mirror of
https://github.com/JimLiu/baoyu-skills.git
synced 2026-07-12 22:09:48 +08:00
Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| db1179e057 | |||
| 7b0c9ca372 |
@@ -6,7 +6,7 @@
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},
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"metadata": {
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"description": "Skills shared by Baoyu for improving daily work efficiency",
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"version": "1.20.0"
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"version": "1.21.1"
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},
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"plugins": [
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{
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@@ -2,6 +2,17 @@
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English | [中文](./CHANGELOG.zh.md)
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## 1.21.1 - 2026-01-24
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### Documentation
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- `baoyu-comic`: adds character sheet compression step after generation to reduce token usage when used as reference image.
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## 1.21.0 - 2026-01-24
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### Features
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- `baoyu-cover-image`: expands aspect ratio options—adds 4:3, 3:2, 3:4 ratios; changes default from 2.35:1 to 16:9 for better versatility. Aspect ratio is now always confirmed unless explicitly specified via `--aspect` flag.
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- `baoyu-image-gen`: refactors Google provider to support both Gemini multimodal and Imagen models with unified API. Adds `--imageSize` parameter support (1K/2K/4K) for Gemini models.
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## 1.20.0 - 2026-01-24
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### Features
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@@ -2,6 +2,17 @@
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[English](./CHANGELOG.md) | 中文
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## 1.21.1 - 2026-01-24
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### 文档
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- `baoyu-comic`:在角色参考图生成后添加压缩步骤,减少作为参考图使用时的 token 消耗。
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## 1.21.0 - 2026-01-24
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### 新功能
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- `baoyu-cover-image`:扩展宽高比选项——新增 4:3、3:2、3:4 比例;默认值从 2.35:1 改为 16:9 以提高通用性。现在除非通过 `--aspect` 标志明确指定,否则始终确认宽高比。
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- `baoyu-image-gen`:重构 Google provider 以统一支持 Gemini 多模态和 Imagen 模型。为 Gemini 模型新增 `--imageSize` 参数支持(1K/2K/4K)。
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## 1.20.0 - 2026-01-24
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### 新功能
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@@ -201,6 +201,12 @@ npx -y bun ${SKILL_DIR}/../baoyu-image-gen/scripts/main.ts \
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--image characters/characters.png --ar 4:3
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```
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**Compress character sheet** (recommended):
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Compress to reduce token usage when used as reference image:
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- Use available image compression skill (if any)
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- Or system tools: `pngquant`, `optipng`, `sips` (macOS)
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- **Keep PNG format**, lossless compression preferred
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**7.2 Generate each page WITH character reference**:
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| Skill Capability | Strategy |
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@@ -40,7 +40,7 @@ Generate elegant cover images for articles with 4-dimensional customization.
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| `--style <name>` | Cover style (see Style Gallery) |
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| `--text <level>` | Text density: none, title-only, title-subtitle, text-rich |
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| `--mood <level>` | Emotional intensity: subtle, balanced, bold |
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| `--aspect <ratio>` | 2.35:1 (default), 16:9, 1:1 |
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| `--aspect <ratio>` | 16:9 (default), 2.35:1, 4:3, 3:2, 1:1, 3:4 |
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| `--lang <code>` | Title language (en, zh, ja, etc.) |
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| `--no-title` | Alias for `--text none` |
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| `--quick` | Skip confirmation, use auto-selection for missing dimensions |
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@@ -357,14 +357,18 @@ options:
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header: "Aspect"
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question: "Default aspect ratio for cover images?"
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options:
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- label: "2.35:1 (Recommended)"
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description: "Cinematic widescreen, best for article headers"
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- label: "16:9"
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description: "Standard widescreen, versatile"
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- label: "16:9 (Recommended)"
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description: "Standard widescreen - YouTube, presentations, versatile"
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- label: "2.35:1"
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description: "Cinematic widescreen - article headers, blog posts"
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- label: "1:1"
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description: "Square, social media friendly"
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description: "Square - Instagram, WeChat, social cards"
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- label: "3:4"
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description: "Portrait - Xiaohongshu, Pinterest, mobile content"
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```
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Note: More ratios (4:3, 3:2) available during generation. This sets the default recommendation.
