Salesforce, Inc.

Bram Wallace

Nikhil Naik

Embodiments described herein provide a method of generating an image. the method comprises receiving, via a data interface, a natural language prompt, obtaining a noised image vector, and generating a denoised image vector by a first forward pass of a plurality of iterations of a denoising diffusion model with the noised image vector as an input and conditioned on the natural language prompt. The method further includes calculating a gradient of a loss function based on the denoised image vector with respect to the noised image vector, and updating the noised image vector based on the gradient. A final image is generated using a final forward pass of the denoising diffusion model with the updated noised image vector as an input and conditioned on the natural language prompt.

Systems and methods for image generation via diffusion

Embodiments described herein provide systems and methods for image editing, a first copy and a second copy of an input image are generated; noise is iteratively added to the first copy and the second copy by: updating the first copy based on a first inverted output of a denoising diffusion model (DDM) based on the second copy and a first caption and updating the second copy based on a second inverted output of the DDM based on the first copy and the first caption. A resultant noised image is iteratively denoised by a reverse process using the DDM conditioned on a second caption, thereby producing a final image.

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Auburn, WA, United States of America

Bram Wallace