18053556. EMBEDDING AN INPUT IMAGE TO A DIFFUSION MODEL simplified abstract (ADOBE INC.)
Contents
- 1 EMBEDDING AN INPUT IMAGE TO A DIFFUSION MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 EMBEDDING AN INPUT IMAGE TO A DIFFUSION MODEL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
EMBEDDING AN INPUT IMAGE TO A DIFFUSION MODEL
Organization Name
Inventor(s)
Yosef Gandelsman of Berkeley CA (US)
Taesung Park of San Francisco CA (US)
Richard Zhang of Burlingame CA (US)
Elya Shechtman of Seattle WA (US)
EMBEDDING AN INPUT IMAGE TO A DIFFUSION MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18053556 titled 'EMBEDDING AN INPUT IMAGE TO A DIFFUSION MODEL
Simplified Explanation
The patent application describes systems and methods for image editing, including tuning a diffusion model based on an image to generate different versions of the image, encoding a prompt to obtain a guidance vector, and generating a modified image based on the image and the encoded prompt.
- Image editing system utilizing a diffusion model:
- The system adjusts a diffusion model based on the characteristics of the image to create various versions of the image.
- Encoding text prompts for image modification:
- The system encodes text prompts to obtain a guidance vector that influences the modifications made to the image.
- Generating modified images based on encoded prompts:
- The system uses the diffusion model and the encoded text prompt to produce a modified version of the original image.
Potential Applications
The technology can be applied in various fields such as graphic design, photography, and digital art to enhance and manipulate images efficiently.
Problems Solved
1. Streamlining the image editing process by utilizing a diffusion model to generate multiple versions of an image. 2. Providing a structured approach to image editing by encoding text prompts to guide the modifications made to the image.
Benefits
1. Increased efficiency in image editing tasks. 2. Enhanced creativity and flexibility in modifying images. 3. Consistent results in image editing based on encoded text prompts.
Potential Commercial Applications
Optimizing image editing workflows in industries such as advertising, marketing, and e-commerce to produce high-quality visuals for products and services.
Possible Prior Art
Prior art in image editing includes software tools like Adobe Photoshop and GIMP, which offer various features for manipulating and enhancing images. However, the specific use of a diffusion model tuned based on the image and encoded text prompts for image editing may be a novel approach not found in existing technologies.
Unanswered Questions
How does the diffusion model adapt to different types of images?
The patent application mentions tuning the diffusion model based on the image, but it does not provide details on how this adaptation process works.
What are the limitations of using encoded text prompts for image modification?
While the application describes encoding text prompts to guide image editing, it does not discuss any potential challenges or constraints associated with this approach.
Original Abstract Submitted
Systems and methods for image editing are described. Embodiments of the present disclosure include obtaining an image and a prompt for editing the image. A diffusion model is tuned based on the image to generate different versions of the image. The prompt is then encoded to obtain a guidance vector, and the diffusion model generates a modified image based on the image and the encoded text prompt.