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**Q5: Quick Mode**
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```yaml
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header: "Quick"
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@@ -415,17 +419,19 @@ Read source content, save it if needed, and perform analysis.
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### Step 2: Confirm Options ⚠️
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**Purpose**: Validate all 4 dimensions. **Skip if `--quick` flag OR all 4 dimensions specified via CLI.**
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**Purpose**: Validate all 4 dimensions + aspect ratio.
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**Skip Conditions**:
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| Condition | Behavior |
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|-----------|----------|
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| `--quick` flag | Auto-select missing dimensions, skip to Step 3 |
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| All 4 dimensions specified | Use specified values, skip to Step 3 |
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| `quick_mode: true` in EXTEND.md | Auto-select missing dimensions, skip to Step 3 |
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| Otherwise | Show confirmation (current behavior) |
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| Condition | Skipped Questions | Still Asked |
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|-----------|-------------------|-------------|
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| `--quick` flag | Type, Style, Text, Mood | **Aspect Ratio** (unless `--aspect` specified) |
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| All 4 dimensions + `--aspect` specified | All | None |
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| `quick_mode: true` in EXTEND.md | Type, Style, Text, Mood | **Aspect Ratio** (unless `--aspect` specified) |
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| Otherwise | None | All 5 questions |
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**Quick Mode Output** (when skipping confirmation):
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**Important**: Aspect ratio is ALWAYS asked unless explicitly specified via `--aspect` CLI flag. User presets in EXTEND.md are shown as recommended option, not auto-selected.
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**Quick Mode Output** (when skipping 4 dimensions):
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```
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Quick Mode: Auto-selected dimensions
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@@ -433,9 +439,8 @@ Quick Mode: Auto-selected dimensions
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• Style: [style] ([reason])
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• Text: [text] ([reason])
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• Mood: [mood] ([reason])
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• Aspect: [aspect]
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Generating...
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[Then ask Question 5: Aspect Ratio]
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```
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**Confirmation Flow** (when NOT skipping):
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@@ -513,19 +518,27 @@ options:
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description: "High contrast, vivid colors, dynamic"
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```
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**Question 5: Aspect Ratio** (if not specified via `--aspect`)
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**Question 5: Aspect Ratio** (ALWAYS ask unless `--aspect` specified via CLI)
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Note: Even if user has a preset in EXTEND.md, still ask this question. The preset is shown as the recommended option.
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```yaml
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header: "Aspect"
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question: "Cover aspect ratio?"
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multiSelect: false
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options:
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- label: "2.35:1 (Recommended)"
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description: "Cinematic widescreen, best for article headers"
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- label: "16:9"
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description: "Standard widescreen, versatile"
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- label: "[user preset or 16:9] (Recommended)"
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description: "[based on preset or default: Standard widescreen, versatile]"
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- label: "2.35:1"
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description: "Cinematic widescreen - article headers, blog posts"
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- label: "4:3"
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description: "Traditional screen - PPT slides, classic displays"
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- label: "3:2"
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description: "Photography ratio - blog articles, Medium posts"
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- label: "1:1"
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description: "Square, social media friendly"
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description: "Square - Instagram, WeChat moments, social cards"
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- label: "3:4"
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description: "Portrait - Xiaohongshu, Pinterest, mobile-first content"
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```
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**After response**: Proceed to Step 3 with confirmed dimensions.
