stable-diffusion-image-generation

A guide and toolkit for generating or changing images from text and reference images with Stable Diffusion models. It covers text-to-image creation, image transformation, masked editing, and extending images.

In plain words
What is it for?
Use it for text-prompted images, style changes, image variations, filling masked areas, extending canvases, and custom pipelines built with Hugging Face Diffusers.
Why use it?
It provides a way to build image-generation workflows that can create new pictures or modify selected parts of existing ones.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/math-inc/opengauss/stable-diffusion
Any agent
npx skills add math-inc/OpenGauss --skill stable-diffusion
Clone the repo
git clone --depth 1 https://github.com/math-inc/OpenGauss

Made for: Claude Code, Codex.

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,315 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00050 $0.03315
Opus 5 $0.00025 $0.01657
Sonnet 5 $0.00010 $0.00663
Haiku 4.5 $0.00005 $0.00331

Measured 2d ago against content hash f3f2c6580af2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

stable-diffusion-image-generation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

4 near-identical copies found in the catalogue:

skills/mlops/models/stable-diffusion/SKILL.md · 523 lines

How it starts

The opening of the file, as written. The whole thing — 523 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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

Basic text-to-image

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)

from diffusers import AutoPipelineForText2Image
import torch

pipe = AutoPipelineForText2Image.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16"
)
pipe.to("cuda")

# Enable memory optimization
pipe.enable_model_cpu_offload()

image = pipe(
    prompt="A futuristic city with flying cars, cinematic lighting",
    height=1024,
    width=1024,
    num_inference_steps=30
).images[0]

Read the full file on GitHub · 523 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 523 lines · 50 tokens per session scan A f3f2c6580af2

Subscribe to this mod's changes

stable-diffusion-image-generation is a skill published in the GitHub repository math-inc/OpenGauss (1,260 stars, last pushed 4mo ago), licensed MIT. It adds 50 tokens to every session and 3,315 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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