skills/jimliu/baoyu-skills/baoyu-image-gen

baoyu-image-gen

Installation
Summary

Multi-provider AI image generation with text-to-image, reference images, batch processing, and quality presets.

  • Supports seven providers: OpenAI, Google, OpenRouter, DashScope, Jimeng, Seedream, and Replicate with automatic provider selection or explicit override
  • Handles single images via CLI flags or batch parallel generation from JSON files with configurable worker counts and per-provider throttling
  • Supports reference images (Google, OpenAI, OpenRouter, Replicate, Seedream), custom aspect ratios, quality presets (normal/2k), and free-form or fixed sizes depending on provider
  • Loads preferences from EXTEND.md (project or user home) for default provider, model, quality, and batch settings; requires first-time setup before any generation
  • Reads prompts from text arguments or markdown files; batch mode auto-retries up to 3 times per image and outputs success/failure summaries
SKILL.md

Image Generation (AI SDK)

Official API-based image generation. Supports OpenAI, Azure OpenAI, Google, OpenRouter, DashScope (阿里通义万象), Z.AI GLM-Image, MiniMax, Jimeng (即梦), Seedream (豆包) and Replicate.

User Input Tools

When this skill prompts the user, follow this tool-selection rule (priority order):

  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.
  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.
  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.

Concrete AskUserQuestion references below are examples — substitute the local equivalent in other runtimes.

Script Directory

{baseDir} = this SKILL.md's directory. Main script: {baseDir}/scripts/main.ts. Resolve ${BUN_X}: prefer bun; else npx -y bun; else suggest brew install oven-sh/bun/bun.

Step 0: Load Preferences ⛔ BLOCKING

This step MUST complete before any image generation — generation is blocked until EXTEND.md exists.

Check these paths in order; first hit wins:

Path Scope
.baoyu-skills/baoyu-image-gen/EXTEND.md Project
${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-image-gen/EXTEND.md XDG
$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md User home
  • Found → load, parse, apply. If default_model.[provider] is null → ask model only.
  • 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.

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.

Usage

Minimum working examples — see references/usage-examples.md for the full set including per-provider invocations and batch mode.

# Basic
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image cat.png

# With aspect ratio and high quality
${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9 --quality 2k

# Prompt from files
${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png

# With reference image
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png

# Specific provider
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider dashscope --model qwen-image-2.0-pro

# Batch mode
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4

Options

Option Description
--prompt <text>, -p Prompt text
--promptfiles <files...> Read prompt from files (concatenated)
--image <path> Output image path (required in single-image mode)
--batchfile <path> JSON batch file for multi-image generation
--jobs <count> Worker count for batch mode (default: auto, max from config, built-in default 10)
--provider google|openai|azure|openrouter|dashscope|zai|minimax|jimeng|seedream|replicate Force provider (default: auto-detect)
--model <id>, -m Model ID — see provider references for defaults and allowed values
--ar <ratio> Aspect ratio (16:9, 1:1, 4:3, …)
--size <WxH> Explicit size (e.g., 1024x1024)
--quality normal|2k Quality preset (default: 2k)
--imageSize 1K|2K|4K Image size for Google/OpenRouter (default: from quality)
--imageApiDialect openai-native|ratio-metadata OpenAI-compatible endpoint dialect — use ratio-metadata for gateways that expect aspect-ratio size plus metadata.resolution
--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. Not supported by Jimeng, Seedream 3.0, SeedEdit 3.0
--n <count> Number of images. Replicate requires --n 1 (single-output save semantics)
--json JSON output

Environment Variables

Variable Description
OPENAI_API_KEY OpenAI API key
AZURE_OPENAI_API_KEY Azure OpenAI API key
OPENROUTER_API_KEY OpenRouter API key
GOOGLE_API_KEY Google API key
DASHSCOPE_API_KEY DashScope API key
ZAI_API_KEY (alias BIGMODEL_API_KEY) Z.AI API key
MINIMAX_API_KEY MiniMax API key
REPLICATE_API_TOKEN Replicate API token
JIMENG_ACCESS_KEY_ID, JIMENG_SECRET_ACCESS_KEY Jimeng (即梦) Volcengine credentials
ARK_API_KEY Seedream (豆包) Volcengine ARK API key
<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)
AZURE_OPENAI_DEPLOYMENT (alias AZURE_OPENAI_IMAGE_MODEL) Azure default deployment
<PROVIDER>_BASE_URL Per-provider endpoint override
AZURE_API_VERSION Azure image API version (default 2025-04-01-preview)
JIMENG_REGION Jimeng region (default cn-north-1)
OPENAI_IMAGE_API_DIALECT openai-native | ratio-metadata
OPENROUTER_HTTP_REFERER, OPENROUTER_TITLE Optional OpenRouter attribution
BAOYU_IMAGE_GEN_MAX_WORKERS Override batch worker cap
BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY Per-provider concurrency (e.g., BAOYU_IMAGE_GEN_REPLICATE_CONCURRENCY)
BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS Per-provider start-gap

