diffusers-workflow

Your GPU, as something an agent can drive.

diffusers-workflow wraps the Hugging Face Diffusers library in an engine that runs image, video and audio generation as jobs, and puts two front ends on it: an MCP server, so Claude Code (or any MCP client) can author, run and inspect generations; and a web UI for doing the same by hand. A CLI and REPL sit underneath for when you want neither.

Python 3.10-3.14 | CUDA (NVIDIA) | MPS (Apple Silicon) | CPU

Getting started

1. Install. The script picks the right torch build for your platform, creates a virtual environment and installs everything, MCP server included.

# Linux / macOS
bash ./install.sh
source ./activate

# Windows
.\install.ps1
.\venv\scripts\activate

python -m dw.test confirms torch and diffusers import and shows which accelerator was found.

2. Start the engine. Leave it running; everything else talks to it.

dw-serve
# diffusers-workflow server on http://127.0.0.1:8765

That address is the web UI. Open it and run templates/text-to-image — a small, ungated model, so the first generation needs no Hugging Face login and downloads only a few GB.

3. Connect Claude Code. Register the MCP server with the absolute path to dw-mcp in the venv you just made (the relative path is the one setup detail that reliably goes wrong):

claude mcp add dw -- "$(pwd)/venv/bin/dw-mcp"

Then, optionally, the dw plugin — one skill per model family that knows which workflow fits a request and the rules that bite:

/plugin marketplace add dkackman/diffusers-workflow
/plugin install dw@diffusers-workflow

Most of the shipped workflows (Flux, LTX-2, MiniMax...) use gated models. Request access on the model's Hugging Face page, then huggingface-cli login once; without it the run fails partway through with a 401/403 from the Hub.

GPU on another machine? Start the engine there with --mcp and connect over HTTP — nothing to install on the laptop:

# on the GPU box
dw-serve --host 0.0.0.0 --token "$DW_API_TOKEN" --mcp --workspace ~/studio

# on your laptop
claude mcp add --transport http dw http://gpu-box:8765/mcp \
  --header "Authorization: Bearer $DW_API_TOKEN"

The server's own Server page composes that line for the address you pick. End to end: Remote GPU server.

Drive it from an agent

Then just ask. The agent has 55 tools covering the whole surface — the workflow catalog, the real diffusers pipeline signatures, the job queue, the gallery, the model cache:

Generation is the long pass, and the agent stays with it — queuing each shot, waiting it out, and reporting what came back:

What a session looks like:

• "What can this box run, and what do I already have?" — get_server_info for the accelerator and workspace, list_workflows for the catalog with each entry's shape, cost and variables, list_models for what is already in the hub cache. The agent knows the device before it proposes anything CUDA-only.
• "Take my Flux workflow, swap in the portrait LoRA, render four at 1024." — get_workflow, get_pipeline_signature to check the arguments exist, validate_workflow (free: schema and signature checking, no model loads), save_workflow, run_workflow. That last one refuses until the agent passes acknowledged_cost=true, so it has to tell you what it is about to spend.
• "How's it going?" — wait_for_job blocks for a bounded interval instead of polling; get_output_image brings the result back into the conversation so the agent can look at what it made.
• "That third frame is the one — keep it and seed the video pass from it." — keep_output promotes the file into the asset library under a name you pick, and the next workflow references asset:hero-frame.png.

Everything that costs real GPU time or real disk (run_workflow, rerun_job, enhance_prompt, download_model, delete_model, update_diffusers, delete_workspace) refuses until it is explicitly acknowledged, so an agent cannot quietly burn an hour of GPU or delete 40GB of weights.

One server holds several workspaces — each with its own workflows, assets and outputs — so two agents, or an agent and you in the browser, share the GPU without saving over each other. An agent calls use_workspace once and the rest of the session lands there.

The complete tool reference, client configuration for other MCP hosts, and the troubleshooting table: MCP Server. Workspaces in depth: Workspaces.

The web UI

Everything the engine does, in a browser, backed by the same persistent GPU worker — models stay loaded between runs.

An editor built from the real pipeline signatures. Forms and argument autocomplete are generated by introspecting diffusers itself, so every knob a pipeline exposes is there with its documentation. Validation catches schema errors and argument typos before any model loads.

A gallery where every image is a recipe. Outputs carry their full workflow and seed; open as workflow drops any image back into the editor, ready to reproduce or riff on. Keep as asset promotes a generated file into the asset library for later workflows to build on.

A prompt library stores a prompt once and lets any workflow reference it, with an Enhance with AI panel that expands an idea into a full prompt using a local language model. A model manager inventories the Hugging Face hub cache — sizes, last use, free space — and downloads or deletes models with live progress.

Jobs queue, stream progress live per denoising step, cancel cooperatively and persist to a searchable history. See Server & Web UI for the pages and the HTTP API.

The command line

The engine also runs standalone, with no server involved:

python -m dw.run workflows/templates/text-to-image.json
python -m dw.run workflows/templates/text-to-image.json prompt="a cat" num_images_per_prompt=4
python -m dw.validate workflows/models/flux-dev.json

An interactive REPL (python -m dw.repl) keeps models resident between runs for 2-4x faster iteration. See REPL Commands.

What's underneath

Every front end reads and writes the same thing: a JSON document of named steps, each a diffusers pipeline or a utility task, whose arguments reference variables, earlier steps' outputs, stored prompts and assets rather than hard-coded values. That is what makes text-to-image chain into image-to-video, and what makes a generated image reopen as the exact recipe that produced it. workflows/ is a corpus of runnable examples across model families; the Workflow Guide is the reference when you do want to write one.

Because a workflow reaches any diffusers pipeline or quantization backend by dynamic import, loading one can execute arbitrary Python. Treat a workflow file from someone else the way you'd treat a .py script — see Trust model.

Under the hood the engine also handles: quantization (BitsAndBytes, TorchAO, GGUF, SDNQ, optimum-quanto); inference acceleration (TeaCache, FirstBlockCache, FasterCache, MagCache, TaylorSeerCache); LoRA and IP-Adapter; A1111-style prompt weighting; long-video chaining with audio-driven length; step-output caching, so re-running a fixed-seed workflow finishes instantly; and utility tasks for upscaling, face restoration, segmentation, captioning, frame interpolation and more.

Documentation

Guides

• MCP Server — The agent tool surface (Claude Code, Claude Desktop)
• Server & Web UI — The web UI, jobs API, and introspection service
• Remote GPU server — Using the server, UI and MCP from another machine
• Workspaces — Where your content lives, run directories, and several workspaces on one server
• Workflow Guide — JSON structure, variables, steps, data flow
• Quantization — BitsAndBytes, TorchAO, GGUF, SDNQ
• Inference Acceleration — torch.compile, FirstBlockCache, MagCache, TaylorSeer, TeaCache
• Fast on 24GB — Recommended speed/memory configurations per model family
• LoRA — Loading and stacking LoRA adapters
• IP-Adapter — Image-prompt conditioning
• Prompt Weighting — A1111-style syntax
• Prompt References — The stored prompt library and prompt: references
• Tasks — Image processing, ControlNet preprocessors, utilities

Reference

• REPL Commands — Interactive REPL command reference
• Worker Guide — GPU persistence and troubleshooting
• Dependencies — Installation details
• Security — Security model
• Testing — Running the test suite
• Releasing — Cutting a release from a version tag
• Agent Loop — What the automated implementer/tester ticket labels mean, for anyone joining an Issue