> ## Documentation Index
> Fetch the complete documentation index at: https://vastai-80aa3a82-ltxv2-serverless.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Image Generation

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  {
    "@type": "HowToStep",
    "name": "Select the Right Template",
    "text": "Navigate to the Templates tab and search for 'SD Web UI Forge' among recommended templates. This template comes pre-installed with the latest SD Web UI version, popular extensions, common models, and optimized settings for Vast.ai. It provides a user-friendly web interface with access to multiple models and extensions."
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  {
    "@type": "HowToStep",
    "name": "Configure Template Environment Variables",
    "text": "Edit the template and add/update key environment variables as needed: AUTO_UPDATE (auto-update to latest release), FORGE_REF (Git reference for updates), FORGE_ARGS (launch arguments), CF_TUNNEL_TOKEN (Cloudflare Zero Trust), CIVITAI_TOKEN (access gated Civitai models), HF_TOKEN (access gated HuggingFace models), and PROVISIONING_SCRIPT (URL to custom setup script). Never save your template as public if you've included tokens."
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    "@type": "HowToStep",
    "name": "Choose an Instance",
    "text": "Select a GPU based on requirements: Minimum 8GB for basic models, 12GB+ recommended for larger models, 24GB+ for advanced techniques. GPU types: 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."
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# 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](/documentation/instances/jupyter)
* [(Optional) SSH client installed on your local machine and SSH public key added the Keys section at cloud.vast.ai](/documentation/instances/sshscp) &#x20;
* (Optional) Basic understanding of model management

## Setting Up Your Environment

### 1. Selecting the Right Template

Navigate to the [Templates tab](https://cloud.vast.ai/templates/) 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 Bash theme={null}
# 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 theme={null}
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:

1. 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
2. Modify it to download your preferred:
   * Models
   * Extensions
   * Configurations
3. Upload to Gist/Pastebin
4. Edit the template and set `PROVISIONING_SCRIPT` environment variable to the raw URL

Example for adding more models:

```bash Bash theme={null}
# 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 Text theme={null}
/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

### &#x20;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 icon="python" Python theme={null}
# 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 Bash theme={null}
/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 Bash theme={null}
# 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](https://vast.ai/docs/)
* [Stable Diffusion Web UI Wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki)
* [CivitAI Models](https://civitai.com/)

## 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.