Agent skill · creative production · nousresearch
stable-diffusion
Text-to-image generation, inpainting, and img2img.
Why this skill is useful
Provides domain-specific knowledge on using Stable Diffusion for image generation, including specific model commands and workflows not commonly known.
What it needs
Requires accelerate, diffusers, torch, transformers installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 226,679 stars on the source repository.
What this skill does
Stable Diffusion Image Generation Guide to generating images with Stable Diffusion using the HuggingFace Diffusers library. When to use Stable Diffusion Use Stable Diffusion when: Generating images from text descriptions Performing image-to-image translation (style transfer, enhancement) Inpainting (filling in masked regions) Outpainting (extending images beyond boundaries) Creating variations of existing images Building custom image generation workflows Key features: Text-to-Image: Generate images from natural language prompts Image-to-Image: Transform existing images with text guidance Inpainting: Fill masked regions with context-aware content ControlNet: Add spatial conditioning (edges, poses, depth) LoRA Support: Efficient fine-tuning and style adaptation Multiple Models: SD 1.5, SDXL, SD 3.0, Flux support Use alternatives instead: DALL-E 3: For API-based generation without GPU Midjourney: For artistic, stylized outputs Imagen: For Google Cloud integration Leonardo.ai: For web-based creative workflows Quick start Installation Basic text-to-image Using SDXL (higher quality) Architecture overview Three-pillar design Diffusers is built around three core components: Pipeline inference flow Core concepts Pipelines Pipelines orchestrate complete workflows: Pipeline Purpose ---------- --------- StableDiffusionPipeline Text-to-image (SD 1.x/2.x) StableDiffusionXLPipeline Text-to-image (SDXL) StableDiffusion3Pipeline Text-to-image (SD 3.0) FluxPipeline Text-to-image (Flux models) StableDiffusionImg2ImgPipeline Image-to-image StableDiffusionInpaintPipeline Inpainting Schedulers Schedulers control the denoising process: Scheduler Steps Quality Use Case ----------- ------- --------- ---------- EulerDiscreteScheduler 20-50 Good Default choice EulerAncestralDiscreteScheduler 20-50 Good More variation DPMSolverMultistepScheduler 15-25 Excellent Fast, high quality DDIMScheduler 50-100 Good Deterministic LCMScheduler 4-8 Good Very fast UniPCMultistepScheduler 15-25 Excellent Fas …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nousresearch/stable-diffusion