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Agent skill

Stable Diffusion Image Generation

by taracodlabs in taracodlabs/aiden

Generate images via Stable Diffusion (HuggingFace Diffusers, local/API)

Apache-2.0Auto-check passedAI & LLM Engineering

Install Stable Diffusion Image Generation

skills CLI
$ npx skills add taracodlabs/aiden --skill stable-diffusion-image-generation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install taracodlabs/aiden stable-diffusion-image-generation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/taracodlabs/aiden.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/stable-diffusion-image-generation .claude/skills/stable-diffusion-image-generation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
stable-diffusion-image-generation
GitHub stars
849
Token cost
~1.1k tokens
SKILL.md length
265 words
Files
2
Skills in repo
63
Repo updated
First seen
Licence
Apache-2.0

At a glance

Generate images via Stable Diffusion (HuggingFace Diffusers, local/API)

  • Works in 5 steps: Quick generation via HuggingFace… → Local generation with Diffusers… → Generate multiple variations → …
  • Tasks that involve Diffusion and image models
  • SKILL.md covers When to Use, How to Use, Examples and Cautions
  • Reaches api-inference.huggingface.co; needs HF_TOKEN

What it does

Stable Diffusion Image Generation is an agent skill from taracodlabs/aiden. Generate images via Stable Diffusion (HuggingFace Diffusers, local/API)

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.json`).

It sits in AI & LLM Engineering, covering Diffusion and image models, Image generation and Model hubs and datasets. It works with Stable Diffusion and Hugging Face. The repository describes itself as: Aiden — an autonomous AI agent and work engine built solo. It can operate your browser, terminal, files, apps, APIs, skills and tools, remember context, recover from failures… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Diffusion and image models
  • Tasks that involve Image generation
  • Tasks that involve Model hubs and datasets

Example prompts

  • “/stable-diffusion-image-generation”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Quick generation via HuggingFace Inference API (no GPU needed)
  2. Local generation with Diffusers (requires GPU or CPU + patience)
  3. Generate multiple variations
  4. Write effective prompts
  5. CPU-only generation (slower but works without GPU)

What it can do on your machine

Read from SKILL.md and the folder at commit 3704204. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api-inference.huggingface.co

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • HF_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Stable Diffusion Image Generation loads about 1.1k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 265 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from taracodlabs/aiden at commit 3704204, republished under its Apache-2.0 licence (© taracodlabs). 265 words, ~1,124 tokens.

Download SKILL.mdSave it as .claude/skills/stable-diffusion-image-generation/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
stable-diffusion-image-generation
description
Generate images via Stable Diffusion (HuggingFace Diffusers, local/API)
category
creative
version
1.0.0
origin
aiden
license
Apache-2.0
tags
stable-diffusion, image-generation, ai-art, diffusers, huggingface, text-to-image, sdxl, creative

Stable Diffusion Image Generation

Generate images from text prompts using Stable Diffusion locally via HuggingFace Diffusers, or via the HuggingFace Inference API for zero-install operation.

When to Use

  • User wants to generate an image from a text description
  • User wants to create concept art, illustrations, or visual mockups
  • User wants to experiment with AI image generation locally
  • User wants to generate multiple variations of an image
  • User wants to use img2img (image-to-image) transformation

How to Use

1. Quick generation via HuggingFace Inference API (no GPU needed)
python
import requests, base64, os

def generate_image_api(prompt, output="output.png"):
  api_url = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
  headers = {"Authorization": f"Bearer {os.environ['HF_TOKEN']}"}
  resp    = requests.post(api_url, headers=headers, json={"inputs": prompt}, timeout=120)
  resp.raise_for_status()
  with open(output, "wb") as f:
    f.write(resp.content)
  print(f"Saved: {output}")

# Set HF_TOKEN in env: huggingface.co/settings/tokens
generate_image_api("a futuristic city at night, cyberpunk style, neon lights, 8k")
2. Local generation with Diffusers (requires GPU or CPU + patience)
python
# pip install diffusers transformers accelerate torch
from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
  "stabilityai/stable-diffusion-xl-base-1.0",
  torch_dtype=torch.float16,
  use_safetensors=True
)
pipe = pipe.to("cuda")   # use "cpu" if no GPU (very slow)

image = pipe(
  prompt="a majestic mountain landscape at golden hour, photorealistic",
  negative_prompt="blurry, low quality, cartoon",
  num_inference_steps=30,
  guidance_scale=7.5,
  width=1024, height=1024
).images[0]

image.save("landscape.png")
print("Saved: landscape.png")
3. Generate multiple variations
python
images = pipe(
  prompt="a robot reading a book in a cozy library",
  num_images_per_prompt=4,
  num_inference_steps=25,
).images

for i, img in enumerate(images):
  img.save(f"variation_{i+1}.png")
  print(f"Saved variation_{i+1}.png")
4. Write effective prompts

Good prompt structure:

[subject], [style], [setting/background], [lighting], [quality tags]

Examples:
"a golden retriever puppy, oil painting style, in a sunlit meadow, warm afternoon light, highly detailed"
"abstract data visualization, dark background, glowing cyan lines, geometric patterns, 4k"
"portrait of a scientist, dramatic studio lighting, photorealistic, sharp focus, professional headshot"

