<!--Copyright 2026 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

# Command line interface

`diffusers-cli` is a command line client for running, inspecting, and packaging Diffusers pipelines. 

## Available commands

| Command | Purpose |
|---|---|
| [`env`](#env) | Print environment info for bug reports. |
| [`schema`](#schema) | Inspect a pipeline's `__call__` signature without downloading weights. |
| [`run`](#run) | Run a pipeline locally or in a Hugging Face Sandbox. |
| [`custom_blocks`](#custom_blocks) | Package a local `ModularPipelineBlocks` subclass for the Hub. |
| [`fp16_safetensors`](#fp16_safetensors) | Convert a checkpoint to fp16 `.safetensors`. |
| [`skills`](#skills) | Install pre-authored skill bundles into your AI coding agent. |

> [!TIP]
> This page does not provide details for all options under each subcommand. For the full, always-current list of options for any subcommand, run `diffusers-cli <command> --help` (`diffusers-cli run --help`).

## `env`

Prints Python/PyTorch/Diffusers versions, CUDA info, and installed optional deps. Use it when opening an
issue so maintainers can reproduce your setup.

```bash
diffusers-cli env
```

## `schema`

Returns the pipeline's accepted inputs without downloading weights. This is useful when building `--pipeline-kwargs`

Only the index file is fetched. Standard pipelines read `model_index.json`; modular pipelines read
`modular_model_index.json`; custom-block repos read `modular_config.json` and need `--trust-remote-code` since
loading them runs code from the Hub.

```bash
diffusers-cli --format json schema --model black-forest-labs/FLUX.1-dev
diffusers-cli schema --model my-org/my-custom-blocks --trust-remote-code
```

## `run`

Run a pipeline end-to-end. Auto-detects standard vs modular repos, auto-loads media inputs from URLs or local
paths, saves outputs by detecting the pipeline's return type, and can run remotely on a Hugging Face
Sandbox via `--remote`.

Minimal example:

```bash
diffusers-cli run \
    --model black-forest-labs/FLUX.1-dev \
    --dtype bf16 \
    --pipeline-kwargs '{"prompt": "an astronaut riding a horse"}'
```

### Passing pipeline arguments

`--pipeline-kwargs` takes a JSON object that's forwarded to `pipeline(**kwargs)`. String values at known
media-input keys are auto-loaded:

- Images (`image`, `mask_image`, `control_image`, `ip_adapter_image`, `image_2`) → `PIL.Image` via
  `load_image`.
- Videos (`video`, `control_video`) → `list[PIL.Image]` via `load_video`.
- Audio (`initial_audio_waveforms`, `reference_audio`, `src_audio`) → `torch.Tensor` via `torchaudio.load`.

```bash
diffusers-cli run \
    --model black-forest-labs/FLUX.2-klein-9B --dtype bf16 \
    --pipeline-kwargs '{"prompt": "make the fur grey", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png", "strength": 0.6}'
```

Both media keys and text keys accept a JSON array to run a batch through a single pipeline call. Each entry
in a media list is loaded individually (URL, local path, or bucket-mount path), and diffusers processes the
whole list in one forward pass on the GPU:

```bash
diffusers-cli run \
    --model black-forest-labs/FLUX.1-Kontext-dev --dtype bf16 \
    --pipeline-kwargs '{
        "prompt": ["make it grey", "make it pink", "make it blue"],
        "image": [
            "https://.../cat1.png",
            "https://.../cat2.png",
            "https://.../cat3.png"
        ]
    }'
```

### Loading

Configure how the CLI loads model weights and custom pipeline code.

- `--dtype {auto, bfloat16, bf16, float16, fp16, float32, fp32}` — weight dtype.
- `--device-map <value>` — component placement. Accepts a torch device string (`cuda`, `cuda:0`, `cpu`, `mps`),
  `balanced` (auto-splits components across visible GPUs), or a JSON dict for explicit per-component placement.
  Auto-detected if omitted. See [device_map](../training/distributed_inference#device_map) for more details
- `--variant fp16` — pick a weight variant.
- `--revision <sha>` — pin a specific model revision.
- `--trust-remote-code` — allow custom code from the Hub (required for repos that ship custom pipeline classes
  or modular blocks). See [Community pipelines](../using-diffusers/custom_pipeline_overview) for standard custom
  pipelines and [Modular Diffusers](../modular_diffusers/overview).
- `--lora <spec>` — attach a LoRA adapter after loading. Each value is a JSON dict; repeat the flag to
  stack multiple adapters. `lora_id` is required per entry; `lora_scale` defaults to `1.0`;
  `adapter_name` is optional (auto-generated as `lora_<i>` when stacking).
  - Single: `--lora '{"lora_id": "alvdansen/flux-koda", "lora_scale": 0.8}'`
  - Multiple: `--lora '{"lora_id": "alvdansen/flux-koda", "lora_scale": 0.6, "adapter_name": "koda"}' --lora '{"lora_id": "Shakker-Labs/FLUX.1-dev-LoRA-AntiBlur", "lora_scale": 0.4}'`

