In the field of AI-powered image generation, Diffusion Models (DMs) are transitioning from Unet-based architectures to Transformer-based architectures (DiT). However, the DiT ecosystem still faces challenges in plugin support, efficiency, and multi-conditional control. Recently, a team led by Xiaojiu-z introduced EasyControl, an innovative framework designed to provide efficient and flexible conditional control for DiT models, acting like a powerful "ControlNet" for DiT.
Core Advantages of EasyControl
EasyControl is not a simple model stacking, but a carefully designed unified conditional DiT framework. Its core advantages lie in its introduction of a lightweight Condition Injection LoRA module, a Position-Aware Training Paradigm, and the combination of Causal Attention and KV Cache technology, achieving significant performance improvements. These innovative designs make EasyControl excel in model compatibility (plug-and-play, style-preserving control), generation flexibility (supporting multiple resolutions, aspect ratios, and multi-conditional combinations), and inference efficiency.
Powerful Control Capabilities: Beyond Canny and OpenPose
One of the most striking features of EasyControl is its powerful multi-conditional control capabilities. Its codebase shows support for various control models, including but not limited to Canny edge detection, depth information, HED edge sketches, image inpainting, human pose (analogous to OpenPose), and semantic segmentation (Seg).
This means users can precisely guide the DiT model to generate images with specific structures, shapes, and layouts by inputting different control signals. For example, Canny control allows users to specify the outline of the generated object; pose control can guide the generation of images with specific human actions. This precise control significantly expands the application scenarios of DiT models.
Stunning Ghibli Style Transfer
Beyond basic structural control, EasyControl also demonstrates powerful style transfer capabilities, particularly in Ghibli style conversion. The research team trained a dedicated LoRA model using only 100 real Asian faces and Ghibli-style corresponding images generated by GPT-4. Surprisingly, this model can convert portraits into classic Ghibli animation style while preserving original facial features well. Users can upload portrait photos and use appropriate prompts to easily generate artistic works with a strong hand-drawn anime style. The project team also provides a Gradio demo for users to experience this functionality online.
The EasyControl team has already released the inference code and pre-trained weights. According to its Todo List, future releases will include spatial pre-trained weights, subject pre-trained weights, and training code, further enhancing EasyControl's functionality and providing researchers and developers with more comprehensive tools.
The emergence of EasyControl undoubtedly injects powerful control capabilities into Transformer-based diffusion models, effectively addressing the shortcomings of DiT models in conditional control. Its support for multiple control modes and impressive Ghibli style transfer capabilities suggest broad application prospects in the AI content generation field. With its efficient, flexible, and user-friendly features, EasyControl is poised to become an important component of the DiT model ecosystem.
Project Link: https://top.aibase.com/tool/easycontrol