Useful negative prompt additions:

"blurry, low quality, watermark, signature, deformed, extra limbs, bad anatomy, poorly drawn"
5. CPU-only generation (slower but works without GPU)
python
from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained(
  "runwayml/stable-diffusion-v1-5",
  torch_dtype=torch.float32
)

image = pipe(
  prompt="a simple landscape, watercolor style",
  num_inference_steps=15,    # fewer steps = faster on CPU
  width=512, height=512      # smaller size for CPU
).images[0]
image.save("output.png")

Examples

"Generate an image of a futuristic AI lab" → Use step 1 (API) if HF_TOKEN is set. Prompt: "futuristic AI research lab, holographic displays, clean aesthetic, cinematic lighting".

"Create 4 variations of a logo concept for a tech startup" → Use step 3 with a logo-style prompt and num_images_per_prompt=4.

"Generate an image locally without internet" → Use step 2 (local Diffusers). SDXL needs ~8GB VRAM; for CPU use step 5 with SD v1.5 at 512×512.

Cautions

  • SDXL requires at least 8GB VRAM for float16; use SD v1.5 (step 5) on CPU or low-VRAM GPUs
  • First run downloads model weights (~6-7GB) — this takes time; subsequent runs use cache
  • HuggingFace Inference API free tier has rate limits — set a delay between requests for batch generation
  • Generated images may reflect biases in training data — review outputs before publishing
  • HF_TOKEN must be set as an environment variable — never hardcode it in scripts

© taracodlabs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/stable-diffusion-image-generation of taracodlabs/aiden.

  • SKILL.md
  • skill.json

Open the folder on GitHubat commit 3704204

Compare with similar skills

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Stable Diffusion Image Generation this skilltaracodlabs/aiden849—~1.1kAutomated safety check: PassApache-2.0
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Stable Diffusion with DiffusersOrchestra-Research/AI-Research-SKILLs13k6 repos~3.2kAutomated safety check: PassMIT
Comfyui Skill OpenclawHuangYuChuh/ComfyUI_Skills_OpenClaw411—~2.7kAutomated safety check: PassApache-2.0
Prompt EngineAgriciDaniel/claude-prompts110—~1.2kAutomated safety check: PassMIT
Workflow Template BuilderMooshieblob1/MooshieUI207—~640Automated safety check: PassAGPL-3.0

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Works with

Categories

Questions about Stable Diffusion Image Generation

What does Stable Diffusion Image Generation do?

Generate images via Stable Diffusion (HuggingFace Diffusers, local/API). Stable Diffusion Image Generation is an agent skill from taracodlabs/aiden.

When should I use Stable Diffusion Image Generation?

Stable Diffusion Image Generation fits situations like: tasks that involve Diffusion and image models; tasks that involve Image generation; tasks that involve Model hubs and datasets.

How do I install Stable Diffusion Image Generation in Claude Code?

Run `npx skills add taracodlabs/aiden --skill stable-diffusion-image-generation -a claude-code`. Or copy the skill folder (skills/stable-diffusion-image-generation in taracodlabs/aiden) into .claude/skills/stable-diffusion-image-generation in your project. Claude Code loads it when a task matches its description.

How do I install Stable Diffusion Image Generation in Codex?

Run `npx skills add taracodlabs/aiden --skill stable-diffusion-image-generation -a codex`. Or copy the skill folder (skills/stable-diffusion-image-generation in taracodlabs/aiden) into .agents/skills/stable-diffusion-image-generation in your project. Codex loads it when a task matches its description.

Can I use Stable Diffusion Image Generation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add taracodlabs/aiden --skill stable-diffusion-image-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stable-diffusion-image-generation, .gemini/skills/stable-diffusion-image-generation, .github/skills/stable-diffusion-image-generation and .opencode/skills/stable-diffusion-image-generation in your project.

What does Stable Diffusion Image Generation need to run?

Going by SKILL.md and its folder, Stable Diffusion Image Generation needs credentials named HF_TOKEN. Our summary lists: Python 3.

Does Stable Diffusion Image Generation access the network?

SKILL.md names 1 domain. In commands or code: api-inference.huggingface.co; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Stable Diffusion Image Generation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Stable Diffusion Image Generation use?

Stable Diffusion Image Generation is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Stable Diffusion Image Generation use?

About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Stable Diffusion Image Generation?

Skills that share tags, products or a category with Stable Diffusion Image Generation: LoRA Space Builder (huggingface/skills, 11k stars), Stable Diffusion with Diffusers (Orchestra-Research/AI-Research-SKILLs, 13k stars), Comfyui Skill Openclaw (HuangYuChuh/ComfyUI_Skills_OpenClaw, 411 stars) and Prompt Engine (AgriciDaniel/claude-prompts, 110 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Stable Diffusion Image Generation?

taracodlabs (a GitHub organization) maintains it in taracodlabs/aiden, which has 849 GitHub stars. The repository holds 63 skills in this directory. The repository was last updated on September 13, 2026.

Source: taracodlabs/aiden on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.