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 skills add humanerd-drew/opencode-drewgent --skill stable-diffusiongit clone --depth 1 https://github.com/humanerd-drew/opencode-drewgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/humanerd-drew/opencode-drewgent/stable-diffusion)<a href="https://agentmods.dev/skills/humanerd-drew/opencode-drewgent/stable-diffusion"><img src="https://agentmods.dev/badge/skills/humanerd-drew/opencode-drewgent/stable-diffusion.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.03360 |
| Opus 5 | $0.00025 | $0.01680 |
| Sonnet 5 | $0.00010 | $0.00672 |
| Haiku 4.5 | $0.00005 | $0.00336 |
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 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
88% identical to stable-diffusion — 14 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 — 528 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]
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 · 528 lines · 0 tokens per session scan A cce76900edcd
stable-diffusion-image-generation is a skill published in the GitHub repository humanerd-drew/opencode-drewgent (2 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 3,360 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to stable-diffusion, differing in 14 lines, and is treated as a copy.
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