rohitgandikota/sliders

Health v2 · maintenance only

Flags: prerelease_only

Adoption not part of the score

1139 stars · 89 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of Concept Sliders, LoRA adaptors that enable precise, plug-and-play control of attributes in diffusion models like Stable Diffusion and FLUX. It provides training scripts and demos for creating sliders from text prompts, image pairs, or StyleGAN stylespace neurons.

Use cases

• train a slider to make generated people look older or younger
• control attribute strength like lighting or eye size in stable diffusion images
• create LoRA adaptors from text prompts or image pairs
• train concept sliders for FLUX-1 models
• use GPT to generate prompts for training a text slider
• edit images without disrupting overall structure

When to choose

• you need fine-grained, strength-adjustable control over concepts in diffusion model outputs
• you want to train custom LoRA sliders from text descriptions or small image sets
• you are doing research on controllable image generation

When to avoid

• you just want simple prompt-based image generation without attribute control
• you need a no-training solution (use the related SliderSpace repo instead)
• you work outside diffusion-based image generation

Facets

library · maturity active

machine-learning deep-learning image-processing llm-training machine-learning deep-learning image-processing artificial-intelligence python cross-platform diffusion-models lora stable-diffusion flux concept-sliders text-to-image eccv-2024 research gpu

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