSizeFromAspectRatio(ar: string | null, quality: CliArgs["quality"]): string {
|
||||
if (quality === "normal") {
|
||||
console.warn(
|
||||
"DashScope qwen-image-max/plus/image models use fixed output sizes; --quality normal does not change the generated resolution."
|
||||
);
|
||||
}
|
||||
|
||||
if (!ar) return QWEN_FIXED_SPEC.defaultSize;
|
||||
|
||||
const ratioKey = findKnownRatioKey(ar, Object.keys(QWEN_FIXED_SIZES_BY_RATIO));
|
||||
if (!ratioKey) {
|
||||
throw new Error(
|
||||
`DashScope model supports only fixed ratios ${Object.keys(QWEN_FIXED_SIZES_BY_RATIO).join(", ")}. ` +
|
||||
`For custom ratios like "${ar}", use --model qwen-image-2.0-pro.`
|
||||
);
|
||||
}
|
||||
|
||||
return QWEN_FIXED_SIZES_BY_RATIO[ratioKey]!;
|
||||
}
|
||||
|
||||
function validateSizeFormat(size: string): { width: number; height: number } {
|
||||
const parsed = parseSize(size);
|
||||
if (!parsed) {
|
||||
throw new Error(`Invalid DashScope size "${size}". Expected <width>x<height> or <width>*<height>.`);
|
||||
}
|
||||
return parsed;
|
||||
}
|
||||
|
||||
function validateQwen2Size(size: string): string {
|
||||
const normalized = normalizeSize(size);
|
||||
const parsed = validateSizeFormat(normalized);
|
||||
const totalPixels = parsed.width * parsed.height;
|
||||
if (totalPixels < MIN_QWEN_2_TOTAL_PIXELS || totalPixels > MAX_QWEN_2_TOTAL_PIXELS) {
|
||||
throw new Error(
|
||||
`DashScope qwen-image-2.0* models require total pixels between ${MIN_QWEN_2_TOTAL_PIXELS} ` +
|
||||
`and ${MAX_QWEN_2_TOTAL_PIXELS}. Received ${normalized} (${totalPixels} pixels).`
|
||||
);
|
||||
}
|
||||
return normalized;
|
||||
}
|
||||
|
||||
function validateQwenFixedSize(size: string): string {
|
||||
const normalized = normalizeSize(size);
|
||||
validateSizeFormat(normalized);
|
||||
if (!QWEN_FIXED_SIZES.includes(normalized)) {
|
||||
throw new Error(
|
||||
`DashScope qwen-image-max/plus/image models support only these sizes: ${QWEN_FIXED_SIZES.join(", ")}. ` +
|
||||
`Received ${normalized}.`
|
||||
);
|
||||
}
|
||||
return normalized;
|
||||
}
|
||||
|
||||
export function resolveSizeForModel(
|
||||
model: string,
|
||||
args: Pick<CliArgs, "size" | "aspectRatio" | "quality"> & { referenceImages?: string[] },
|
||||
): string {
|
||||
const spec = getModelSpec(model);
|
||||
const referenceCount = args.referenceImages?.length ?? 0;
|
||||
|
||||
if (spec.family === "wan27") {
|
||||
const maxPixels = getWan27MaxPixels(model, referenceCount > 0);
|
||||
if (args.size) return validateWan27Size(args.size, maxPixels, model);
|
||||
return getWan27SizeFromAspectRatio(args.aspectRatio, args.quality, maxPixels);
|
||||
}
|
||||
|
||||
if (args.size) {
|
||||
if (spec.family === "qwen2") return validateQwen2Size(args.size);
|
||||
if (spec.family === "qwenFixed") return validateQwenFixedSize(args.size);
|
||||
validateSizeFormat(args.size);
|
||||
return normalizeSize(args.size);
|
||||
}
|
||||
|
||||
if (spec.family === "qwen2") {
|
||||
return getQwen2SizeFromAspectRatio(args.aspectRatio, args.quality);
|
||||
}
|
||||
|
||||
if (spec.family === "qwenFixed") {
|
||||
return getQwenFixedSizeFromAspectRatio(args.aspectRatio, args.quality);
|
||||
}
|
||||
|
||||
return getSizeFromAspectRatio(args.aspectRatio, args.quality);
|
||||
}
|
||||
|
||||
function buildParameters(
|
||||
family: DashScopeModelFamily,
|
||||
size: string,
|
||||
): Record<string, unknown> {
|
||||
if (family === "wan27") {
|
||||
return {
|
||||
size,
|
||||
n: 1,
|
||||
watermark: false,
|
||||
};
|
||||
}
|
||||
|
||||
const parameters: Record<string, unknown> = {
|
||||
prompt_extend: false,
|
||||
size,
|
||||
};
|
||||
|
||||
if (family === "qwen2" || family === "qwenFixed") {
|
||||
parameters.watermark = false;
|
||||
parameters.negative_prompt = QWEN_NEGATIVE_PROMPT;
|
||||
}
|
||||
|
||||
return parameters;
|
||||
}
|
||||
|
||||
type DashScopeResponse = {
|
||||
output?: {
|
||||
result_image?: string;
|
||||
choices?: Array<{
|
||||
message?: {
|
||||
content?: Array<{ image?: string }>;
|
||||
};
|
||||
}>;
|
||||
};
|
||||
};
|
||||
|
||||
async function extractImageFromResponse(result: DashScopeResponse): Promise<Uint8Array> {
|
||||
let imageData: string | null = null;
|
||||
|
||||
if (result.output?.result_image) {
|
||||
imageData = result.output.result_image;
|
||||
} else if (result.output?.choices?.[0]?.message?.content) {
|
||||
const content = result.output.choices[0].message.content;
|
||||
for (const item of content) {
|
||||
if (item.image) {
|
||||
imageData = item.image;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (!imageData) {
|
||||
console.error("Response:", JSON.stringify(result, null, 2));
|
||||
throw new Error("No image in response");
|
||||
}
|
||||
|
||||
if (imageData.startsWith("http://") || imageData.startsWith("https://")) {
|
||||
const imgRes = await fetch(imageData);
|
||||
if (!imgRes.ok) throw new Error("Failed to download image");
|
||||
const buf = await imgRes.arrayBuffer();
|
||||
return new Uint8Array(buf);
|
||||
}
|
||||
|
||||
return Uint8Array.from(Buffer.from(imageData, "base64"));
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
const apiKey = getApiKey();
|
||||
if (!apiKey) throw new Error("DASHSCOPE_API_KEY is required");
|
||||
|
||||
const spec = getModelSpec(model);
|
||||
|
||||
if (args.referenceImages.length > 0 && spec.family !== "wan27") {
|
||||
throw new Error(
|
||||
"Reference images are not supported with this DashScope model. Use a wan2.7 image model (--model wan2.7-image-pro or wan2.7-image), or switch to --provider google with a Gemini multimodal model."
|
||||
);
|
||||
}
|
||||
|
||||
if (args.referenceImages.length > WAN27_MAX_REFERENCE_IMAGES) {
|
||||
throw new Error(
|
||||
`DashScope wan2.7 image models accept at most ${WAN27_MAX_REFERENCE_IMAGES} reference images. Received ${args.referenceImages.length}.`
|
||||
);
|
||||
}
|
||||
|
||||
if (spec.family === "wan27" && args.n !== 1) {
|
||||
throw new Error(
|
||||
"DashScope wan2.7 image models in baoyu-image-gen support exactly one output image per request (extra images would be billed but discarded). Remove --n or use --n 1."
|
||||
);
|
||||
}
|
||||
|
||||
const size = resolveSizeForModel(model, args);
|
||||
const url = `${getBaseUrl()}/api/v1/services/aigc/multimodal-generation/generation`;
|
||||
|
||||
const content: Array<Record<string, unknown>> = [];
|
||||
if (spec.family === "wan27" && args.referenceImages.length > 0) {
|
||||
for (const refPath of args.referenceImages) {
|
||||
content.push({ image: await loadReferenceImage(refPath) });
|
||||
}
|
||||
}
|
||||
content.push({ text: prompt });
|
||||
|
||||
const body = {
|
||||
model,
|
||||
input: {
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content,
|
||||
},
|
||||
],
|
||||
},
|
||||
parameters: buildParameters(spec.family, size),
|
||||
};
|
||||
|
||||
console.log(`Generating image with DashScope (${model})...`, { family: spec.family, size });
|
||||
|
||||
const res = await fetch(url, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
},
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`DashScope API error (${res.status}): ${err}`);
|
||||
}
|
||||
|
||||
const result = await res.json() as DashScopeResponse;
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
@@ -0,0 +1,146 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
addAspectRatioToPrompt,
|
||||
buildGoogleUrl,
|
||||
buildPromptWithAspect,
|
||||
extractInlineImageData,
|
||||
extractPredictedImageData,
|
||||
getGoogleImageSize,
|
||||
isGoogleImagen,
|
||||
is1KOnlyGoogleModel,
|
||||
isGoogleMultimodal,
|
||||
normalizeGoogleModelId,
|
||||
resolveGeminiImageSize,
|
||||
} from "./google.ts";
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
test("Google provider helpers normalize model IDs and select image size defaults", () => {
|
||||
assert.equal(
|
||||
normalizeGoogleModelId("models/gemini-3.1-flash-image-preview"),
|
||||
"gemini-3.1-flash-image-preview",
|
||||
);
|
||||
assert.equal(isGoogleMultimodal("models/gemini-3-pro-image-preview"), true);
|
||||
assert.equal(isGoogleMultimodal("gemini-3-pro-image"), true);
|
||||
assert.equal(isGoogleMultimodal("gemini-3.1-flash-image"), true);
|
||||
assert.equal(isGoogleMultimodal("models/gemini-3-pro-image"), true);
|
||||
assert.equal(isGoogleImagen("imagen-3.0-generate-002"), true);
|
||||
assert.equal(getGoogleImageSize(makeArgs({ imageSize: null, quality: "2k" })), "2K");
|
||||
assert.equal(getGoogleImageSize(makeArgs({ imageSize: "4K", quality: "normal" })), "4K");
|
||||
});
|
||||
|
||||
test("Google clamps 1K-only models to 1K output", () => {
|
||||
assert.equal(isGoogleMultimodal("gemini-3.1-flash-lite-image"), true);
|
||||
assert.equal(is1KOnlyGoogleModel("gemini-3.1-flash-lite-image"), true);
|
||||
assert.equal(is1KOnlyGoogleModel("gemini-3.1-flash-image"), false);
|
||||
assert.equal(
|
||||
resolveGeminiImageSize("gemini-3.1-flash-lite-image", makeArgs({ imageSize: "4K", quality: "2k" })),
|
||||
"1K",
|
||||
);
|
||||
assert.equal(
|
||||
resolveGeminiImageSize("gemini-3-pro-image", makeArgs({ imageSize: null, quality: "2k" })),
|
||||
"2K",
|
||||
);
|
||||
});
|
||||
|
||||
test("Google URL builder appends v1beta when the base URL does not already include it", (t) => {
|
||||
useEnv(t, { GOOGLE_BASE_URL: "https://generativelanguage.googleapis.com" });
|
||||
assert.equal(
|
||||
buildGoogleUrl("models/demo:generateContent"),
|
||||
"https://generativelanguage.googleapis.com/v1beta/models/demo:generateContent",
|
||||
);
|
||||
});
|
||||
|
||||
test("Google URL and prompt helpers preserve existing v1beta paths and aspect hints", (t) => {
|
||||
useEnv(t, { GOOGLE_BASE_URL: "https://example.com/custom/v1beta/" });
|
||||
assert.equal(
|
||||
buildGoogleUrl("/models/demo:predict"),
|
||||
"https://example.com/custom/v1beta/models/demo:predict",
|
||||
);
|
||||
|
||||
assert.equal(
|
||||
addAspectRatioToPrompt("A city skyline", "16:9"),
|
||||
"A city skyline Aspect ratio: 16:9.",
|
||||
);
|
||||
assert.equal(
|
||||
buildPromptWithAspect("A city skyline", "16:9", "2k"),
|
||||
"A city skyline Aspect ratio: 16:9. High resolution 2048px.",
|
||||
);
|
||||
});
|
||||
|
||||
test("Google response extractors find inline and predicted image payloads", () => {
|
||||
assert.equal(
|
||||
extractInlineImageData({
|
||||
candidates: [
|
||||
{
|
||||
content: {
|
||||
parts: [{ inlineData: { data: "inline-base64" } }],
|
||||
},
|
||||
},
|
||||
],
|
||||
}),
|
||||
"inline-base64",
|
||||
);
|
||||
|
||||
assert.equal(
|
||||
extractPredictedImageData({
|
||||
predictions: [{ image: { imageBytes: "predicted-base64" } }],
|
||||
}),
|
||||
"predicted-base64",
|
||||
);
|
||||
|
||||
assert.equal(
|
||||
extractPredictedImageData({
|
||||
generatedImages: [{ bytesBase64Encoded: "generated-base64" }],
|
||||
}),
|
||||
"generated-base64",
|
||||
);
|
||||
});
|
||||
372
baoyu-skills/skills/baoyu-image-gen/scripts/providers/google.ts
Normal file
372
baoyu-skills/skills/baoyu-image-gen/scripts/providers/google.ts
Normal file
@@ -0,0 +1,372 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
import { execFileSync } from "node:child_process";
|
||||
import type { CliArgs } from "../types";
|
||||
|
||||
const GOOGLE_MULTIMODAL_MODELS = [
|
||||
"gemini-3-pro-image",
|
||||
"gemini-3.1-flash-image",
|
||||
"gemini-3.1-flash-lite-image",
|
||||
"gemini-3-pro-image-preview",
|
||||
"gemini-3-flash-preview",
|
||||
"gemini-3.1-flash-image-preview",
|
||||
];
|
||||
const GOOGLE_1K_ONLY_MODELS = ["gemini-3.1-flash-lite-image"];
|
||||
const GOOGLE_IMAGEN_MODELS = [
|
||||
"imagen-3.0-generate-002",
|
||||
"imagen-3.0-generate-001",
|
||||
];
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.GOOGLE_IMAGE_MODEL || "gemini-3-pro-image";
|
||||
}
|
||||
|
||||
export function normalizeGoogleModelId(model: string): string {
|
||||
return model.startsWith("models/") ? model.slice("models/".length) : model;
|
||||
}
|
||||
|
||||
export function isGoogleMultimodal(model: string): boolean {
|
||||
const normalized = normalizeGoogleModelId(model);
|
||||
return GOOGLE_MULTIMODAL_MODELS.some((m) => normalized.includes(m));
|
||||
}
|
||||
|
||||
export function isGoogleImagen(model: string): boolean {
|
||||
const normalized = normalizeGoogleModelId(model);
|
||||
return GOOGLE_IMAGEN_MODELS.some((m) => normalized.includes(m));
|
||||
}
|
||||
|
||||
function getGoogleApiKey(): string | null {
|
||||
return process.env.GOOGLE_API_KEY || process.env.GEMINI_API_KEY || null;
|
||||
}
|
||||
|
||||
export function getGoogleImageSize(args: CliArgs): "1K" | "2K" | "4K" {
|
||||
if (args.imageSize) return args.imageSize as "1K" | "2K" | "4K";
|
||||
return args.quality === "2k" ? "2K" : "1K";
|
||||
}
|
||||
|
||||
export function is1KOnlyGoogleModel(model: string): boolean {
|
||||
const normalized = normalizeGoogleModelId(model);
|
||||
return GOOGLE_1K_ONLY_MODELS.some((m) => normalized.includes(m));
|
||||
}
|
||||
|
||||
export function resolveGeminiImageSize(
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
): "1K" | "2K" | "4K" {
|
||||
const size = getGoogleImageSize(args);
|
||||
if (size !== "1K" && is1KOnlyGoogleModel(model)) {
|
||||
console.error(
|
||||
`Warning: ${normalizeGoogleModelId(model)} only supports 1K output, using 1K instead of ${size}.`,
|
||||
);
|
||||
return "1K";
|
||||
}
|
||||
return size;
|
||||
}
|
||||
|
||||
function getGoogleBaseUrl(): string {
|
||||
const base =
|
||||
process.env.GOOGLE_BASE_URL || "https://generativelanguage.googleapis.com";
|
||||
return base.replace(/\/+$/g, "");
|
||||
}
|
||||
|
||||
export function buildGoogleUrl(pathname: string): string {
|
||||
const base = getGoogleBaseUrl();
|
||||
const cleanedPath = pathname.replace(/^\/+/g, "");
|
||||
if (base.endsWith("/v1beta")) return `${base}/${cleanedPath}`;
|
||||
return `${base}/v1beta/${cleanedPath}`;
|
||||
}
|
||||
|
||||
function toModelPath(model: string): string {
|
||||
const modelId = normalizeGoogleModelId(model);
|
||||
return `models/${modelId}`;
|
||||
}
|
||||
|
||||
function getHttpProxy(): string | null {
|
||||
return (
|
||||
process.env.https_proxy ||
|
||||
process.env.HTTPS_PROXY ||
|
||||
process.env.http_proxy ||
|
||||
process.env.HTTP_PROXY ||
|
||||
process.env.ALL_PROXY ||
|
||||
null
|
||||
);
|
||||
}
|
||||
|
||||
async function postGoogleJsonViaCurl<T>(
|
||||
url: string,
|
||||
apiKey: string,
|
||||
body: unknown,
|
||||
): Promise<T> {
|
||||
const proxy = getHttpProxy();
|
||||
const bodyStr = JSON.stringify(body);
|
||||
const args = [
|
||||
"-s",
|
||||
"--connect-timeout",
|
||||
"30",
|
||||
"--max-time",
|
||||
"300",
|
||||
...(proxy ? ["-x", proxy] : []),
|
||||
url,
|
||||
"-H",
|
||||
"Content-Type: application/json",
|
||||
"-H",
|
||||
`x-goog-api-key: ${apiKey}`,
|
||||
"-d",
|
||||
"@-",
|
||||
];
|
||||
|
||||
let result = "";
|
||||
try {
|
||||
result = execFileSync("curl", args, {
|
||||
input: bodyStr,
|
||||
encoding: "utf8",
|
||||
maxBuffer: 100 * 1024 * 1024,
|
||||
timeout: 310000,
|
||||
});
|
||||
} catch (error) {
|
||||
const e = error as { message?: string; stderr?: string | Buffer };
|
||||
const stderrText =
|
||||
typeof e.stderr === "string"
|
||||
? e.stderr
|
||||
: e.stderr
|
||||
? e.stderr.toString("utf8")
|
||||
: "";
|
||||
const details = stderrText.trim() || e.message || "curl request failed";
|
||||
throw new Error(`Google API request failed via curl: ${details}`);
|
||||
}
|
||||
|
||||
const parsed = JSON.parse(result) as any;
|
||||
if (parsed.error) {
|
||||
throw new Error(
|
||||
`Google API error (${parsed.error.code}): ${parsed.error.message}`,
|
||||
);
|
||||
}
|
||||
return parsed as T;
|
||||
}
|
||||
|
||||
async function postGoogleJsonViaFetch<T>(
|
||||
url: string,
|
||||
apiKey: string,
|
||||
body: unknown,
|
||||
): Promise<T> {
|
||||
const res = await fetch(url, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
"x-goog-api-key": apiKey,
|
||||
},
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`Google API error (${res.status}): ${err}`);
|
||||
}
|
||||
|
||||
return (await res.json()) as T;
|
||||
}
|
||||
|
||||
async function postGoogleJson<T>(pathname: string, body: unknown): Promise<T> {
|
||||
const apiKey = getGoogleApiKey();
|
||||
if (!apiKey) throw new Error("GOOGLE_API_KEY or GEMINI_API_KEY is required");
|
||||
|
||||
const url = buildGoogleUrl(pathname);
|
||||
const proxy = getHttpProxy();
|
||||
|
||||
// When an HTTP proxy is detected, use curl instead of fetch.
|
||||
// Bun's fetch has a known issue where long-lived connections through
|
||||
// HTTP proxies get their sockets closed unexpectedly, causing image
|
||||
// generation requests to fail with "socket connection was closed
|
||||
// unexpectedly". Using curl as the HTTP client works around this.
