Xiaojiu-z/EasyControl

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1737 stars · 124 forks observed · 2026-08-28

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

EasyControl is the official implementation of an ICCV 2025 paper adding efficient and flexible conditional control to Diffusion Transformer (DiT) image generation models. It provides lightweight Condition Injection LoRA modules, causal attention with KV cache, and training/inference code for multi-condition controlled generation.

Use cases

• add control conditions like depth or pose to a diffusion transformer model
• generate images conditioned on multiple inputs with a DiT model
• train a lightweight LoRA adapter for conditional image generation
• speed up conditional diffusion inference with KV cache and causal attention
• apply stylized controlled generation like Ghibli-style images
• run controlled image generation locally on a GPU

When to choose

• you need plug-and-play conditional control for DiT-based models like FLUX
• you want multi-condition combinations with flexible resolutions and aspect ratios
• you are reproducing or building on the EasyControl paper
• you have high-VRAM GPUs (80GB) for training custom condition adapters

When to avoid

• you use UNet-based diffusion models instead of DiT architectures
• you have limited GPU memory and only need inference without heavy training
• you need a production-ready, well-abstracted API rather than research code
• you need non-image modalities like audio or video generation

Facets

library · maturity active

machine-learning image-processing llm-inference artificial-intelligence image-processing deep-learning python diffusion-transformer conditional-generation lora image-generation controlnet research-code gpu linux

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