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diffusers-cli

Use when the user wants to run a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference time. Prefer this over

Quelle prüfen Auf GitHub ansehen

Preis unbestätigt★ 29 GitHub-StarsVerzeichnis aktualisiert · 20. Sept. 2026agent-skill

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Overview

diffusers-cli is the shipped CLI in src/diffusers/commands/. Subcommands relevant to agentic use:

Command	Purpose
`run`	Run any `DiffusionPipeline` or `ModularPipeline`. Forwards `--pipeline-kwargs` verbatim, saves output by detecting its runtime type, optionally runs on HF Jobs via `--remote`.
`schema`	Print the input schema for a pipeline repo (kwarg names, types, defaults, descriptions). No weights downloaded — only the small index file.
`custom_blocks`	Package a local `ModularPipelineBlocks` subclass for the Hub.
`env`	Print versions of diffusers + torch + transformers + accelerate + safetensors + CUDA + GPU info. Use when investigating environment issues, dtype/precision support, or building bug reports.

When to read which file

Most agentic work goes through run. Read the matching reference file before constructing a command:

run.md — full reference for diffusers-cli run. Covers --pipeline-kwargs semantics and the shell-quoting gotcha, LoRA via --lora, optimization flags (--dtype, --cpu-offload, --attention-backend, --vae-tiling/slicing), output handling and --push-to bucket uploads, the full --remote HF Jobs flow (image, container command, log streaming, timing payload, artifact download), and context parallel (--context-parallel) for both local-torchrun and --remote paths.

The other commands are small enough that diffusers-cli <command> --help is the canonical reference:

diffusers-cli schema --help
diffusers-cli custom_blocks --help
diffusers-cli env --help

When NOT to use this skill

Multi-stage workflows where you need intermediate tensor manipulation between pipelines → write Python.
Training or fine-tuning → CLI only covers inference.
Anything requiring quantization_config or other low-level loader knobs not exposed by the CLI flags → write Python. (device_map is exposed as --device-map; see run.md.)

Verifying the CLI is installed

The console entry point is registered in pyproject.toml (diffusers-cli = "diffusers.commands.diffusers_cli:main"). If diffusers-cli is not on PATH after pip install -e ., reinstall with pip install -e . --force-reinstall --no-deps and check which diffusers-cli. If the installed binary is missing recent features (e.g. you see unrecognized arguments: --lora), reinstall.

Output formats

--format {auto, human, agent, json} (top-level flag, must appear before the subcommand):

human — plain-text indented output for terminals (default when not running under an agent harness). No ANSI color.
agent — TSV tables and key=value lines. Auto-selected when an agent env var is present (CLAUDECODE, CLAUDE_CODE, CODEX_SANDBOX, CURSOR_AI, AIDER_AI_CONTEXT, GH_COPILOT_AGENT, AI_AGENT). Token-cheap for LLM agents to read.
json — compact JSON. Use for programmatic parsing (scripts, services) where type fidelity and nested structures matter.

stdout carries data; stderr carries hints/warnings/progress — parseable output is never polluted.

Rule of thumb: --format json for scripts that will json.loads() the output, otherwise leave it on auto-detect (agent for LLMs, human for terminals).

Quelle ansehen ↗

Dateimetadaten

name: diffusers-cli
description: >
  Use when the user wants to run a diffusers pipeline from a terminal (one-off
  generation, batch jobs, smoke-testing a new model), run on HF Sandbox
  hardware via `--remote`, introspect a pipeline's input schema before
  calling it, or attach a LoRA at inference time. Prefer this over writing
  ad-hoc Python scripts for generation tasks.

Originaltext anzeigen

---
name: diffusers-cli
description: >
  Use when the user wants to run a diffusers pipeline from a terminal (one-off
  generation, batch jobs, smoke-testing a new model), run on HF Sandbox
  hardware via `--remote`, introspect a pipeline's input schema before
  calling it, or attach a LoRA at inference time. Prefer this over writing
  ad-hoc Python scripts for generation tasks.
---

## Overview

`diffusers-cli` is the shipped CLI in `src/diffusers/commands/`. Subcommands relevant to agentic use:

| Command         | Purpose                                                                                                                                                                                       |
| --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `run`      | Run any `DiffusionPipeline` or `ModularPipeline`. Forwards `--pipeline-kwargs` verbatim, saves output by detecting its runtime type, optionally runs on HF Jobs via `--remote`.                |
| `schema`      | Print the input schema for a pipeline repo (kwarg names, types, defaults, descriptions). **No weights downloaded** — only the small index file.                                               |
| `custom_blocks` | Package a local `ModularPipelineBlocks` subclass for the Hub.                                                                                                                                 |
| `env`           | Print versions of diffusers + torch + transformers + accelerate + safetensors + CUDA + GPU info. Use when investigating environment issues, dtype/precision support, or building bug reports. |