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@@ -2,19 +2,25 @@ import path from "node:path";
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import { readFile } from "node:fs/promises";
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import type { CliArgs } from "../types";
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const GOOGLE_MULTIMODAL_MODELS = ["gemini-3-pro-image-preview"];
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const GOOGLE_MULTIMODAL_MODELS = ["gemini-3-pro-image-preview", "gemini-3-flash-preview"];
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const GOOGLE_IMAGEN_MODELS = ["imagen-3.0-generate-002", "imagen-3.0-generate-001"];
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export function getDefaultModel(): string {
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return process.env.GOOGLE_IMAGE_MODEL || "gemini-3-pro-image-preview";
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}
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function normalizeGoogleModelId(model: string): string {
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return model.startsWith("models/") ? model.slice("models/".length) : model;
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}
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function isGoogleMultimodal(model: string): boolean {
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return GOOGLE_MULTIMODAL_MODELS.some((m) => model.includes(m));
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const normalized = normalizeGoogleModelId(model);
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return GOOGLE_MULTIMODAL_MODELS.some((m) => normalized.includes(m));
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}
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function isGoogleImagen(model: string): boolean {
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return GOOGLE_IMAGEN_MODELS.some((m) => model.includes(m));
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const normalized = normalizeGoogleModelId(model);
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return GOOGLE_IMAGEN_MODELS.some((m) => normalized.includes(m));
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}
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function getGoogleApiKey(): string | null {
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@@ -26,6 +32,44 @@ function getGoogleImageSize(args: CliArgs): "1K" | "2K" | "4K" {
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return args.quality === "2k" ? "2K" : "1K";
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}
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function getGoogleBaseUrl(): string {
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const base = process.env.GOOGLE_BASE_URL || "https://generativelanguage.googleapis.com";
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return base.replace(/\/+$/g, "");
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}
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function buildGoogleUrl(pathname: string): string {
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const base = getGoogleBaseUrl();
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const cleanedPath = pathname.replace(/^\/+/g, "");
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if (base.endsWith("/v1beta")) return `${base}/${cleanedPath}`;
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return `${base}/v1beta/${cleanedPath}`;
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}
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function toModelPath(model: string): string {
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const modelId = normalizeGoogleModelId(model);
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return `models/${modelId}`;
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}
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async function postGoogleJson<T>(pathname: string, body: unknown): Promise<T> {
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const apiKey = getGoogleApiKey();
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if (!apiKey) throw new Error("GOOGLE_API_KEY or GEMINI_API_KEY is required");
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const res = await fetch(buildGoogleUrl(pathname), {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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"x-goog-api-key": apiKey,
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},
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body: JSON.stringify(body),
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});
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if (!res.ok) {
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const err = await res.text();
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throw new Error(`Google API error (${res.status}): ${err}`);
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}
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return (await res.json()) as T;
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}
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function buildPromptWithAspect(prompt: string, ar: string | null, quality: CliArgs["quality"]): string {
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let result = prompt;
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if (ar) {
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@@ -37,6 +81,11 @@ function buildPromptWithAspect(prompt: string, ar: string | null, quality: CliAr
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return result;
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}
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function addAspectRatioToPrompt(prompt: string, ar: string | null): string {
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if (!ar) return prompt;
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return `${prompt} Aspect ratio: ${ar}.`;
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}
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async function readImageAsBase64(p: string): Promise<{ data: string; mimeType: string }> {
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const buf = await readFile(p);
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const ext = path.extname(p).toLowerCase();
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@@ -47,53 +96,74 @@ async function readImageAsBase64(p: string): Promise<{ data: string; mimeType: s
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return { data: buf.toString("base64"), mimeType };
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}
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function extractInlineImageData(response: {
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candidates?: Array<{ content?: { parts?: Array<{ inlineData?: { data?: string } }> } }>;
