# Stable Diffusion Image Generation

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Media
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill stable-diffusion-image-generation -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill stable-diffusion-image-generation -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill stable-diffusion-image-generation -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill stable-diffusion-image-generation -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Stable Diffusion Image Generation

Comprehensive 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

```bash
pip install diffusers transformers accelerate torch
pip install xformers  # Optional: memory-efficient attention
```

### Basic text-to-image

```python
from diffusers import DiffusionPipeline
import torch

# Load pipeline (auto-detects model type)
pipe = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    torch_dtype=torch.float16
)
pipe.to("cuda")

# Generate image
image = pipe(
    "A serene mountain landscape at sunset, highly detailed",
    num_inference_steps=50,
    guidance_scale=7.5
).images[0]

image.save("output.png")
```

### Using SDXL (higher quality)

```python
from diffusers import AutoPipelineForText2Image
import torch

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/18-multimodal/stable-diffusion/SKILL.md