I built a Telegram bot that generates AI images using Stable Diffusion — here's how

Rahul K — Mon, 27 Apr 2026 23:28:34 +0000

🔗 Live portfolio page

AI Image Generation Telegram Bot - Rahul Khunte

Full-stack AI-as-a-Service bot with GPU inference, payment integration, and production deployment

rahulkhunte.github.io

A few months ago I wanted AI image generation accessible
directly in Telegram — no web UI, no waiting, just type
a prompt and get an image back in the chat. So I built it.

128 developers cloned the repo in the last 2 weeks with
zero promotion. Apparently others wanted the same thing.

Here's exactly how it works.

What it does

Send any text prompt to the bot
Bot generates an image using SDXL / Juggernaut XL
Returns image directly in Telegram chat
Supports custom models via ComfyUI workflows
Runs on any GPU server (I use free Kaggle/Colab and a VPS with GPU)

The Stack

python-telegram-bot — handles all Telegram API
ComfyUI — the image generation backend
SDXL / Juggernaut XL — the actual models
websockets — connects bot to ComfyUI API
Oracle Cloud ARM — hosts the bot 24/7 for free

How it works

ComfyUI exposes a REST API. The bot sends your prompt
to ComfyUI via websocket, waits for the image, then
sends it back to Telegram. That's the whole loop.

async def generate_image(prompt: str) -> bytes:
    # Build ComfyUI workflow with your prompt
    workflow = build_workflow(prompt)

    # Send to ComfyUI API
    async with websockets.connect(COMFYUI_WS) as ws:
        await queue_prompt(workflow, ws)
        image_data = await wait_for_output(ws)

    return image_data

async def handle_message(update, context):
    prompt = update.message.text
    await update.message.reply_text("Generating... ")

    image = await generate_image(prompt)
    await update.message.reply_photo(image)

The tricky parts

1. ComfyUI workflow as JSON
ComfyUI workflows are JSON graphs. Export any workflow
from the UI, save as a template, then inject the user
prompt at runtime. This lets you swap models without
changing bot code.

2. Queue management
Multiple users sending prompts simultaneously = queue
needed. I use asyncio queues to process one generation
at a time, with the user getting a "position X in queue"
message.

3. GPU access without paying
For development: Kaggle notebooks (30hrs GPU/week free),
Google Colab free tier.
For production hosting the bot itself (not the GPU),
you need almost nothing — it's just a Python process
making API calls.

Good free/cheap options:

Railway.app — free tier, deploy straight from GitHub, zero config
Render.com — free tier, auto-deploys on git push
Fly.io — free allowance, great for bots
Any $4/month VPS — DigitalOcean, Hetzner, Vultr

The bot uses ~80MB RAM idle. A $4 Hetzner ARM server
handles it perfectly.

Results

The bot handles ~20-30 image requests per day on a
single deployment. Average generation time: 8-15 seconds
on a mid-range GPU.

Users can also send style keywords:

"realistic portrait of a cat --style photorealistic"
"cyberpunk city at night --style anime"

What's next

Currently adding:

Image-to-image (send a photo + prompt = transformed image)
Multiple model selection via /model command
DALL-E 3 fallback when GPU is unavailable

Full source code

Everything is open source:
👉 Github

rahulkhunte / ai-image-bot

🤖 Jenerator Bot - AI Image Generation

Production-ready Telegram bot with GPU-accelerated image generation using Stable Diffusion XL Turbo. Generate high-quality images in under 4 seconds with natural language prompts.

📄 View Full Project Portfolio

⚠️ Note: Demo currently paused for infrastructure optimization. Full codebase and deployment guide available.

✨ Features

Core Capabilities

⚡ Lightning-fast generation - Sub-4 second image creation (L40 GPU)
🎨 High-quality outputs - SDXL Turbo model for photorealistic results
🔧 ComfyUI backend - Professional workflow management
💬 Telegram integration - Intuitive bot interface with inline buttons
💰 Payment system - Cryptocurrency payment integration
📊 Usage tracking - Credit system with transaction history
🖼️ Gallery system - Browse and regenerate previous images

Technical Highlights

GPU-optimized inference pipeline
Asynchronous request handling
Docker containerization
Production-grade error handling
Automatic queue management
Real-time generation status updates

🎯 Why This Project Matters

This bot demonstrates:

Production ML deployment - Not a toy…

View on GitHub

👉 🔗 Live portfolio page

AI Image Generation Telegram Bot - Rahul Khunte

Full-stack AI-as-a-Service bot with GPU inference, payment integration, and production deployment

rahulkhunte.github.io

Includes the full bot, ComfyUI workflow templates,
deployment guide on Cloud, and systemd service
config for 24/7 running.

If you build something with it or have questions,
drop a comment — happy to help.

DEV Community: Rahul K

I built a Telegram bot that generates AI images using Stable Diffusion — here's how

AI Image Generation Telegram Bot - Rahul Khunte

What it does

The Stack

How it works

The tricky parts

Results

What's next

Full source code

rahulkhunte / ai-image-bot

🤖 Jenerator Bot - AI Image Generation

✨ Features

Core Capabilities

Technical Highlights

🎯 Why This Project Matters

AI Image Generation Telegram Bot - Rahul Khunte