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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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Price unconfirmed★ 29 GitHub starsRegistry updated · Sep 20, 2026agent-skill

Overview

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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).

View source ↗

File metadata

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.

View original text

---
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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Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
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

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Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: modem-dev/ossrules
License: MIT
Version: Unknown
Last GitHub push: Sep 19, 2026
Registry updated: Sep 20, 2026
Instruction path: public/files/diffusers/.ai/skills/diffusers-cli/SKILL.md @ d2b677576df8

Version reported in registry metadata; check source releases before relying on it.

Quality

56/100

Promising

Trust

61/100

Sandbox only

Audit

72/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
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

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More details

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diffusers-cli

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Price unconfirmed★ 29 GitHub starsRegistry updated · Sep 20, 2026agent-skill

Overview

Read full documentation

Source documentation, not instructions for this website. Review permissions before running any commands.

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

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).

View source ↗

File metadata

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.

View original text

---
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).

Review the source

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License: MIT
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Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
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

Open full audit

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Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: modem-dev/ossrules
License: MIT
Version: Unknown
Last GitHub push: Sep 19, 2026
Registry updated: Sep 20, 2026
Instruction path: public/files/diffusers/.ai/skills/diffusers-cli/SKILL.md @ d2b677576df8

Version reported in registry metadata; check source releases before relying on it.

Quality

56/100

Promising

Trust

61/100

Sandbox only

Audit

72/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
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: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

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More details

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Creator: modem-dev
Source: modem-dev/ossrules
Indexed by: OpenAgentSkill community index

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Overview

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When to read which file

When NOT to use this skill

Verifying the CLI is installed

Output formats

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Start with one small task

Source & usage notes

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Related skills

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Registry indexed

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