## When to read which file

Most agentic work goes through `run`. Read the matching reference file before constructing a command:

- **[`run.md`](run.md)** — full reference for `diffusers-cli run`. Covers `--pipeline-kwargs`
  semantics and the shell-quoting gotcha, LoRA via `--lora`, optimization flags (`--dtype`, `--cpu-offload`,
  `--attention-backend`, `--vae-tiling/slicing`), output handling and `--push-to` bucket uploads, the full
  `--remote` HF Jobs flow (image, container command, log streaming, timing payload, artifact download), and
  context parallel (`--context-parallel`) for both local-torchrun and `--remote` paths.

The other commands are small enough that `diffusers-cli <command> --help` is the canonical reference:

```bash
diffusers-cli schema --help
diffusers-cli custom_blocks --help
diffusers-cli env --help
```

## When NOT to use this skill

- Multi-stage workflows where you need intermediate tensor manipulation between pipelines → write Python.
- Training or fine-tuning → CLI only covers inference.
- Anything requiring `quantization_config` or other low-level loader knobs not exposed by the CLI flags → write
  Python. (`device_map` is exposed as `--device-map`; see [run.md](run.md#optimization-flags).)

## Verifying the CLI is installed

The console entry point is registered in `pyproject.toml` (`diffusers-cli =
"diffusers.commands.diffusers_cli:main"`). If `diffusers-cli` is not on PATH after `pip install -e .`, reinstall
with `pip install -e . --force-reinstall --no-deps` and check `which diffusers-cli`. If the installed binary is
missing recent features (e.g. you see `unrecognized arguments: --lora`), reinstall.

## Output formats

`--format {auto, human, agent, json}` (top-level flag, must appear before the subcommand):

- **`human`** — plain-text indented output for terminals (default when not running under an agent harness). No ANSI color.
- **`agent`** — TSV tables and `key=value` lines. Auto-selected when an agent env var is present
  (`CLAUDECODE`, `CLAUDE_CODE`, `CODEX_SANDBOX`, `CURSOR_AI`, `AIDER_AI_CONTEXT`, `GH_COPILOT_AGENT`,
  `AI_AGENT`). Token-cheap for LLM agents to read.
- **`json`** — compact JSON. Use for programmatic parsing (scripts, services) where type fidelity and nested
  structures matter.

`stdout` carries data; `stderr` carries hints/warnings/progress — parseable output is never polluted.

Rule of thumb: `--format json` for scripts that will `json.loads()` the output, otherwise leave it on
auto-detect (`agent` for LLMs, `human` for terminals).

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Preis und Betriebskosten

Skill beziehen: Preis unbestätigt
Ausführen: Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz: MIT
Preis unbestätigt: Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 29 GitHub stars
Stars/forks activity: 29 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

ErfasstStatisch geprüft

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: modem-dev/ossrules
Lizenz: MIT
Version: Unknown
Letzter GitHub-Push: 19. Sept. 2026
Verzeichnis aktualisiert: 20. Sept. 2026
Anleitungspfad: public/files/diffusers/.ai/skills/diffusers-cli/SKILL.md @ d2b677576df8

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

56/100

Vielversprechend

Vertrauen

61/100

Nur Sandbox

Audit

72/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
KI-Prüffreigabe fehlt
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 29 GitHub stars
Stars/forks activity: 29 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20diffusers-cli%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20diffusers-cli%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/modem-dev-diffusers-cli/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/modem-dev-diffusers-cli"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: modem-dev
Quelle: modem-dev/ossrules
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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Dieser Registry-indexiert-Eintrag wird modem-dev zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/modem-dev-diffusers-cli?metric=listed&label=Listed)](https://www.openagentskill.com/skills/modem-dev-diffusers-cli?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/modem-dev-diffusers-cli?metric=trust&label=Trust)](https://www.openagentskill.com/skills/modem-dev-diffusers-cli?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/modem-dev-diffusers-cli?metric=audit&label=Audit)](https://www.openagentskill.com/skills/modem-dev-diffusers-cli/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/modem-dev-diffusers-cli?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/modem-dev-diffusers-cli?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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