| --- |
| license: other |
| tags: |
| - ai-generated |
| - text-to-image |
| - prompt-engineering |
| - chinese |
| - image-caption |
| --- |
| |
| # Telegram AI Image Dataset — Cleaned for VLM LoRA Training |
|
|
| A cleaned dataset of **1,050 AI-generated images** with their generation prompts, collected from a Chinese Telegram channel focused on GPT-Image-2 prompt engineering. |
|
|
| Each image is paired with a structured generation prompt in Chinese/English. Prompts have been cleaned of channel boilerplate — no bot instructions, hashtags, source credits, model name prefixes, emoji title lines, or channel footer ads. |
|
|
| ## Dataset Structure |
|
|
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `message_id` | int64 | Telegram message ID | |
| | `datetime` | string | Message timestamp | |
| | `width` | int64 | Image width in pixels | |
| | `height` | int64 | Image height in pixels | |
| | `image` | binary | Embedded WebP image bytes | |
| | `text` | string | Cleaned generation prompt (Chinese/English) | |
|
|
| ## Cleaning Applied |
|
|
| - **Removed 18 noise columns** — Telegram metadata, author info, file paths, entity data, etc. |
| - **Batch grouping** — Consecutive same-generation images grouped by matching dimensions; prompts propagated to all images in the batch. |
| - **Noise removal** — Stripped model name prefixes (`GPT-Image-2｜`), emoji title lines, bot instructions (`直接在 Bot 里输入提示词`), hashtags, source credits, channel footer ads (`VPN推荐`, `教程目录`, `邪修频道`). |
| - **Ad filtering** — Removed promotional posts, channel announcements, and non-prompt content. |
| - **Remaining** — 1,050 rows from 321 prompt groups, typically 2–4 images per prompt. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("GCStream/telegram-channel-dataset", split="train") |
| print(ds[0]["text"]) # Clean prompt |
| print(ds[0]["image"]) # PIL image |
| ``` |
|
|
| ## Notes |
|
|
| - Images are embedded as binary WebP in the parquet `image` column. |
| - Prompts are primarily Chinese with English keywords, structured as field-value formats (任务, 主体, 场景, 光线, 镜头, 风格, etc.). |
| - Suitable for VLM fine-tuning (e.g., FLUX, SD3, DeepFloyd), prompt engineering analysis, and image-caption training. |
|
|
