Rebuilding AUTOMATIC1111 with Gradio Workflow

What happened

Workflow graphs and hinted at what it would take to build something as complex as AUTOMATIC1111's stable-diffusion-webui. In this post we walk you through Workflow1111, where we have rebuilt most of AUTOMATIC1111's feature set as a single workflow canvas. Workflow1111 is a graph of eleven media pipelines built using seventy-three nodes.

DETR finds six objects in a street photo (three people, a dog, a bicycle, and a car), and from there the workflow splits into two branches: one draws the detected boxes on the original image, the other turns them into a mask you can feed into an inpaint pipeline downstream. The drawing and the mask creation both happen locally with Pillow and NumPy. Only the detection call leaves the machine.

This is like AUTOMATIC1111's prompt matrix. A base prompt, "a lone oak tree," gets combined with four suffixes (at sunrise, in a thunderstorm, under the Milky Way, in autumn fog) by a fn node, and each variant goes to its own text-to-image node. A final node stitches the four results into one contact sheet.

Workflow has no loop operator, so the four text-to-image nodes sit side by side on the canvas. Since they're at the same dependency depth they run in parallel, and all four images start generating at once. This is like the Extras tab in Automatic1111. There are two upscaler nodes, and they take different routes.

The first is a local Lanczos resample in an fn node, which needs no network call and finishes as fast as Pillow can resize. The second is AuraSR ×4, and it's the first space node on the canvas: it calls a Space on the Hub and treats the result like any other node output. Background removal works the same way.

Why it matters

It brings together SOTA models for text-to-image, hi-resolution fix, image-to-image, prompt-matrix grids, VLM interrogate, detection-to-inpaint masks, ControlNet-style annotators, background removal, PNG Info storing, and image-to-video. You can run any of these pipelines by signing in with your Hugging Face account or providing an access token. Once you sign in, the model calls use your own quota.

👉 Try Workflow1111, or duplicate the Space and start rewiring it for your own use case. All the media pipelines are built from the same four operator kinds covered in our last post and the official guide. Each node on the canvas wraps one operator, and the operator's inputs and outputs become the ports you connect edges to.

As a quick reference on our four operator kinds: fn is a Python function, model is a model called through InferenceClient, space is another Gradio Space, and dataset is a row from a Hub dataset. Let's go through the pipelines one by one. This is the core pipeline.

It has the controls you'd expect from A1111's txt2img tab: negative prompt, steps, CFG, seed, width and height, plus a model_id field for choosing the checkpoint. The prompt goes through a prompt-builder fn node first, which appends the selected style preset and cleans up the text, then into a model node that calls the checkpoint through Inference Providers.

A post-process fn node writes the generation parameters into the PNG's metadata on the way out, which is what the PNG Info pipeline reads back later. In Automatic1111, hi-resolution fix first upscales the txt2img output and then runs a second denoising pass. Here it's a two-node detour instead. 1-Kontext model node with a refine instruction ("enhance fine detail and micro-texture, keep the composition identical") and comes back sharper and larger.

That same Kontext node doubles as the image-to-image tab. Upload an image, describe the change you want, and it returns the edited image. " You can connect any diffusion model node to this output to render the image. There's no custom node involved, unlike in ComfyUI. In a Gradio workflow the LLM and the diffusion model are both ordinary model operators on the same canvas.

This is like AUTOMATIC1111's Interrogate button, with a VLM doing the interrogating instead of CLIP. 5-VL looks at a night-market photo and writes a prompt that could have produced it. 1%. Workflow runs them in parallel and you get both answers in roughly the time it takes to run one. AUTOMATIC1111 makes you paint an inpaint mask by hand. This pipeline generates one from a detector instead.

What to watch

0 is another space node, so the whole model lives in its own Space and this canvas just calls it in. Canny, line art, sketch, luma-depth, and posterize are the preprocessors you'd normally get from the ControlNet extension in Automatic1111. Here, each one is a fn node written in plain NumPy, with no model behind it.

On a pre-loaded example photo of a building facade, each annotator takes about half a second on CPU. There are 36 operator nodes in the app, 32 are fn nodes, and 22 of those run entirely in-process without a network call. Roughly two-thirds of the canvas keeps working if you lose your connection.