  All specs are loaded by `pipeline.load_lora_weights(...)`, then activated together with a single
  `pipeline.set_adapters(names, adapter_weights=scales)` call. See [LoRA](../tutorials/using_peft_for_inference)
  for a deeper walkthrough of adapter stacking, scale scheduling, and hotswapping.

### Optimizations

- `--cpu-offload {model, group}` — `model` calls `enable_model_cpu_offload`; `group` calls
  `enable_group_offload(offload_type="leaf_level", use_stream=True)`. Onload target comes from `--device-map`
  (which must be a plain device string for offload). See
  [Model offloading](../optimization/memory#model-offloading) and
  [Group offloading](../optimization/memory#group-offloading).
- `--attention-backend {default, flash_hub, flash_varlen_hub, flash_4_hub, sage_hub}` — Hub-hosted attention
  kernels, auto-downloaded on first use. Transformer-based pipelines only; ignored with a warning on legacy UNet
  pipelines. See [Attention backends](../optimization/attention_backends).
- `--vae-tiling` / `--vae-slicing` — lower VAE decode VRAM. See
  [VAE tiling](../optimization/memory#vae-tiling) and [VAE slicing](../optimization/memory#vae-slicing).
- `--compile [JSON]` — compile denoiser modules with [torch.compile](../optimization/fp16#torchcompile). The
  CLI prefers [regional compilation](../optimization/fp16#regional-compilation) for modules with repeated
  blocks. Bare `--compile` uses `fullgraph=true`. A JSON object is forwarded to `torch.compile`. Not supported
  with `--context-parallel`.
- `--context-parallel` — Ulysses-style context parallelism on a DiT-based pipeline. Locally requires torchrun;
  under `--remote` the CLI wraps `torchrun --nproc-per-node=gpu` for you. See
  [Context parallelism](../training/distributed_inference#context-parallelism).

### Outputs

`run` detects the pipeline output type:

- `PIL.Image`/list → `<NNNN>.png` (zero-padded index, e.g. `0000.png`)
- Image sequence → `0000.mp4` (`--fps` controls framerate, default 8)
- Audio array → `0000.wav` (`--sampling-rate` controls rate)
- Anything else → JSON dump

The default output directory format is `~/.diffusers/cli/run/outputs/diffusers-run-<YYYYMMDDTHHMMSS>-<uuid>/`. Each
run gets its own subdirectory so consecutive invocations don't overwrite.

Override with `--output <path>`. How the path expands depends on its shape and the batch size:

| `--output` | 1 output | N outputs |
|---|---|---|
| _omitted_ | default dir → `0000.png` | default dir → `0000.png`, `0001.png`, `0002.png`, … |
| `./results/` (trailing `/` or an existing directory) | `./results/0000.png` | `./results/0000.png`, `./results/0001.png`, … |
| `my-cat.png` (file path) | `my-cat.png` (used verbatim) | `my-cat-0000.png`, `my-cat-0001.png`, … |

Directory outputs always use bare padded names (`0000`, `0001`, …). Explicit file paths preserve your chosen
stem and get the padded index appended when the batch produces multiple outputs.

Use `--push-to` to upload outputs to a
[Hugging Face storage bucket](https://huggingface.co/docs/hub/en/storage-buckets). Accepts an HF bucket
id (`<namespace>/<name>`), an `hf://buckets/<namespace>/<name>[/<subpath>]`
[HF URI](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/hf_uris), or a browser URL
for the same — a subpath is used as a folder prefix. The bucket is created if missing; objects land under
`[<subpath>/]<run_id>/<filename>`.

```bash
# HF bucket id — files at hf://buckets/alice/edit-outputs/<run_id>/<file>
--push-to alice/edit-outputs

# URI with subpath — files at hf://buckets/alice/edit-outputs/greyscale/2026-07/<run_id>/<file>
--push-to hf://buckets/alice/edit-outputs/greyscale/2026-07

# Browser URL copy-paste from the Hub also works.
--push-to https://huggingface.co/buckets/alice/edit-outputs/tree/greyscale/2026-07
```

The table below describes remote runs. For local runs, `--push-to` uploads the locally saved output. It does
not suppress local file creation.

| `--push-to` set? | `--output` set? | Result |
|---|---|---|
| no | no | download to default local dir |
| no | yes | download to `--output` |
| yes | no | bucket only, no local download |
| yes | yes | bucket AND `--output` |

`--format` shapes the stdout metadata (paths, timing, sandbox info) — it does not change the file format of
the media itself. Written images are always PNG, videos MP4, audio WAV.

