diffusers-cli

por huggingface

Use quando o usuário quiser executar um pipeline de diffusers a partir de um terminal (geração pontual, trabalhos em lote, teste rápido de um novo modelo), executar no hardware do HF Sandbox via…

npx skills add https://github.com/huggingface/diffusers --skill diffusers-cli

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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).