|
||||
if (proxy) {
|
||||
return postGoogleJsonViaCurl<T>(url, apiKey, body);
|
||||
}
|
||||
|
||||
return postGoogleJsonViaFetch<T>(url, apiKey, body);
|
||||
}
|
||||
|
||||
export function buildPromptWithAspect(
|
||||
prompt: string,
|
||||
ar: string | null,
|
||||
quality: CliArgs["quality"],
|
||||
): string {
|
||||
let result = prompt;
|
||||
if (ar) {
|
||||
result += ` Aspect ratio: ${ar}.`;
|
||||
}
|
||||
if (quality === "2k") {
|
||||
result += " High resolution 2048px.";
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
export function addAspectRatioToPrompt(prompt: string, ar: string | null): string {
|
||||
if (!ar) return prompt;
|
||||
return `${prompt} Aspect ratio: ${ar}.`;
|
||||
}
|
||||
|
||||
async function readImageAsBase64(
|
||||
p: string,
|
||||
): Promise<{ data: string; mimeType: string }> {
|
||||
const buf = await readFile(p);
|
||||
const ext = path.extname(p).toLowerCase();
|
||||
let mimeType = "image/png";
|
||||
if (ext === ".jpg" || ext === ".jpeg") mimeType = "image/jpeg";
|
||||
else if (ext === ".gif") mimeType = "image/gif";
|
||||
else if (ext === ".webp") mimeType = "image/webp";
|
||||
return { data: buf.toString("base64"), mimeType };
|
||||
}
|
||||
|
||||
export function extractInlineImageData(response: {
|
||||
candidates?: Array<{
|
||||
content?: { parts?: Array<{ inlineData?: { data?: string } }> };
|
||||
}>;
|
||||
}): string | null {
|
||||
for (const candidate of response.candidates || []) {
|
||||
for (const part of candidate.content?.parts || []) {
|
||||
const data = part.inlineData?.data;
|
||||
if (typeof data === "string" && data.length > 0) return data;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
export function extractPredictedImageData(response: {
|
||||
predictions?: Array<any>;
|
||||
generatedImages?: Array<any>;
|
||||
}): string | null {
|
||||
const candidates = [
|
||||
...(response.predictions || []),
|
||||
...(response.generatedImages || []),
|
||||
];
|
||||
for (const candidate of candidates) {
|
||||
if (!candidate || typeof candidate !== "object") continue;
|
||||
if (typeof candidate.imageBytes === "string") return candidate.imageBytes;
|
||||
if (typeof candidate.bytesBase64Encoded === "string")
|
||||
return candidate.bytesBase64Encoded;
|
||||
if (typeof candidate.data === "string") return candidate.data;
|
||||
const image = candidate.image;
|
||||
if (image && typeof image === "object") {
|
||||
if (typeof image.imageBytes === "string") return image.imageBytes;
|
||||
if (typeof image.bytesBase64Encoded === "string")
|
||||
return image.bytesBase64Encoded;
|
||||
if (typeof image.data === "string") return image.data;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
async function generateWithGemini(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
const promptWithAspect = addAspectRatioToPrompt(prompt, args.aspectRatio);
|
||||
const parts: Array<{
|
||||
text?: string;
|
||||
inlineData?: { data: string; mimeType: string };
|
||||
}> = [];
|
||||
for (const refPath of args.referenceImages) {
|
||||
const { data, mimeType } = await readImageAsBase64(refPath);
|
||||
parts.push({ inlineData: { data, mimeType } });
|
||||
}
|
||||
parts.push({ text: promptWithAspect });
|
||||
|
||||
const imageConfig: { imageSize: "1K" | "2K" | "4K" } = {
|
||||
imageSize: resolveGeminiImageSize(model, args),
|
||||
};
|
||||
|
||||
console.log("Generating image with Gemini...", imageConfig);
|
||||
const response = await postGoogleJson<{
|
||||
candidates?: Array<{
|
||||
content?: { parts?: Array<{ inlineData?: { data?: string } }> };
|
||||
}>;
|
||||
}>(`${toModelPath(model)}:generateContent`, {
|
||||
contents: [
|
||||
{
|
||||
role: "user",
|
||||
parts,
|
||||
},
|
||||
],
|
||||
generationConfig: {
|
||||
responseModalities: ["IMAGE"],
|
||||
imageConfig,
|
||||
},
|
||||
});
|
||||
console.log("Generation completed.");
|
||||
|
||||
const imageData = extractInlineImageData(response);
|
||||
if (imageData) return Uint8Array.from(Buffer.from(imageData, "base64"));
|
||||
|
||||
throw new Error("No image in response");
|
||||
}
|
||||
|
||||
async function generateWithImagen(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
const fullPrompt = buildPromptWithAspect(
|
||||
prompt,
|
||||
args.aspectRatio,
|
||||
args.quality,
|
||||
);
|
||||
const imageSize = getGoogleImageSize(args);
|
||||
if (imageSize === "4K") {
|
||||
console.error(
|
||||
"Warning: Imagen models do not support 4K imageSize, using 2K instead.",
|
||||
);
|
||||
}
|
||||
|
||||
const parameters: Record<string, unknown> = {
|
||||
sampleCount: args.n,
|
||||
};
|
||||
if (args.aspectRatio) {
|
||||
parameters.aspectRatio = args.aspectRatio;
|
||||
}
|
||||
if (imageSize === "1K" || imageSize === "2K") {
|
||||
parameters.imageSize = imageSize;
|
||||
} else {
|
||||
parameters.imageSize = "2K";
|
||||
}
|
||||
|
||||
const response = await postGoogleJson<{
|
||||
predictions?: Array<any>;
|
||||
generatedImages?: Array<any>;
|
||||
}>(`${toModelPath(model)}:predict`, {
|
||||
instances: [
|
||||
{
|
||||
prompt: fullPrompt,
|
||||
},
|
||||
],
|
||||
parameters,
|
||||
});
|
||||
|
||||
const imageData = extractPredictedImageData(response);
|
||||
if (imageData) return Uint8Array.from(Buffer.from(imageData, "base64"));
|
||||
|
||||
throw new Error("No image in response");
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
if (isGoogleImagen(model)) {
|
||||
if (args.referenceImages.length > 0) {
|
||||
throw new Error(
|
||||
"Reference images are not supported with Imagen models. Use a Gemini multimodal model such as gemini-3-pro-image, gemini-3.1-flash-image, gemini-3.1-flash-lite-image, gemini-3-pro-image-preview, gemini-3-flash-preview, or gemini-3.1-flash-image-preview.",
|
||||
);
|
||||
}
|
||||
return generateWithImagen(prompt, model, args);
|
||||
}
|
||||
|
||||
if (!isGoogleMultimodal(model) && args.referenceImages.length > 0) {
|
||||
throw new Error(
|
||||
"Reference images are only supported with Gemini multimodal models such as gemini-3-pro-image, gemini-3.1-flash-image, gemini-3.1-flash-lite-image, gemini-3-pro-image-preview, gemini-3-flash-preview, or gemini-3.1-flash-image-preview.",
|
||||
);
|
||||
}
|
||||
|
||||
return generateWithGemini(prompt, model, args);
|
||||
}
|
||||
@@ -0,0 +1,115 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import { generateImage } from "./jimeng.ts";
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
test("Jimeng submit request uses prompt field expected by current API", async (t) => {
|
||||
useEnv(t, {
|
||||
JIMENG_ACCESS_KEY_ID: "test-access-key",
|
||||
JIMENG_SECRET_ACCESS_KEY: "test-secret-key",
|
||||
JIMENG_BASE_URL: null,
|
||||
JIMENG_REGION: null,
|
||||
});
|
||||
|
||||
const originalFetch = globalThis.fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
const calls: Array<{
|
||||
input: string;
|
||||
init?: RequestInit;
|
||||
}> = [];
|
||||
|
||||
globalThis.fetch = async (input, init) => {
|
||||
calls.push({
|
||||
input: String(input),
|
||||
init,
|
||||
});
|
||||
|
||||
if (calls.length === 1) {
|
||||
return Response.json({
|
||||
code: 10000,
|
||||
data: {
|
||||
task_id: "task-123",
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
return Response.json({
|
||||
code: 10000,
|
||||
data: {
|
||||
status: "done",
|
||||
binary_data_base64: [Buffer.from("jimeng-image").toString("base64")],
|
||||
},
|
||||
});
|
||||
};
|
||||
|
||||
const image = await generateImage(
|
||||
"A quiet bamboo forest",
|
||||
"jimeng_t2i_v40",
|
||||
makeArgs({ quality: "normal" }),
|
||||
);
|
||||
|
||||
assert.equal(Buffer.from(image).toString("utf8"), "jimeng-image");
|
||||
assert.equal(calls.length, 2);
|
||||
assert.equal(
|
||||
calls[0]?.input,
|
||||
"https://visual.volcengineapi.com/?Action=CVSync2AsyncSubmitTask&Version=2022-08-31",
|
||||
);
|
||||
|
||||
const submitBody = JSON.parse(String(calls[0]?.init?.body)) as Record<string, unknown>;
|
||||
assert.equal(submitBody.req_key, "jimeng_t2i_v40");
|
||||
assert.equal(submitBody.prompt, "A quiet bamboo forest");
|
||||
assert.ok(!("prompt_text" in submitBody));
|
||||
assert.equal(submitBody.width, 1024);
|
||||
assert.equal(submitBody.height, 1024);
|
||||
});
|
||||
467
baoyu-skills/skills/baoyu-image-gen/scripts/providers/jimeng.ts
Normal file
467
baoyu-skills/skills/baoyu-image-gen/scripts/providers/jimeng.ts
Normal file
@@ -0,0 +1,467 @@
|
||||
import type { CliArgs } from "../types";
|
||||
import * as crypto from "node:crypto";
|
||||
|
||||
type JimengSizePreset = "normal" | "2k" | "4k";
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.JIMENG_IMAGE_MODEL || "jimeng_t2i_v40";
|
||||
}
|
||||
|
||||
function getAccessKey(): string | null {
|
||||
return process.env.JIMENG_ACCESS_KEY_ID || null;
|
||||
}
|
||||
|
||||
function getSecretKey(): string | null {
|
||||
return process.env.JIMENG_SECRET_ACCESS_KEY || null;
|
||||
}
|
||||
|
||||
function getRegion(): string {
|
||||
return process.env.JIMENG_REGION || "cn-north-1";
|
||||
}
|
||||
|
||||
function getBaseUrl(): string {
|
||||
return process.env.JIMENG_BASE_URL || "https://visual.volcengineapi.com";
|
||||
}
|
||||
|
||||
function resolveEndpoint(query: Record<string, string>): {
|
||||
url: string;
|
||||
host: string;
|
||||
canonicalUri: string;
|
||||
} {
|
||||
let baseUrl: URL;
|
||||
try {
|
||||
baseUrl = new URL(getBaseUrl());
|
||||
} catch {
|
||||
throw new Error(`Invalid JIMENG_BASE_URL: ${getBaseUrl()}`);
|
||||
}
|
||||
|
||||
baseUrl.search = "";
|
||||
for (const [key, value] of Object.entries(query).sort(([a], [b]) => a.localeCompare(b))) {
|
||||
baseUrl.searchParams.set(key, value);
|
||||
}
|
||||
|
||||
return {
|
||||
url: baseUrl.toString(),
|
||||
host: baseUrl.host,
|
||||
canonicalUri: baseUrl.pathname || "/",
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Volcengine HMAC-SHA256 signature generation
|
||||
* Following the official documentation at:
|
||||
* https://www.volcengine.com/docs/85621/1817045
|
||||
*/
|
||||
function generateSignature(
|
||||
method: string,
|
||||
query: Record<string, string>,
|
||||
headers: Record<string, string>,
|
||||
body: string,
|
||||
accessKey: string,
|
||||
secretKey: string,
|
||||
region: string,
|
||||
service: string,
|
||||
canonicalUri: string
|
||||
): string {
|
||||
// 1. Create canonical request
|
||||
// Sort query parameters alphabetically
|
||||
const sortedQuery = Object.entries(query)
|
||||
.sort(([a], [b]) => a.localeCompare(b))
|
||||
.map(([k, v]) => `${encodeURIComponent(k)}=${encodeURIComponent(v)}`)
|
||||
.join("&");
|
||||
|
||||
// Sort headers alphabetically and create canonical headers
|
||||
const sortedHeaders = Object.entries(headers)
|
||||
.sort(([a], [b]) => a.localeCompare(b))
|
||||
.map(([k, v]) => `${k.toLowerCase()}:${v.trim()}\n`)
|
||||
.join("");
|
||||
|
||||
const signedHeaders = Object.keys(headers)
|
||||
.sort()
|
||||
.map(k => k.toLowerCase())
|
||||
.join(";");
|
||||
|
||||
const hashedPayload = crypto.createHash("sha256").update(body, "utf8").digest("hex");
|
||||
|
||||
const canonicalRequest = [
|
||||
method,
|
||||
canonicalUri,
|
||||
sortedQuery,
|
||||
sortedHeaders,
|
||||
signedHeaders,
|
||||
hashedPayload,
|
||||
].join("\n");
|
||||
|
||||
const hashedCanonicalRequest = crypto
|
||||
.createHash("sha256")
|
||||
.update(canonicalRequest, "utf8")
|
||||
.digest("hex");
|
||||
|
||||
// 2. Create string to sign
|
||||
const algorithm = "HMAC-SHA256";
|
||||
const timestamp = headers["X-Date"] || headers["x-date"];
|
||||
if (!timestamp) {
|
||||
throw new Error("Jimeng signature generation requires an X-Date header.");
|
||||
}
|
||||
const dateStamp = timestamp.slice(0, 8);
|
||||
|
||||
const credentialScope = `${dateStamp}/${region}/${service}/request`;
|
||||
|
||||
const stringToSign = [
|
||||
algorithm,
|
||||
timestamp,
|
||||
credentialScope,
|
||||
hashedCanonicalRequest,
|
||||
].join("\n");
|
||||
|
||||
// 3. Calculate signature
|
||||
const kDate = crypto
|
||||
.createHmac("sha256", secretKey)
|
||||
.update(dateStamp)
|
||||
.digest();
|
||||
|
||||
const kRegion = crypto.createHmac("sha256", kDate).update(region).digest();
|
||||
const kService = crypto.createHmac("sha256", kRegion).update(service).digest();
|
||||
const kSigning = crypto.createHmac("sha256", kService).update("request").digest();
|
||||
|
||||
const signature = crypto
|
||||
.createHmac("sha256", kSigning)
|
||||
.update(stringToSign)
|
||||
.digest("hex");
|
||||
|
||||
// 4. Create authorization header
|
||||
return `${algorithm} Credential=${accessKey}/${credentialScope}, SignedHeaders=${signedHeaders}, Signature=${signature}`;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse aspect ratio string like "16:9", "1:1", "4:3" into width and height
|
||||
*/
|
||||
function parseAspectRatio(ar: string): { width: number; height: number } | null {
|
||||
const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
|
||||
if (!match) return null;
|
||||
const w = parseFloat(match[1]!);
|
||||
const h = parseFloat(match[2]!);
|
||||
if (w <= 0 || h <= 0) return null;
|
||||
return { width: w, height: h };
|
||||
}
|
||||
|
||||
/**
|
||||
* Supported size presets for different quality levels
|
||||
* Based on Volcengine Jimeng documentation
|
||||
*/
|
||||
const SIZE_PRESETS: Record<string, Record<string, string>> = {
|
||||
normal: {
|
||||
"1:1": "1024x1024",
|
||||
"4:3": "1360x1020",
|
||||
"16:9": "1536x864",
|
||||
"3:2": "1440x960",
|
||||
"21:9": "1920x824",
|
||||
},
|
||||
"2k": {
|
||||
"1:1": "2048x2048",
|
||||
"4:3": "2304x1728",
|
||||
"16:9": "2560x1440",
|
||||
"3:2": "2496x1664",
|
||||
"21:9": "3024x1296",
|
||||
},
|
||||
"4k": {
|
||||
"1:1": "4096x4096",
|
||||
"4:3": "4694x3520",
|
||||
"16:9": "5404x3040",
|
||||
"3:2": "4992x3328",
|
||||
"21:9": "6198x2656",
|
||||
},
|
||||
};
|
||||
|
||||
function normalizeDimensions(value: string): string | null {
|
||||
const match = value.trim().match(/^(\d+)\s*[xX*]\s*(\d+)$/);
|
||||
if (!match) return null;
|
||||
return `${match[1]}x${match[2]}`;
|
||||
}
|
||||
|
||||
function getClosestPresetSize(ar: string | null, qualityLevel: JimengSizePreset): string {
|
||||
const presets = SIZE_PRESETS[qualityLevel];
|
||||
const defaultSize = presets["1:1"]!;
|
||||
|
||||
if (!ar) return defaultSize;
|
||||
|
||||
const parsed = parseAspectRatio(ar);
|
||||
if (!parsed) return defaultSize;
|
||||
|
||||
const targetRatio = parsed.width / parsed.height;
|
||||
let bestMatch = defaultSize;
|
||||
let bestDiff = Infinity;
|
||||
|
||||
for (const [ratio, size] of Object.entries(presets)) {
|
||||
const [w, h] = ratio.split(":").map(Number);
|
||||
const presetRatio = w / h;
|
||||
const diff = Math.abs(presetRatio - targetRatio);
|
||||
if (diff < bestDiff) {
|
||||
bestDiff = diff;
|
||||
bestMatch = size;
|
||||
}
|
||||
}
|
||||
|
||||
return bestMatch;
|
||||
}
|
||||
|
||||
function normalizeImageSizePreset(imageSize: string, ar: string | null): string | null {
|
||||
const preset = imageSize.trim().toUpperCase();
|
||||
if (preset === "1K") return getClosestPresetSize(ar, "normal");
|
||||
if (preset === "2K") return getClosestPresetSize(ar, "2k");
|
||||
if (preset === "4K") return getClosestPresetSize(ar, "4k");
|
||||
return normalizeDimensions(imageSize);
|
||||
}
|
||||
|
||||
function getImageSize(ar: string | null, quality: CliArgs["quality"], imageSize?: string | null): string {
|
||||
if (imageSize) {
|
||||
const normalizedSize = normalizeImageSizePreset(imageSize, ar);
|
||||
if (normalizedSize) return normalizedSize;
|
||||
}
|
||||
|
||||
// Default to 2K quality if not specified
|
||||
const qualityLevel: JimengSizePreset = quality === "normal" ? "normal" : "2k";
|
||||
return getClosestPresetSize(ar, qualityLevel);
|
||||
}
|
||||
|
||||
/**
|
||||
* Step 1: Submit async task to Volcengine Jimeng API
|
||||
*/
|
||||
async function submitTask(
|
||||
prompt: string,
|
||||
model: string,
|
||||
size: string,
|
||||
accessKey: string,
|
||||
secretKey: string,
|
||||
region: string
|
||||
): Promise<string> {
|
||||
// Query parameters for submit endpoint
|
||||
const query = {
|
||||
Action: "CVSync2AsyncSubmitTask",
|
||||
Version: "2022-08-31",
|
||||
};
|
||||
const endpoint = resolveEndpoint(query);
|
||||
|
||||
// Request body - Jimeng API expects width/height as separate integers
|
||||
const [width, height] = size.split("x").map(Number);
|
||||
const bodyObj = {
|
||||
req_key: model,
|
||||
prompt,
|
||||
// Use separate width and height parameters instead of size string
|
||||
width: width,
|
||||
height: height,
|
||||
// Optional: seed for reproducibility
|
||||
// seed: Math.floor(Math.random() * 999999),
|
||||
};
|
||||
|
||||
const body = JSON.stringify(bodyObj);
|
||||
|
||||
// Headers
|
||||
const timestampHeader = new Date().toISOString().replace(/[:\-]|\.\d{3}/g, "");
|
||||
const headers = {
|
||||
"Content-Type": "application/json",
|
||||
"X-Date": timestampHeader,
|
||||
"Host": endpoint.host,
|
||||
};
|
||||
|
||||
// Generate signature
|
||||
const authorization = generateSignature(
|
||||
"POST",
|
||||
query,
|
||||
headers,
|
||||
body,
|
||||
accessKey,
|
||||
secretKey,
|
||||
region,
|
||||
"cv",
|
||||
endpoint.canonicalUri
|
||||
);
|
||||
|
||||
console.error(`Submitting task to Jimeng (${model})...`, { width, height });
|
||||
|
||||
const res = await fetch(endpoint.url, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
...headers,
|
||||
"Authorization": authorization,
|
||||
},
|
||||
body,
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`Jimeng API submit error (${res.status}): ${err}`);
|
||||
}
|
||||
|
||||
const result = (await res.json()) as {
|
||||
code?: number;
|
||||
message?: string;
|
||||
data?: {
|
||||
task_id?: string;
|
||||
};
|
||||
};
|
||||
|
||||
// Volcengine API returns code 10000 for success
|
||||
if (result.code !== 10000 || !result.data?.task_id) {
|
||||
console.error("Submit response:", JSON.stringify(result, null, 2));
|
||||
throw new Error(`Failed to submit task: ${result.message || "Unknown error"}`);
|
||||
}
|
||||
|
||||
return result.data.task_id;
|
||||
}
|
||||
|
||||
/**
|
||||
* Step 2: Poll for task result
|
||||
* Returns image data directly as Uint8Array
|
||||
*/
|
||||
async function pollForResult(
|
||||
taskId: string,
|
||||
model: string,
|
||||
accessKey: string,
|
||||
secretKey: string,
|
||||
region: string
|
||||
): Promise<Uint8Array> {
|
||||
const maxAttempts = 60;
|
||||
const pollIntervalMs = 2000;
|
||||
|
||||
for (let attempt = 0; attempt < maxAttempts; attempt++) {
|
||||
// Query parameters for result endpoint
|
||||
const query = {
|
||||
Action: "CVSync2AsyncGetResult",
|
||||
Version: "2022-08-31",
|
||||
};
|
||||
const endpoint = resolveEndpoint(query);
|
||||
|
||||
// Request body - include req_key and task_id
|
||||
const bodyObj = {
|
||||
req_key: model,
|
||||
task_id: taskId,
|
||||
};
|
||||
|
||||
const body = JSON.stringify(bodyObj);
|
||||
|
||||
// Headers
|
||||
const timestampHeader = new Date().toISOString().replace(/[:\-]|\.\d{3}/g, "");
|
||||
const headers = {
|
||||
"Content-Type": "application/json",
|
||||
"X-Date": timestampHeader,
|
||||
"Host": endpoint.host,
|
||||
};
|
||||
|
||||
// Generate signature
|
||||
const authorization = generateSignature(
|
||||
"POST",
|
||||
query,
|
||||
headers,
|
||||
body,
|
||||
accessKey,
|
||||
secretKey,
|
||||
region,
|
||||
"cv",
|
||||
endpoint.canonicalUri
|
||||
);
|
||||
|
||||
const res = await fetch(endpoint.url, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
...headers,
|
||||
"Authorization": authorization,
|
||||
},
|
||||
body,
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`Jimeng API poll error (${res.status}): ${err}`);
|
||||
}
|
||||
|
||||
const result = (await res.json()) as {
|
||||
code?: number;
|
||||
message?: string;
|
||||
data?: {
|
||||
status?: string;
|
||||
image_urls?: string[];
|
||||
binary_data_base64?: string[];
|
||||
};
|
||||
};
|
||||
|
||||
// Volcengine API returns code 10000 for success
|
||||
if (result.code === 10000 && result.data) {
|
||||
const { status, image_urls, binary_data_base64 } = result.data;
|
||||
|
||||
// Check for base64 image data (preferred by Jimeng)
|
||||
if (binary_data_base64 && binary_data_base64.length > 0) {
|
||||
console.error("Image received as base64 data");
|
||||
const base64Data = binary_data_base64[0]!;
|
||||
// Convert base64 to Uint8Array
|
||||
const binaryString = Buffer.from(base64Data, "base64").toString("binary");
|
||||
const bytes = new Uint8Array(binaryString.length);
|
||||
for (let i = 0; i < binaryString.length; i++) {
|
||||
bytes[i] = binaryString.charCodeAt(i);
|
||||
}
|
||||
return bytes;
|
||||
}
|
||||
|
||||
// Fallback to URL format
|
||||
if (status === "done" && image_urls && image_urls.length > 0) {
|
||||
// Download from URL
|
||||
console.error(`Downloading image from ${image_urls[0]}...`);
|
||||
const imgRes = await fetch(image_urls[0]!);
|
||||
if (!imgRes.ok) {
|
||||
throw new Error(`Failed to download image from ${image_urls[0]}`);
|
||||
}
|
||||
const buffer = await imgRes.arrayBuffer();
|
||||
return new Uint8Array(buffer);
|
||||
}
|
||||
|
||||
if (status === "in_queue" || status === "generating") {
|
||||
console.error(`Task status: ${status} (${attempt + 1}/${maxAttempts})`);
|
||||
await new Promise(resolve => setTimeout(resolve, pollIntervalMs));
|
||||
continue;
|
||||
}
|
||||
|
||||
if (status === "fail") {
|
||||
throw new Error(`Jimeng task failed: ${result.message || "Generation failed"}`);
|
||||
}
|
||||
}
|
||||
|
||||
console.error("Poll response:", JSON.stringify(result, null, 2));
|
||||
throw new Error(`Unexpected response during polling: ${result.message || "Unknown error"}`);
|
||||
}
|
||||
|
||||
throw new Error("Task timeout: image generation took too long");
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
if (args.referenceImages.length > 0) {
|
||||
throw new Error(
|
||||
"Jimeng does not support reference images. Use --provider google, openai, openrouter, or replicate."
