TianLin0509
Community indexed
Img2ppt Lite
Turn a slide mockup image into an editable PPTX: native text + semantic draggable sprites + inpainted background. Agent-as-VLM skill for Claude Code / Codex / any CLI agent.
Overview
Turn a slide mockup image into an editable PPTX: native text + semantic draggable sprites + inpainted background. Agent-as-VLM skill for Claude Code / Codex / any CLI agent.
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img2ppt-lite
把一张 PPT 效果图（PNG/JPG）还原成高保真、可编辑的 PPTX。
Turn a slide mockup image (e.g. generated by GPT-image / Midjourney / any design tool) into an editable PowerPoint file: native text boxes + semantically-split draggable sprites + inpainted clean background. Built as an agent skill — the executing AI (Claude Code / Codex / any CLI agent with vision) acts as the VLM, deterministic Python scripts do everything else.
左：合成样张源图 · 右：管线重建后由 PowerPoint 渲染回的图（文字全部为原生可编辑文本框，图表/图标为独立贴图）
它解决什么问题
AI 出图工具能生成非常漂亮的 PPT 效果图，但那只是一张图片。想拿它当真正的工作底稿，你需要：
- 文字可编辑——改标题、改数据是最高频操作；
- 元素可拖拽——每个图标、图表、色块都是独立对象，能单独移动缩放；
- 视觉尽量 1:1——还原完的页面和效果图肉眼几乎一致。
传统"图转 PPT"路线要么把整页塞成一张图（完全不可编辑），要么全原生重建（图标丑、收敛循环极慢）。img2ppt-lite 走中间路线，并把速度做到了单页 3-5 分钟（脚本部分约 40-60 秒）。
核心设计
可编辑 PPTX = 干净底图 (inpaint 去前景)
+ 语义元素贴图 (每个图标/图表一张独立 picture，可拖拽)
+ 原生文本框 (PIL 墨水高度反解字号，可编辑)
+ 纯色矩形原生化 (KPI 条/按钮/色块 → 可改色的原生 shape)
三个反直觉的关键决策：
- 视觉一致性的最大敌人恰恰是"原生重建"。 从原图裁出来的贴图天然 100% 一致；会漂的只有文本层——而文本字号可以用 PIL 按墨水高度离线反解，不需要"渲染→对比→调整"的收敛循环。这是比前代管线快一个量级的根本原因。
- 执行 agent 自身就是 VLM。 不调外部视觉 API：agent 看图产出一份
elements.json（文本/色块/图形的语义标注），脚本吃 JSON 确定性完成其余一切。LLM 参与固定 2 次（标注 + 终检），零额外 token 成本。 - 测量代替猜测。 字号、文字颜色、卡片填充色、渐变、行首 marker 全部从像素实测，agent 只负责语义；每个测量器都经过阳性+阴性用例双验。
环境要求
- Windows（PowerPoint COM 用于渲染回读验证；没有 PowerPoint 也能出 PPTX，只是跳过渲染验证）
- Python 3.10+
pip install -r requirements.txt
装好后先跑自检（生成合成样张走全链路，含 marker 正/负回归用例）：
python scripts/pipeline.py --selftest
使用方式
作为 agent skill（推荐）
把本仓库放进你的 agent skill 目录（如 Claude Code 的 ~/.claude/skills/img2ppt-lite/），agent 会按 SKILL.md 的 5 步工作流执行：
1. pipeline.py --run <run> --seed # OCR 冷启动，产出待审阅的文本骨架
2. [agent 看图] 校对 seed、补图形/色块标注 → elements.json
3. pipeline.py --run <run> # OCR校准 → 切图 → 装配 → COM 渲染验收
4. [agent 看图] 对比 compare.png，必要时改 JSON 秒级重跑
5. 交付 pptx + 验收报告（修补硬上限 2 轮，禁无限迭代）
手动使用
不用 agent 也可以：
View original text
# img2ppt-lite
**把一张 PPT 效果图（PNG/JPG）还原成高保真、可编辑的 PPTX。**
Turn a slide mockup image (e.g. generated by GPT-image / Midjourney / any design tool) into an editable PowerPoint file: native text boxes + semantically-split draggable sprites + inpainted clean background. Built as an agent skill — the executing AI (Claude Code / Codex / any CLI agent with vision) acts as the VLM, deterministic Python scripts do everything else.
