Composition Encoder for Precise Image Synthesis Control
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Solution Overview
Problem
Existing image generation techniques struggle to produce high-quality images with specific composition control, often resulting in low-quality outputs that lack fidelity to the original subject and fail to generalize to new subjects easily.
Innovation Solution
The proposed solution involves an image processing apparatus that includes a composition encoder and an image generation model. The composition encoder generates a composition embedding from a composition input, which is then combined with the image generation model during image synthesis to impart the generated content with a specific structure. The image generation model is finetuned to learn specific image elements, allowing for the synthesis of images with target elements and compositions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image generation techniques are used, then image generation capability is provided, but composition control precision deteriorates and output quality becomes low
Solution Approach 1:
The system segments the image generation process into two independent components: a composition encoder that processes composition inputs (depth maps, pose images) to extract structural features, and an image generation model that synthesizes final images. This segmentation allows each component to specialize, improving both composition control precision and output quality without mutual interference.
Solution Approach 2:
A composition embedding serves as an intermediary between the composition input and the image generation model. The composition encoder transforms composition inputs into this embedding representation, which then guides the image generation model to produce images with accurate compositional structure while maintaining high visual quality.
2Measurement precision
If image generation models are trained to learn specific image elements, then accuracy of image element representation improves, but model complexity increases
Solution Approach 1:
The image generation model employs local quality by treating different image elements (identity, lighting, texture, scene attributes) with differentiated attention. Each image element can be independently controlled through specific tokens in the text prompt, allowing precise control over individual elements without requiring the entire model to be retrained for each element.
Solution Approach 2:
The system controls image element representation accuracy by changing parameters dynamically through text prompts containing specific tokens. Rather than modifying model architecture or retraining, the model responds to parameter changes in the input representation, maintaining simplicity while achieving high accuracy in representing specific image elements.
3Productivity
If composition encoder and image generation model are independently trained, then training efficiency improves, but integration complexity increases
Solution Approach 1:
Both the composition encoder and image generation model are based on the same ancestor pre-trained model architecture, providing universality. This shared foundation enables independent training of each component while ensuring seamless integration, as they speak the same architectural language and can be swapped or combined flexibly.
Solution Approach 2:
The composition encoder is created as a specialized copy of the ancestor model, trained independently to extract compositional features. The image generation model is another copy, trained to synthesize images. This copying approach allows independent training for efficiency while the common architecture ensures easy integration without complex adaptation layers.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for generating synthetic images depicting an image element with a target composition include obtaining a content input and a composition input. The content input indicates an image element and the composition input indicates a target composition of the image element. Embodiments then encode the composition input to obtain a composition embedding representing the target composition. Subsequently, embodiments generate, using an image generation model, a synthetic image based on the content input and the composition embedding. The synthetic image depicts the image element with the target composition.