|
||||
);
|
||||
}
|
||||
|
||||
const accessKey = getAccessKey();
|
||||
const secretKey = getSecretKey();
|
||||
const region = getRegion();
|
||||
|
||||
if (!accessKey || !secretKey) {
|
||||
throw new Error(
|
||||
"JIMENG_ACCESS_KEY_ID and JIMENG_SECRET_ACCESS_KEY are required. " +
|
||||
"Get your credentials from https://console.volcengine.com/iam/keymanage"
|
||||
);
|
||||
}
|
||||
|
||||
const size = getImageSize(args.aspectRatio, args.quality, args.imageSize);
|
||||
|
||||
// Step 1: Submit task
|
||||
const taskId = await submitTask(prompt, model, size, accessKey, secretKey, region);
|
||||
|
||||
// Step 2: Poll for result (returns image data directly)
|
||||
const imageData = await pollForResult(taskId, model, accessKey, secretKey, region);
|
||||
|
||||
console.error("Image generation complete!");
|
||||
return imageData;
|
||||
}
|
||||
@@ -0,0 +1,175 @@
|
||||
import assert from "node:assert/strict";
|
||||
import fs from "node:fs/promises";
|
||||
import os from "node:os";
|
||||
import path from "node:path";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
buildMinimaxUrl,
|
||||
buildRequestBody,
|
||||
buildSubjectReference,
|
||||
extractImageFromResponse,
|
||||
parsePixelSize,
|
||||
validateArgs,
|
||||
} from "./minimax.ts";
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
test("MiniMax URL builder uses documented default and normalizes /v1 suffixes", (t) => {
|
||||
useEnv(t, { MINIMAX_BASE_URL: null });
|
||||
assert.equal(buildMinimaxUrl(), "https://api.minimaxi.com/v1/image_generation");
|
||||
|
||||
process.env.MINIMAX_BASE_URL = "https://api.minimax.io";
|
||||
assert.equal(buildMinimaxUrl(), "https://api.minimax.io/v1/image_generation");
|
||||
|
||||
process.env.MINIMAX_BASE_URL = "https://proxy.example.com/custom/v1/";
|
||||
assert.equal(buildMinimaxUrl(), "https://proxy.example.com/custom/v1/image_generation");
|
||||
});
|
||||
|
||||
test("MiniMax size parsing and validation follow documented constraints", () => {
|
||||
assert.deepEqual(parsePixelSize("1536x1024"), { width: 1536, height: 1024 });
|
||||
assert.deepEqual(parsePixelSize("1536*1024"), { width: 1536, height: 1024 });
|
||||
assert.equal(parsePixelSize("wide"), null);
|
||||
|
||||
validateArgs("image-01", makeArgs({ size: "1536x1024", n: 9 }));
|
||||
|
||||
assert.throws(
|
||||
() => validateArgs("image-01-live", makeArgs({ size: "1536x1024" })),
|
||||
/only supported with model image-01/,
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs("image-01", makeArgs({ size: "1537x1024" })),
|
||||
/divisible by 8/,
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs("image-01", makeArgs({ aspectRatio: "2.35:1" })),
|
||||
/aspect_ratio must be one of/,
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs("image-01", makeArgs({ n: 10 })),
|
||||
/at most 9 images/,
|
||||
);
|
||||
});
|
||||
|
||||
test("MiniMax request body maps aspect ratio, size, n, and subject references", async (t) => {
|
||||
const dir = await fs.mkdtemp(path.join(os.tmpdir(), "minimax-test-"));
|
||||
t.after(() => fs.rm(dir, { recursive: true, force: true }));
|
||||
|
||||
const refPath = path.join(dir, "portrait.png");
|
||||
await fs.writeFile(refPath, Buffer.from("portrait"));
|
||||
|
||||
const ratioBody = await buildRequestBody(
|
||||
"A portrait by the window",
|
||||
"image-01",
|
||||
makeArgs({ aspectRatio: "16:9", n: 2, referenceImages: [refPath] }),
|
||||
);
|
||||
assert.equal(ratioBody.aspect_ratio, "16:9");
|
||||
assert.equal(ratioBody.n, 2);
|
||||
assert.equal(ratioBody.response_format, "base64");
|
||||
assert.match(ratioBody.subject_reference?.[0]?.image_file || "", /^data:image\/png;base64,/);
|
||||
|
||||
const sizeBody = await buildRequestBody(
|
||||
"A portrait by the window",
|
||||
"image-01",
|
||||
makeArgs({ size: "1536x1024" }),
|
||||
);
|
||||
assert.equal(sizeBody.width, 1536);
|
||||
assert.equal(sizeBody.height, 1024);
|
||||
assert.equal(sizeBody.aspect_ratio, undefined);
|
||||
});
|
||||
|
||||
test("MiniMax subject references require supported file types", async (t) => {
|
||||
const dir = await fs.mkdtemp(path.join(os.tmpdir(), "minimax-ref-"));
|
||||
t.after(() => fs.rm(dir, { recursive: true, force: true }));
|
||||
|
||||
const good = path.join(dir, "portrait.jpg");
|
||||
const bad = path.join(dir, "portrait.webp");
|
||||
await fs.writeFile(good, Buffer.from("portrait"));
|
||||
await fs.writeFile(bad, Buffer.from("portrait"));
|
||||
|
||||
const subjectReference = await buildSubjectReference([good]);
|
||||
assert.equal(subjectReference?.[0]?.type, "character");
|
||||
|
||||
await assert.rejects(
|
||||
() => buildSubjectReference([bad]),
|
||||
/only supports JPG, JPEG, or PNG/,
|
||||
);
|
||||
});
|
||||
|
||||
test("MiniMax response extraction supports base64 and URL payloads", async (t) => {
|
||||
const originalFetch = globalThis.fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
const fromBase64 = await extractImageFromResponse({
|
||||
data: {
|
||||
image_base64: [Buffer.from("hello").toString("base64")],
|
||||
},
|
||||
});
|
||||
assert.equal(Buffer.from(fromBase64).toString("utf8"), "hello");
|
||||
|
||||
globalThis.fetch = async () =>
|
||||
new Response(Uint8Array.from([1, 2, 3]), {
|
||||
status: 200,
|
||||
headers: { "Content-Type": "image/jpeg" },
|
||||
});
|
||||
|
||||
const fromUrl = await extractImageFromResponse({
|
||||
data: {
|
||||
image_urls: ["https://example.com/output.jpg"],
|
||||
},
|
||||
});
|
||||
assert.deepEqual([...fromUrl], [1, 2, 3]);
|
||||
|
||||
await assert.rejects(
|
||||
() => extractImageFromResponse({ base_resp: { status_code: 1001, status_msg: "blocked" } }),
|
||||
/blocked/,
|
||||
);
|
||||
});
|
||||
220
baoyu-skills/skills/baoyu-image-gen/scripts/providers/minimax.ts
Normal file
220
baoyu-skills/skills/baoyu-image-gen/scripts/providers/minimax.ts
Normal file
@@ -0,0 +1,220 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
|
||||
import type { CliArgs } from "../types";
|
||||
|
||||
const DEFAULT_MODEL = "image-01";
|
||||
const MAX_REFERENCE_IMAGE_BYTES = 10 * 1024 * 1024;
|
||||
const SUPPORTED_ASPECT_RATIOS = new Set(["1:1", "16:9", "4:3", "3:2", "2:3", "3:4", "9:16", "21:9"]);
|
||||
|
||||
type MinimaxSubjectReference = {
|
||||
type: "character";
|
||||
image_file: string;
|
||||
};
|
||||
|
||||
type MinimaxRequestBody = {
|
||||
model: string;
|
||||
prompt: string;
|
||||
response_format: "base64";
|
||||
aspect_ratio?: string;
|
||||
width?: number;
|
||||
height?: number;
|
||||
n?: number;
|
||||
subject_reference?: MinimaxSubjectReference[];
|
||||
};
|
||||
|
||||
type MinimaxResponse = {
|
||||
id?: string;
|
||||
data?: {
|
||||
image_urls?: string[];
|
||||
image_base64?: string[];
|
||||
};
|
||||
base_resp?: {
|
||||
status_code?: number;
|
||||
status_msg?: string;
|
||||
};
|
||||
};
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.MINIMAX_IMAGE_MODEL || DEFAULT_MODEL;
|
||||
}
|
||||
|
||||
function getApiKey(): string | null {
|
||||
return process.env.MINIMAX_API_KEY || null;
|
||||
}
|
||||
|
||||
export function buildMinimaxUrl(): string {
|
||||
const base = (process.env.MINIMAX_BASE_URL || "https://api.minimaxi.com").replace(/\/+$/g, "");
|
||||
return base.endsWith("/v1") ? `${base}/image_generation` : `${base}/v1/image_generation`;
|
||||
}
|
||||
|
||||
function getMimeType(filename: string): "image/jpeg" | "image/png" {
|
||||
const ext = path.extname(filename).toLowerCase();
|
||||
if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
|
||||
if (ext === ".png") return "image/png";
|
||||
throw new Error(
|
||||
`MiniMax subject_reference only supports JPG, JPEG, or PNG files: ${filename}`
|
||||
);
|
||||
}
|
||||
|
||||
export function parsePixelSize(size: string): { width: number; height: number } | null {
|
||||
const match = size.trim().match(/^(\d+)\s*[xX*]\s*(\d+)$/);
|
||||
if (!match) return null;
|
||||
|
||||
const width = parseInt(match[1]!, 10);
|
||||
const height = parseInt(match[2]!, 10);
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
function validatePixelSize(width: number, height: number): void {
|
||||
if (width < 512 || width > 2048 || height < 512 || height > 2048) {
|
||||
throw new Error("MiniMax custom size must keep width and height between 512 and 2048.");
|
||||
}
|
||||
if (width % 8 !== 0 || height % 8 !== 0) {
|
||||
throw new Error("MiniMax custom size requires width and height divisible by 8.");
|
||||
}
|
||||
}
|
||||
|
||||
export function validateArgs(model: string, args: CliArgs): void {
|
||||
if (args.n > 9) {
|
||||
throw new Error("MiniMax supports at most 9 images per request.");
|
||||
}
|
||||
|
||||
if (args.aspectRatio && !SUPPORTED_ASPECT_RATIOS.has(args.aspectRatio)) {
|
||||
throw new Error(
|
||||
`MiniMax aspect_ratio must be one of: ${Array.from(SUPPORTED_ASPECT_RATIOS).join(", ")}.`
|
||||
);
|
||||
}
|
||||
|
||||
if (args.size && !args.aspectRatio) {
|
||||
if (model !== "image-01") {
|
||||
throw new Error("MiniMax custom --size is only supported with model image-01. Use --model image-01 or pass --ar instead.");
|
||||
}
|
||||
const parsed = parsePixelSize(args.size);
|
||||
if (!parsed) {
|
||||
throw new Error("MiniMax --size must be in WxH format, for example 1536x1024.");
|
||||
}
|
||||
validatePixelSize(parsed.width, parsed.height);
|
||||
}
|
||||
}
|
||||
|
||||
export async function buildSubjectReference(
|
||||
referenceImages: string[],
|
||||
): Promise<MinimaxSubjectReference[] | undefined> {
|
||||
if (referenceImages.length === 0) return undefined;
|
||||
|
||||
const subjectReference: MinimaxSubjectReference[] = [];
|
||||
for (const refPath of referenceImages) {
|
||||
const bytes = await readFile(refPath);
|
||||
if (bytes.length > MAX_REFERENCE_IMAGE_BYTES) {
|
||||
throw new Error(`MiniMax subject_reference images must be smaller than 10MB: ${refPath}`);
|
||||
}
|
||||
|
||||
subjectReference.push({
|
||||
type: "character",
|
||||
image_file: `data:${getMimeType(refPath)};base64,${bytes.toString("base64")}`,
|
||||
});
|
||||
}
|
||||
|
||||
return subjectReference;
|
||||
}
|
||||
|
||||
export async function buildRequestBody(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
): Promise<MinimaxRequestBody> {
|
||||
validateArgs(model, args);
|
||||
|
||||
const body: MinimaxRequestBody = {
|
||||
model,
|
||||
prompt,
|
||||
response_format: "base64",
|
||||
};
|
||||
|
||||
if (args.aspectRatio) {
|
||||
body.aspect_ratio = args.aspectRatio;
|
||||
} else if (args.size) {
|
||||
const parsed = parsePixelSize(args.size);
|
||||
if (!parsed) {
|
||||
throw new Error("MiniMax --size must be in WxH format, for example 1536x1024.");
|
||||
}
|
||||
body.width = parsed.width;
|
||||
body.height = parsed.height;
|
||||
}
|
||||
|
||||
if (args.n > 1) {
|
||||
body.n = args.n;
|
||||
}
|
||||
|
||||
const subjectReference = await buildSubjectReference(args.referenceImages);
|
||||
if (subjectReference) {
|
||||
body.subject_reference = subjectReference;
|
||||
}
|
||||
|
||||
return body;
|
||||
}
|
||||
|
||||
async function downloadImage(url: string): Promise<Uint8Array> {
|
||||
const response = await fetch(url);
|
||||
if (!response.ok) {
|
||||
throw new Error(`Failed to download image from MiniMax: ${response.status}`);
|
||||
}
|
||||
return new Uint8Array(await response.arrayBuffer());
|
||||
}
|
||||
|
||||
export async function extractImageFromResponse(result: MinimaxResponse): Promise<Uint8Array> {
|
||||
const baseResp = result.base_resp;
|
||||
if (baseResp && baseResp.status_code !== undefined && baseResp.status_code !== 0) {
|
||||
throw new Error(baseResp.status_msg || `MiniMax API returned status_code=${baseResp.status_code}`);
|
||||
}
|
||||
|
||||
const base64Image = result.data?.image_base64?.[0];
|
||||
if (base64Image) {
|
||||
return Uint8Array.from(Buffer.from(base64Image, "base64"));
|
||||
}
|
||||
|
||||
const url = result.data?.image_urls?.[0];
|
||||
if (url) {
|
||||
return downloadImage(url);
|
||||
}
|
||||
|
||||
throw new Error("No image data in MiniMax response");
|
||||
}
|
||||
|
||||
export function getDefaultOutputExtension(): ".jpg" {
|
||||
return ".jpg";
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
const apiKey = getApiKey();
|
||||
if (!apiKey) {
|
||||
throw new Error("MINIMAX_API_KEY is required. Get one from https://platform.minimaxi.com/");
|
||||
}
|
||||
|
||||
const body = await buildRequestBody(prompt, model, args);
|
||||
const response = await fetch(buildMinimaxUrl(), {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
},
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const err = await response.text();
|
||||
throw new Error(`MiniMax API error (${response.status}): ${err}`);
|
||||
}
|
||||
|
||||
const result = (await response.json()) as MinimaxResponse;
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
@@ -0,0 +1,192 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
buildOpenAIGenerationsBody,
|
||||
extractImageFromResponse,
|
||||
getDefaultModel,
|
||||
getOpenAIAspectRatio,
|
||||
getOpenAIImageApiDialect,
|
||||
getOpenAIResolution,
|
||||
getMimeType,
|
||||
getOpenAISize,
|
||||
getOrientationFromAspectRatio,
|
||||
inferAspectRatioFromSize,
|
||||
inferResolutionFromSize,
|
||||
parseAspectRatio,
|
||||
validateArgs,
|
||||
} from "./openai.ts";
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: "2k",
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
test("OpenAI aspect-ratio parsing and size selection match model families", () => {
|
||||
assert.equal(getDefaultModel(), "gpt-image-2.5-flare");
|
||||
assert.deepEqual(parseAspectRatio("16:9"), { width: 16, height: 9 });
|
||||
assert.equal(parseAspectRatio("wide"), null);
|
||||
assert.equal(parseAspectRatio("0:1"), null);
|
||||
|
||||
assert.equal(getOpenAISize("dall-e-3", "16:9", "2k"), "1792x1024");
|
||||
assert.equal(getOpenAISize("dall-e-3", "9:16", "normal"), "1024x1792");
|
||||
assert.equal(getOpenAISize("dall-e-2", "16:9", "2k"), "1024x1024");
|
||||
assert.equal(getOpenAISize("gpt-image-1.5", "16:9", "2k"), "1536x1024");
|
||||
assert.equal(getOpenAISize("gpt-image-1.5", "4:3", "2k"), "1024x1024");
|
||||
assert.equal(getOpenAISize("gpt-image-2", "16:9", "2k"), "2048x1152");
|
||||
assert.equal(getOpenAISize("gpt-image-2", "9:16", "2k"), "1152x2048");
|
||||
assert.equal(getOpenAISize("gpt-image-2", "4:3", "2k"), "2048x1536");
|
||||
assert.equal(getOpenAISize("gpt-image-2", "2.35:1", "normal"), "1248x528");
|
||||
assert.equal(getOpenAISize("gpt-image-2.5-flare", "16:9", "2k"), "2048x1152");
|
||||
assert.equal(getOpenAISize("gpt-image-2.5-sunburst", "9:16", "normal"), "608x1088");
|
||||
assert.equal(getOpenAISize("gpt-image-2.5-flare-2026-09-08", "1:1", "2k"), "2048x2048");
|
||||
assert.equal(inferAspectRatioFromSize("1536x1024"), "3:2");
|
||||
assert.equal(inferResolutionFromSize("1536x1024"), "2K");
|
||||
assert.equal(getOpenAIAspectRatio({ aspectRatio: null, size: "2048x1152" }), "16:9");
|
||||
assert.equal(getOpenAIResolution({ imageSize: null, size: "2048x1152", quality: "normal" }), "2K");
|
||||
assert.equal(getOrientationFromAspectRatio("16:9"), "landscape");
|
||||
assert.equal(getOrientationFromAspectRatio("9:16"), "portrait");
|
||||
assert.equal(getOrientationFromAspectRatio("1:1"), null);
|
||||
assert.equal(getOpenAIImageApiDialect({ imageApiDialect: null }), "openai-native");
|
||||
});
|
||||
|
||||
test("OpenAI generations body switches between native and ratio-metadata dialects", () => {
|
||||
assert.deepEqual(
|
||||
buildOpenAIGenerationsBody("Draw a skyline", "gpt-image-2", {
|
||||
aspectRatio: "16:9",
|
||||
size: null,
|
||||
quality: "2k",
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
}),
|
||||
{
|
||||
model: "gpt-image-2",
|
||||
prompt: "Draw a skyline",
|
||||
size: "2048x1152",
|
||||
quality: "high",
|
||||
},
|
||||
);
|
||||
|
||||
assert.deepEqual(
|
||||
buildOpenAIGenerationsBody("Draw a skyline", "gemini-3-pro-image-preview", {
|
||||
aspectRatio: "16:9",
|
||||
size: null,
|
||||
quality: "2k",
|
||||
imageSize: null,
|
||||
imageApiDialect: "ratio-metadata",
|
||||
}),
|
||||
{
|
||||
model: "gemini-3-pro-image-preview",
|
||||
prompt: "Draw a skyline",
|
||||
size: "16:9",
|
||||
metadata: {
|
||||
resolution: "2K",
|
||||
orientation: "landscape",
|
||||
},
|
||||
},
|
||||
);
|
||||
|
||||
assert.deepEqual(
|
||||
buildOpenAIGenerationsBody("Draw a portrait", "gemini-3-pro-image-preview", {
|
||||
aspectRatio: null,
|
||||
size: "1152x2048",
|
||||
quality: "normal",
|
||||
imageSize: null,
|
||||
imageApiDialect: "ratio-metadata",
|
||||
}),
|
||||
{
|
||||
model: "gemini-3-pro-image-preview",
|
||||
prompt: "Draw a portrait",
|
||||
size: "9:16",
|
||||
metadata: {
|
||||
resolution: "2K",
|
||||
orientation: "portrait",
|
||||
},
|
||||
},
|
||||
);
|
||||
});
|
||||
|
||||
test("OpenAI validates GPT Image custom size constraints", () => {
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs("gpt-image-2", makeArgs({ size: "3840x2160" })),
|
||||
);
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs("gpt-image-2-2026-04-21", makeArgs({ aspectRatio: "2.35:1" })),
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() => validateArgs("gpt-image-2", makeArgs({ size: "1024x576" })),
|
||||
/total pixels/,
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs("gpt-image-2", makeArgs({ size: "1025x1024" })),
|
||||
/multiples of 16px/,
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs("gpt-image-2", makeArgs({ aspectRatio: "4:1" })),
|
||||
/must not exceed 3:1/,
|
||||
);
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs("gpt-image-2.5-flare", makeArgs({ size: "3840x2160" })),
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs("gpt-image-2.5-sunburst", makeArgs({ aspectRatio: "4:1" })),
|
||||
/gpt-image-2\.5-sunburst aspect ratio must not exceed 3:1/,
|
||||
);
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs("gpt-image-1.5", makeArgs({ size: "1536x1024" })),
|
||||
);
|
||||
});
|
||||
|
||||
test("OpenAI mime-type detection covers supported reference image extensions", () => {
|
||||
assert.equal(getMimeType("frame.png"), "image/png");
|
||||
assert.equal(getMimeType("frame.jpg"), "image/jpeg");
|
||||
assert.equal(getMimeType("frame.webp"), "image/webp");
|
||||
assert.equal(getMimeType("frame.gif"), "image/gif");
|
||||
