diffusers-cli

Overview

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

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).