Model Registry
artokun/comfyui-mcp
Curated download URLs and target directories, organized by family (Flux, WAN, LTX, Qwen, Z-Image, SD15/SDXL), for every model the comfyui-mcp skills reference, covering checkpoints, VAEs, text…
Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти.
Install Diffusion Engineering
$ npx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config diffusion-engineering --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-ml/diffusion-engineering .claude/skills/diffusion-engineering && rm -rf skills-srcUse ~/.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/
Install the "diffusion-engineering" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/diffusion-engineering into .claude/skills/diffusion-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diffusion-engineering", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/diffusion-engineeringType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config diffusion-engineering --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-ml/diffusion-engineering .agents/skills/diffusion-engineering && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "diffusion-engineering" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/diffusion-engineering into .agents/skills/diffusion-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diffusion-engineering", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config diffusion-engineering --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-ml/diffusion-engineering .cursor/skills/diffusion-engineering && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "diffusion-engineering" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/diffusion-engineering into .cursor/skills/diffusion-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diffusion-engineering", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/AnastasiyaW/codex-claude-code-config.git --path skills/ai-ml/diffusion-engineering--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config diffusion-engineering --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-ml/diffusion-engineering .gemini/skills/diffusion-engineering && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "diffusion-engineering" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/diffusion-engineering into .gemini/skills/diffusion-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diffusion-engineering", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install AnastasiyaW/codex-claude-code-config diffusion-engineeringInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-ml/diffusion-engineering .github/skills/diffusion-engineering && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "diffusion-engineering" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/diffusion-engineering into .github/skills/diffusion-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diffusion-engineering", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install AnastasiyaW/codex-claude-code-config diffusion-engineering --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-ml/diffusion-engineering .opencode/skills/diffusion-engineering && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "diffusion-engineering" agent skill from https://github.com/AnastasiyaW/codex-claude-code-config/tree/main/skills/ai-ml/diffusion-engineering into .opencode/skills/diffusion-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "diffusion-engineering", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
diffusion-engineering- GitHub stars
- 154
- Token cost
- ~1.2k tokens
- SKILL.md length
- 383 words
- Files
- 7 (incl. references)
- Skills in repo
- 52
- Repo updated
- First seen
- Licence
- MIT
At a glance
Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти.
- Works in 3 steps: Где идёт диффузия → пиксели (дорого) или… → Backbone денойзера → UNet (классика,… → Управление сэмплингом → scheduler, число…
- Это общая диффузионная инженерия
- SKILL.md covers Быстрая ориентация, Reference files — читать по…, Быстрый чеклист «я… and Trade-offs на один экран, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Diffusion Engineering is an agent skill from AnastasiyaW/codex-claude-code-config. Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти. Использовать при любых задачах с диффузионными моделями: проектирование или модификация архитектуры (UNet/DiT/Flow/Flux), выбор и настройка schedulers/samplers, дообучение (LoRA/DreamBooth/full fine-tune), оптимизация памяти (AMP/checkpointing/ZeRO/FSDP/quantization), замена или fusion текст-энкодеров (CLIP/Qwen), работа с Diffusers, отладка диффузионных пайплайнов, оценка качества (FID/CLIPScore/LPIPS), latent…
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `references/architectures.md`, `references/encoders-data.md` and `references/eval-debug.md`).
It sits in AI & LLM Engineering, covering Diffusion and image models and Fine-tuning. It works with Stable Diffusion and Qwen. The repository describes itself as: Claude Code, Codex, and multi-agent configuration system: principles, hooks, skills, and workflow patterns for AI-assisted development. The licence is MIT.
When your agent uses it
- Это общая диффузионная инженерия
- Не специфика FLUX.2 Klein
Example prompts
- “/diffusion-engineering”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Где идёт диффузия → пиксели (дорого) или латентное пространство (LDM/SD-семейство — практично)
- Backbone денойзера → UNet (классика, проще) или Transformer/DiT/Flow (масштабируется лучше)
- Управление сэмплингом → scheduler, число шагов, guidance_scale — часто дают больше, чем правка сети
What it can do on your machine
Read from SKILL.md and the folder at commit cdcb11d. 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.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Diffusion Engineering loads about 1.2k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 248 tokens; SKILL.md has 383 words of instructions outside code blocks.
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 AnastasiyaW/codex-claude-code-config at commit cdcb11d, republished under its MIT licence (© AnastasiyaW). 383 words, ~1,207 tokens.