});
|
||||
|
||||
test("OpenAI response extraction supports base64 and URL download flows", async (t) => {
|
||||
const originalFetch = globalThis.fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
const fromBase64 = await extractImageFromResponse({
|
||||
data: [{ b64_json: Buffer.from("hello").toString("base64") }],
|
||||
});
|
||||
assert.equal(Buffer.from(fromBase64).toString("utf8"), "hello");
|
||||
|
||||
globalThis.fetch = async () =>
|
||||
new Response(Uint8Array.from([1, 2, 3]), {
|
||||
status: 200,
|
||||
headers: { "Content-Type": "application/octet-stream" },
|
||||
});
|
||||
|
||||
const fromUrl = await extractImageFromResponse({
|
||||
data: [{ url: "https://example.com/image.png" }],
|
||||
});
|
||||
assert.deepEqual([...fromUrl], [1, 2, 3]);
|
||||
|
||||
await assert.rejects(
|
||||
() => extractImageFromResponse({ data: [{}] }),
|
||||
/No image in response/,
|
||||
);
|
||||
});
|
||||
444
baoyu-skills/skills/baoyu-image-gen/scripts/providers/openai.ts
Normal file
444
baoyu-skills/skills/baoyu-image-gen/scripts/providers/openai.ts
Normal file
@@ -0,0 +1,444 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
import type { CliArgs, OpenAIImageApiDialect } from "../types";
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.OPENAI_IMAGE_MODEL || "gpt-image-2.5-flare";
|
||||
}
|
||||
|
||||
type OpenAIImageResponse = { data: Array<{ url?: string; b64_json?: string }> };
|
||||
|
||||
export function parseAspectRatio(ar: string): { width: number; height: number } | null {
|
||||
const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
|
||||
if (!match) return null;
|
||||
const w = parseFloat(match[1]!);
|
||||
const h = parseFloat(match[2]!);
|
||||
if (w <= 0 || h <= 0) return null;
|
||||
return { width: w, height: h };
|
||||
}
|
||||
|
||||
type SizeMapping = {
|
||||
square: string;
|
||||
landscape: string;
|
||||
portrait: string;
|
||||
};
|
||||
|
||||
type OpenAIGenerationsBody = Record<string, unknown>;
|
||||
|
||||
function isGptImageModel(model: string): boolean {
|
||||
return model.includes("gpt-image");
|
||||
}
|
||||
|
||||
function isGptImageCustomSizeModel(model: string): boolean {
|
||||
return model.includes("gpt-image-2");
|
||||
}
|
||||
|
||||
function roundToMultiple(value: number, multiple: number): number {
|
||||
return Math.max(multiple, Math.round(value / multiple) * multiple);
|
||||
}
|
||||
|
||||
function buildCustomSizeFromAspectRatio(
|
||||
ar: string | null,
|
||||
quality: CliArgs["quality"],
|
||||
): string {
|
||||
const parsed = ar ? parseAspectRatio(ar) : null;
|
||||
const ratio = parsed ? parsed.width / parsed.height : 1;
|
||||
|
||||
if (!parsed || Math.abs(ratio - 1) < 0.1) {
|
||||
const edge = quality === "2k" ? 2048 : 1024;
|
||||
return `${edge}x${edge}`;
|
||||
}
|
||||
|
||||
const targetLongEdge = quality === "2k" ? 2048 : 1024;
|
||||
let width: number;
|
||||
let height: number;
|
||||
|
||||
if (ratio > 1) {
|
||||
width = targetLongEdge;
|
||||
height = roundToMultiple(width / ratio, 16);
|
||||
} else {
|
||||
height = targetLongEdge;
|
||||
width = roundToMultiple(height * ratio, 16);
|
||||
}
|
||||
|
||||
while (width * height < 655_360) {
|
||||
if (ratio > 1) {
|
||||
width += 16;
|
||||
height = roundToMultiple(width / ratio, 16);
|
||||
} else {
|
||||
height += 16;
|
||||
width = roundToMultiple(height * ratio, 16);
|
||||
}
|
||||
}
|
||||
|
||||
return `${width}x${height}`;
|
||||
}
|
||||
|
||||
export function getOpenAISize(
|
||||
model: string,
|
||||
ar: string | null,
|
||||
quality: CliArgs["quality"]
|
||||
): string {
|
||||
const isDalle3 = model.includes("dall-e-3");
|
||||
const isDalle2 = model.includes("dall-e-2");
|
||||
|
||||
if (isDalle2) {
|
||||
return "1024x1024";
|
||||
}
|
||||
|
||||
if (isGptImageCustomSizeModel(model)) {
|
||||
return buildCustomSizeFromAspectRatio(ar, quality);
|
||||
}
|
||||
|
||||
const sizes: SizeMapping = isDalle3
|
||||
? {
|
||||
square: "1024x1024",
|
||||
landscape: "1792x1024",
|
||||
portrait: "1024x1792",
|
||||
}
|
||||
: {
|
||||
square: "1024x1024",
|
||||
landscape: "1536x1024",
|
||||
portrait: "1024x1536",
|
||||
};
|
||||
|
||||
if (!ar) return sizes.square;
|
||||
|
||||
const parsed = parseAspectRatio(ar);
|
||||
if (!parsed) return sizes.square;
|
||||
|
||||
const ratio = parsed.width / parsed.height;
|
||||
|
||||
if (Math.abs(ratio - 1) < 0.1) return sizes.square;
|
||||
if (ratio > 1.5) return sizes.landscape;
|
||||
if (ratio < 0.67) return sizes.portrait;
|
||||
return sizes.square;
|
||||
}
|
||||
|
||||
function parsePixelSize(value: string): { width: number; height: number } | null {
|
||||
const match = value.match(/^(\d+)\s*[xX]\s*(\d+)$/);
|
||||
if (!match) return null;
|
||||
|
||||
const width = parseInt(match[1]!, 10);
|
||||
const height = parseInt(match[2]!, 10);
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
function gcd(a: number, b: number): number {
|
||||
let x = Math.abs(a);
|
||||
let y = Math.abs(b);
|
||||
while (y !== 0) {
|
||||
const next = x % y;
|
||||
x = y;
|
||||
y = next;
|
||||
}
|
||||
return x || 1;
|
||||
}
|
||||
|
||||
export function getOpenAIImageApiDialect(args: Pick<CliArgs, "imageApiDialect">): OpenAIImageApiDialect {
|
||||
return args.imageApiDialect ?? "openai-native";
|
||||
}
|
||||
|
||||
export function inferAspectRatioFromSize(size: string | null): string | null {
|
||||
if (!size) return null;
|
||||
const parsed = parsePixelSize(size);
|
||||
if (!parsed) return null;
|
||||
|
||||
const divisor = gcd(parsed.width, parsed.height);
|
||||
return `${parsed.width / divisor}:${parsed.height / divisor}`;
|
||||
}
|
||||
|
||||
export function inferResolutionFromSize(size: string | null): "1K" | "2K" | "4K" | null {
|
||||
if (!size) return null;
|
||||
const parsed = parsePixelSize(size);
|
||||
if (!parsed) return null;
|
||||
|
||||
const longestEdge = Math.max(parsed.width, parsed.height);
|
||||
if (longestEdge <= 1024) return "1K";
|
||||
if (longestEdge <= 2048) return "2K";
|
||||
return "4K";
|
||||
}
|
||||
|
||||
export function getOpenAIAspectRatio(args: Pick<CliArgs, "aspectRatio" | "size">): string {
|
||||
return args.aspectRatio ?? inferAspectRatioFromSize(args.size) ?? "1:1";
|
||||
}
|
||||
|
||||
export function getOpenAIResolution(
|
||||
args: Pick<CliArgs, "imageSize" | "size" | "quality">
|
||||
): "1K" | "2K" | "4K" {
|
||||
if (args.imageSize === "1K" || args.imageSize === "2K" || args.imageSize === "4K") {
|
||||
return args.imageSize;
|
||||
}
|
||||
|
||||
const inferred = inferResolutionFromSize(args.size);
|
||||
if (inferred) return inferred;
|
||||
|
||||
return args.quality === "normal" ? "1K" : "2K";
|
||||
}
|
||||
|
||||
function getOpenAIQuality(model: string, quality: CliArgs["quality"]): "standard" | "hd" | "medium" | "high" | null {
|
||||
if (model.includes("dall-e-3")) {
|
||||
return quality === "2k" ? "hd" : "standard";
|
||||
}
|
||||
|
||||
if (isGptImageModel(model)) {
|
||||
return quality === "2k" ? "high" : "medium";
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
export function getOrientationFromAspectRatio(ar: string): "landscape" | "portrait" | null {
|
||||
const parsed = parseAspectRatio(ar);
|
||||
if (!parsed) return null;
|
||||
|
||||
const ratio = parsed.width / parsed.height;
|
||||
if (Math.abs(ratio - 1) < 0.1) return null;
|
||||
return ratio > 1 ? "landscape" : "portrait";
|
||||
}
|
||||
|
||||
export function buildOpenAIGenerationsBody(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: Pick<CliArgs, "aspectRatio" | "size" | "quality" | "imageSize" | "imageApiDialect">
|
||||
): OpenAIGenerationsBody {
|
||||
if (getOpenAIImageApiDialect(args) === "ratio-metadata") {
|
||||
const aspectRatio = getOpenAIAspectRatio(args);
|
||||
const metadata: Record<string, string> = {
|
||||
resolution: getOpenAIResolution(args),
|
||||
};
|
||||
const orientation = getOrientationFromAspectRatio(aspectRatio);
|
||||
if (orientation) metadata.orientation = orientation;
|
||||
|
||||
return {
|
||||
model,
|
||||
prompt,
|
||||
size: aspectRatio,
|
||||
metadata,
|
||||
};
|
||||
}
|
||||
|
||||
const body: OpenAIGenerationsBody = {
|
||||
model,
|
||||
prompt,
|
||||
size: args.size || getOpenAISize(model, args.aspectRatio, args.quality),
|
||||
};
|
||||
|
||||
const quality = getOpenAIQuality(model, args.quality);
|
||||
if (quality) {
|
||||
body.quality = quality;
|
||||
}
|
||||
|
||||
return body;
|
||||
}
|
||||
|
||||
export function validateArgs(model: string, args: CliArgs): void {
|
||||
if (!isGptImageCustomSizeModel(model)) return;
|
||||
|
||||
if (args.aspectRatio && !args.size) {
|
||||
const parsed = parseAspectRatio(args.aspectRatio);
|
||||
if (!parsed) {
|
||||
throw new Error(`Invalid ${model} aspect ratio: ${args.aspectRatio}`);
|
||||
}
|
||||
const ratio = parsed.width / parsed.height;
|
||||
if (Math.max(ratio, 1 / ratio) > 3) {
|
||||
throw new Error(`${model} aspect ratio must not exceed 3:1.`);
|
||||
}
|
||||
}
|
||||
|
||||
if (!args.size) return;
|
||||
|
||||
const parsedSize = parsePixelSize(args.size);
|
||||
if (!parsedSize) {
|
||||
throw new Error(`Invalid ${model} --size: ${args.size}. Expected <width>x<height>.`);
|
||||
}
|
||||
|
||||
const { width, height } = parsedSize;
|
||||
const totalPixels = width * height;
|
||||
const ratio = Math.max(width, height) / Math.min(width, height);
|
||||
|
||||
if (Math.max(width, height) > 3840) {
|
||||
throw new Error(`${model} --size maximum edge length must be 3840px or less.`);
|
||||
}
|
||||
if (width % 16 !== 0 || height % 16 !== 0) {
|
||||
throw new Error(`${model} --size width and height must both be multiples of 16px.`);
|
||||
}
|
||||
if (ratio > 3) {
|
||||
throw new Error(`${model} --size long edge to short edge ratio must not exceed 3:1.`);
|
||||
}
|
||||
if (totalPixels < 655_360 || totalPixels > 8_294_400) {
|
||||
throw new Error(`${model} --size total pixels must be between 655,360 and 8,294,400.`);
|
||||
}
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
const baseURL = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1";
|
||||
const apiKey = process.env.OPENAI_API_KEY;
|
||||
|
||||
if (!apiKey) {
|
||||
throw new Error(
|
||||
"OPENAI_API_KEY is required. Codex/ChatGPT desktop login does not automatically grant OpenAI Images API access to this script."
|
||||
);
|
||||
}
|
||||
|
||||
if (process.env.OPENAI_IMAGE_USE_CHAT === "true") {
|
||||
return generateWithChatCompletions(baseURL, apiKey, prompt, model);
|
||||
}
|
||||
|
||||
const imageApiDialect = getOpenAIImageApiDialect(args);
|
||||
|
||||
if (args.referenceImages.length > 0) {
|
||||
if (imageApiDialect !== "openai-native") {
|
||||
throw new Error(
|
||||
"Reference images are not supported with the ratio-metadata OpenAI dialect yet. Use openai-native, Google, Azure, OpenRouter, MiniMax, Seedream, or Replicate for image-edit workflows."
|
||||
);
|
||||
}
|
||||
if (model.includes("dall-e-2") || model.includes("dall-e-3")) {
|
||||
throw new Error(
|
||||
"Reference images with OpenAI in this skill require GPT Image models. Use --model gpt-image-2.5-flare (or another gpt-image model)."
|
||||
);
|
||||
}
|
||||
const size = args.size || getOpenAISize(model, args.aspectRatio, args.quality);
|
||||
return generateWithOpenAIEdits(baseURL, apiKey, prompt, model, size, args.referenceImages, args.quality);
|
||||
}
|
||||
|
||||
return generateWithOpenAIGenerations(
|
||||
baseURL,
|
||||
apiKey,
|
||||
buildOpenAIGenerationsBody(prompt, model, args)
|
||||
);
|
||||
}
|
||||
|
||||
async function generateWithChatCompletions(
|
||||
baseURL: string,
|
||||
apiKey: string,
|
||||
prompt: string,
|
||||
model: string
|
||||
): Promise<Uint8Array> {
|
||||
const res = await fetch(`${baseURL}/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model,
|
||||
messages: [{ role: "user", content: prompt }],
|
||||
}),
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`OpenAI API error: ${err}`);
|
||||
}
|
||||
|
||||
const result = (await res.json()) as { choices: Array<{ message: { content: string } }> };
|
||||
const content = result.choices[0]?.message?.content ?? "";
|
||||
|
||||
const match = content.match(/data:image\/[^;]+;base64,([A-Za-z0-9+/=]+)/);
|
||||
if (match) {
|
||||
return Uint8Array.from(Buffer.from(match[1]!, "base64"));
|
||||
}
|
||||
|
||||
throw new Error("No image found in chat completions response");
|
||||
}
|
||||
|
||||
async function generateWithOpenAIGenerations(
|
||||
baseURL: string,
|
||||
apiKey: string,
|
||||
body: OpenAIGenerationsBody
|
||||
): Promise<Uint8Array> {
|
||||
const res = await fetch(`${baseURL}/images/generations`, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
},
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`OpenAI API error: ${err}`);
|
||||
}
|
||||
|
||||
const result = (await res.json()) as OpenAIImageResponse;
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
|
||||
async function generateWithOpenAIEdits(
|
||||
baseURL: string,
|
||||
apiKey: string,
|
||||
prompt: string,
|
||||
model: string,
|
||||
size: string,
|
||||
referenceImages: string[],
|
||||
quality: CliArgs["quality"]
|
||||
): Promise<Uint8Array> {
|
||||
const form = new FormData();
|
||||
form.append("model", model);
|
||||
form.append("prompt", prompt);
|
||||
form.append("size", size);
|
||||
|
||||
const openAIQuality = getOpenAIQuality(model, quality);
|
||||
if (openAIQuality && openAIQuality !== "standard" && openAIQuality !== "hd") {
|
||||
form.append("quality", openAIQuality);
|
||||
}
|
||||
|
||||
for (const refPath of referenceImages) {
|
||||
const bytes = await readFile(refPath);
|
||||
const filename = path.basename(refPath);
|
||||
const mimeType = getMimeType(filename);
|
||||
const blob = new Blob([bytes], { type: mimeType });
|
||||
form.append("image[]", blob, filename);
|
||||
}
|
||||
|
||||
const res = await fetch(`${baseURL}/images/edits`, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
},
|
||||
body: form,
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`OpenAI edits API error: ${err}`);
|
||||
}
|
||||
|
||||
const result = (await res.json()) as OpenAIImageResponse;
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
|
||||
export function getMimeType(filename: string): string {
|
||||
const ext = path.extname(filename).toLowerCase();
|
||||
if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
|
||||
if (ext === ".webp") return "image/webp";
|
||||
if (ext === ".gif") return "image/gif";
|
||||
return "image/png";
|
||||
}
|
||||
|
||||
export async function extractImageFromResponse(result: OpenAIImageResponse): Promise<Uint8Array> {
|
||||
const img = result.data[0];
|
||||
|
||||
if (img?.b64_json) {
|
||||
return Uint8Array.from(Buffer.from(img.b64_json, "base64"));
|
||||
}
|
||||
|
||||
if (img?.url) {
|
||||
const imgRes = await fetch(img.url);
|
||||
if (!imgRes.ok) throw new Error("Failed to download image");
|
||||
const buf = await imgRes.arrayBuffer();
|
||||
return new Uint8Array(buf);
|
||||
}
|
||||
|
||||
throw new Error("No image in response");
|
||||
}
|
||||
@@ -0,0 +1,169 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
buildContent,
|
||||
buildRequestBody,
|
||||
extractImageFromResponse,
|
||||
getAspectRatio,
|
||||
getImageSize,
|
||||
validateArgs,
|
||||
} from "./openrouter.ts";
|
||||
|
||||
const GEMINI_MODEL = "google/gemini-3.1-flash-image-preview";
|
||||
const GEMINI_25_MODEL = "google/gemini-2.5-flash-image";
|
||||
const GPT_5_IMAGE_MODEL = "openai/gpt-5-image";
|
||||
const OPENROUTER_AUTO_MODEL = "openrouter/auto";
|
||||
const FLUX_MODEL = "black-forest-labs/flux.2-pro";
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
test("OpenRouter request body uses image_config and string content for text-only prompts", () => {
|
||||
const args = makeArgs({ aspectRatio: "16:9", quality: "2k" });
|
||||
const body = buildRequestBody("hello", GEMINI_MODEL, args, []);
|
||||
|
||||
assert.deepEqual(body.image_config, {
|
||||
image_size: "2K",
|
||||
aspect_ratio: "16:9",
|
||||
});
|
||||
assert.deepEqual(body.provider, {
|
||||
require_parameters: true,
|
||||
});
|
||||
assert.deepEqual(body.modalities, ["image", "text"]);
|
||||
assert.equal(body.stream, false);
|
||||
assert.equal(body.messages[0].content, "hello");
|
||||
});
|
||||
|
||||
test("OpenRouter request body keeps text+image modalities for current text+image models", () => {
|
||||
for (const model of [GEMINI_MODEL, GEMINI_25_MODEL, GPT_5_IMAGE_MODEL, OPENROUTER_AUTO_MODEL]) {
|
||||
const body = buildRequestBody("hello", model, makeArgs({ quality: "2k" }), []);
|
||||
|
||||
assert.deepEqual(body.image_config, {
|
||||
image_size: "2K",
|
||||
});
|
||||
assert.deepEqual(body.provider, {
|
||||
require_parameters: true,
|
||||
});
|
||||
assert.deepEqual(body.modalities, ["image", "text"]);
|
||||
assert.equal(body.messages[0].content, "hello");
|
||||
}
|
||||
});
|
||||
|
||||
test("OpenRouter request body uses image-only modalities for image-only models under CLI defaults", () => {
|
||||
const body = buildRequestBody("hello", FLUX_MODEL, makeArgs({ quality: "2k" }), []);
|
||||
|
||||
assert.deepEqual(body.image_config, {
|
||||
image_size: "2K",
|
||||
});
|
||||
assert.deepEqual(body.provider, {
|
||||
require_parameters: true,
|
||||
});
|
||||
assert.deepEqual(body.modalities, ["image"]);
|
||||
assert.equal(body.stream, false);
|
||||
assert.equal(body.messages[0].content, "hello");
|
||||
});
|
||||
|
||||
test("OpenRouter helper omits image_config when no size or quality is passed", () => {
|
||||
const body = buildRequestBody("hello", FLUX_MODEL, makeArgs(), []);
|
||||
|
||||
assert.equal(body.image_config, undefined);
|
||||
assert.equal(body.provider, undefined);
|
||||
assert.deepEqual(body.modalities, ["image"]);
|
||||
assert.equal(body.stream, false);
|
||||
assert.equal(body.messages[0].content, "hello");
|
||||
});
|
||||
|
||||
test("OpenRouter request body keeps multimodal array content when references are provided", () => {
|
||||
const content = buildContent("hello", ["data:image/png;base64,abc"]);
|
||||
assert.ok(Array.isArray(content));
|
||||
assert.deepEqual(content[0], { type: "text", text: "hello" });
|
||||
assert.deepEqual(content[1], {
|
||||
type: "image_url",
|
||||
image_url: { url: "data:image/png;base64,abc" },
|
||||
