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.
npx agentmods add skills/graniet/kheish/stable-diffusionnpx skills add graniet/kheish --skill stable-diffusiongit clone --depth 1 https://github.com/graniet/kheishWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00043 | $0.03452 |
| Opus 5 | $0.00022 | $0.01726 |
| Sonnet 5 | $0.00009 | $0.00690 |
| Haiku 4.5 | $0.00004 | $0.00345 |
Grade A, and why
stable-diffusion 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 3d 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.
This is a copy
84% identical to stable-diffusion-image-generation — 35 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 546 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kheish Compatibility
This skill is repo-local and stays inactive until explicitly activated.
When the original instructions refer to legacy tool names, use these Kheish mappings:
terminal=>bashweb_extract=>web_fetch, plusweb_searchwhen discovery is neededsearch_files=>grep_searchandglob_searchbrowser_*tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly
When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.
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")
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.
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.
- 3d ago First seen · 546 lines · 43 tokens per session scan A 6477743844af
stable-diffusion is a skill published in the GitHub repository graniet/kheish (227 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 3,452 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to stable-diffusion-image-generation, differing in 35 lines, and is treated as a copy.
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