Controllable Diffusion Image Generation With Task-Adaptive Modulation
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Solution Overview
Problem
Existing denoising diffusion models (DDMs) exhibit poor performance when used for multiple different image generation tasks, requiring separate training or fine-tuning for each task, which is computationally expensive in terms of memory and resources.
Innovation Solution
A framework comprising a fixed DDM modulated by a trainable DDM, where the trainable DDM controls the fixed DDM through internal image representation modulation, using an adapter and task encoder to manage convolutional layers specific to each task, allowing the model to perform a variety of tasks without separate training for each.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a separate DDM is trained for each task, then task-specific performance is improved, but computational cost and memory requirements increase
Solution Approach 1:
The patent implements a universal DDM framework where a single pre-trained model performs multiple image generation tasks through task-specific adapters. Instead of training separate DDMs for each task (sketch-to-image, depth map-to-image, normal map-to-image, etc.), the system uses one base model that can be adapted to different tasks via lightweight adapter modules, achieving multi-functionality without proportional increases in computational resources.
Solution Approach 2:
The patent segments the DDM into a fixed pre-trained base model and trainable task-specific adapters. The base model contains general image generation capabilities learned from large-scale data, while adapters are separate modular components that learn task-specific transformations. This segmentation allows the heavy computational lifting to be done once during pre-training, with subsequent tasks requiring only adapter training.
2Reliability
If a separate DDM is trained for each task, then task-specific performance is improved, but memory requirements increase
Solution Approach 1:
The universal DDM framework allows a single model to serve multiple tasks, eliminating the need to store multiple complete DDM parameter sets in memory. The base model parameters are shared across all tasks, and only small adapter parameters need to be stored and loaded for each specific task, dramatically reducing memory requirements while maintaining task-specific performance.
Solution Approach 2:
Instead of creating and storing full copies of the DDM for each task, the patent uses lightweight adapter modules that can be attached to the base model. These adapters are minimal parameter sets that capture task-specific behavior without requiring complete model copies, reducing memory usage from O(N) separate models to O(1) base model plus O(k) small adapters where k is the number of tasks.
3Productivity
If the same DDM is used for multiple tasks, then computational efficiency is improved, but performance deteriorates
Solution Approach 1:
The patent applies local quality by making different parts of the system have different trainability characteristics. The base model remains fixed with pre-trained general capabilities, while task-specific adapters are trainable to capture local task nuances. This allows the system to maintain high computational efficiency through model sharing while achieving high performance through task-specific adaptation where needed.
Solution Approach 2:
The system dynamically adjusts between using the fixed base model for general image generation and engaging trainable adapters for task-specific refinement. During inference, the base model provides efficient general-purpose generation, while adapters can be selectively activated or fine-tuned based on the specific task requirements, creating a dynamic balance between efficiency and performance.
Data Source
AI summary
Embodiments described herein provide a method of image generation. The method includes a fixed diffusion model, and a trainable diffusion model. The fixed diffusion model may be pretrained on a large training corpus. The trainable diffusion model may be used to control the image generation of the fixed diffusion model by modifying internal representations of the fixed diffusion model. A task instruction may be provided in addition to a text prompt, and the task instruction may guide the trainable diffusion model together with the visual conditions. The visual conditions may be adapted according to the task instruction. During training, a fixed number of task instructions may be used. At inference, unseen task instructions may be used by combining convolutional kernels of the visual condition adapter.