How do I install the Machin Diffusion Prompting skill?

Run npx skillmds@latest add javimosch/machin-diffusion-prompting in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Machin Diffusion Prompting safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Machin Diffusion Prompting?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Machin Diffusion Prompting free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Machin Diffusion Prompting?

javimosch (@javimosch) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Machin Diffusion Prompting by javimosch

Name: Machin Diffusion Prompting
Author: javimosch

File contents

SD-Turbo Prompting Guide

What SD-Turbo is

SD-Turbo is a distilled version of Stable Diffusion 2.1, built for single-step, real-time text-to-image generation instead of the usual 20-50 step diffusion process. Stability AI labels it a "research artifact" rather than a production model — SDXL-Turbo is recommended for better prompt understanding and image quality.

Think of it as a latency optimization: the tradeoff is speed for fidelity and prompt adherence.

Key differences from standard SD

Aspect	SD-Turbo	Standard SD/SDXL
Steps needed	1 (single network eval)	20–50
Guidance scale	Not used — `guidance_scale=0.0`	7–12 typical
Negative prompts	Not effective at 1 step	Fully supported
Resolution	512×512 preferred	Up to 1024×1024
Best for	Real-time previews, rapid prototyping, research	Final, detailed outputs

Prompting rules

1. Write dense, front-loaded prompts

Since there's no CFG steering, the model relies purely on how well the prompt tokens map to its distilled understanding. Put the most important descriptors first.

Good: a girl photo, close take
Good: planet earth seen from the moon, detailed, cinematic
Bad: a photo of a girl who is standing close to the camera and looking at the viewer (too verbose, key info buried)

2. Skip negative prompts entirely

They have no effect at one step. Don't waste tokens on them.

3. Use concrete visual descriptors

Subject first, then style/mood
close take, top view, wide shot for framing
cinematic, photo, detailed for style
Color/lighting descriptors work well: warm lighting, golden hour

4. Resolution

512×512 is the preferred resolution. Higher works but quality degrades.

Best use cases

Live preview loops — iterating on a prompt with instant visual feedback
Rapid prototyping — quick concept exploration before a slower high-quality pass
Research — real-time generative model experiments

What SD-Turbo is NOT good at

Fine detail (hair, text, texture) — softer than full SD
Complex prompt adherence — distilled model loses some nuance
Production-quality final outputs — use SDXL for that

Future direction

Newer turbo variants (2025-2026) use Adversarial Diffusion Distillation (ADD) for 4-step generation with better quality retention. SD3.5 Large Turbo is an example. These may be better fits if you want speed without sacrificing as much prompt fidelity.

Model

HuggingFace: https://huggingface.co/stabilityai/sd-turbo
Local path: models/sd-turbo/

Generating an image

# 1. Tokenize
python3 scripts/tokenize.py models/sd-turbo/tokenizer/vocab.json models/sd-turbo/tokenizer/merges.txt "your prompt here" /tmp/tokens.txt

# 2. Deploy tokens
rcc ordi-jla /tmp/tokens.txt M:/machin-diffusion/tokens.txt 30

# 3. Run (uses seed 12345 by default)
rcx ordi-jla "cmd /c M:\machin-diffusion\machin-diffusion.exe M:\machin-diffusion\models\sd-turbo M:\machin-diffusion\tokens.txt M:\machin-diffusion\output.ppm" 600

---name: machin-diffusion-promptingdescription: SD-Turbo prompting guide. How to write prompts that work with 1-step distilled generation (no CFG, no negative prompts). Read this before generating images.---# SD-Turbo Prompting Guide## What SD-Turbo isSD-Turbo is a **distilled** version of Stable Diffusion 2.1, built for **single-step, real-time** text-to-image generation instead of the usual 20-50 step diffusion process. Stability AI labels it a "research artifact" rather than a production model — SDXL-Turbo is recommended for better prompt understanding and image quality.Think of it as a **latency optimization**: the tradeoff is speed for fidelity and prompt adherence.## Key differences from standard SD| Aspect | SD-Turbo | Standard SD/SDXL ||--------|----------|-------------------|| Steps needed | 1 (single network eval) | 20–50 || Guidance scale | Not used — `guidance_scale=0.0` | 7–12 typical || Negative prompts | Not effective at 1 step | Fully supported || Resolution | 512×512 preferred | Up to 1024×1024 || Best for | Real-time previews, rapid prototyping, research | Final, detailed outputs |## Prompting rules### 1. Write dense, front-loaded promptsSince there's no CFG steering, the model relies **purely on how well the prompt tokens map to its distilled understanding**. Put the most important descriptors first.**Good:** `a girl photo, close take` **Good:** `planet earth seen from the moon, detailed, cinematic` **Bad:** `a photo of a girl who is standing close to the camera and looking at the viewer` (too verbose, key info buried)### 2. Skip negative prompts entirelyThey have **no effect** at one step. Don't waste tokens on them.### 3. Use concrete visual descriptors- Subject first, then style/mood- `close take`, `top view`, `wide shot` for framing- `cinematic`, `photo`, `detailed` for style- Color/lighting descriptors work well: `warm lighting`, `golden hour`### 4. Resolution512×512 is the preferred resolution. Higher works but quality degrades.## Best use cases- **Live preview loops** — iterating on a prompt with instant visual feedback- **Rapid prototyping** — quick concept exploration before a slower high-quality pass- **Research** — real-time generative model experiments## What SD-Turbo is NOT good at- Fine detail (hair, text, texture) — softer than full SD- Complex prompt adherence — distilled model loses some nuance- Production-quality final outputs — use SDXL for that## Future directionNewer turbo variants (2025-2026) use Adversarial Diffusion Distillation (ADD) for 4-step generation with better quality retention. SD3.5 Large Turbo is an example. These may be better fits if you want speed without sacrificing as much prompt fidelity.## Model- HuggingFace: https://huggingface.co/stabilityai/sd-turbo- Local path: `models/sd-turbo/`## Generating an image```bash# 1. Tokenizepython3 scripts/tokenize.py models/sd-turbo/tokenizer/vocab.json models/sd-turbo/tokenizer/merges.txt "your prompt here" /tmp/tokens.txt# 2. Deploy tokensrcc ordi-jla /tmp/tokens.txt M:/machin-diffusion/tokens.txt 30# 3. Run (uses seed 12345 by default)rcx ordi-jla "cmd /c M:\machin-diffusion\machin-diffusion.exe M:\machin-diffusion\models\sd-turbo M:\machin-diffusion\tokens.txt M:\machin-diffusion\output.ppm" 600```

Frequently asked questions

Machin Diffusion Prompting