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||||
}): string | null {
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||||
for (const candidate of response.candidates || []) {
|
||||
for (const part of candidate.content?.parts || []) {
|
||||
const data = part.inlineData?.data;
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if (typeof data === "string" && data.length > 0) return data;
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||||
}
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||||
}
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return null;
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||||
}
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function extractPredictedImageData(response: {
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predictions?: Array<any>;
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generatedImages?: Array<any>;
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}): string | null {
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const candidates = [...(response.predictions || []), ...(response.generatedImages || [])];
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for (const candidate of candidates) {
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||||
if (!candidate || typeof candidate !== "object") continue;
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if (typeof candidate.imageBytes === "string") return candidate.imageBytes;
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if (typeof candidate.bytesBase64Encoded === "string") return candidate.bytesBase64Encoded;
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if (typeof candidate.data === "string") return candidate.data;
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const image = candidate.image;
|
||||
if (image && typeof image === "object") {
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if (typeof image.imageBytes === "string") return image.imageBytes;
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if (typeof image.bytesBase64Encoded === "string") return image.bytesBase64Encoded;
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if (typeof image.data === "string") return image.data;
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}
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}
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return null;
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}
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async function generateWithGemini(
|
||||
prompt: string,
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model: string,
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||||
args: CliArgs
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||||
): Promise<Uint8Array> {
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const { GoogleGenAI } = await import("@google/genai");
|
||||
|
||||
const apiKey = getGoogleApiKey();
|
||||
if (!apiKey) throw new Error("GOOGLE_API_KEY or GEMINI_API_KEY is required");
|
||||
|
||||
const ai = new GoogleGenAI({
|
||||
apiKey,
|
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httpOptions: {
|
||||
baseUrl: process.env.GOOGLE_BASE_URL || undefined,
|
||||
},
|
||||
});
|
||||
|
||||
const input: Array<{ type: "text" | "image"; text?: string; data?: string; mime_type?: string }> = [];
|
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const promptWithAspect = addAspectRatioToPrompt(prompt, args.aspectRatio);
|
||||
const parts: Array<{ text?: string; inlineData?: { data: string; mimeType: string } }> = [];
|
||||
for (const refPath of args.referenceImages) {
|
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const { data, mimeType } = await readImageAsBase64(refPath);
|
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input.push({ type: "image", data, mime_type: mimeType });
|
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parts.push({ inlineData: { data, mimeType } });
|
||||
}
|
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input.push({ type: "text", text: prompt });
|
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parts.push({ text: promptWithAspect });
|
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|
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const imageConfig: { image_size: "1K" | "2K" | "4K"; aspect_ratio?: string } = {
|
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image_size: getGoogleImageSize(args),
|
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const imageConfig: { imageSize: "1K" | "2K" | "4K" } = {
|
||||
imageSize: getGoogleImageSize(args),
|
||||
};
|
||||
if (args.aspectRatio) {
|
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imageConfig.aspect_ratio = args.aspectRatio;
|
||||
}
|
||||
|
||||
console.log("Generating image with Gemini...", imageConfig);
|
||||
const interaction = await ai.interactions.create({
|
||||
model,
|
||||
input,
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||||
response_modalities: ["image"],
|
||||
generation_config: {
|
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image_config: 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.");
|
||||
|
||||
for (const output of interaction.outputs || []) {
|
||||
if (output.type === "image" && output.data) {
|
||||
return Uint8Array.from(Buffer.from(output.data, "base64"));
|
||||
}
|
||||
}
|
||||
const imageData = extractInlineImageData(response);
|
||||
if (imageData) return Uint8Array.from(Buffer.from(imageData, "base64"));
|
||||
|
||||
throw new Error("No image in response");
|
||||
}
|
||||
@@ -103,30 +173,40 @@ async function generateWithImagen(
|
||||
model: string,
|
||||
args: CliArgs
|
||||
): Promise<Uint8Array> {
|
||||
const { experimental_generateImage: generateImage } = await import("ai");
|
||||
const { createGoogleGenerativeAI } = await import("@ai-sdk/google");
|
||||
|
||||
const google = createGoogleGenerativeAI({
|
||||
apiKey: getGoogleApiKey() || undefined,
|
||||
baseURL: process.env.GOOGLE_BASE_URL,
|
||||
});
|
||||
|
||||
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 result = await generateImage({
|
||||
model: google.image(model),
|
||||
prompt: fullPrompt,
|
||||
n: args.n,
|
||||
aspectRatio: args.aspectRatio || undefined,
|
||||
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 img = result.images[0];
|
||||
if (!img) throw new Error("No image in response");
|
||||
const imageData = extractPredictedImageData(response);
|
||||
if (imageData) return Uint8Array.from(Buffer.from(imageData, "base64"));
|
||||
|
||||
if (img.uint8Array) return img.uint8Array;
|
||||
if (img.base64) return Uint8Array.from(Buffer.from(img.base64, "base64"));
|
||||
|
||||
throw new Error("Cannot extract image data");
|
||||
throw new Error("No image in response");
|
||||
}
|
||||
|
||||
export async function generateImage(
|
||||
|
||||
Reference in New Issue
Block a user