Diffusion Model Equivariance Training for Semantic Image Control
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Solution Overview
Problem
Current diffusion models have limited control over the semantic content of generated images, especially when applying transforms like flipping, rotating, or moving objects, leading to unpredictable changes and loss of semantic meaning, which is problematic for data augmentation and image editing tasks requiring realistic and labeled images.
Innovation Solution
Training a diffusion model to be equivariant with respect to specific transforms, allowing predictable changes to the output image, by using a loss function that optimizes both noise prediction and transform equivariance, enabling fine-grained image control and preserving semantic content during edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data augmentation is performed using traditional methods, then the quantity of training images is increased, but the semantic consistency and realism of generated images deteriorate
Solution Approach 1:
The patent uses a pre-trained diffusion model to generate synthetic training images by copying and transforming existing images through learned probabilistic processes. The model copies semantic content from source images while generating realistic variations, maintaining semantic consistency through the equivariance property that ensures transformed images preserve meaningful relationships.
Solution Approach 2:
The patent applies parameter changes by modifying the diffusion model's training objective to include equivariance constraints. By changing the loss function to incorporate transform equivariance terms, the model learns to generate images that maintain semantic consistency under various transformations, thus improving reliability while increasing quantity.
2Adaptability or versatility
If diffusion models are trained without equivariance constraints, then training flexibility is improved, but control over semantic content and predictability of transformations deteriorate
Solution Approach 1:
The patent implements feedback by incorporating equivariance constraints into the training loss function. The model receives feedback during training about how well its generated images maintain semantic consistency under transformations, allowing it to adjust its parameters to achieve both flexibility and control. This feedback mechanism ensures that the model learns to preserve semantic content while maintaining training adaptability.
3Adaptability or versatility
If transforms are applied to input images for data augmentation, then the variety of training data is increased, but the predictability of output changes and semantic preservation deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-training the diffusion model on large datasets to learn the underlying data distribution and transformation patterns before fine-tuning with equivariance constraints. This preliminary training phase enables the model to inherently understand how transformations affect semantic content, making subsequent transformations more predictable and reliable while still providing data variety.
Data Source
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AI summary
A method (100) for training a diffusion model (1) that is configured to generate, from an input image i comprising at least a noise sample ε, a de-noised output image o, the method (100) comprising the steps of: • providing (110) training samples ε of noise; • providing (120) training images x∗; • providing (130) at least one transform T with respect to which the diffusion model (1) shall be equivariant, said transform T mapping an image I to a transformed image T(I); • applying (140) each noise sample ε to one or more training images x∗, thereby obtaining a noisy image xt; • applying (150) the transform T to the noisy image xt, and/or to the noise sample ε before forming the noisy image xt, thereby obtaining an input i = T(xt) for the to-be-trained diffusion model (1); • generating (160), by the to-be-trained diffusion model (1), from the input i, an output o; • computing (170), based at least on the transform T and the noise sample ε, an expected output o#; • rating (180), by means of a predetermined loss function L, a deviation of the output o from the expected output o#; and • optimizing (190) parameters (1a) that characterize the behavior of the diffusion model (1) towards the goal that, when further training samples ε of noise are processed, the value of the loss function L improves.