Langevin Diffusion Priors for Constraint-Guided Image and 3D Synthesis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing diffusion models struggle with generating high-quality images or 3D objects when conditioned post-hoc, especially when transitioning between different domains, and lack effective methods to incorporate additional constraints during synthesis.
Innovation Solution
A generalized method using Langevin diffusion to optimize an original representation towards a learned data distribution, incorporating neutral noise and stochasticity to refine the latent representation, and applying external constraints such as text prompts to guide the synthesis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If diffusion model is trained on realistic images, then generated images look realistic, but the model cannot effectively incorporate additional constraints during synthesis
Solution Approach 1:
The patent separates the diffusion model into two independent components: (1) a pre-trained diffusion model that learns the data distribution from realistic images, and (2) an external constraint module that incorporates additional conditions during synthesis. This segmentation allows each component to specialize in its function while working together, resolving the contradiction between image quality and constraint incorporation capability
Solution Approach 2:
The patent introduces an intermediary optimization process that acts as a bridge between the pre-trained diffusion model and the external constraints. This intermediary layer allows the model to incorporate additional conditions during synthesis without retraining, maintaining both image quality and adaptability
2Adaptability or versatility
If diffusion model is trained with conditioning input, then it can incorporate constraints during synthesis, but training complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the diffusion model on unconditional realistic images first, establishing the data distribution understanding before any constraint incorporation is needed. This preliminary training simplifies the overall process by separating distribution learning from constraint learning, reducing training complexity while maintaining conditioning capability
3Manufacturing precision
If diffusion model is trained on one domain, then it specializes in that domain, but it cannot generate samples in other domains
Solution Approach 1:
The patent creates a universal framework where a diffusion model trained on one domain (e.g., 2D images) can be applied to generate samples in other domains (e.g., 3D objects) by incorporating domain-specific constraints during the synthesis process. The pre-trained model provides generalizable priors that work across domains when combined with appropriate external constraints
Data Source
AI summary
Embodiments of the present disclosure relate to a general image prior based on Langevin diffusion. An original representation (image, 3D model, audio) is optimized to resemble a data distribution learned by a trained diffusion model. The original representation may be incomplete and is completed by the optimization process. In an embodiment, the diffusion model may be trained to use an additional conditioning input, such as a text prompt. The diffusion model receives a noisy latent as input and generates a denoised output, such as an image. Generally, the diffusion model samples the learned data distribution to produce the output. The conditioning input provides additional constraint that causes the output to be “nudged” towards the learned data distribution. An example synthesis problem is to generate a panorama image that is much larger compared with images used to train the diffusion model.