README.md

metadata

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

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.