instantX-research/InstantStyle
A diffusion model framework for style-preserving text-to-image generation using CLIP feature subtraction to separate style and content.
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InstantStyle is a text-to-image generation framework that achieves effective disentanglement of style and content from reference images. It uses two techniques: subtracting content text features from image features to decouple style, and injecting style information into specific attention layers. The method aims to mitigate content leakage while preserving the artistic style of reference images.
Frequently asked
- What is instantX-research/InstantStyle?
- A diffusion model framework for style-preserving text-to-image generation using CLIP feature subtraction to separate style and content.
- Is InstantStyle open source?
- Yes — instantX-research/InstantStyle is an open-source project tracked on heatdrop.
- What language is InstantStyle written in?
- instantX-research/InstantStyle is primarily written in Jupyter Notebook.
- How popular is InstantStyle?
- instantX-research/InstantStyle has 2k stars on GitHub.
- Where can I find InstantStyle?
- instantX-research/InstantStyle is on GitHub at https://github.com/instantX-research/InstantStyle.