stable-diffusion
by Community
A latent text-to-image diffusion model
OSS
stable-diffusion
Added 1 June 2026
Overview
Stable Diffusion is a latent text-to-image diffusion model that generates images from text prompts by iteratively denoising latent representations. It runs locally on consumer hardware and is open source, making it accessible for integration into applications without cloud dependencies.
Best for
Best for
Developers building image generation features who need local control and can manage infrastructure requirements
Use cases
- Generate custom images from text descriptions in applications
- Fine-tune the model on domain-specific image datasets
- Build image generation features without API rate limits or costs
Notes
Stable Diffusion is a latent text-to-image diffusion model that generates images from text prompts by iteratively denoising latent representations. It runs locally on consumer hardware and is open source, making it accessible for integration into applications without cloud dependencies.
73,065 stars on GitHub. Last updated 2024-06-18.
Use cases
- Generate custom images from text descriptions in applications
- Fine-tune the model on domain-specific image datasets
- Build image generation features without API rate limits or costs
Pros
- Runs on consumer GPUs and CPUs, no cloud service required
- Open source with large community and extensive tooling ecosystem
- Generates high-quality images with reasonable inference speed
Cons
- Requires significant local compute resources for acceptable generation speed
- Output quality and consistency depend heavily on prompt engineering
- Model weights are large (several GB), adding deployment complexity
Indexed from awesome-llmops and enriched against its public facts.
Pros
- Runs on consumer GPUs and CPUs, no cloud service required
- Open source with large community and extensive tooling ecosystem
- Generates high-quality images with reasonable inference speed
Cons
- Requires significant local compute resources for acceptable generation speed
- Output quality and consistency depend heavily on prompt engineering
- Model weights are large (several GB), adding deployment complexity
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