Build a Style Transfer Tool
Build a style transfer tool using SDXL's IP-Adapter to apply a reference image's visual style to new generations, then add ControlNet to preserve the structure of a source image while restyling it.
Prerequisites: Text-to-Image Pipeline with Stable Diffusion; a CUDA GPU; a reference style image and a source photo to restyle.
Targeted versions: Diffusers (current release); h94/IP-Adapter SDXL weights; diffusers/controlnet-canny-sdxl-1.0 — verified against Hugging Face documentation, mid-2026.
Classic neural style transfer (the 2015 Gatys et al. approach, optimizing pixel values directly against style/content losses from a VGG network) still exists, but the current generative-AI toolkit for style transfer runs through diffusion adapters instead — specifically IP-Adapter, which lets you condition an SDXL generation on a reference image's visual style the same way a text prompt conditions it on words.
You'll use IP-Adapter to transfer a reference image's style onto new prompts, then layer in ControlNet so you can restyle an existing image while keeping its structure (pose, composition, edges) intact — the combination that most "turn my photo into an anime/oil painting/etc." tools are actually built on.
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