Stable Diffusion

by Stability AI

Freemium

Open-source text-to-image model offering high-quality image generation with extensive customization, LoRA support, and local deployment.

4.4
out of 5.0
Visit Stable Diffusion
Category Image & Design
Platform WebmacOSWindowsLinuxAPI
Last Updated March 22, 2026
Website stability.ai

Overview

Stable Diffusion is Stability AI's open-source text-to-image generation model, the most widely used AI image model in the world. Version 3.5 (January 2026) features enhanced prompt adherence, diverse outputs, improved text rendering, better composition, and ControlNet support for precise image control.

Being open-source, Stable Diffusion can be downloaded and run locally on consumer GPUs, fine-tuned with custom LoRA models, and integrated into any pipeline. It supports resolutions up to 1 megapixel and generates everything from photorealism to artistic renders. The ecosystem includes thousands of community models, extensions, and GUIs (ComfyUI, Automatic1111, Forge).

Available as open-weight downloads, through the DreamStudio web app, or via the Stability API for developers.

Pricing

Free (Self
Hosted)
  • Download open-weight models from Hugging Face
  • Run locally on your own GPU
  • Community License free for individuals and businesses under $1M annual revenue

Pros & Cons

Pros

Fully open-source — download, modify, fine-tune, run locally
Massive community ecosystem — thousands of LoRA models, extensions, GUIs
Free to use for individuals and small businesses (under $1M revenue)
ControlNet for precise composition control (pose, depth, edges)
No content restrictions when self-hosted (user responsibility)
Hardware-efficient — runs on consumer GPUs (8GB+ VRAM)
Full commercial rights under Community License

Cons

Requires technical setup for local deployment (GPU, Python, dependencies)
Base image quality below Midjourney and DALL-E 3 without fine-tuning
Text rendering still inconsistent despite v3.5 improvements
GPU required for decent speed — CPU generation is extremely slow
Model management and updates require manual effort
Quality depends heavily on prompt engineering and model selection

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