Color-Conditioned Diffusion Prior for Efficient Image Generation
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Solution Overview
Problem
Conventional image processing models face high computational burden and memory usage, lack flexibility, and do not effectively incorporate color conditioning in image generation tasks.
Innovation Solution
An image processing apparatus utilizing a combination of a diffusion prior model and a latent diffusion model, which generates images based on both text and color prompts, enabling efficient training and flexible image generation by mapping text and color embeddings to image embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image generation models are used, then image generation capability is achieved, but computational burden and memory usage are high
Solution Approach 1:
The image generation task is segmented into two independent parts: a diffusion prior model that maps text and color embeddings to image embeddings, and a latent diffusion model that generates images from image embeddings. This segmentation allows the computationally intensive embedding mapping to be pre-computed separately from the image generation process, reducing the computational burden during actual image generation.
Solution Approach 2:
Image embeddings serve as an intermediary representation between text/color prompts and final generated images. The diffusion prior model operates on these intermediate embeddings rather than directly generating pixel data, which significantly reduces computational requirements while maintaining generation quality.
2Productivity
If conventional image generation models are used, then image generation capability is achieved, but memory usage is high
Solution Approach 1:
The model architecture is segmented into separate functional components: a diffusion prior model for embedding mapping and a latent diffusion model for image generation. This segmentation enables independent optimization of each component's memory requirements, with the embedding mapping layer using minimal memory compared to traditional end-to-end models.
Solution Approach 2:
The diffusion prior model learns to map text and color embeddings to image embeddings, creating a reusable representation library. This copied embedding space can be applied to multiple image generation tasks without requiring retraining of the entire model, reducing memory usage for storage and computation.
3Adaptability or versatility
If conventional image generation models are used, then basic image generation is achieved, but flexibility is limited
Solution Approach 1:
The diffusion prior model serves multiple functions: it processes text prompts, processes color prompts, and combines them to generate image embeddings. This multi-functionality allows a single model component to handle diverse generation tasks without requiring separate specialized models for each condition type, thereby increasing flexibility without proportionally increasing complexity.
Solution Approach 2:
The system dynamically combines text and color embeddings in the diffusion prior model, allowing flexible weighting and integration of different condition types. This dynamic combination mechanism enables the model to adapt to various generation scenarios (text-only, color-only, or combined conditioning) without requiring rigid architectural changes, thus enhancing versatility.
4Manufacturing precision
If conventional models are used, then image generation is achieved, but color conditioning is not effectively incorporated
Solution Approach 1:
Color embeddings serve as an intermediary conditioning signal that is integrated into the diffusion prior model's embedding space. By representing color information as embeddings that can be directly combined with text embeddings, the model achieves precise color control without requiring complex separate color processing modules, thus improving color accuracy while maintaining architectural efficiency.
Data Source
AI summary
Systems and methods for image processing are described. Embodiments of the present disclosure, via a multi-modal encoder of an image processing apparatus, encodes a text prompt to obtain a text embedding. A color encoder of the image processing apparatus encodes a color prompt to obtain a color embedding. A diffusion prior model of the image processing apparatus generates an image embedding based on the text embedding and the color embedding. A latent diffusion model of the image processing apparatus generates an image based on the image embedding, where the image includes an element from the text prompt and a color from the color prompt.