Yuanshi9815/OminiControl

Health v2 · maintenance only

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1927 stars · 150 forks observed · 2026-08-28

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

OminiControl is a universal control framework for Diffusion Transformer models like FLUX, supporting subject-driven and spatial control (edge-guided generation, inpainting) with only ~0.1% additional parameters. It includes training code, a companion efficient-conditioning method (OminiControl2), and pretrained models on HuggingFace.

Use cases

• control FLUX image generation with a reference subject
• generate images guided by edge maps or sketches
• inpaint parts of an image with a diffusion transformer
• train a subject-driven LoRA adapter for FLUX
• speed up conditioned diffusion inference with KV-cache
• add spatial control to diffusion transformer models

When to choose

• you need subject-driven or spatially controlled generation with FLUX or similar DiT models
• you want a lightweight control adapter that barely modifies the base model
• you want published, paper-backed methods (ICCV 2025) with training code

When to avoid

• you use UNet-based diffusion models like SD 1.5 instead of diffusion transformers
• you need a production image-generation service rather than a research library
• you lack a GPU for training or inference

Facets

library · maturity active

image-processing machine-learning deep-learning artificial-intelligence image-processing deep-learning python diffusion-transformer flux controllable-generation subject-driven-generation inpainting lora research gpu

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