Diffusion Guidance Distillation for Single-Pass Multimodal Inference
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Solution Overview
Problem
Existing diffusion models require multiple inference runs for classifier-free guidance with multiple conditionings, which is inefficient and resource-intensive, especially for constrained hardware.
Innovation Solution
Implement multimodal guidance distillation that integrates guidance scales as inputs to a denoising neural network, allowing a single inference run to achieve results comparable to multiple runs without sacrificing quality or control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple inference runs are performed for classifier-free guidance with multiple conditionings, then image quality and control are maintained, but computational efficiency and resource utilization deteriorate
Solution Approach 1:
The patent merges multiple inference runs into a single unified inference process by integrating multiple conditioning inputs (text, image, video) and their corresponding guidance scales into one neural network forward pass. This combining approach maintains the quality control benefits of multiple runs while achieving the efficiency gains of a single run, directly resolving the contradiction between reliability and productivity.
Solution Approach 2:
The neural network is designed with multi-functionality to handle multiple types of conditioning inputs (text, image, video) simultaneously through a unified architecture. By making the system universal in handling different conditioning modalities within a single inference run, it achieves both the quality control of specialized multiple runs and the efficiency of a consolidated process.
2Adaptability or versatility
If multiple inference runs are performed for classifier-free guidance, then guidance control over multiple conditionings is achieved, but hardware resource consumption increases
Solution Approach 1:
Multiple conditioning inputs and their guidance controls are merged into a single inference process. The neural network accepts multiple conditioning tensors and their corresponding guidance scales as unified inputs, maintaining adaptability and versatility in controlling different conditionings while reducing hardware resource consumption by eliminating repeated inference passes.
Solution Approach 2:
The patent introduces guidance scales as adjustable parameters that control the influence of each conditioning input. By changing the parameter values of guidance scales, the system maintains flexible control over multiple conditionings within a single inference run, achieving adaptability without increasing hardware resource usage.
3Productivity
If a single inference run is used, then computational efficiency improves, but the ability to apply classifier-free guidance with multiple conditionings deteriorates
Solution Approach 1:
The patent combines the functionality of multiple inference runs with classifier-free guidance into a single inference run architecture. By merging multiple conditioning inputs and guidance scale applications into one unified process, it achieves computational efficiency while preserving the adaptability and versatility of guidance control over multiple conditionings.
Solution Approach 2:
The patent adds a new dimension to the input space by incorporating guidance scales as additional input parameters alongside the conditioning inputs. This dimensional expansion allows the single inference run to encode multiple guidance controls, maintaining adaptability while improving computational efficiency.
Data Source
AI summary
Systems and techniques are described for image processing. For example, a computing device can obtain, via a neural network of a diffusion model, features associated with an input image, a plurality of conditioning inputs, and a plurality of guidance scale inputs. Each guidance scale input is associated with a respective conditioning input. The computing device can generate, using the neural network, output features based on the features associated with the input image, the plurality of conditioning inputs, and the plurality of guidance scale inputs. The computing device can generate, using the diffusion model, an output image based on the output features. The output image is a modified version of the input image based on the plurality of conditioning inputs.