});
|
||||
});
|
||||
|
||||
test("OpenRouter size and aspect helpers infer supported values", () => {
|
||||
assert.equal(getImageSize(makeArgs()), null);
|
||||
assert.equal(getImageSize(makeArgs({ quality: "normal" })), "1K");
|
||||
assert.equal(getImageSize(makeArgs({ size: "2048x1024" })), "2K");
|
||||
assert.equal(getAspectRatio(GEMINI_MODEL, makeArgs({ size: "1600x900" })), "16:9");
|
||||
assert.equal(getAspectRatio(GEMINI_MODEL, makeArgs({ size: "1024x4096" })), "1:4");
|
||||
assert.equal(getAspectRatio(GEMINI_25_MODEL, makeArgs({ size: "1600x900" })), "16:9");
|
||||
assert.equal(getAspectRatio(FLUX_MODEL, makeArgs({ size: "1024x4096" })), null);
|
||||
});
|
||||
|
||||
test("OpenRouter validates explicit aspect ratios and inferred size ratios against model support", () => {
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs(GEMINI_MODEL, makeArgs({ aspectRatio: "1:4" })),
|
||||
);
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs(GEMINI_MODEL, makeArgs({ size: "1024x4096" })),
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs(GEMINI_25_MODEL, makeArgs({ aspectRatio: "1:4" })),
|
||||
/does not support aspect ratio 1:4/,
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs(FLUX_MODEL, makeArgs({ aspectRatio: "1:4" })),
|
||||
/does not support aspect ratio 1:4/,
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs(GEMINI_MODEL, makeArgs({ size: "2048x1024" })),
|
||||
/does not support size 2048x1024 \(aspect ratio 2:1\)/,
|
||||
);
|
||||
});
|
||||
|
||||
test("OpenRouter response extraction supports inline image data and finish_reason errors", async () => {
|
||||
const bytes = await extractImageFromResponse({
|
||||
choices: [
|
||||
{
|
||||
message: {
|
||||
images: [
|
||||
{
|
||||
image_url: {
|
||||
url: `data:image/png;base64,${Buffer.from("hello").toString("base64")}`,
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
],
|
||||
});
|
||||
assert.equal(Buffer.from(bytes).toString("utf8"), "hello");
|
||||
|
||||
await assert.rejects(
|
||||
() =>
|
||||
extractImageFromResponse({
|
||||
choices: [
|
||||
{
|
||||
finish_reason: "error",
|
||||
native_finish_reason: "MALFORMED_FUNCTION_CALL",
|
||||
message: { content: null },
|
||||
},
|
||||
],
|
||||
}),
|
||||
/finish_reason=MALFORMED_FUNCTION_CALL/,
|
||||
);
|
||||
});
|
||||
@@ -0,0 +1,372 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
import type { CliArgs } from "../types";
|
||||
|
||||
const DEFAULT_MODEL = "google/gemini-3.1-flash-image";
|
||||
const COMMON_ASPECT_RATIOS = [
|
||||
"1:1",
|
||||
"2:3",
|
||||
"3:2",
|
||||
"3:4",
|
||||
"4:3",
|
||||
"4:5",
|
||||
"5:4",
|
||||
"9:16",
|
||||
"16:9",
|
||||
"21:9",
|
||||
];
|
||||
const GEMINI_EXTENDED_ASPECT_RATIOS = ["1:4", "4:1", "1:8", "8:1"];
|
||||
|
||||
type OpenRouterImageEntry = {
|
||||
image_url?: string | { url?: string | null } | null;
|
||||
imageUrl?: string | { url?: string | null } | null;
|
||||
};
|
||||
|
||||
type OpenRouterMessagePart = {
|
||||
type?: string;
|
||||
text?: string;
|
||||
image_url?: string | { url?: string | null } | null;
|
||||
imageUrl?: string | { url?: string | null } | null;
|
||||
};
|
||||
|
||||
type OpenRouterResponse = {
|
||||
choices?: Array<{
|
||||
finish_reason?: string | null;
|
||||
native_finish_reason?: string | null;
|
||||
message?: {
|
||||
images?: OpenRouterImageEntry[];
|
||||
content?: string | OpenRouterMessagePart[] | null;
|
||||
};
|
||||
}>;
|
||||
};
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.OPENROUTER_IMAGE_MODEL || DEFAULT_MODEL;
|
||||
}
|
||||
|
||||
function normalizeModelId(model: string): string {
|
||||
return model.trim().toLowerCase().split(":")[0]!;
|
||||
}
|
||||
|
||||
function isTextAndImageModel(model: string): boolean {
|
||||
const normalized = normalizeModelId(model);
|
||||
if (normalized === "openrouter/auto") {
|
||||
return true;
|
||||
}
|
||||
|
||||
if (normalized.startsWith("google/gemini-") && normalized.includes("image")) {
|
||||
return true;
|
||||
}
|
||||
|
||||
if (normalized.startsWith("openai/gpt-") && normalized.includes("image")) {
|
||||
return true;
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
function getSupportedAspectRatios(model: string): Set<string> {
|
||||
const normalized = normalizeModelId(model);
|
||||
if (
|
||||
normalized !== "google/gemini-3.1-flash-image" &&
|
||||
normalized !== "google/gemini-3.1-flash-image-preview"
|
||||
) {
|
||||
return new Set(COMMON_ASPECT_RATIOS);
|
||||
}
|
||||
|
||||
return new Set([...COMMON_ASPECT_RATIOS, ...GEMINI_EXTENDED_ASPECT_RATIOS]);
|
||||
}
|
||||
|
||||
function getApiKey(): string | null {
|
||||
return process.env.OPENROUTER_API_KEY || null;
|
||||
}
|
||||
|
||||
function getBaseUrl(): string {
|
||||
const base = process.env.OPENROUTER_BASE_URL || "https://openrouter.ai/api/v1";
|
||||
return base.replace(/\/+$/g, "");
|
||||
}
|
||||
|
||||
function getHeaders(apiKey: string): Record<string, string> {
|
||||
const headers: Record<string, string> = {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
};
|
||||
|
||||
const referer = process.env.OPENROUTER_HTTP_REFERER?.trim();
|
||||
if (referer) {
|
||||
headers["HTTP-Referer"] = referer;
|
||||
}
|
||||
|
||||
const title = process.env.OPENROUTER_TITLE?.trim();
|
||||
if (title) {
|
||||
headers["X-OpenRouter-Title"] = title;
|
||||
headers["X-Title"] = title;
|
||||
}
|
||||
|
||||
return headers;
|
||||
}
|
||||
|
||||
function parsePixelSize(value: string): { width: number; height: number } | null {
|
||||
const match = value.match(/^(\d+)\s*[xX]\s*(\d+)$/);
|
||||
if (!match) return null;
|
||||
|
||||
const width = parseInt(match[1]!, 10);
|
||||
const height = parseInt(match[2]!, 10);
|
||||
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
function gcd(a: number, b: number): number {
|
||||
let x = Math.abs(a);
|
||||
let y = Math.abs(b);
|
||||
while (y !== 0) {
|
||||
const next = x % y;
|
||||
x = y;
|
||||
y = next;
|
||||
}
|
||||
return x || 1;
|
||||
}
|
||||
|
||||
function inferAspectRatio(size: string | null): string | null {
|
||||
if (!size) return null;
|
||||
const parsed = parsePixelSize(size);
|
||||
if (!parsed) return null;
|
||||
|
||||
const divisor = gcd(parsed.width, parsed.height);
|
||||
return `${parsed.width / divisor}:${parsed.height / divisor}`;
|
||||
}
|
||||
|
||||
function inferImageSize(size: string | null): "1K" | "2K" | "4K" | null {
|
||||
if (!size) return null;
|
||||
const parsed = parsePixelSize(size);
|
||||
if (!parsed) return null;
|
||||
|
||||
const longestEdge = Math.max(parsed.width, parsed.height);
|
||||
if (longestEdge <= 1024) return "1K";
|
||||
if (longestEdge <= 2048) return "2K";
|
||||
return "4K";
|
||||
}
|
||||
|
||||
export function getImageSize(args: CliArgs): "1K" | "2K" | "4K" | null {
|
||||
if (args.imageSize) return args.imageSize as "1K" | "2K" | "4K";
|
||||
|
||||
const inferredFromSize = inferImageSize(args.size);
|
||||
if (inferredFromSize) return inferredFromSize;
|
||||
|
||||
if (args.quality === "normal") return "1K";
|
||||
if (args.quality === "2k") return "2K";
|
||||
return null;
|
||||
}
|
||||
|
||||
export function getAspectRatio(model: string, args: CliArgs): string | null {
|
||||
if (args.aspectRatio) return args.aspectRatio;
|
||||
|
||||
const inferred = inferAspectRatio(args.size);
|
||||
if (!inferred || !getSupportedAspectRatios(model).has(inferred)) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return inferred;
|
||||
}
|
||||
|
||||
function getModalities(model: string): string[] {
|
||||
return isTextAndImageModel(model) ? ["image", "text"] : ["image"];
|
||||
}
|
||||
|
||||
export function validateArgs(model: string, args: CliArgs): void {
|
||||
const requestedAspectRatio = args.aspectRatio || inferAspectRatio(args.size);
|
||||
if (!requestedAspectRatio) {
|
||||
return;
|
||||
}
|
||||
|
||||
const supported = getSupportedAspectRatios(model);
|
||||
if (supported.has(requestedAspectRatio)) {
|
||||
return;
|
||||
}
|
||||
|
||||
const requestedValue = args.aspectRatio
|
||||
? `aspect ratio ${requestedAspectRatio}`
|
||||
: `size ${args.size} (aspect ratio ${requestedAspectRatio})`;
|
||||
|
||||
throw new Error(
|
||||
`OpenRouter model ${model} does not support ${requestedValue}. Supported values: ${Array.from(supported).join(", ")}`
|
||||
);
|
||||
}
|
||||
|
||||
function getMimeType(filename: string): string {
|
||||
const ext = path.extname(filename).toLowerCase();
|
||||
if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
|
||||
if (ext === ".webp") return "image/webp";
|
||||
if (ext === ".gif") return "image/gif";
|
||||
return "image/png";
|
||||
}
|
||||
|
||||
async function readImageAsDataUrl(filePath: string): Promise<string> {
|
||||
const bytes = await readFile(filePath);
|
||||
return `data:${getMimeType(filePath)};base64,${bytes.toString("base64")}`;
|
||||
}
|
||||
|
||||
export function buildContent(
|
||||
prompt: string,
|
||||
referenceImages: string[],
|
||||
): string | Array<Record<string, unknown>> {
|
||||
if (referenceImages.length === 0) {
|
||||
return prompt;
|
||||
}
|
||||
|
||||
const content: Array<Record<string, unknown>> = [{ type: "text", text: prompt }];
|
||||
|
||||
for (const imageUrl of referenceImages) {
|
||||
content.push({
|
||||
type: "image_url",
|
||||
image_url: { url: imageUrl },
|
||||
});
|
||||
}
|
||||
|
||||
return content;
|
||||
}
|
||||
|
||||
function extractImageUrl(entry: OpenRouterImageEntry | OpenRouterMessagePart): string | null {
|
||||
const value = entry.image_url ?? entry.imageUrl;
|
||||
if (!value) return null;
|
||||
if (typeof value === "string") return value;
|
||||
return value.url ?? null;
|
||||
}
|
||||
|
||||
function decodeDataUrl(value: string): Uint8Array | null {
|
||||
const match = value.match(/^data:image\/[^;]+;base64,([A-Za-z0-9+/=]+)$/);
|
||||
if (!match) return null;
|
||||
return Uint8Array.from(Buffer.from(match[1]!, "base64"));
|
||||
}
|
||||
|
||||
async function downloadImage(value: string): Promise<Uint8Array> {
|
||||
const inline = decodeDataUrl(value);
|
||||
if (inline) return inline;
|
||||
|
||||
if (value.startsWith("http://") || value.startsWith("https://")) {
|
||||
const response = await fetch(value);
|
||||
if (!response.ok) {
|
||||
throw new Error(`Failed to download OpenRouter image: ${response.status}`);
|
||||
}
|
||||
const buffer = await response.arrayBuffer();
|
||||
return new Uint8Array(buffer);
|
||||
}
|
||||
|
||||
return Uint8Array.from(Buffer.from(value, "base64"));
|
||||
}
|
||||
|
||||
export async function extractImageFromResponse(result: OpenRouterResponse): Promise<Uint8Array> {
|
||||
const choice = result.choices?.[0];
|
||||
const message = choice?.message;
|
||||
|
||||
for (const image of message?.images ?? []) {
|
||||
const imageUrl = extractImageUrl(image);
|
||||
if (imageUrl) return downloadImage(imageUrl);
|
||||
}
|
||||
|
||||
if (Array.isArray(message?.content)) {
|
||||
for (const item of message.content) {
|
||||
const imageUrl = extractImageUrl(item);
|
||||
if (imageUrl) return downloadImage(imageUrl);
|
||||
|
||||
if (item.type === "text" && item.text) {
|
||||
const inline = decodeDataUrl(item.text);
|
||||
if (inline) return inline;
|
||||
}
|
||||
}
|
||||
} else if (typeof message?.content === "string") {
|
||||
const inline = decodeDataUrl(message.content);
|
||||
if (inline) return inline;
|
||||
}
|
||||
|
||||
const finishReason =
|
||||
choice?.native_finish_reason || choice?.finish_reason || "unknown";
|
||||
throw new Error(
|
||||
`No image in OpenRouter response (finish_reason=${finishReason})`,
|
||||
);
|
||||
}
|
||||
|
||||
export function buildRequestBody(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
referenceImages: string[],
|
||||
): Record<string, unknown> {
|
||||
validateArgs(model, args);
|
||||
|
||||
const imageConfig: Record<string, string> = {};
|
||||
|
||||
const imageSize = getImageSize(args);
|
||||
if (imageSize) {
|
||||
imageConfig.image_size = imageSize;
|
||||
}
|
||||
|
||||
const aspectRatio = getAspectRatio(model, args);
|
||||
if (aspectRatio) {
|
||||
imageConfig.aspect_ratio = aspectRatio;
|
||||
}
|
||||
|
||||
const body: Record<string, unknown> = {
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: buildContent(prompt, referenceImages),
|
||||
},
|
||||
],
|
||||
modalities: getModalities(model),
|
||||
stream: false,
|
||||
};
|
||||
|
||||
if (Object.keys(imageConfig).length > 0) {
|
||||
body.image_config = imageConfig;
|
||||
body.provider = {
|
||||
require_parameters: true,
|
||||
};
|
||||
}
|
||||
|
||||
return body;
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
const apiKey = getApiKey();
|
||||
if (!apiKey) {
|
||||
throw new Error("OPENROUTER_API_KEY is required. Get one at https://openrouter.ai/settings/keys");
|
||||
}
|
||||
|
||||
const referenceImages: string[] = [];
|
||||
for (const refPath of args.referenceImages) {
|
||||
referenceImages.push(await readImageAsDataUrl(refPath));
|
||||
}
|
||||
|
||||
const body = {
|
||||
model,
|
||||
...buildRequestBody(prompt, model, args, referenceImages),
|
||||
};
|
||||
|
||||
console.log(
|
||||
`Generating image with OpenRouter (${model})...`,
|
||||
(body.image_config as Record<string, string>),
|
||||
);
|
||||
|
||||
const response = await fetch(`${getBaseUrl()}/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: getHeaders(apiKey),
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
throw new Error(`OpenRouter API error (${response.status}): ${errorText}`);
|
||||
}
|
||||
|
||||
const result = (await response.json()) as OpenRouterResponse;
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
@@ -0,0 +1,310 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
buildInput,
|
||||
extractOutputUrl,
|
||||
getDefaultModel,
|
||||
getModelFamily,
|
||||
parseModelId,
|
||||
validateArgs,
|
||||
} from "./replicate.ts";
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
aspectRatioSource: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageSizeSource: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
test("Replicate default model now points at nano-banana-2", () => {
|
||||
const previous = process.env.REPLICATE_IMAGE_MODEL;
|
||||
delete process.env.REPLICATE_IMAGE_MODEL;
|
||||
try {
|
||||
assert.equal(getDefaultModel(), "google/nano-banana-2");
|
||||
} finally {
|
||||
if (previous == null) {
|
||||
delete process.env.REPLICATE_IMAGE_MODEL;
|
||||
} else {
|
||||
process.env.REPLICATE_IMAGE_MODEL = previous;
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
test("Replicate model parsing and family detection accept supported official ids", () => {
|
||||
assert.deepEqual(parseModelId("google/nano-banana-2"), {
|
||||
owner: "google",
|
||||
name: "nano-banana-2",
|
||||
version: null,
|
||||
});
|
||||
assert.deepEqual(parseModelId("owner/model:abc123"), {
|
||||
owner: "owner",
|
||||
name: "model",
|
||||
version: "abc123",
|
||||
});
|
||||
|
||||
assert.equal(getModelFamily("google/nano-banana-pro"), "nano-banana");
|
||||
assert.equal(getModelFamily("bytedance/seedream-4.5"), "seedream45");
|
||||
assert.equal(getModelFamily("bytedance/seedream-5-lite"), "seedream5lite");
|
||||
assert.equal(getModelFamily("wan-video/wan-2.7-image"), "wan27image");
|
||||
assert.equal(getModelFamily("wan-video/wan-2.7-image-pro"), "wan27imagepro");
|
||||
assert.equal(getModelFamily("stability-ai/sdxl"), "unknown");
|
||||
|
||||
assert.throws(
|
||||
() => parseModelId("just-a-model-name"),
|
||||
/Invalid Replicate model format/,
|
||||
);
|
||||
});
|
||||
|
||||
test("Replicate nano-banana input builder maps refs, aspect ratio, and quality presets", () => {
|
||||
assert.deepEqual(
|
||||
buildInput(
|
||||
"google/nano-banana-2",
|
||||
"A robot painter",
|
||||
makeArgs({
|
||||
aspectRatio: "16:9",
|
||||
quality: "2k",
|
||||
}),
|
||||
["data:image/png;base64,AAAA"],
|
||||
),
|
||||
{
|
||||
prompt: "A robot painter",
|
||||
resolution: "2K",
|
||||
output_format: "png",
|
||||
aspect_ratio: "16:9",
|
||||
image_input: ["data:image/png;base64,AAAA"],
|
||||
},
|
||||
);
|
||||
|
||||
assert.deepEqual(
|
||||
buildInput(
|
||||
"google/nano-banana-2",
|
||||
"A robot painter",
|
||||
makeArgs({ size: "1024x1024", quality: "normal" }),
|
||||
[],
|
||||
),
|
||||
{
|
||||
prompt: "A robot painter",
|
||||
resolution: "1K",
|
||||
output_format: "png",
|
||||
aspect_ratio: "1:1",
|
||||
},
|
||||
);
|
||||
});
|
||||
|
||||
test("Replicate Seedream and Wan inputs use family-specific request fields", () => {
|
||||
assert.deepEqual(
|
||||
buildInput(
|
||||
"bytedance/seedream-4.5",
|
||||
"A cinematic portrait",
|
||||
makeArgs({ quality: "2k", referenceImages: ["local.png"] }),
|
||||
["data:image/png;base64,AAAA"],
|
||||
),
|
||||
{
|
||||
prompt: "A cinematic portrait",
|
||||
size: "4K",
|
||||
image_input: ["data:image/png;base64,AAAA"],
|
||||
aspect_ratio: "match_input_image",
|
||||
},
|
||||
);
|
||||
|
||||
assert.deepEqual(
|
||||
buildInput(
|
||||
"bytedance/seedream-4.5",
|
||||
"A cinematic portrait",
|
||||
makeArgs({ size: "1536x1024" }),
|
||||
[],
|
||||
),
|
||||
{
|
||||
prompt: "A cinematic portrait",
|
||||
size: "custom",
|
||||
width: 1536,
|
||||
height: 1024,
|
||||
},
|
||||
);
|
||||
|
||||
assert.deepEqual(
|
||||
buildInput(
|
||||
"bytedance/seedream-5-lite",
|
||||
"A poster",
|
||||
makeArgs({ aspectRatio: "21:9", quality: "2k" }),
|
||||
[],
|
||||
),
|
||||
{
|
||||
prompt: "A poster",
|
||||
size: "3K",
|
||||
aspect_ratio: "21:9",
|
||||
},
|
||||
);
|
||||
|
||||
assert.deepEqual(
|
||||
buildInput(
|
||||
"wan-video/wan-2.7-image",
|
||||
"A storyboard frame",
|
||||
makeArgs({ aspectRatio: "16:9", quality: "2k" }),
|
||||
[],
|
||||
),
|
||||
{
|
||||
prompt: "A storyboard frame",
|
||||
size: "2048*1152",
|
||||
},
|
||||
);
|
||||
|
||||
assert.deepEqual(
|
||||
buildInput(
|
||||
"wan-video/wan-2.7-image-pro",
|
||||
"Blend these references",
|
||||
makeArgs({ size: "2K", referenceImages: ["a.png", "b.png"] }),
|
||||
["ref-a", "ref-b"],
|
||||
),
|
||||
{
|
||||
prompt: "Blend these references",
|
||||
size: "2K",
|
||||
images: ["ref-a", "ref-b"],
|
||||
},
|
||||
);
|
||||
});
|
||||
|
||||
test("Replicate validateArgs blocks misleading multi-output and unsupported family options locally", () => {