Download SKILL.mdSave it as
.claude/skills/diffusion-engineering/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- diffusion-engineering
- description
- Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти. Использовать при любых задачах с диффузионными моделями: проектирование или модификация архитектуры (UNet/DiT/Flow/Flux), выбор и настройка schedulers/samplers, дообучение (LoRA/DreamBooth/full fine-tune), оптимизация памяти (AMP/checkpointing/ZeRO/FSDP/quantization), замена или fusion текст-энкодеров (CLIP/Qwen), работа с Diffusers, отладка диффузионных пайплайнов, оценка качества (FID/CLIPScore/LPIPS), latent diffusion, VAE, guidance/CFG, rectified flow, Stable Diffusion, SDXL, Flux. Также применять при вопросах про GPU-память при обучении генеративных моделей, text-to-image пайплайны, ControlNet, multi-encoder fusion, WebDataset. Do NOT use for writing FLUX.2 Klein generation prompts (use flux2-klein-prompting) или для FLUX.2 Klein / Qwen-Edit LoRA-тренинга и edit-LoRA (use flux2-lora-training); это общая диффузионная инженерия, не специфика FLUX.2 Klein.
Diffusion Engineering Skill
Быстрая ориентация
Три инженерных решения, которые больше всего влияют на качество/скорость/стоимость:
- Где идёт диффузия → пиксели (дорого) или латентное пространство (LDM/SD-семейство — практично)
- Backbone денойзера → UNet (классика, проще) или Transformer/DiT/Flow (масштабируется лучше)
- Управление сэмплингом → scheduler, число шагов, guidance_scale — часто дают больше, чем правка сети
Reference files — читать по задаче
| Тема | Файл | Когда читать |
|---|---|---|
| Архитектуры и data flow | references/architectures.md | DDPM/SDE/LDM/DiT/Flux/VAE/SDXL, схема пайплайна |
| Schedulers и guidance | references/samplers.md | DDIM/Euler/Heun/DPM-Solver/PNDM, CFG, prediction_type |
| Обучение и дообучение | references/training.md | Loss/цели, LoRA/DreamBooth/full FT, гиперпараметры |
| Память и распределённость | references/memory.md | AMP, checkpointing, ZeRO, FSDP, quantization, FP8 |
| Текст-энкодеры и данные | references/encoders-data.md | CLIP/Qwen/multi-encoder, токенизация, data pipeline |
| Оценка и траблшутинг | references/eval-debug.md | FID/CLIPScore/LPIPS, типовые поломки и фиксы, лицензии |
Быстрый чеклист «я строю/модифицирую diffusion»
- Backbone: UNet (проще) или DiT/Flow (масштабирование)?
- Модули зафиксированы: tokenizer → text encoder →
encoder_hidden_states→ denoiser → VAE decode - Scheduler выбран: DDIM / Euler / DPM-Solver — A/B на фиксированных seed
- Дообучение: начинать с LoRA, в full fine-tune только при необходимости
- Память: AMP включён, при необходимости checkpointing, при масштабе ZeRO/FSDP
- Данные: стриминг/шардинг (HF streaming, WebDataset), валидировать throughput dataloader
- Оценка: выбрать метрики под требуемый результат и доступные данные, а не обязательный набор FID/CLIPScore/LPIPS. Для парной ретуши проверять сохранность личности/геометрии/цвета и целевое изменение на фиксированном наборе; FID не заменяет эти проверки. См.
references/eval-debug.md.
Trade-offs на один экран
| Ручка | Увеличить | Уменьшить |
|---|---|---|
num_inference_steps | ↑ время; качество проверять для данной модели | ↓ время; качество проверять, особенно у distilled моделей |
guidance_scale (CFG) | ↑ adherence к промпту, риск «пережога» | ↑ разнообразие |
| LoRA rank | ↑ выразительность | ↑ параметры, риск overfitting |
| Шаги дообучения | ↑ адаптация | ↑ риск catastrophic forgetting |
| Batch size | ↑ стабильность градиентов | ↑ VRAM |
Show full SKILL.md (140 more words)Show less
Мини-рецепты по бюджету GPU
| Бюджет | Что делать |
|---|---|
| 8–16 GB (1 GPU) | LoRA вместо full FT; grad accumulation; BF16/FP16; xFormers/SDPA; 8-bit оптимизатор |
| 24–48 GB (1–4 GPU) | LoRA или partial FT; иногда FSDP; большее разрешение |
| 8+ GPU, H100 | Full FT, ZeRO-3/FSDP, float8, WebDataset стриминг, масштабный датапайплайн |
Gotchas
- Универсальный набор метрик превращал малую парную edit-задачу в ненужный сбор тысяч генераций. Метрика должна проверять конкретное требование; численный балл не заменяет визуальную приёмку.
