Auxiliary Model Guiding Diffusion Denoising to Prevent Mode Collapse
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Solution Overview
Problem
Conventional generative models suffer from mode collapse and lack diversity in generation, being highly sensitive to hyperparameters and input data, and failing to produce different outputs even with varying random seeds.
Innovation Solution
A new architecture for diffusion machine learning models is introduced, where a diffusion model is frozen and an auxiliary machine learning model is trained to assist the denoising backbone during the inverse diffusion phase, using inputs like reference image embeddings, prompt embeddings, and noisy latent tensors to generate intermediate tensors that guide the denoising process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional generative models are used, then the model structure is simple, but mode collapse occurs and generative diversity is poor
Solution Approach 1:
The patent introduces an auxiliary machine learning model as an intermediary component that receives reference latent tensor and current latent tensor, processes them through intermediate layers, and generates intermediate tensors that guide the denoising backbone. This intermediary auxiliary model enables the system to achieve diverse generation without requiring complete redesign of the core diffusion architecture, thus improving generative diversity while controlling overall system complexity.
Solution Approach 2:
The patent segments the diffusion model into distinct functional components: a frozen denoising backbone and a trainable auxiliary model. The auxiliary model is further divided into input processing layers, intermediate layers, and output layers. This segmentation allows each component to be optimized independently - the denoising backbone maintains its original simplicity while the auxiliary model handles the complexity of generating diverse outputs through its specialized architecture.
2Reliability
If conventional generative models are used, then training is straightforward, but the model is highly sensitive to hyperparameters and input data
Solution Approach 1:
The auxiliary model incorporates feedback mechanisms where the current latent tensor (output from the denoising backbone) is fed back into the auxiliary model along with the reference latent tensor. This feedback loop allows the auxiliary model to continuously adjust its intermediate tensor generation based on the current state, stabilizing the overall generation process against hyperparameter sensitivity and input data variations while maintaining reliable output.
3Adaptability or versatility
If conventional generative models are used, then the model is simple, but it fails to produce different outputs with varying random seeds
Solution Approach 1:
The auxiliary model introduces dynamic processing through its intermediate layers that transform the reference latent tensor and current latent tensor into varied intermediate tensors. These dynamic transformations, combined with the feedback mechanism, enable the system to generate different outputs even with the same random seed, while the modular architecture keeps the complexity manageable through specialized rather than generalization-based solutions.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a reference latent tensor generated based on a reference input to a diffusion machine learning model is accessed. A first latent tensor generated during a first iteration of processing data using a denoising backbone of the diffusion machine learning model is accessed, and a first intermediate tensor is generated based on processing the reference latent tensor and the first latent tensor using an auxiliary machine learning model. A second latent tensor is generated, during a second iteration of processing data using the denoising backbone, based on the first latent tensor and at least in part on the first intermediate tensor.