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"google/nano-banana-2",
|
||||
makeArgs({ n: 2 }),
|
||||
),
|
||||
/exactly one output image/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"bytedance/seedream-4.5",
|
||||
makeArgs({ size: "1K" }),
|
||||
),
|
||||
/2K, 4K, or an explicit WxH size/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"bytedance/seedream-5-lite",
|
||||
makeArgs({ size: "4K" }),
|
||||
),
|
||||
/supports 2K or 3K output/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"wan-video/wan-2.7-image",
|
||||
makeArgs({ referenceImages: new Array(10).fill("ref.png") }),
|
||||
),
|
||||
/at most 9 reference images/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"wan-video/wan-2.7-image-pro",
|
||||
makeArgs({ referenceImages: ["ref.png"], size: "4K" }),
|
||||
),
|
||||
/only supports 4K text-to-image/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"stability-ai/sdxl",
|
||||
makeArgs({ aspectRatio: "16:9" }),
|
||||
),
|
||||
/compatibility list/,
|
||||
);
|
||||
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs(
|
||||
"google/nano-banana-2",
|
||||
makeArgs({ imageSize: "2K", imageSizeSource: "config" }),
|
||||
),
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"google/nano-banana-2",
|
||||
makeArgs({ imageSize: "2K", imageSizeSource: "cli" }),
|
||||
),
|
||||
/do not use --imageSize/,
|
||||
);
|
||||
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs(
|
||||
"stability-ai/sdxl",
|
||||
makeArgs({ aspectRatio: "16:9", aspectRatioSource: "config" }),
|
||||
),
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"stability-ai/sdxl",
|
||||
makeArgs({ aspectRatio: "16:9", aspectRatioSource: "cli" }),
|
||||
),
|
||||
/compatibility list/,
|
||||
);
|
||||
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs(
|
||||
"stability-ai/sdxl",
|
||||
makeArgs(),
|
||||
),
|
||||
);
|
||||
});
|
||||
|
||||
test("Replicate output extraction supports single outputs and rejects silent multi-image drops", () => {
|
||||
assert.equal(
|
||||
extractOutputUrl({ output: "https://example.com/a.png" } as never),
|
||||
"https://example.com/a.png",
|
||||
);
|
||||
assert.equal(
|
||||
extractOutputUrl({ output: ["https://example.com/b.png"] } as never),
|
||||
"https://example.com/b.png",
|
||||
);
|
||||
assert.equal(
|
||||
extractOutputUrl({ output: { url: "https://example.com/c.png" } } as never),
|
||||
"https://example.com/c.png",
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
extractOutputUrl({
|
||||
output: [
|
||||
"https://example.com/one.png",
|
||||
"https://example.com/two.png",
|
||||
],
|
||||
} as never),
|
||||
/supports saving exactly one image/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() => extractOutputUrl({ output: { invalid: true } } as never),
|
||||
/Unexpected Replicate output format/,
|
||||
);
|
||||
});
|
||||
@@ -0,0 +1,616 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
import type { CliArgs } from "../types";
|
||||
|
||||
const DEFAULT_MODEL = "google/nano-banana-2";
|
||||
const SYNC_WAIT_SECONDS = 60;
|
||||
const POLL_INTERVAL_MS = 2000;
|
||||
const MAX_POLL_MS = 300_000;
|
||||
const DOCUMENTED_REPLICATE_ASPECT_RATIOS = new Set([
|
||||
"1:1",
|
||||
"2:3",
|
||||
"3:2",
|
||||
"3:4",
|
||||
"4:3",
|
||||
"5:4",
|
||||
"4:5",
|
||||
"9:16",
|
||||
"16:9",
|
||||
"21:9",
|
||||
]);
|
||||
|
||||
export type ReplicateModelFamily =
|
||||
| "nano-banana"
|
||||
| "seedream45"
|
||||
| "seedream5lite"
|
||||
| "wan27image"
|
||||
| "wan27imagepro"
|
||||
| "unknown";
|
||||
|
||||
type PixelSize = {
|
||||
width: number;
|
||||
height: number;
|
||||
};
|
||||
|
||||
type Seedream45Size = "2K" | "4K" | { width: number; height: number };
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.REPLICATE_IMAGE_MODEL || DEFAULT_MODEL;
|
||||
}
|
||||
|
||||
function getApiToken(): string | null {
|
||||
return process.env.REPLICATE_API_TOKEN || null;
|
||||
}
|
||||
|
||||
function getBaseUrl(): string {
|
||||
const base = process.env.REPLICATE_BASE_URL || "https://api.replicate.com";
|
||||
return base.replace(/\/+$/g, "");
|
||||
}
|
||||
|
||||
function normalizeModelId(model: string): string {
|
||||
return model.trim().toLowerCase().split(":")[0]!;
|
||||
}
|
||||
|
||||
export function getModelFamily(model: string): ReplicateModelFamily {
|
||||
const normalized = normalizeModelId(model);
|
||||
|
||||
if (
|
||||
normalized === "google/nano-banana" ||
|
||||
normalized === "google/nano-banana-pro" ||
|
||||
normalized === "google/nano-banana-2"
|
||||
) {
|
||||
return "nano-banana";
|
||||
}
|
||||
|
||||
if (normalized === "bytedance/seedream-4.5") {
|
||||
return "seedream45";
|
||||
}
|
||||
|
||||
if (normalized === "bytedance/seedream-5-lite") {
|
||||
return "seedream5lite";
|
||||
}
|
||||
|
||||
if (normalized === "wan-video/wan-2.7-image") {
|
||||
return "wan27image";
|
||||
}
|
||||
|
||||
if (normalized === "wan-video/wan-2.7-image-pro") {
|
||||
return "wan27imagepro";
|
||||
}
|
||||
|
||||
return "unknown";
|
||||
}
|
||||
|
||||
export function parseModelId(model: string): { owner: string; name: string; version: string | null } {
|
||||
const [ownerName, version] = model.split(":");
|
||||
const parts = ownerName!.split("/");
|
||||
if (parts.length !== 2 || !parts[0] || !parts[1]) {
|
||||
throw new Error(
|
||||
`Invalid Replicate model format: "${model}". Expected "owner/name" or "owner/name:version".`
|
||||
);
|
||||
}
|
||||
return { owner: parts[0], name: parts[1], version: version || null };
|
||||
}
|
||||
|
||||
function parsePixelSize(value: string): PixelSize | null {
|
||||
const match = value.trim().match(/^(\d+)\s*[xX*]\s*(\d+)$/);
|
||||
if (!match) return null;
|
||||
|
||||
const width = parseInt(match[1]!, 10);
|
||||
const height = parseInt(match[2]!, 10);
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
function parseAspectRatio(value: string): PixelSize | null {
|
||||
const match = value.trim().match(/^(\d+)\s*:\s*(\d+)$/);
|
||||
if (!match) return null;
|
||||
|
||||
const width = parseInt(match[1]!, 10);
|
||||
const height = parseInt(match[2]!, 10);
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
function gcd(a: number, b: number): number {
|
||||
let x = Math.abs(a);
|
||||
let y = Math.abs(b);
|
||||
|
||||
while (y !== 0) {
|
||||
const next = x % y;
|
||||
x = y;
|
||||
y = next;
|
||||
}
|
||||
|
||||
return x || 1;
|
||||
}
|
||||
|
||||
function inferAspectRatioFromSize(size: string): string | null {
|
||||
const parsed = parsePixelSize(size);
|
||||
if (!parsed) return null;
|
||||
|
||||
const divisor = gcd(parsed.width, parsed.height);
|
||||
const normalized = `${parsed.width / divisor}:${parsed.height / divisor}`;
|
||||
if (!DOCUMENTED_REPLICATE_ASPECT_RATIOS.has(normalized)) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return normalized;
|
||||
}
|
||||
|
||||
function getQualityPreset(args: CliArgs): "normal" | "2k" {
|
||||
return args.quality === "normal" ? "normal" : "2k";
|
||||
}
|
||||
|
||||
function validateDocumentedAspectRatio(model: string, aspectRatio: string): void {
|
||||
if (aspectRatio === "match_input_image") {
|
||||
return;
|
||||
}
|
||||
|
||||
if (DOCUMENTED_REPLICATE_ASPECT_RATIOS.has(aspectRatio)) {
|
||||
return;
|
||||
}
|
||||
|
||||
throw new Error(
|
||||
`Replicate model ${model} does not support aspect ratio ${aspectRatio}. Supported values: ${Array.from(DOCUMENTED_REPLICATE_ASPECT_RATIOS).join(", ")}`
|
||||
);
|
||||
}
|
||||
|
||||
function getRequestedAspectRatio(model: string, args: CliArgs): string | null {
|
||||
if (args.aspectRatio) {
|
||||
validateDocumentedAspectRatio(model, args.aspectRatio);
|
||||
return args.aspectRatio;
|
||||
}
|
||||
|
||||
if (!args.size) return null;
|
||||
|
||||
const inferred = inferAspectRatioFromSize(args.size);
|
||||
if (!inferred) {
|
||||
throw new Error(
|
||||
`Replicate model ${model} cannot derive a supported aspect ratio from --size ${args.size}. Use one of: ${Array.from(DOCUMENTED_REPLICATE_ASPECT_RATIOS).join(", ")}`
|
||||
);
|
||||
}
|
||||
|
||||
return inferred;
|
||||
}
|
||||
|
||||
function getNanoBananaResolution(args: CliArgs): "1K" | "2K" {
|
||||
if (args.size) {
|
||||
const parsed = parsePixelSize(args.size);
|
||||
if (!parsed) {
|
||||
throw new Error("Replicate nano-banana --size must be in WxH format, for example 1536x1024.");
|
||||
}
|
||||
|
||||
const longestEdge = Math.max(parsed.width, parsed.height);
|
||||
if (longestEdge <= 1024) return "1K";
|
||||
if (longestEdge <= 2048) return "2K";
|
||||
throw new Error("Replicate nano-banana only supports sizes that map to 1K or 2K output.");
|
||||
}
|
||||
|
||||
return getQualityPreset(args) === "normal" ? "1K" : "2K";
|
||||
}
|
||||
|
||||
function resolveSeedream45Size(args: CliArgs): Seedream45Size {
|
||||
if (args.size) {
|
||||
const upper = args.size.trim().toUpperCase();
|
||||
if (upper === "2K" || upper === "4K") {
|
||||
return upper;
|
||||
}
|
||||
|
||||
const parsed = parsePixelSize(args.size);
|
||||
if (!parsed) {
|
||||
throw new Error("Replicate Seedream 4.5 --size must be 2K, 4K, or an explicit WxH size.");
|
||||
}
|
||||
if (parsed.width < 1024 || parsed.width > 4096 || parsed.height < 1024 || parsed.height > 4096) {
|
||||
throw new Error("Replicate Seedream 4.5 custom --size must keep width and height between 1024 and 4096.");
|
||||
}
|
||||
return parsed;
|
||||
}
|
||||
|
||||
return getQualityPreset(args) === "normal" ? "2K" : "4K";
|
||||
}
|
||||
|
||||
function resolveSeedream5LiteSize(args: CliArgs): "2K" | "3K" {
|
||||
if (args.size) {
|
||||
const upper = args.size.trim().toUpperCase();
|
||||
if (upper === "2K" || upper === "3K") {
|
||||
return upper;
|
||||
}
|
||||
|
||||
throw new Error("Replicate Seedream 5 Lite currently supports 2K or 3K output in this tool.");
|
||||
}
|
||||
|
||||
return getQualityPreset(args) === "normal" ? "2K" : "3K";
|
||||
}
|
||||
|
||||
function formatCustomWanSize(size: PixelSize): string {
|
||||
return `${size.width}*${size.height}`;
|
||||
}
|
||||
|
||||
function resolveWanSizeFromAspectRatio(
|
||||
aspectRatio: string,
|
||||
maxDimension: number,
|
||||
): string {
|
||||
const parsedRatio = parseAspectRatio(aspectRatio);
|
||||
if (!parsedRatio) {
|
||||
throw new Error(`Replicate Wan aspect ratio must be in W:H format, got ${aspectRatio}.`);
|
||||
}
|
||||
|
||||
const scale = Math.min(maxDimension / parsedRatio.width, maxDimension / parsedRatio.height);
|
||||
const width = Math.max(1, Math.floor(parsedRatio.width * scale));
|
||||
const height = Math.max(1, Math.floor(parsedRatio.height * scale));
|
||||
return formatCustomWanSize({ width, height });
|
||||
}
|
||||
|
||||
function resolveWanSize(family: "wan27image" | "wan27imagepro", args: CliArgs): "1K" | "2K" | "4K" | string {
|
||||
const referenceMode = args.referenceImages.length > 0;
|
||||
const maxDimension = family === "wan27imagepro" && !referenceMode ? 4096 : 2048;
|
||||
|
||||
if (args.size) {
|
||||
const upper = args.size.trim().toUpperCase();
|
||||
if (upper === "1K" || upper === "2K" || upper === "4K") {
|
||||
if (upper === "4K" && family !== "wan27imagepro") {
|
||||
throw new Error("Replicate Wan 2.7 Image only supports 1K, 2K, or custom sizes up to 2048px.");
|
||||
}
|
||||
if (upper === "4K" && referenceMode) {
|
||||
throw new Error("Replicate Wan 2.7 Image Pro only supports 4K text-to-image. Remove --ref or lower the size.");
|
||||
}
|
||||
return upper;
|
||||
}
|
||||
|
||||
const parsed = parsePixelSize(args.size);
|
||||
if (!parsed) {
|
||||
throw new Error("Replicate Wan --size must be 1K, 2K, 4K, or an explicit WxH size.");
|
||||
}
|
||||
if (parsed.width > maxDimension || parsed.height > maxDimension) {
|
||||
throw new Error(
|
||||
`Replicate ${family === "wan27imagepro" ? "Wan 2.7 Image Pro" : "Wan 2.7 Image"} custom --size must keep width and height at or below ${maxDimension}px in the current mode.`
|
||||
);
|
||||
}
|
||||
return formatCustomWanSize(parsed);
|
||||
}
|
||||
|
||||
if (args.aspectRatio) {
|
||||
return resolveWanSizeFromAspectRatio(
|
||||
args.aspectRatio,
|
||||
getQualityPreset(args) === "normal" ? 1024 : 2048,
|
||||
);
|
||||
}
|
||||
|
||||
return getQualityPreset(args) === "normal" ? "1K" : "2K";
|
||||
}
|
||||
|
||||
function buildNanoBananaInput(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
referenceImages: string[],
|
||||
): Record<string, unknown> {
|
||||
const input: Record<string, unknown> = {
|
||||
prompt,
|
||||
resolution: getNanoBananaResolution(args),
|
||||
output_format: "png",
|
||||
};
|
||||
|
||||
const aspectRatio = getRequestedAspectRatio(model, args);
|
||||
if (aspectRatio) {
|
||||
input.aspect_ratio = aspectRatio;
|
||||
} else if (referenceImages.length > 0) {
|
||||
input.aspect_ratio = "match_input_image";
|
||||
}
|
||||
|
||||
if (referenceImages.length > 0) {
|
||||
input.image_input = referenceImages;
|
||||
}
|
||||
|
||||
return input;
|
||||
}
|
||||
|
||||
function buildSeedreamInput(
|
||||
family: "seedream45" | "seedream5lite",
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
referenceImages: string[],
|
||||
): Record<string, unknown> {
|
||||
const size = family === "seedream45" ? resolveSeedream45Size(args) : resolveSeedream5LiteSize(args);
|
||||
const input: Record<string, unknown> = {
|
||||
prompt,
|
||||
};
|
||||
|
||||
if (family === "seedream45" && typeof size === "object") {
|
||||
input.size = "custom";
|
||||
input.width = size.width;
|
||||
input.height = size.height;
|
||||
} else {
|
||||
input.size = size;
|
||||
}
|
||||
|
||||
if (referenceImages.length > 0) {
|
||||
input.image_input = referenceImages;
|
||||
}
|
||||
|
||||
if (args.aspectRatio) {
|
||||
validateDocumentedAspectRatio(model, args.aspectRatio);
|
||||
input.aspect_ratio = args.aspectRatio;
|
||||
} else if (referenceImages.length > 0 && family === "seedream45") {
|
||||
input.aspect_ratio = "match_input_image";
|
||||
}
|
||||
|
||||
return input;
|
||||
}
|
||||
|
||||
function buildWanInput(
|
||||
family: "wan27image" | "wan27imagepro",
|
||||
prompt: string,
|
||||
args: CliArgs,
|
||||
referenceImages: string[],
|
||||
): Record<string, unknown> {
|
||||
const input: Record<string, unknown> = {
|
||||
prompt,
|
||||
size: resolveWanSize(family, args),
|
||||
};
|
||||
|
||||
if (referenceImages.length > 0) {
|
||||
input.images = referenceImages;
|
||||
}
|
||||
|
||||
return input;
|
||||
}
|
||||
|
||||
export function validateArgs(model: string, args: CliArgs): void {
|
||||
parseModelId(model);
|
||||
|
||||
if (args.n !== 1) {
|
||||
throw new Error("Replicate integration currently supports exactly one output image per request. Remove --n or use --n 1.");
|
||||
}
|
||||
|
||||
if (args.imageSize && args.imageSizeSource !== "config") {
|
||||
throw new Error("Replicate models in baoyu-image-gen do not use --imageSize. Use --quality, --ar, or --size instead.");
|
||||
}
|
||||
|
||||
const family = getModelFamily(model);
|
||||
|
||||
if (family === "nano-banana") {
|
||||
if (args.referenceImages.length > 14) {
|
||||
throw new Error("Replicate nano-banana supports at most 14 reference images.");
|
||||
}
|
||||
if (args.aspectRatio) {
|
||||
validateDocumentedAspectRatio(model, args.aspectRatio);
|
||||
}
|
||||
if (args.size) {
|
||||
getRequestedAspectRatio(model, args);
|
||||
getNanoBananaResolution(args);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (family === "seedream45") {
|
||||
if (args.referenceImages.length > 14) {
|
||||
throw new Error("Replicate Seedream 4.5 supports at most 14 reference images.");
|
||||
}
|
||||
if (args.aspectRatio) {
|
||||
validateDocumentedAspectRatio(model, args.aspectRatio);
|
||||
}
|
||||
resolveSeedream45Size(args);
|
||||
return;
|
||||
}
|
||||
|
||||
if (family === "seedream5lite") {
|
||||
if (args.referenceImages.length > 14) {
|
||||
throw new Error("Replicate Seedream 5 Lite supports at most 14 reference images.");
|
||||
}
|
||||
if (args.aspectRatio) {
|
||||
validateDocumentedAspectRatio(model, args.aspectRatio);
|
||||
}
|
||||
resolveSeedream5LiteSize(args);
|
||||
return;
|
||||
}
|
||||
|
||||
if (family === "wan27image" || family === "wan27imagepro") {
|
||||
if (args.referenceImages.length > 9) {
|
||||
throw new Error("Replicate Wan 2.7 image models support at most 9 reference images.");
|
||||
}
|
||||
if (args.aspectRatio) {
|
||||
const parsed = parseAspectRatio(args.aspectRatio);
|
||||
if (!parsed) {
|
||||
throw new Error(`Replicate Wan aspect ratio must be in W:H format, got ${args.aspectRatio}.`);
|
||||
}
|
||||
}
|
||||
resolveWanSize(family, args);
|
||||
return;
|
||||
}
|
||||
|
||||
const hasExplicitAspectRatio = !!args.aspectRatio && args.aspectRatioSource !== "config";
|
||||
|
||||
if (args.referenceImages.length > 0 || hasExplicitAspectRatio || args.size) {
|
||||
throw new Error(
|
||||
`Replicate model ${model} is not in the baoyu-image-gen compatibility list. Supported families: google/nano-banana*, bytedance/seedream-4.5, bytedance/seedream-5-lite, wan-video/wan-2.7-image, wan-video/wan-2.7-image-pro.`
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
export function getDefaultOutputExtension(model: string): ".png" {
|
||||
const _family = getModelFamily(model);
|
||||
return ".png";
|
||||
}
|
||||
|
||||
export function buildInput(
|
||||
model: string,
|
||||
prompt: string,
|
||||
args: CliArgs,
|
||||
referenceImages: string[],
|
||||
): Record<string, unknown> {
|
||||
const family = getModelFamily(model);
|
||||
|
||||
if (family === "nano-banana") {
|
||||
return buildNanoBananaInput(prompt, model, args, referenceImages);
|
||||
}
|
||||
|
||||
if (family === "seedream45" || family === "seedream5lite") {
|
||||
return buildSeedreamInput(family, prompt, model, args, referenceImages);
|
||||
}
|
||||
|
||||
if (family === "wan27image" || family === "wan27imagepro") {
|
||||
return buildWanInput(family, prompt, args, referenceImages);
|
||||
}
|
||||
|
||||
return { prompt };
|
||||
}
|
||||
|
||||
async function readImageAsDataUrl(p: string): Promise<string> {
|
||||
const buf = await readFile(p);
|
||||
const ext = path.extname(p).toLowerCase();
|
||||
let mimeType = "image/png";
|
||||
if (ext === ".jpg" || ext === ".jpeg") mimeType = "image/jpeg";
|
||||
else if (ext === ".gif") mimeType = "image/gif";
|
||||
else if (ext === ".webp") mimeType = "image/webp";
