> ## Documentation Index > Fetch the complete documentation index at: https://vastai-80aa3a82-docs-screenshot-updates.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Image Generation # 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](/guides/instances/jupyter) * [(Optional) SSH client installed on your local machine and SSH public key added the Keys section at cloud.vast.ai](/guides/instances/sshscp) * (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 ' \ ... ``` * **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 ### 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.