rohitgandikota/sliders
LoRA adapters that add controllable slider attributes to diffusion models for precise image generation control.
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Concept Sliders trains lightweight LoRA adaptors that modify how diffusion models interpret latents, enabling fine-grained control over generated attributes like age, style, or mood. The method requires minimal training data and allows users to compose multiple sliders to steer image generation through intuitive interfaces. Supports SDXL and FLUX-1 diffusion models.
Frequently asked
- What is rohitgandikota/sliders?
- LoRA adapters that add controllable slider attributes to diffusion models for precise image generation control.
- Is sliders open source?
- Yes — rohitgandikota/sliders is open source, released under the MIT license.
- What language is sliders written in?
- rohitgandikota/sliders is primarily written in Jupyter Notebook.
- How popular is sliders?
- rohitgandikota/sliders has 1.1k stars on GitHub.
- Where can I find sliders?
- rohitgandikota/sliders is on GitHub at https://github.com/rohitgandikota/sliders.