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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

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Harga belum dikonfirmasi★ 29 Star GitHubDirektori diperbarui · 20 Sep 2026agent-skill

Ringkasan

Baca dokumentasi lengkap

Dokumentasi sumber, bukan instruksi untuk situs ini. Periksa izin sebelum menjalankan perintah.

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).

Lihat sumber ↗

Metadata berkas

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.

Lihat teks asli

---
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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Harga dan biaya penggunaan

Dapatkan skill: Harga belum dikonfirmasi
Jalankan: Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
Lisensi: MIT
Harga belum dikonfirmasi: Harga belum dikonfirmasi. Tautan sumber dan instalasi yang ada tetap tersedia.

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Sumber skill tercatat

Jalur instruksi telah dicatat. Ini bukan uji eksekusi, jaminan keamanan, atau sertifikasi kompatibilitas.

Tinjau sebelum memasang: Hindari pemasangan otomatis

Lisensi: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
Persetujuan tinjauan AI belum ada
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

Buka audit lengkap

Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.

Mulai dengan tugas kecil

1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.

Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

Sumber dan catatan penggunaan

TerindeksDiperiksa statis

Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.

Repositori sumber: modem-dev/ossrules
Lisensi: MIT
Versi: Unknown
Push GitHub terakhir: 19 Sep 2026
Direktori diperbarui: 20 Sep 2026
Jalur instruksi: public/files/diffusers/.ai/skills/diffusers-cli/SKILL.md @ d2b677576df8

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

56/100

Menjanjikan

Kepercayaan

61/100

Hanya sandbox

Audit

72/100

Perlu ditinjau

Buka audit lengkap Lihat laporan evaluasi

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
Persetujuan tinjauan AI belum ada
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: —
Hasil: —

Menyalin bukan memasang. Jumlah instalasi memerlukan laporan berhasil dan bukan jaminan kualitas menyeluruh.

Akses agent

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Buka API pemasangan Format teks LLM Buka Manifest Rencana resolusi otomatis

Detail lainnya

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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"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: modem-dev
Sumber: modem-dev/ossrules
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan modem-dev, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![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)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.