*左：合成样张源图 · 右：管线重建后由 PowerPoint 渲染回的图（文字全部为原生可编辑文本框，图表/图标为独立贴图）*
## 它解决什么问题
AI 出图工具能生成非常漂亮的 PPT 效果图，但那只是一张图片。想拿它当真正的工作底稿，你需要：
- **文字可编辑**——改标题、改数据是最高频操作；
- **元素可拖拽**——每个图标、图表、色块都是独立对象，能单独移动缩放；
- **视觉尽量 1:1**——还原完的页面和效果图肉眼几乎一致。
传统"图转 PPT"路线要么把整页塞成一张图（完全不可编辑），要么全原生重建（图标丑、收敛循环极慢）。img2ppt-lite 走中间路线，并把速度做到了**单页 3-5 分钟**（脚本部分约 40-60 秒）。
## 核心设计
```
可编辑 PPTX = 干净底图 (inpaint 去前景)
+ 语义元素贴图 (每个图标/图表一张独立 picture，可拖拽)
+ 原生文本框 (PIL 墨水高度反解字号，可编辑)
+ 纯色矩形原生化 (KPI 条/按钮/色块 → 可改色的原生 shape)
```
三个反直觉的关键决策：
1. **视觉一致性的最大敌人恰恰是"原生重建"。** 从原图裁出来的贴图天然 100% 一致；会漂的只有文本层——而文本字号可以用 PIL 按墨水高度离线反解，**不需要"渲染→对比→调整"的收敛循环**。这是比前代管线快一个量级的根本原因。
2. **执行 agent 自身就是 VLM。** 不调外部视觉 API：agent 看图产出一份 `elements.json`（文本/色块/图形的语义标注），脚本吃 JSON 确定性完成其余一切。LLM 参与固定 2 次（标注 + 终检），零额外 token 成本。
3. **测量代替猜测。** 字号、文字颜色、卡片填充色、渐变、行首 marker 全部从像素实测，agent 只负责语义；每个测量器都经过阳性+阴性用例双验。
## 环境要求
- Windows（PowerPoint COM 用于渲染回读验证；**没有 PowerPoint 也能出 PPTX**，只是跳过渲染验证）
- Python 3.10+
```bash
pip install -r requirements.txt
```
装好后先跑自检（生成合成样张走全链路，含 marker 正/负回归用例）：
```bash
python scripts/pipeline.py --selftest
```
## 使用方式
### 作为 agent skill（推荐）
把本仓库放进你的 agent skill 目录（如 Claude Code 的 `~/.claude/skills/img2ppt-lite/`），agent 会按 `SKILL.md` 的 5 步工作流执行：
```
1. pipeline.py --run <run> --seed # OCR 冷启动，产出待审阅的文本骨架
2. [agent 看图] 校对 seed、补图形/色块标注 → elements.json
3. pipeline.py --run <run> # OCR校准 → 切图 → 装配 → COM 渲染验收
4. [agent 看图] 对比 compare.png，必要时改 JSON 秒级重跑
5. 交付 pptx + 验收报告（修补硬上限 2 轮，禁无限迭代）
```
### 手动使用
不用 agent 也可以：Review the source
Price & running costs
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- License
- MIT
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Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Source needs review
The tracked source changed or could not be synchronized. Review the current source before installing.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 23 GitHub stars
- Stars/forks activity: 23 stars, 4 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
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Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
Indexed
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- TianLin0509/img2ppt-lite
- License
- MIT
- Version
- 1.0.0
- Last GitHub push
- Jul 24, 2026
- Registry updated
- Sep 10, 2026
- Instruction path
- Source structure unverified
Version reported in registry metadata; check source releases before relying on it.
Quality
71/100
Strong
Trust
59/100
Do not auto-install
Audit
75/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 23 GitHub stars
- Stars/forks activity: 23 stars, 4 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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}For the creator
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