|
||||
return `data:${mimeType};base64,${buf.toString("base64")}`;
|
||||
}
|
||||
|
||||
type PredictionResponse = {
|
||||
id: string;
|
||||
status: string;
|
||||
output: unknown;
|
||||
error: string | null;
|
||||
urls?: { get?: string };
|
||||
};
|
||||
|
||||
async function createPrediction(
|
||||
apiToken: string,
|
||||
model: { owner: string; name: string; version: string | null },
|
||||
input: Record<string, unknown>,
|
||||
sync: boolean
|
||||
): Promise<PredictionResponse> {
|
||||
const baseUrl = getBaseUrl();
|
||||
|
||||
let url: string;
|
||||
const body: Record<string, unknown> = { input };
|
||||
|
||||
if (model.version) {
|
||||
url = `${baseUrl}/v1/predictions`;
|
||||
body.version = model.version;
|
||||
} else {
|
||||
url = `${baseUrl}/v1/models/${model.owner}/${model.name}/predictions`;
|
||||
}
|
||||
|
||||
const headers: Record<string, string> = {
|
||||
Authorization: `Bearer ${apiToken}`,
|
||||
"Content-Type": "application/json",
|
||||
};
|
||||
|
||||
if (sync) {
|
||||
headers["Prefer"] = `wait=${SYNC_WAIT_SECONDS}`;
|
||||
}
|
||||
|
||||
const res = await fetch(url, {
|
||||
method: "POST",
|
||||
headers,
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`Replicate API error (${res.status}): ${err}`);
|
||||
}
|
||||
|
||||
return (await res.json()) as PredictionResponse;
|
||||
}
|
||||
|
||||
async function pollPrediction(apiToken: string, getUrl: string): Promise<PredictionResponse> {
|
||||
const start = Date.now();
|
||||
|
||||
while (Date.now() - start < MAX_POLL_MS) {
|
||||
const res = await fetch(getUrl, {
|
||||
headers: { Authorization: `Bearer ${apiToken}` },
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const err = await res.text();
|
||||
throw new Error(`Replicate poll error (${res.status}): ${err}`);
|
||||
}
|
||||
|
||||
const prediction = (await res.json()) as PredictionResponse;
|
||||
|
||||
if (prediction.status === "succeeded") return prediction;
|
||||
if (prediction.status === "failed" || prediction.status === "canceled") {
|
||||
throw new Error(`Replicate prediction ${prediction.status}: ${prediction.error || "unknown error"}`);
|
||||
}
|
||||
|
||||
await new Promise((r) => setTimeout(r, POLL_INTERVAL_MS));
|
||||
}
|
||||
|
||||
throw new Error(`Replicate prediction timed out after ${MAX_POLL_MS / 1000}s`);
|
||||
}
|
||||
|
||||
export function extractOutputUrl(prediction: PredictionResponse): string {
|
||||
const output = prediction.output;
|
||||
|
||||
if (typeof output === "string") return output;
|
||||
|
||||
if (Array.isArray(output)) {
|
||||
if (output.length !== 1) {
|
||||
throw new Error(
|
||||
`Replicate returned ${output.length} outputs, but baoyu-image-gen currently supports saving exactly one image per request.`
|
||||
);
|
||||
}
|
||||
const first = output[0];
|
||||
if (typeof first === "string") return first;
|
||||
}
|
||||
|
||||
if (output && typeof output === "object" && "url" in output) {
|
||||
const url = (output as Record<string, unknown>).url;
|
||||
if (typeof url === "string") return url;
|
||||
}
|
||||
|
||||
throw new Error(`Unexpected Replicate output format: ${JSON.stringify(output)}`);
|
||||
}
|
||||
|
||||
async function downloadImage(url: string): Promise<Uint8Array> {
|
||||
const res = await fetch(url);
|
||||
if (!res.ok) throw new Error(`Failed to download image from Replicate: ${res.status}`);
|
||||
const buf = await res.arrayBuffer();
|
||||
return new Uint8Array(buf);
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
const apiToken = getApiToken();
|
||||
if (!apiToken) throw new Error("REPLICATE_API_TOKEN is required. Get one at https://replicate.com/account/api-tokens");
|
||||
|
||||
const parsedModel = parseModelId(model);
|
||||
validateArgs(model, args);
|
||||
|
||||
const refDataUrls: string[] = [];
|
||||
for (const refPath of args.referenceImages) {
|
||||
refDataUrls.push(await readImageAsDataUrl(refPath));
|
||||
}
|
||||
|
||||
const input = buildInput(model, prompt, args, refDataUrls);
|
||||
|
||||
console.log(`Generating image with Replicate (${model})...`);
|
||||
|
||||
let prediction = await createPrediction(apiToken, parsedModel, input, true);
|
||||
|
||||
if (prediction.status !== "succeeded") {
|
||||
if (!prediction.urls?.get) {
|
||||
throw new Error("Replicate prediction did not return a poll URL");
|
||||
}
|
||||
console.log("Waiting for prediction to complete...");
|
||||
prediction = await pollPrediction(apiToken, prediction.urls.get);
|
||||
}
|
||||
|
||||
console.log("Generation completed.");
|
||||
|
||||
const outputUrl = extractOutputUrl(prediction);
|
||||
return downloadImage(outputUrl);
|
||||
}
|
||||
@@ -0,0 +1,245 @@
|
||||
import assert from "node:assert/strict";
|
||||
import fs from "node:fs/promises";
|
||||
import os from "node:os";
|
||||
import path from "node:path";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
buildImageInput,
|
||||
buildRequestBody,
|
||||
generateImage,
|
||||
getDefaultOutputExtension,
|
||||
resolveSeedreamSize,
|
||||
validateArgs,
|
||||
} from "./seedream.ts";
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
async function makeTempPng(t: TestContext, name: string): Promise<string> {
|
||||
const dir = await fs.mkdtemp(path.join(os.tmpdir(), "seedream-test-"));
|
||||
t.after(() => fs.rm(dir, { recursive: true, force: true }));
|
||||
|
||||
const filePath = path.join(dir, name);
|
||||
const png1x1 =
|
||||
"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/x8AAwMCAO+a7m0AAAAASUVORK5CYII=";
|
||||
await fs.writeFile(filePath, Buffer.from(png1x1, "base64"));
|
||||
return filePath;
|
||||
}
|
||||
|
||||
test("Seedream request body and default extensions follow official model capabilities", () => {
|
||||
const five = buildRequestBody(
|
||||
"A robot illustrator",
|
||||
"doubao-seedream-5-0-260128",
|
||||
makeArgs(),
|
||||
);
|
||||
assert.equal(five.size, "2K");
|
||||
assert.equal(five.response_format, "url");
|
||||
assert.equal(five.output_format, "png");
|
||||
assert.equal(getDefaultOutputExtension("doubao-seedream-5-0-260128"), ".png");
|
||||
|
||||
const fourFive = buildRequestBody(
|
||||
"A robot illustrator",
|
||||
"doubao-seedream-4-5-251128",
|
||||
makeArgs(),
|
||||
);
|
||||
assert.equal(fourFive.size, "2K");
|
||||
assert.equal(fourFive.response_format, "url");
|
||||
assert.ok(!("output_format" in fourFive));
|
||||
assert.equal(getDefaultOutputExtension("doubao-seedream-4-5-251128"), ".jpg");
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
buildRequestBody(
|
||||
"Change the bubbles into hearts",
|
||||
"doubao-seededit-3-0-i2i-250628",
|
||||
makeArgs({ referenceImages: ["ref.png"] }),
|
||||
"data:image/png;base64,AAAA",
|
||||
),
|
||||
/no longer supported/,
|
||||
);
|
||||
});
|
||||
|
||||
test("Seedream size selection validates model-specific presets", () => {
|
||||
assert.equal(
|
||||
resolveSeedreamSize("doubao-seedream-4-0-250828", makeArgs({ quality: "normal" })),
|
||||
"1K",
|
||||
);
|
||||
assert.equal(
|
||||
resolveSeedreamSize("doubao-seedream-3-0-t2i-250415", makeArgs({ quality: "2k" })),
|
||||
"2048x2048",
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSeedreamSize("doubao-seedream-5-0-260128", makeArgs({ size: "4K" })),
|
||||
/only supports 2K, 3K/,
|
||||
);
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSeedreamSize("doubao-seedream-3-0-t2i-250415", makeArgs({ imageSize: "2K" })),
|
||||
/only supports explicit WxH sizes/,
|
||||
);
|
||||
assert.throws(
|
||||
() =>
|
||||
resolveSeedreamSize("doubao-seededit-3-0-i2i-250628", makeArgs({ size: "1024x1024" })),
|
||||
/no longer supported/,
|
||||
);
|
||||
});
|
||||
|
||||
test("Seedream reference-image support is model-specific", () => {
|
||||
assert.doesNotThrow(() =>
|
||||
validateArgs(
|
||||
"doubao-seedream-5-0-260128",
|
||||
makeArgs({ referenceImages: ["a.png", "b.png"] }),
|
||||
),
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"doubao-seedream-3-0-t2i-250415",
|
||||
makeArgs({ referenceImages: ["a.png"] }),
|
||||
),
|
||||
/does not support reference images/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"doubao-seededit-3-0-i2i-250628",
|
||||
makeArgs(),
|
||||
),
|
||||
/no longer supported/,
|
||||
);
|
||||
|
||||
assert.throws(
|
||||
() =>
|
||||
validateArgs(
|
||||
"ep-20260315171508-t8br2",
|
||||
makeArgs({ referenceImages: ["a.png"] }),
|
||||
),
|
||||
/require a known model ID/,
|
||||
);
|
||||
});
|
||||
|
||||
test("Seedream image input encodes local references as data URLs", async (t) => {
|
||||
const refOne = await makeTempPng(t, "one.png");
|
||||
const refTwo = await makeTempPng(t, "two.png");
|
||||
|
||||
const single = await buildImageInput("doubao-seedream-4-5-251128", [refOne]);
|
||||
assert.match(String(single), /^data:image\/png;base64,/);
|
||||
|
||||
const multiple = await buildImageInput("doubao-seedream-5-0-260128", [refOne, refTwo]);
|
||||
assert.ok(Array.isArray(multiple));
|
||||
assert.equal(multiple.length, 2);
|
||||
});
|
||||
|
||||
test("Seedream generateImage posts the documented response_format and downloads the returned URL", async (t) => {
|
||||
useEnv(t, { ARK_API_KEY: "test-key", SEEDREAM_BASE_URL: null });
|
||||
|
||||
const originalFetch = globalThis.fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
const calls: Array<{
|
||||
input: string;
|
||||
init?: RequestInit;
|
||||
}> = [];
|
||||
|
||||
globalThis.fetch = async (input, init) => {
|
||||
calls.push({
|
||||
input: String(input),
|
||||
init,
|
||||
});
|
||||
|
||||
if (calls.length === 1) {
|
||||
return Response.json({
|
||||
model: "doubao-seedream-4-5-251128",
|
||||
created: 1740000000,
|
||||
data: [
|
||||
{
|
||||
url: "https://example.com/generated-image",
|
||||
size: "2048x2048",
|
||||
},
|
||||
],
|
||||
usage: {
|
||||
generated_images: 1,
|
||||
output_tokens: 1,
|
||||
total_tokens: 1,
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
return new Response(Uint8Array.from([7, 8, 9]), {
|
||||
status: 200,
|
||||
headers: { "Content-Type": "image/jpeg" },
|
||||
});
|
||||
};
|
||||
|
||||
const image = await generateImage(
|
||||
"A robot illustrator",
|
||||
"doubao-seedream-4-5-251128",
|
||||
makeArgs(),
|
||||
);
|
||||
|
||||
assert.deepEqual([...image], [7, 8, 9]);
|
||||
assert.equal(calls.length, 2);
|
||||
assert.equal(
|
||||
calls[0]?.input,
|
||||
"https://ark.cn-beijing.volces.com/api/v3/images/generations",
|
||||
);
|
||||
|
||||
const requestBody = JSON.parse(String(calls[0]?.init?.body)) as Record<string, unknown>;
|
||||
assert.equal(requestBody.model, "doubao-seedream-4-5-251128");
|
||||
assert.equal(requestBody.size, "2K");
|
||||
assert.equal(requestBody.response_format, "url");
|
||||
assert.ok(!("output_format" in requestBody));
|
||||
assert.equal(calls[1]?.input, "https://example.com/generated-image");
|
||||
});
|
||||
@@ -0,0 +1,341 @@
|
||||
import path from "node:path";
|
||||
import { readFile } from "node:fs/promises";
|
||||
|
||||
import type { CliArgs } from "../types";
|
||||
|
||||
export type SeedreamModelFamily =
|
||||
| "seedream5"
|
||||
| "seedream45"
|
||||
| "seedream40"
|
||||
| "seedream30"
|
||||
| "unknown";
|
||||
|
||||
type SeedreamRequestImage = string | string[];
|
||||
|
||||
type SeedreamRequestBody = {
|
||||
model: string;
|
||||
prompt: string;
|
||||
size: string;
|
||||
response_format: "url";
|
||||
watermark: boolean;
|
||||
image?: SeedreamRequestImage;
|
||||
output_format?: "png";
|
||||
};
|
||||
|
||||
type SeedreamImageResponse = {
|
||||
model?: string;
|
||||
created?: number;
|
||||
data?: Array<{
|
||||
url?: string;
|
||||
b64_json?: string;
|
||||
size?: string;
|
||||
error?: {
|
||||
code?: string;
|
||||
message?: string;
|
||||
};
|
||||
}>;
|
||||
usage?: {
|
||||
generated_images: number;
|
||||
output_tokens: number;
|
||||
total_tokens: number;
|
||||
};
|
||||
error?: {
|
||||
code?: string;
|
||||
message?: string;
|
||||
};
|
||||
};
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.SEEDREAM_IMAGE_MODEL || "doubao-seedream-5-0-260128";
|
||||
}
|
||||
|
||||
function getApiKey(): string | null {
|
||||
return process.env.ARK_API_KEY || null;
|
||||
}
|
||||
|
||||
function getBaseUrl(): string {
|
||||
return process.env.SEEDREAM_BASE_URL || "https://ark.cn-beijing.volces.com/api/v3";
|
||||
}
|
||||
|
||||
function parsePixelSize(value: string): { width: number; height: number } | null {
|
||||
const match = value.trim().match(/^(\d+)\s*[xX]\s*(\d+)$/);
|
||||
if (!match) return null;
|
||||
|
||||
const width = parseInt(match[1]!, 10);
|
||||
const height = parseInt(match[2]!, 10);
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
function normalizePixelSize(value: string): string | null {
|
||||
const parsed = parsePixelSize(value);
|
||||
if (!parsed) return null;
|
||||
return `${parsed.width}x${parsed.height}`;
|
||||
}
|
||||
|
||||
function normalizeSizePreset(value: string): string | null {
|
||||
const upper = value.trim().toUpperCase();
|
||||
if (upper === "ADAPTIVE") return "adaptive";
|
||||
if (upper === "1K" || upper === "2K" || upper === "3K" || upper === "4K") return upper;
|
||||
return null;
|
||||
}
|
||||
|
||||
function normalizeSizeValue(value: string): string | null {
|
||||
return normalizeSizePreset(value) ?? normalizePixelSize(value);
|
||||
}
|
||||
|
||||
function getMimeType(filename: string): string {
|
||||
const ext = path.extname(filename).toLowerCase();
|
||||
if (ext === ".jpg" || ext === ".jpeg") return "image/jpeg";
|
||||
if (ext === ".webp") return "image/webp";
|
||||
if (ext === ".gif") return "image/gif";
|
||||
if (ext === ".bmp") return "image/bmp";
|
||||
if (ext === ".tiff" || ext === ".tif") return "image/tiff";
|
||||
return "image/png";
|
||||
}
|
||||
|
||||
async function readImageAsDataUrl(filePath: string): Promise<string> {
|
||||
const bytes = await readFile(filePath);
|
||||
return `data:${getMimeType(filePath)};base64,${bytes.toString("base64")}`;
|
||||
}
|
||||
|
||||
export function getModelFamily(model: string): SeedreamModelFamily {
|
||||
const normalized = model.trim();
|
||||
if (/^doubao-seedream-5-0(?:-lite)?-\d+$/.test(normalized)) return "seedream5";
|
||||
if (/^doubao-seedream-4-5-\d+$/.test(normalized)) return "seedream45";
|
||||
if (/^doubao-seedream-4-0-\d+$/.test(normalized)) return "seedream40";
|
||||
if (/^doubao-seedream-3-0-t2i-\d+$/.test(normalized)) return "seedream30";
|
||||
return "unknown";
|
||||
}
|
||||
|
||||
function isRemovedSeededitModel(model: string): boolean {
|
||||
return /^doubao-seededit-3-0-i2i-\d+$/.test(model.trim());
|
||||
}
|
||||
|
||||
function assertSupportedModel(model: string): void {
|
||||
if (isRemovedSeededitModel(model)) {
|
||||
throw new Error(
|
||||
`${model} is no longer supported. SeedEdit 3.0 support has been removed from this tool; use Seedream 5.0/4.5/4.0/3.0 instead.`
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
export function supportsReferenceImages(model: string): boolean {
|
||||
const family = getModelFamily(model);
|
||||
return family === "seedream5" || family === "seedream45" || family === "seedream40";
|
||||
}
|
||||
|
||||
function supportsOutputFormat(model: string): boolean {
|
||||
return getModelFamily(model) === "seedream5";
|
||||
}
|
||||
|
||||
export function getDefaultOutputExtension(model: string): ".png" | ".jpg" {
|
||||
assertSupportedModel(model);
|
||||
return supportsOutputFormat(model) ? ".png" : ".jpg";
|
||||
}
|
||||
|
||||
export function getDefaultSeedreamSize(model: string, args: CliArgs): string {
|
||||
assertSupportedModel(model);
|
||||
const family = getModelFamily(model);
|
||||
|
||||
if (family === "seedream5") return "2K";
|
||||
if (family === "seedream45") return "2K";
|
||||
if (family === "seedream40") return args.quality === "normal" ? "1K" : "2K";
|
||||
if (family === "seedream30") return args.quality === "2k" ? "2048x2048" : "1024x1024";
|
||||
return "2K";
|
||||
}
|
||||
|
||||
export function resolveSeedreamSize(model: string, args: CliArgs): string {
|
||||
assertSupportedModel(model);
|
||||
const family = getModelFamily(model);
|
||||
const requested = args.size || args.imageSize || null;
|
||||
const normalized = requested ? normalizeSizeValue(requested) : null;
|
||||
|
||||
if (!normalized) {
|
||||
return getDefaultSeedreamSize(model, args);
|
||||
}
|
||||
|
||||
if (family === "seedream30") {
|
||||
const pixelSize = normalizePixelSize(normalized);
|
||||
if (!pixelSize) {
|
||||
throw new Error("Seedream 3.0 only supports explicit WxH sizes such as 1024x1024.");
|
||||
}
|
||||
return pixelSize;
|
||||
}
|
||||
|
||||
if (family === "seedream5") {
|
||||
if (normalized === "4K" || normalized === "1K" || normalized === "adaptive") {
|
||||
throw new Error("Seedream 5.0 only supports 2K, 3K, or explicit WxH sizes.");
|
||||
}
|
||||
return normalized;
|
||||
}
|
||||
|
||||
if (family === "seedream45") {
|
||||
if (normalized === "1K" || normalized === "3K" || normalized === "adaptive") {
|
||||
throw new Error("Seedream 4.5 only supports 2K, 4K, or explicit WxH sizes.");
|
||||
}
|
||||
return normalized;
|
||||
}
|
||||
|
||||
if (family === "seedream40") {
|
||||
if (normalized === "3K" || normalized === "adaptive") {
|
||||
throw new Error("Seedream 4.0 only supports 1K, 2K, 4K, or explicit WxH sizes.");
|
||||
}
|
||||
return normalized;
|
||||
}
|
||||
|
||||
if (normalized === "adaptive") {
|
||||
throw new Error("Adaptive size is not supported by Seedream image generation.");
|
||||
}
|
||||
|
||||
if (normalized === "1K" || normalized === "3K" || normalized === "4K") {
|
||||
throw new Error(
|
||||
"Unknown Seedream model ID. Use a documented model ID or pass an explicit WxH size instead of preset imageSize."
|
||||
);
|
||||
}
|
||||
|
||||
return normalized;
|
||||
}
|
||||
|
||||
export function validateArgs(model: string, args: CliArgs): void {
|
||||
assertSupportedModel(model);
|
||||
const family = getModelFamily(model);
|
||||
const refCount = args.referenceImages.length;
|
||||
|
||||
if (refCount === 0) {
|
||||
resolveSeedreamSize(model, args);
|
||||
return;
|
||||
}
|
||||
|
||||
if (family === "unknown") {
|
||||
throw new Error(
|
||||
"Reference images with Seedream require a known model ID. Use Seedream 5.0/4.5/4.0 model IDs instead of an endpoint ID."
