Image Generation

29min
Running Image Generation on Vast.ai: A Complete Guide
Introduction
This guide walks you through setting up and running image generation workloads on Vast.ai, a marketplace for renting GPU compute power. Whether you're using Stable Diffusion or other image generation models, this guide will help you get started efficiently.
Prerequisites
A Vast.ai account
Basic familiarity with image generation models
﻿(Optional) Read Jupyter guide﻿
﻿(Optional) SSH client installed on your local machine and SSH public key added in Account tab at cloud.vast.ai 
(Optional) Basic understanding of model management
Setting Up Your Environment
1. Selecting the Right Template
Navigate to the Templates tab to view available templates. For image generation, we recommend searching for "SD Web UI Forge" among the recommended templates.
Stable Diffusion Web UI Forge Template
Pre-installed with:
Latest SD Web UI version
Popular extensions
Common models
Optimized settings for vast.ai
Choose this template if:
You want a ready-to-use environment for image generation
You need a user-friendly web interface
You want access to multiple models and extensions
You're looking for an optimized setup
Edit the template and add/update key environment variables if needed:
Bash
# Core Configuration
AUTO_UPDATE=false        # Auto-update to latest release
FORGE_REF=latest        # Git reference for updates
FORGE_ARGS=""           # Launch arguments

# Authentication Tokens
CF_TUNNEL_TOKEN=""      # Cloudflare Zero Trust
CIVITAI_TOKEN=""        # Access gated Civitai models
HF_TOKEN=""             # Access gated HuggingFace models

# Custom Setup
PROVISIONING_SCRIPT=""  # URL to custom setup script
# Core Configuration
AUTO_UPDATE=false        # Auto-update to latest release
FORGE_REF=latest        # Git reference for updates
FORGE_ARGS=""           # Launch arguments

# Authentication Tokens
CF_TUNNEL_TOKEN=""      # Cloudflare Zero Trust
CIVITAI_TOKEN=""        # Access gated Civitai models
HF_TOKEN=""             # Access gated HuggingFace models

# Custom Setup
PROVISIONING_SCRIPT=""  # URL to custom setup script
﻿
Important: Never save your template as public if you've included tokens in Docker Options or added your docker login password.
2. Choosing an Instance
When selecting a GPU for image generation, consider:
GPU Memory:
Minimum 8GB for basic models
12GB+ recommended for larger models
24GB+ for advanced techniques (img2img, inpainting, etc.)
GPU Type:
RTX 3090, 4090 for best performance
RTX 3080, 3080 Ti for good balance
A4000, A5000 for stability
Disk Space:
Minimum 50GB for base models
100GB+ recommended for multiple models
Consider SSD speed for model loading
3. Connecting to Your Instance
The Forge template provides multiple ways to access your instance:
AI-Dock Landing Page (Recommended):
Click the "Open" button in Instances tab once the blue button says "Open" on your instance
You'll be automatically logged in to the AI-Dock landing page
Access Forge and other management tools from there
Direct Access:
Basic authentication is enabled by default
Username: vastai
Password: Check OPEN_BUTTON_TOKEN value
To find token: echo $OPEN_BUTTON_TOKEN in terminal
API Access:
Bash
curl -X POST https://[INSTANCE_IP]:[MAPPED_PORT]/endpoint \
-H 'Authorization: Bearer <OPEN_BUTTON_TOKEN>' \
...
curl -X POST https://[INSTANCE_IP]:[MAPPED_PORT]/endpoint \
-H 'Authorization: Bearer <OPEN_BUTTON_TOKEN>' \
...
﻿
Security Setup:
HTTPS and token authentication enabled by default
Install TLS certificate to avoid browser warnings
Configure via WEB_ENABLE_HTTPS and WEB_ENABLE_AUTH variables
Jupyter Access for Uploading/Downloading:
You can access jupyter clicking on the jupyter button on the instance card to easily upload and download files
Working with Models
Managing Models
Default Setup
The template includes a default provisioning script that downloads:
Base Stable Diffusion XL models
Popular extensions
Common configurations
Custom Provisioning
Create your own setup by:
Copy the default provisioning script by editing the SD Web UI Forge template and grabbing the value of PROVISIONING_SCRIPT environment variable and downloading it
Modify it to download your preferred:
Models
Extensions
Configurations
Upload to Gist/Pastebin
Edit the template and set PROVISIONING_SCRIPT environment variable to the raw URL
Example for adding more models:
Bash
# Navigate to models directory
cd /workspace/stable-diffusion-webui/models/Stable-diffusion

