Hugging Face が Rebuilding AUTOMATIC1111 with Gradio Workflow をリリース
Hugging Face から Rebuilding AUTOMATIC1111 with Gradio Workflow がリリースされました。
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Rebuilding AUTOMATIC1111 with Gradio Workflow
Published
September 10, 2026
Update on GitHub
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yuvraj sharma
ysharma
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Abubakar Abid
abidlabs
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In our
last post
, we built five small
gr.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
. 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.
Let’s walk the canvas.
What’s on the canvas
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.
Text-to-image
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.
Hi-resolution fix
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. The text-to-image result goes into a
FLUX.1-Kontext
model
node with a refine instruction (“enhance fine detail and micro-texture, keep the composition identical”) and comes back sharper and larger.
Image-to-image
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.
Let an LLM write the prompt
Start with a rough prompt like “A lighthouse in a storm.” This pipeline sends it to a
Qwen3-4B
model
node, and a small
fn
node turns the reply into a clean list of tags, capped at forty: “stormy sea, wet rocks, dramatic composition, low angle shot, volumetric lighting, ominous tone.” You can connect any diffusion model node
SOURCE: Hugging Face (2026-09-10)