### Remote execution (`--remote`)

Run the same call inside a [Hugging Face Sandbox](https://huggingface.co/docs/huggingface_hub/en/guides/sandbox)
— an isolated cloud VM the CLI drives over HTTP: it uploads inputs, installs deps, runs the pipeline, downloads
outputs, then terminates the sandbox. Requires `huggingface_hub>=1.23`.

```bash
diffusers-cli run \
    --model black-forest-labs/FLUX.1-dev --dtype bf16 \
    --pipeline-kwargs '{"prompt": "an astronaut riding a horse"}' \
    --remote --flavor a100-large
```

Remote flags:

- `--flavor <name>` — sandbox hardware (e.g. `a10g-small`, `h200`, `rtx-pro-6000`).
- `--timeout <duration>` — max wallclock for the run command inside the sandbox (default `10m`).
- `--dependencies <pkg>` — extra pip deps (repeatable). Useful for pinning a diffusers branch tarball or
  adding pipeline-specific extras.
- `--namespace <name>` — create the sandbox under a different HF org/account.
- `--image <ref>` — override the sandbox image. Must ship torch + CUDA compatible with your `--flavor`'s
  driver.
- `--volume <bucket-id>[:<mount-path>]` — mount an [HF storage bucket](https://huggingface.co/docs/hub/en/storage-buckets)
  into the sandbox as a read-write directory. Repeatable. Default mount path is
  `/mnt/buckets/<bucket-id>`. Reference mounted files from `--pipeline-kwargs` like any other local path.
  Applied only on new sandbox creation — ignored when reconnecting via `--sandbox-id`.

By default each `--remote` run is ephemeral (create → run → download → kill). To reuse a warm sandbox across
runs — keeping deps, the model weight cache, and the `torch.compile` cache on its disk — keep it alive and
reconnect:

- `--keep-alive` — don't terminate the sandbox after the run; its id is printed.
- `--sandbox-id <id>` — reconnect to a kept-alive sandbox instead of creating a new one. 
- `--idle-timeout <duration>` — auto-shutdown after this much inactivity (default `10m`). Applied only on new sandbox creation — ignored when reconnecting via `--sandbox-id`.

```bash
# First run keeps the sandbox alive and prints sandbox_id=<id>.
diffusers-cli run -m black-forest-labs/FLUX.1-dev --dtype bf16 \
    --pipeline-kwargs '{"prompt": "a cat"}' --remote --flavor a100-large --keep-alive

# Reconnect for the next run — the model is already cached, so only inference runs.
diffusers-cli run -m black-forest-labs/FLUX.1-dev --dtype bf16 \
    --pipeline-kwargs '{"prompt": "a dog"}' --remote --flavor a100-large --sandbox-id <id>

# Stop it when done (or let it timeout).
hf sandbox kill <id>
```

## `custom_blocks`

Package a local `ModularPipelineBlocks` subclass for upload to the Hub. Reads a Python file, AST-scans it for
subclasses of `ModularPipelineBlocks`, instantiates the chosen one, and calls `save_pretrained` in the current
working directory.

```bash
# Package the first block found in ./block.py
diffusers-cli custom_blocks

# Point at a different file / pick a specific class
diffusers-cli custom_blocks --block_module_name my_block.py --block_class_name MyDenoiseBlock
```

The block class must be instantiable with zero constructor args — hardcode defaults in `__init__` or read
config from the pipeline `state` at call time.

## `fp16_safetensors`

Convert a checkpoint on the Hub to fp16 `.safetensors` and push the result. Useful for shrinking a repo's
weight size for faster loading. See `diffusers-cli fp16_safetensors --help` for the exact args.

## `skills`

Install skills from the diffusers repo ([`.ai/skills/`](https://github.com/huggingface/diffusers/tree/main/.ai/skills)).

```bash
# Install a single skill
diffusers-cli skills add "<skill name>"

# Install every skill in the registry
diffusers-cli skills add --all

# List available skills
diffusers-cli skills list

# Preview a skill's SKILL.md without installing
diffusers-cli skills preview diffusers-cli

# Refetch and reinstall every managed skill
diffusers-cli skills update

# Install to the user-level directory instead of the current project
diffusers-cli skills add diffusers-cli --global
```