Load priority: CLI args > EXTEND.md > env vars > <cwd>/.baoyu-skills/.env > ~/.baoyu-skills/.env

Model Resolution

Priority (highest → lowest) applies to every provider:

  1. CLI flag --model <id>
  2. EXTEND.md default_model.[provider]
  3. Env var <PROVIDER>_IMAGE_MODEL
  4. Built-in default

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.

EXTEND.md overrides env vars: if EXTEND.md sets default_model.google: "gemini-3-pro-image-preview" and the env var sets GOOGLE_IMAGE_MODEL=gemini-3.1-flash-image-preview, EXTEND.md wins.

Display model info before each generation:

  • Using [provider] / [model]
  • Switch model: --model <id> | EXTEND.md default_model.[provider] | env <PROVIDER>_IMAGE_MODEL

OpenAI-Compatible Gateway Dialects

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:

  • openai-native: pixel size (1536x1024) and native OpenAI quality fields
  • ratio-metadata: aspect-ratio size (16:9) plus metadata.resolution (1K|2K|4K) and metadata.orientation

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.

Provider-Specific Guides

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:

Provider Reference
DashScope (Qwen-Image families, custom sizes) references/providers/dashscope.md
Z.AI (GLM-Image, cogview-4) references/providers/zai.md
MiniMax (image-01, subject-reference) references/providers/minimax.md
OpenRouter (multimodal models, /chat/completions flow) references/providers/openrouter.md
Replicate (nano-banana, Seedream, Wan) references/providers/replicate.md

Provider Selection

  1. --ref provided + no --provider → auto-select Google → OpenAI → Azure → OpenRouter → Replicate → Seedream → MiniMax (MiniMax's subject reference is more specialized toward character/portrait consistency)
  2. --provider specified → use it (if --ref, must be google/openai/azure/openrouter/replicate/seedream/minimax)
  3. Only one API key present → use that provider
  4. Multiple keys → default priority: Google → OpenAI → Azure → OpenRouter → DashScope → Z.AI → MiniMax → Replicate → Jimeng → Seedream

Quality Presets

Preset Google imageSize OpenAI size OpenRouter size Replicate resolution Use case
normal 1K 1024px 1K 1K Quick previews
2k (default) 2K 2048px 2K 2K Covers, illustrations, infographics

Google/OpenRouter imageSize can be overridden with --imageSize 1K|2K|4K.

Aspect Ratios

Supported: 1:1, 16:9, 9:16, 4:3, 3:4, 2.35:1.

  • Google multimodal: imageConfig.aspectRatio
  • OpenAI: closest supported size
  • OpenRouter: imageGenerationOptions.aspect_ratio; if only --size <WxH> is given, the ratio is inferred
  • 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
  • MiniMax: official aspect_ratio values; if --size <WxH> is given without --ar, sends width/height for image-01

Generation Mode

Default: sequential. Batch parallel: enabled automatically when --batchfile contains 2+ pending tasks.

Situation Prefer Why
One image, or 1-2 simple images Sequential Lower coordination overhead, easier debugging
Multiple images with saved prompt files Batch (--batchfile) Reuses finalized prompts, applies shared throttling/retries, predictable throughput
Each image still needs its own reasoning / prompt writing / style exploration Subagents Work is still exploratory, each needs independent analysis
Input is outline.md + prompts/ (e.g. from baoyu-article-illustrator) Batch — use scripts/build-batch.ts to assemble the payload The outline + prompt files already contain everything needed

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.

Parallel behavior:

  • Default worker count is automatic, capped by config, built-in default 10
  • Provider-specific throttling applies only in batch mode; defaults are tuned for throughput while avoiding RPM bursts
  • Override with --jobs <count>
  • Each image retries up to 3 attempts
  • Final output includes success count, failure count, and per-image failure reasons

Error Handling

  • Missing API key → error with setup instructions
  • Generation failure → auto-retry up to 3 attempts per image
  • Invalid aspect ratio → warning, proceed with default
  • Reference images with unsupported provider/model → error with fix hint

References

File Content
references/usage-examples.md Extended CLI examples across providers and batch mode
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/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.

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