UTILIZING A DIFFUSION PRIOR NEURAL NETWORK FOR TEXT GUIDED DIGITAL IMAGE EDITING

Smart overview of the Invention

The disclosed systems, methods, and computer-readable media introduce a diffusion prior neural network for text-guided digital image editing. This system employs a text-image encoder to produce embeddings from both a base digital image and edit text. By integrating these embeddings within a diffusion prior neural network, the system effectively guides image modifications using text prompts. This approach enhances the flexibility and accuracy of digital image editing, overcoming limitations of traditional methods.

Background

Advancements in digital image editing have been driven by improvements in hardware and software, yet many existing systems still face challenges. Traditional methods often lack flexibility, accuracy, and efficiency, requiring complex processes to generate images from text inputs. Conventional systems typically involve embedding optimizations or model fine-tuning, which are resource-intensive and time-consuming.

Technical Advancements

The diffusion prior image editing system addresses these challenges by utilizing a conceptual editing process within a diffusion prior neural network. This method allows for seamless integration of text prompts into image modifications without the need for additional inputs or optimizations. The system leverages a diffusion noising model to perform structural edits, enhancing both the realism and adaptability of the generated images.

System Flexibility

This innovative system offers significant flexibility through conceptual and structural edit controllers. These controllers enable users to adjust the influence of base images and edit texts on the final output. By varying the editing steps within the neural network, users can control the degree of fidelity to the original image structure or explore creative modifications beyond initial constraints.

Accuracy and Efficiency

The diffusion prior image editing system improves accuracy by aligning modified images closely with input texts and base images. It eliminates inefficiencies associated with traditional methods, such as embedding optimizations or additional training requirements. This results in faster processing times and reduced computational demands while maintaining high-quality outputs that meet user expectations.