|
||||
);
|
||||
}
|
||||
|
||||
if (!supportsReferenceImages(model)) {
|
||||
throw new Error(`${model} does not support reference images.`);
|
||||
}
|
||||
|
||||
if ((family === "seedream5" || family === "seedream45" || family === "seedream40") && refCount > 14) {
|
||||
throw new Error(`${model} supports at most 14 reference images.`);
|
||||
}
|
||||
|
||||
resolveSeedreamSize(model, args);
|
||||
}
|
||||
|
||||
export async function buildImageInput(
|
||||
model: string,
|
||||
referenceImages: string[],
|
||||
): Promise<SeedreamRequestImage | undefined> {
|
||||
if (referenceImages.length === 0) return undefined;
|
||||
assertSupportedModel(model);
|
||||
|
||||
const encoded = await Promise.all(referenceImages.map((refPath) => readImageAsDataUrl(refPath)));
|
||||
|
||||
return encoded.length === 1 ? encoded[0]! : encoded;
|
||||
}
|
||||
|
||||
export function buildRequestBody(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
imageInput?: SeedreamRequestImage,
|
||||
): SeedreamRequestBody {
|
||||
validateArgs(model, args);
|
||||
|
||||
const requestBody: SeedreamRequestBody = {
|
||||
model,
|
||||
prompt,
|
||||
size: resolveSeedreamSize(model, args),
|
||||
response_format: "url",
|
||||
watermark: false,
|
||||
};
|
||||
|
||||
if (imageInput) {
|
||||
requestBody.image = imageInput;
|
||||
}
|
||||
|
||||
if (supportsOutputFormat(model)) {
|
||||
requestBody.output_format = "png";
|
||||
}
|
||||
|
||||
return requestBody;
|
||||
}
|
||||
|
||||
async function downloadImage(url: string): Promise<Uint8Array> {
|
||||
const imgResponse = await fetch(url);
|
||||
if (!imgResponse.ok) {
|
||||
throw new Error(`Failed to download image from ${url}`);
|
||||
}
|
||||
|
||||
const buffer = await imgResponse.arrayBuffer();
|
||||
return new Uint8Array(buffer);
|
||||
}
|
||||
|
||||
export async function extractImageFromResponse(result: SeedreamImageResponse): Promise<Uint8Array> {
|
||||
const first = result.data?.find((item) => item.url || item.b64_json || item.error);
|
||||
|
||||
if (!first) {
|
||||
throw new Error("No image data in Seedream response");
|
||||
}
|
||||
|
||||
if (first.error) {
|
||||
throw new Error(first.error.message || "Seedream returned an image generation error");
|
||||
}
|
||||
|
||||
if (first.b64_json) {
|
||||
return Uint8Array.from(Buffer.from(first.b64_json, "base64"));
|
||||
}
|
||||
|
||||
if (first.url) {
|
||||
console.error(`Downloading image from ${first.url}...`);
|
||||
return downloadImage(first.url);
|
||||
}
|
||||
|
||||
throw new Error("No image URL or base64 data in Seedream response");
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
const apiKey = getApiKey();
|
||||
if (!apiKey) {
|
||||
throw new Error(
|
||||
"ARK_API_KEY is required. " +
|
||||
"Get your API key from https://console.volcengine.com/ark"
|
||||
);
|
||||
}
|
||||
|
||||
validateArgs(model, args);
|
||||
const imageInput = await buildImageInput(model, args.referenceImages);
|
||||
const requestBody = buildRequestBody(prompt, model, args, imageInput);
|
||||
|
||||
console.error(`Calling Seedream API (${model}) with size: ${requestBody.size}`);
|
||||
|
||||
const response = await fetch(`${getBaseUrl()}/images/generations`, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
},
|
||||
body: JSON.stringify(requestBody),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const err = await response.text();
|
||||
throw new Error(`Seedream API error (${response.status}): ${err}`);
|
||||
}
|
||||
|
||||
const result = (await response.json()) as SeedreamImageResponse;
|
||||
if (result.error) {
|
||||
throw new Error(result.error.message || "Seedream API returned an error");
|
||||
}
|
||||
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
@@ -0,0 +1,181 @@
|
||||
import assert from "node:assert/strict";
|
||||
import test, { type TestContext } from "node:test";
|
||||
|
||||
import type { CliArgs } from "../types.ts";
|
||||
import {
|
||||
buildRequestBody,
|
||||
buildZaiUrl,
|
||||
extractImageFromResponse,
|
||||
getDefaultModel,
|
||||
getModelFamily,
|
||||
parseAspectRatio,
|
||||
parseSize,
|
||||
resolveSizeForModel,
|
||||
validateArgs,
|
||||
} from "./zai.ts";
|
||||
|
||||
function makeArgs(overrides: Partial<CliArgs> = {}): CliArgs {
|
||||
return {
|
||||
prompt: null,
|
||||
promptFiles: [],
|
||||
imagePath: null,
|
||||
provider: null,
|
||||
model: null,
|
||||
aspectRatio: null,
|
||||
size: null,
|
||||
quality: null,
|
||||
imageSize: null,
|
||||
imageApiDialect: null,
|
||||
referenceImages: [],
|
||||
n: 1,
|
||||
batchFile: null,
|
||||
jobs: null,
|
||||
json: false,
|
||||
help: false,
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
function useEnv(
|
||||
t: TestContext,
|
||||
values: Record<string, string | null>,
|
||||
): void {
|
||||
const previous = new Map<string, string | undefined>();
|
||||
for (const [key, value] of Object.entries(values)) {
|
||||
previous.set(key, process.env[key]);
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
t.after(() => {
|
||||
for (const [key, value] of previous.entries()) {
|
||||
if (value == null) {
|
||||
delete process.env[key];
|
||||
} else {
|
||||
process.env[key] = value;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
test("Z.AI default model prefers env override and otherwise uses glm-image", (t) => {
|
||||
useEnv(t, {
|
||||
ZAI_IMAGE_MODEL: null,
|
||||
BIGMODEL_IMAGE_MODEL: null,
|
||||
});
|
||||
assert.equal(getDefaultModel(), "glm-image");
|
||||
|
||||
process.env.BIGMODEL_IMAGE_MODEL = "cogview-4-250304";
|
||||
assert.equal(getDefaultModel(), "cogview-4-250304");
|
||||
});
|
||||
|
||||
test("Z.AI URL builder normalizes host, v4 base, and full endpoint inputs", (t) => {
|
||||
useEnv(t, { ZAI_BASE_URL: "https://api.z.ai" });
|
||||
assert.equal(buildZaiUrl(), "https://api.z.ai/api/paas/v4/images/generations");
|
||||
|
||||
process.env.ZAI_BASE_URL = "https://proxy.example.com/api/paas/v4/";
|
||||
assert.equal(buildZaiUrl(), "https://proxy.example.com/api/paas/v4/images/generations");
|
||||
|
||||
process.env.ZAI_BASE_URL = "https://proxy.example.com/custom/images/generations";
|
||||
assert.equal(buildZaiUrl(), "https://proxy.example.com/custom/images/generations");
|
||||
});
|
||||
|
||||
test("Z.AI model family and parsing helpers recognize documented formats", () => {
|
||||
assert.equal(getModelFamily("glm-image"), "glm");
|
||||
assert.equal(getModelFamily("cogview-4-250304"), "legacy");
|
||||
assert.deepEqual(parseAspectRatio("16:9"), { width: 16, height: 9 });
|
||||
assert.equal(parseAspectRatio("wide"), null);
|
||||
assert.deepEqual(parseSize("1280x1280"), { width: 1280, height: 1280 });
|
||||
assert.deepEqual(parseSize("1472*1088"), { width: 1472, height: 1088 });
|
||||
assert.equal(parseSize("big"), null);
|
||||
});
|
||||
|
||||
test("Z.AI size resolution follows documented recommended ratios and validates custom sizes", () => {
|
||||
assert.equal(
|
||||
resolveSizeForModel("glm-image", makeArgs({ aspectRatio: "16:9", quality: "2k" })),
|
||||
"1728x960",
|
||||
);
|
||||
assert.equal(
|
||||
resolveSizeForModel("cogview-4-250304", makeArgs({ aspectRatio: "4:3", quality: "normal" })),
|
||||
"1152x864",
|
||||
);
|
||||
assert.equal(
|
||||
resolveSizeForModel("glm-image", makeArgs({ size: "1568x1056", quality: "2k" })),
|
||||
"1568x1056",
|
||||
);
|
||||
|
||||
const uncommon = resolveSizeForModel(
|
||||
"glm-image",
|
||||
makeArgs({ aspectRatio: "5:2", quality: "normal" }),
|
||||
);
|
||||
const parsed = parseSize(uncommon);
|
||||
assert.ok(parsed);
|
||||
assert.ok(parsed.width % 32 === 0);
|
||||
assert.ok(parsed.height % 32 === 0);
|
||||
assert.ok(parsed.width * parsed.height <= 2 ** 22);
|
||||
|
||||
assert.throws(
|
||||
() => resolveSizeForModel("glm-image", makeArgs({ size: "1000x1000", quality: "2k" })),
|
||||
/between 1024 and 2048/,
|
||||
);
|
||||
assert.throws(
|
||||
() => resolveSizeForModel("glm-image", makeArgs({ size: "1280x1260", quality: "2k" })),
|
||||
/divisible by 32/,
|
||||
);
|
||||
assert.throws(
|
||||
() => resolveSizeForModel("cogview-4-250304", makeArgs({ size: "2048x2048", quality: "2k" })),
|
||||
/must not exceed 2\^21 total pixels/,
|
||||
);
|
||||
});
|
||||
|
||||
test("Z.AI validation rejects unsupported refs and multi-image requests", () => {
|
||||
assert.throws(
|
||||
() => validateArgs("glm-image", makeArgs({ referenceImages: ["ref.png"] })),
|
||||
/text-to-image only/,
|
||||
);
|
||||
assert.throws(
|
||||
() => validateArgs("glm-image", makeArgs({ n: 2 })),
|
||||
/single image per request/,
|
||||
);
|
||||
});
|
||||
|
||||
test("Z.AI request body maps skill quality and resolved size into provider fields", () => {
|
||||
const body = buildRequestBody(
|
||||
"A cinematic science poster",
|
||||
"glm-image",
|
||||
makeArgs({ aspectRatio: "4:3", quality: "normal" }),
|
||||
);
|
||||
|
||||
assert.deepEqual(body, {
|
||||
model: "glm-image",
|
||||
prompt: "A cinematic science poster",
|
||||
quality: "standard",
|
||||
size: "1472x1088",
|
||||
});
|
||||
});
|
||||
|
||||
test("Z.AI response extraction downloads the returned image URL", async (t) => {
|
||||
const originalFetch = globalThis.fetch;
|
||||
t.after(() => {
|
||||
globalThis.fetch = originalFetch;
|
||||
});
|
||||
|
||||
globalThis.fetch = async () =>
|
||||
new Response(Uint8Array.from([1, 2, 3]), {
|
||||
status: 200,
|
||||
headers: { "Content-Type": "image/png" },
|
||||
});
|
||||
|
||||
const image = await extractImageFromResponse({
|
||||
data: [{ url: "https://cdn.example.com/glm-image.png" }],
|
||||
});
|
||||
assert.deepEqual([...image], [1, 2, 3]);
|
||||
|
||||
await assert.rejects(
|
||||
() => extractImageFromResponse({ data: [{}] }),
|
||||
/No image URL/,
|
||||
);
|
||||
});
|
||||
306
baoyu-skills/skills/baoyu-image-gen/scripts/providers/zai.ts
Normal file
306
baoyu-skills/skills/baoyu-image-gen/scripts/providers/zai.ts
Normal file
@@ -0,0 +1,306 @@
|
||||
import type { CliArgs, Quality } from "../types";
|
||||
|
||||
type ZaiModelFamily = "glm" | "legacy";
|
||||
|
||||
type ZaiRequestBody = {
|
||||
model: string;
|
||||
prompt: string;
|
||||
quality: "hd" | "standard";
|
||||
size: string;
|
||||
};
|
||||
|
||||
type ZaiResponse = {
|
||||
data?: Array<{ url?: string }>;
|
||||
};
|
||||
|
||||
const DEFAULT_MODEL = "glm-image";
|
||||
const GLM_MAX_PIXELS = 2 ** 22;
|
||||
const LEGACY_MAX_PIXELS = 2 ** 21;
|
||||
const GLM_SIZE_STEP = 32;
|
||||
const LEGACY_SIZE_STEP = 16;
|
||||
|
||||
const GLM_RECOMMENDED_SIZES: Record<string, string> = {
|
||||
"1:1": "1280x1280",
|
||||
"3:2": "1568x1056",
|
||||
"2:3": "1056x1568",
|
||||
"4:3": "1472x1088",
|
||||
"3:4": "1088x1472",
|
||||
"16:9": "1728x960",
|
||||
"9:16": "960x1728",
|
||||
};
|
||||
|
||||
const LEGACY_RECOMMENDED_SIZES: Record<string, string> = {
|
||||
"1:1": "1024x1024",
|
||||
"9:16": "768x1344",
|
||||
"3:4": "864x1152",
|
||||
"16:9": "1344x768",
|
||||
"4:3": "1152x864",
|
||||
"2:1": "1440x720",
|
||||
"1:2": "720x1440",
|
||||
};
|
||||
|
||||
export function getDefaultModel(): string {
|
||||
return process.env.ZAI_IMAGE_MODEL || process.env.BIGMODEL_IMAGE_MODEL || DEFAULT_MODEL;
|
||||
}
|
||||
|
||||
function getApiKey(): string | null {
|
||||
return process.env.ZAI_API_KEY || process.env.BIGMODEL_API_KEY || null;
|
||||
}
|
||||
|
||||
export function buildZaiUrl(): string {
|
||||
const base = (process.env.ZAI_BASE_URL || process.env.BIGMODEL_BASE_URL || "https://api.z.ai/api/paas/v4")
|
||||
.replace(/\/+$/g, "");
|
||||
if (base.endsWith("/images/generations")) return base;
|
||||
if (base.endsWith("/api/paas/v4")) return `${base}/images/generations`;
|
||||
if (base.endsWith("/v4")) return `${base}/images/generations`;
|
||||
return `${base}/api/paas/v4/images/generations`;
|
||||
}
|
||||
|
||||
export function getModelFamily(model: string): ZaiModelFamily {
|
||||
return model.trim().toLowerCase() === "glm-image" ? "glm" : "legacy";
|
||||
}
|
||||
|
||||
export function parseAspectRatio(ar: string): { width: number; height: number } | null {
|
||||
const match = ar.match(/^(\d+(?:\.\d+)?):(\d+(?:\.\d+)?)$/);
|
||||
if (!match) return null;
|
||||
const width = Number(match[1]);
|
||||
const height = Number(match[2]);
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
export function parseSize(size: string): { width: number; height: number } | null {
|
||||
const match = size.trim().match(/^(\d+)\s*[xX*]\s*(\d+)$/);
|
||||
if (!match) return null;
|
||||
const width = parseInt(match[1]!, 10);
|
||||
const height = parseInt(match[2]!, 10);
|
||||
if (!Number.isFinite(width) || !Number.isFinite(height) || width <= 0 || height <= 0) {
|
||||
return null;
|
||||
}
|
||||
return { width, height };
|
||||
}
|
||||
|
||||
function formatSize(width: number, height: number): string {
|
||||
return `${width}x${height}`;
|
||||
}
|
||||
|
||||
function roundToStep(value: number, step: number): number {
|
||||
return Math.max(step, Math.round(value / step) * step);
|
||||
}
|
||||
|
||||
function getRatioValue(ar: string): number | null {
|
||||
const parsed = parseAspectRatio(ar);
|
||||
if (!parsed) return null;
|
||||
return parsed.width / parsed.height;
|
||||
}
|
||||
|
||||
function findClosestRatioKey(ar: string, candidates: string[]): string | null {
|
||||
const targetRatio = getRatioValue(ar);
|
||||
if (targetRatio == null) return null;
|
||||
|
||||
let bestKey: string | null = null;
|
||||
let bestDiff = Infinity;
|
||||
for (const candidate of candidates) {
|
||||
const candidateRatio = getRatioValue(candidate);
|
||||
if (candidateRatio == null) continue;
|
||||
const diff = Math.abs(candidateRatio - targetRatio);
|
||||
if (diff < bestDiff) {
|
||||
bestDiff = diff;
|
||||
bestKey = candidate;
|
||||
}
|
||||
}
|
||||
|
||||
return bestDiff <= 0.05 ? bestKey : null;
|
||||
}
|
||||
|
||||
function getTargetPixels(quality: Quality): number {
|
||||
return quality === "normal" ? 1024 * 1024 : 1536 * 1536;
|
||||
}
|
||||
|
||||
function fitToPixelBudget(
|
||||
width: number,
|
||||
height: number,
|
||||
targetPixels: number,
|
||||
maxPixels: number,
|
||||
step: number,
|
||||
): { width: number; height: number } {
|
||||
let nextWidth = width;
|
||||
let nextHeight = height;
|
||||
const pixels = nextWidth * nextHeight;
|
||||
|
||||
if (pixels > maxPixels) {
|
||||
const scale = Math.sqrt(maxPixels / pixels);
|
||||
nextWidth *= scale;
|
||||
nextHeight *= scale;
|
||||
} else {
|
||||
const scale = Math.sqrt(targetPixels / pixels);
|
||||
nextWidth *= scale;
|
||||
nextHeight *= scale;
|
||||
}
|
||||
|
||||
let roundedWidth = roundToStep(nextWidth, step);
|
||||
let roundedHeight = roundToStep(nextHeight, step);
|
||||
let roundedPixels = roundedWidth * roundedHeight;
|
||||
|
||||
while (roundedPixels > maxPixels && (roundedWidth > step || roundedHeight > step)) {
|
||||
if (roundedWidth >= roundedHeight && roundedWidth > step) {
|
||||
roundedWidth -= step;
|
||||
} else if (roundedHeight > step) {
|
||||
roundedHeight -= step;
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
roundedPixels = roundedWidth * roundedHeight;
|
||||
}
|
||||
|
||||
return { width: roundedWidth, height: roundedHeight };
|
||||
}
|
||||
|
||||
function validateCustomSize(
|
||||
size: string,
|
||||
family: ZaiModelFamily,
|
||||
): string {
|
||||
const parsed = parseSize(size);
|
||||
if (!parsed) {
|
||||
throw new Error("Z.AI --size must be in WxH format, for example 1280x1280.");
|
||||
}
|
||||
|
||||
const widthStep = family === "glm" ? GLM_SIZE_STEP : LEGACY_SIZE_STEP;
|
||||
const minEdge = family === "glm" ? 1024 : 512;
|
||||
const maxPixels = family === "glm" ? GLM_MAX_PIXELS : LEGACY_MAX_PIXELS;
|
||||
|
||||
if (parsed.width < minEdge || parsed.width > 2048 || parsed.height < minEdge || parsed.height > 2048) {
|
||||
throw new Error(
|
||||
family === "glm"
|
||||
? "GLM-image custom size requires width and height between 1024 and 2048."
|
||||
: "Z.AI legacy image models require width and height between 512 and 2048."
|
||||
);
|
||||
}
|
||||
|
||||
if (parsed.width % widthStep !== 0 || parsed.height % widthStep !== 0) {
|
||||
throw new Error(
|
||||
family === "glm"
|
||||
? "GLM-image custom size requires width and height divisible by 32."
|
||||
: "Z.AI legacy image models require width and height divisible by 16."
|
||||
);
|
||||
}
|
||||
|
||||
if (parsed.width * parsed.height > maxPixels) {
|
||||
throw new Error(
|
||||
family === "glm"
|
||||
? "GLM-image custom size must not exceed 2^22 total pixels."
|
||||
: "Z.AI legacy image size must not exceed 2^21 total pixels."
|
||||
);
|
||||
}
|
||||
|
||||
return formatSize(parsed.width, parsed.height);
|
||||
}
|
||||
|
||||
export function resolveSizeForModel(
|
||||
model: string,
|
||||
args: Pick<CliArgs, "size" | "aspectRatio" | "quality">,
|
||||
): string {
|
||||
const family = getModelFamily(model);
|
||||
const quality = args.quality === "normal" ? "normal" : "2k";
|
||||
|
||||
if (args.size) {
|
||||
return validateCustomSize(args.size, family);
|
||||
}
|
||||
|
||||
const recommended = family === "glm" ? GLM_RECOMMENDED_SIZES : LEGACY_RECOMMENDED_SIZES;
|
||||
const defaultSize = family === "glm" ? "1280x1280" : "1024x1024";
|
||||
|
||||
if (!args.aspectRatio) return defaultSize;
|
||||
|
||||
const recommendedRatio = findClosestRatioKey(args.aspectRatio, Object.keys(recommended));
|
||||
if (recommendedRatio) {
|
||||
return recommended[recommendedRatio]!;
|
||||
}
|
||||
|
||||
const parsedRatio = parseAspectRatio(args.aspectRatio);
|
||||
if (!parsedRatio) return defaultSize;
|
||||
|
||||
const targetPixels = getTargetPixels(quality);
|
||||
const maxPixels = family === "glm" ? GLM_MAX_PIXELS : LEGACY_MAX_PIXELS;
|
||||
const step = family === "glm" ? GLM_SIZE_STEP : LEGACY_SIZE_STEP;
|
||||
const fit = fitToPixelBudget(
|
||||
parsedRatio.width,
|
||||
parsedRatio.height,
|
||||
targetPixels,
|
||||
maxPixels,
|
||||
step,
|
||||
);
|
||||
return formatSize(fit.width, fit.height);
|
||||
}
|
||||
|
||||
function getZaiQuality(quality: CliArgs["quality"]): "hd" | "standard" {
|
||||
return quality === "normal" ? "standard" : "hd";
|
||||
}
|
||||
|
||||
export function validateArgs(_model: string, args: CliArgs): void {
|
||||
if (args.referenceImages.length > 0) {
|
||||
throw new Error("Z.AI GLM-image currently supports text-to-image only in baoyu-image-gen. Remove --ref or choose another provider.");
|
||||
}
|
||||
|
||||
if (args.n > 1) {
|
||||
throw new Error("Z.AI image generation currently returns a single image per request in baoyu-image-gen.");
|
||||
}
|
||||
}
|
||||
|
||||
export function buildRequestBody(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
): ZaiRequestBody {
|
||||
validateArgs(model, args);
|
||||
return {
|
||||
model,
|
||||
prompt,
|
||||
quality: getZaiQuality(args.quality),
|
||||
size: resolveSizeForModel(model, args),
|
||||
};
|
||||
}
|
||||
|
||||
export async function extractImageFromResponse(result: ZaiResponse): Promise<Uint8Array> {
|
||||
const url = result.data?.[0]?.url;
|
||||
if (!url) {
|
||||
throw new Error("No image URL in Z.AI response");
|
||||
}
|
||||
|
||||
const imageResponse = await fetch(url);
|
||||
if (!imageResponse.ok) {
|
||||
throw new Error(`Failed to download image from Z.AI: ${imageResponse.status}`);
|
||||
}
|
||||
|
||||
return new Uint8Array(await imageResponse.arrayBuffer());
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
prompt: string,
|
||||
model: string,
|
||||
args: CliArgs,
|
||||
): Promise<Uint8Array> {
|
||||
const apiKey = getApiKey();
|
||||
if (!apiKey) {
|
||||
throw new Error("ZAI_API_KEY is required. Get one from https://docs.z.ai/.");
|
||||
}
|
||||
|
||||
const response = await fetch(buildZaiUrl(), {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
},
|
||||
body: JSON.stringify(buildRequestBody(prompt, model, args)),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const err = await response.text();
|
||||
throw new Error(`Z.AI API error (${response.status}): ${err}`);
|
||||
}
|
||||
|
||||
const result = (await response.json()) as ZaiResponse;
|
||||
return extractImageFromResponse(result);
|
||||
}
|
||||
97
baoyu-skills/skills/baoyu-image-gen/scripts/types.ts
Normal file
97
baoyu-skills/skills/baoyu-image-gen/scripts/types.ts
Normal file
@@ -0,0 +1,97 @@
|
||||
export type Provider =
|
||||
| "google"
|
||||
| "openai"
|
||||
| "openrouter"
|
||||
| "dashscope"
|
||||
| "zai"
|
||||
| "minimax"
|
||||
| "replicate"
|
||||
| "jimeng"
|
||||
| "seedream"
|
||||
| "azure"
|
||||
| "codex-cli"
|
||||
| "agnes";
|
||||
export type Quality = "normal" | "2k";
|
||||
export type OpenAIImageApiDialect = "openai-native" | "ratio-metadata";
|
||||
export type ResponseFormat = "file" | "url";
|
||||
|
||||
export type CliArgs = {
|
||||
prompt: string | null;
|
||||
promptFiles: string[];
|
||||
imagePath: string | null;
|
||||
provider: Provider | null;
|
||||
model: string | null;
|
||||
aspectRatio: string | null;
|
||||
aspectRatioSource?: "cli" | "task" | "config" | null;
|
||||
size: string | null;
|
||||
quality: Quality | null;
|
||||
imageSize: string | null;
|
||||
imageSizeSource?: "cli" | "task" | "config" | null;
|
||||
imageApiDialect: OpenAIImageApiDialect | null;
|
||||
responseFormat: ResponseFormat | null;
|
||||
referenceImages: string[];
|
||||
n: number;
|
||||
batchFile: string | null;
|
||||
jobs: number | null;
|
||||
json: boolean;
|
||||
help: boolean;
|
||||
};
|
||||
|
||||
export type BatchTaskInput = {
|
||||
id?: string;
|
||||
prompt?: string | null;
|
||||
promptFiles?: string[];
|
||||
image?: string;
|
||||
provider?: Provider | null;
|
||||
model?: string | null;
|
||||
ar?: string | null;
|
||||
size?: string | null;
|
||||
quality?: Quality | null;
|
||||
imageSize?: "1K" | "2K" | "4K" | null;
|
||||
imageApiDialect?: OpenAIImageApiDialect | null;
|
||||
responseFormat?: ResponseFormat | null;
|
||||
ref?: string[];
|
||||
n?: number;
|
||||
};
|
||||
|
||||
export type BatchFile =
|
||||
| BatchTaskInput[]
|
||||
| {
|
||||
tasks: BatchTaskInput[];
|
||||
jobs?: number | null;
|
||||
};
|
||||
|
||||
export type ExtendConfig = {
|
||||
version: number;
|
||||
default_provider: Provider | null;
|
||||
default_quality: Quality | null;
|
||||
default_aspect_ratio: string | null;
|
||||
default_image_size: "1K" | "2K" | "4K" | null;
|
||||
default_image_api_dialect: OpenAIImageApiDialect | null;
|
||||
default_model: {
|
||||
google: string | null;
|
||||
openai: string | null;
|
||||
openrouter: string | null;
|
||||
dashscope: string | null;
|
||||
zai: string | null;
|
||||
minimax: string | null;
|
||||
replicate: string | null;
|
||||
jimeng: string | null;
|
||||
seedream: string | null;
|
||||
azure: string | null;
|
||||
"codex-cli": string | null;
|
||||
agnes: string | null;
|
||||
};
|
||||
batch?: {
|
||||
max_workers?: number | null;
|
||||
provider_limits?: Partial<
|
||||
Record<
|
||||
Provider,
|
||||
{
|
||||
concurrency?: number | null;
|
||||
start_interval_ms?: number | null;
|
||||
}
|
||||
>
|
||||
>;
|
||||
};
|
||||
};
|
||||
Reference in New Issue
Block a user