# Download new models (example)
wget https://civitai.com/api/download/models/[MODEL_ID]
# Navigate to models directory
cd /workspace/stable-diffusion-webui/models/Stable-diffusion

# Download new models (example)
wget https://civitai.com/api/download/models/[MODEL_ID]
﻿
Model Organization
Keep your models organized:
Text
/workspace/stable-diffusion-webui/models/
├── Stable-diffusion/      # Main models
├── Lora/                  # LoRA models
├── VAE/                   # VAE files
└── embeddings/            # Textual inversions
/workspace/stable-diffusion-webui/models/
├── Stable-diffusion/      # Main models
├── Lora/                  # LoRA models
├── VAE/                   # VAE files
└── embeddings/            # Textual inversions
﻿
You can access jupyter clicking on the jupyter button on the instance card to easily upload and download files.
Optimization Tips
 Performance Settings
Access Settings > Performance in Web UI:
Enable xformers memory efficient attention
Use float16 precision when possible
Optimize VRAM usage based on your GPU
Batch Processing
For multiple images:
Use batch count for variations
Use batch size for parallel processing
Monitor GPU memory usage
Memory Management
Python
# Recommended settings for different GPU sizes
8GB GPU:
- max_batch_count: 4
- max_batch_size: 2

12GB GPU:
- max_batch_count: 6
- max_batch_size: 3

24GB+ GPU:
- max_batch_count: 10
- max_batch_size: 5
# Recommended settings for different GPU sizes
8GB GPU:
- max_batch_count: 4
- max_batch_size: 2

12GB GPU:
- max_batch_count: 6
- max_batch_size: 3

24GB+ GPU:
- max_batch_count: 10
- max_batch_size: 5
﻿
Advanced Features
Custom Scripts
Place custom scripts in:
Bash
/workspace/stable-diffusion-webui/scripts/
/workspace/stable-diffusion-webui/scripts/
﻿
Extensions Management
Popular extensions are pre-installed. Add more via Web UI:
Extensions tab
Install from URL
Restart UI to apply
API Usage
Enable API in settings:
Bash
# Add to config.json
{
    "api": {
        "enable_api": true,
        "api_auth": false
    }
}
# Add to config.json
{
    "api": {
        "enable_api": true,
        "api_auth": false
    }
}
﻿
Troubleshooting
Common Issues and Solutions
Out of Memory (OOM)
Reduce batch size
Lower resolution
Enable optimization settings
Slow Generation
Check GPU utilization
Verify model loading
Consider switching to half precision
Connection Issues
Use --listen flag for network access
Check instance status
Verify network settings
Best Practices
Workflow Management
Save prompts for reuse
Use version control for custom scripts
Document model combinations
Resource Optimization
Monitor costs in Billing tab
Use appropriate batch sizes
Clean up unused models
Quality Control
Maintain prompt libraries
Document successful settings
Track model performance
Cost Optimization
Instance Selection
Compare GPU prices
Consider spot instances
Monitor usage patterns
Storage Management
Remove unused models
Archive generated images
Use efficient formats
Additional Resources
﻿Vast.ai Documentation﻿
﻿Stable Diffusion Web UI Wiki﻿
﻿CivitAI Models﻿
Conclusion
Running image generation workloads on Vast.ai provides a cost-effective way to access powerful GPUs. By following this guide and best practices, you can efficiently set up and manage your image generation pipeline while optimizing costs and performance.
Updated 21 Jan 2025
Did this page help you?
vLLM (LLM inference and serving)
Stable Diffusion