Laguna by Poolside vs Stable Diffusion Web UI: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Laguna by Poolside and Stable Diffusion Web UI — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Laguna by Poolside
Poolside
Poolside's family of open Mixture-of-Experts foundation models for agentic coding — XS.2 runs locally, M.1 reaches 72.5% on SWE-bench Verified.
Key features
- Two Model Sizes: Laguna XS.2 (33B total / 3B active) and Laguna M.1 (225B total / 23B active) target different latency and capability needs.
- Mixture-of-Experts Architecture: Routes each token through a subset of experts for efficiency at large scale.
- Local Deployment: XS.2 is small enough to run on a Mac with 36 GB of RAM via Ollama under an Apache 2.0 license.
- Strong SWE-bench Results: XS.2 hits 68.2% and M.1 reaches 72.5% on SWE-bench Verified.
- Bundled Coding Agent: Ships 'pool,' a lightweight terminal-based coding agent.
- Agent Client Protocol: Includes a dual ACP client-server used internally for agent RL training and evaluation.
Best for
- Local Agentic Coding: Running XS.2 on a laptop for private, offline code generation and editing.
- High-Capability Code Tasks: Using M.1 for harder, long-horizon software engineering work.
- Self-Hosted Deployments: Building on open weights to avoid third-party API dependencies.
- Research & Fine-Tuning: Adapting permissively licensed weights for custom coding workflows.
- Benchmarking: Evaluating agentic coding performance against SWE-bench Verified and Pro.
Stable Diffusion Web UI
AUTOMATIC1111
A Gradio-based local web interface for running, managing, and extending Stable Diffusion models with extensive scripts and platform support.
Key features
- Gradio Web Interface: A browser-based UI built on Gradio for interactive image generation and editing, exposing common parameters and visual previews to users.
- Cross-Platform Launchers: Includes platform-specific launch and helper scripts (webui-user.bat, webui.sh, update.bat) and documented installation steps for Windows, Linux, and Apple Silicon systems.
- Model and Checkpoint Management: Clear model placement and management workflow (e.g., models/Stable-diffusion folder), support for multiple checkpoints and configuration YAMLs to run different Stable Diffusion variants.
- Custom Scripts & Extensions: Official wiki and community resources for custom scripts and extensions, enabling added functionality, experimental features, and plugin-style enhancements.
- Integration with Restoration Tools: Examples and support for integrating tools like GFPGAN for face restoration and other post-processing/upscaling utilities.
- Multi-GPU and Hardware Guidance: Documentation and installation guidance for NVIDIA (recommended), AMD GPUs, and Apple Silicon to optimize inference and compatibility.
- Image Generation Modes: Provides common generation workflows (e.g., txt2img and img2img) with prebuilt screenshots and examples in the repository to illustrate workflows and parameters.
- Community-driven Forks & Optimization Layers: Ecosystem of forks (Forge, reForge) and community projects that add resource management, inference speedups, and experimental APIs on top of the core web UI.
- Gradio-based browser GUI for Stable Diffusion (txt2img and other interfaces referenced)
- Local model management: place checkpoints/models in models/Stable-diffusion folder
- Platform launch scripts: webui-user.bat (Windows), webui.sh/webui.py (Linux/macOS), WSL2 environment YAML
- Automatic virtual environment creation and dependency installation (venv) on launch
- Explicit installation guidance and scripts for NVidia (recommended), AMD GPUs and Apple Silicon
- Support for community extensions and custom scripts (wiki pages for scripts/extensions)
- Support for different Stable Diffusion model versions and configs (v1, v2 depth model note)
- High-resolution fix handling and hooks for sampling (extension/script API within WebUI)
- Works with online services such as Google Colab for remote/quick setup
- Requirements and environment files included: requirements.txt, requirements_npu.txt, environment-wsl2.yaml, requirements_versions.txt
Best for
- Local Image Generation: Run Stable Diffusion locally through a web browser to generate images from text prompts without relying on external hosted services.
- Prompt Engineering and Iteration: Rapidly prototype and refine prompts using the interactive UI and visual previews to achieve desired outputs.
- Batch and Automated Renders: Use built-in batching and launch scripts to produce large sets of images or run parameter sweeps for dataset creation or experimentation.
- Model Testing and Comparison: Load multiple checkpoints and configurations to compare model outputs, test new checkpoints, or evaluate fine-tuned models.
- Image Editing and Restoration: Perform image-to-image edits, inpainting, and integrate face-restoration/upscaling tools (e.g., GFPGAN) for higher-quality results.
- Extension Development and Experimentation: Develop and test custom scripts or extensions via the project's wiki and community repositories (Forge/reForge) to prototype new sampler or optimization features.
- Cross-hardware Deployment: Deploy and run Stable Diffusion on a variety of local hardware setups (NVIDIA, AMD, Apple Silicon) using documented installation procedures.
- Local image generation and experimentation with Stable Diffusion models via GUI
- Rapid prototyping and visual parameter tuning (prompt weighting, sampler settings)
- Research and development of custom sampling scripts and model extensions
- Resource optimization and experimental features via community 'Forge' forks
- Running Stable Diffusion on Apple Silicon, NVidia/AMD GPUs, WSL2 or via Colab