- Число workers и оптимизации памяти — кандидаты для замера на данном runtime, не обязательная лестница. Больше workers может замедлить Windows-пайплайн или дублировать iterable-данные.
Troubleshooting
| Симптом | Проверка | Действие |
|---|---|---|
| Оценка не отвечает, исправлена ли ретушь | Связать каждую метрику с требованием и эталонной парой | Заменить нерелевантную метрику проверкой нужного свойства; сохранить визуальный контроль |
| Загрузка данных медленнее после настройки | Сравнить throughput, память и уникальность данных с num_workers=0 | Оставить измеренно подходящие параметры; шардировать iterable dataset при нескольких workers |
© AnastasiyaW, MIT. 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 6 other files (references) in skills/ai-ml/diffusion-engineering of AnastasiyaW/codex-claude-code-config.
- SKILL.md
- references/architectures.md
- references/encoders-data.md
- references/eval-debug.md
- references/memory.md
- references/samplers.md
- references/training.md
Open the folder on GitHubat commit cdcb11d
Compare with similar skills
Diffusion Engineering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Diffusion Engineering this skillAnastasiyaW/codex-claude-code-config | 154 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Model Registryartokun/comfyui-mcp | 793 | — | ~2.1k | Automated safety check: Pass | MIT | |
| Castguaardvark/guaardvark | 251 | — | ~673 | Automated safety check: Pass | MIT | |
| LoRA Space Builderhuggingface/skills | 11k | 2 repos | ~8.4k | Automated safety check: Pass | Apache-2.0 | |
| Workflow Template BuilderMooshieblob1/MooshieUI | 207 | — | ~640 | Automated safety check: Pass | AGPL-3.0 | |
| Z Image Txt2imgartokun/comfyui-mcp | 793 | — | ~2.8k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Diffusion Engineering
What does Diffusion Engineering do?
Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти. Diffusion Engineering is an agent skill from AnastasiyaW/codex-claude-code-config. Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти.
When should I use Diffusion Engineering?
Diffusion Engineering fits situations like: Это общая диффузионная инженерия; Не специфика FLUX.2 Klein.
How do I install Diffusion Engineering in Claude Code?
Run `npx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a claude-code`. Or copy the skill folder (skills/ai-ml/diffusion-engineering in AnastasiyaW/codex-claude-code-config) into .claude/skills/diffusion-engineering in your project. Claude Code loads it when a task matches its description.
How do I install Diffusion Engineering in Codex?
Run `npx skills add AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a codex`. Or copy the skill folder (skills/ai-ml/diffusion-engineering in AnastasiyaW/codex-claude-code-config) into .agents/skills/diffusion-engineering in your project. Codex loads it when a task matches its description.
Can I use Diffusion Engineering 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 AnastasiyaW/codex-claude-code-config --skill diffusion-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/diffusion-engineering, .gemini/skills/diffusion-engineering, .github/skills/diffusion-engineering and .opencode/skills/diffusion-engineering in your project.
What does Diffusion Engineering need to run?
SKILL.md names no scripts, command-line tools or credentials: Diffusion Engineering is instructions for the agent only.
Does Diffusion Engineering access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Diffusion Engineering 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 Diffusion Engineering use?
Diffusion Engineering is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Diffusion Engineering use?
About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 15k tokens, read only when the agent opens those files.
What are the alternatives to Diffusion Engineering?
Skills that share tags, products or a category with Diffusion Engineering: Model Registry (artokun/comfyui-mcp, 793 stars), Cast (guaardvark/guaardvark, 251 stars), LoRA Space Builder (huggingface/skills, 11k stars) and Workflow Template Builder (Mooshieblob1/MooshieUI, 207 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Diffusion Engineering?
AnastasiyaW (a GitHub user) maintains it in AnastasiyaW/codex-claude-code-config, which has 154 GitHub stars. The repository holds 52 skills in this directory. The repository was last updated on October 2, 2026.
Source: AnastasiyaW/codex-claude-code-config on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.