Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.
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.
Review the instructions the agent will receive before you install.
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. |
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
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.)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.
--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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Package an existing custom diffusers block into a directory ready for Hub upload. Select a class from a Python file and save its source, configuration, and associated files so others can load the block.
Integrate a model or pipeline into diffusers, from file structure and weight conversion to tests and documentation. Verify numerical agreement with the original implementation, with modular pipelines as the preferred starting point for new integrations.
Review a diffusers contribution before opening a pull request. Check the full change against project rules, trace potentially unused code, and receive a severity-based report separating required fixes from points to discuss with reviewers.