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HiCo - 360 AI Research Institute's layout-controllable AI painting model

HiCo is a hierarchical, controllable layout-to-image generation model developed by 360 AI Research Institute, based on a diffusion model. HiCo utilizes a multi-branch structure design to achieve precise control over object positioning and text description. HiCo's key features include...

What is HiCo?

HiCo is a hierarchical, controllable layout-to-image generation model developed by 360 AI Research Institute, based on a diffusion model. HiCo's multi-branch structure design enables precise control over object positions and text descriptions. A key feature of HiCo is its spatial decoupling, effectively handling complex layouts and reducing issues such as missing objects and viewpoint conflicts. HiCo performs exceptionally well in generating controllable layouts for multiple objects in natural scenes and is included in the HiCo-7K benchmark set. The HiCo model demonstrates compatibility with fast generation plugins (such as LoRA and LCM) and can generate high-resolution images. However, there is still room for improvement in handling multi-concept combined layouts.

HiCo's main functions

  • Hierarchical layout controlHiCo models layouts based on a hierarchical structure, enabling precise control over background, foreground, and spatial relationships.
  • Object-level controllable generationThe model generates each object independently based on its textual description and spatial location conditions, ensuring the accuracy and consistency of the generated images.
  • Multi-branch structure fusionThe algorithm uses a multi-branch network to process different regions independently, and merges features based on the fusion module (Fuse Net) to generate images with complex layouts.
  • Quickly generate plugin compatibilityHiCo is compatible with fast generation plugins (such as LoRA and LCM), accelerating the image generation process and maintaining high-quality output.
  • HiCo-7K Benchmark Test: Introduce the HiCo-7K benchmark set to evaluate the model's performance in multi-objective controllable layout generation.
  • Flexible scalabilityThe model supports the integration of different plugins or parameter adjustments to adapt to different generation tasks, such as personalized generation or multilingual control.

HiCo's technical principles

  • Hierarchical modelingHiCo uses a hierarchical structure to model the input layout information, capturing spatial layout details from coarse to fine.
  • Separable conditional branchesEach branch independently processes and generates content for a specific region, generating images based on the object's text description and spatial location conditions.
  • diffusion modelBased on a diffusion model, the iterative denoising process recovers a clear image from noisy data, and the generation process is guided by conditions.
  • Fuse Net (fusion module)Masking technology separates the content of different foreground and background areas, maintaining their independence during the merging process.
  • Low-rank adaptation (LoRA)It is compatible with LoRA technology, can quickly adapt to new tasks or styles, and does not require training the entire model from scratch.
  • Rapid inference capabilityDesign fast inference mechanisms, such as HiCo-LCM (Lightning) and HiCo-Lightning, to accelerate image generation with parallel processing and optimized network structures.

HiCo's project address

HiCo's application scenarios

  • Image editing and compositingIn image editing, it allows for the precise addition, modification, or removal of objects in an image based on text descriptions and location information, making it suitable for scenarios requiring fine-grained control over visual layout.
  • Games and entertainmentIn game design or film special effects production, it generates complex scene layouts, including characters, props, and background elements, improving creative efficiency and visual effects.
  • Virtual Reality (VR) and Augmented Reality (AR)In VR and AR applications, virtual environments that meet specific layout requirements are generated to provide users with a more immersive experience.
  • Advertising designIt can quickly generate attractive advertising images based on advertising creativity and layout requirements, improving the efficiency and quality of advertising design.
  • Data AugmentationIn machine learning and computer vision tasks, it generates training data, especially when datasets requiring specific layouts or scenes are needed, to enhance the generalization ability of models.
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