Rebuilding AUTOMATIC1111 with Gradio Workflow

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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.

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.

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. 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.

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.

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 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. Qwen2.5-VL looks at a night-market photo and writes a prompt that could have produced it. A ViT classifier node reads the same image and returns labels: restaurant 51.9%, tobacco shop 15.6%, toyshop 9.1%.

Both nodes use the same image input, so gr.Workflow runs them in parallel and you get both