Image style transfer

An electronic device, comprising:

• a processor;
• and
• a memory communicatively connected to the processor,
• wherein the memory stores instructions executable by the processor, and the instructions, when executed by the processor, cause the processor to perform operations comprising:
• obtaining a reference image and a description text, wherein the description text comprises a content description text describing content of the reference image and a style description text describing a style of a target image to be generated;
• extracting a text feature of the description text;
• and
• performing the following operations based on a pre-trained diffusion model to generate the target image:
• in each time step of the diffusion model:
• calculating a first cross-attention feature of a first image feature and the text feature, wherein the first image feature in a first time step is an image feature of an initial image, and the first image feature in each of a second time step and subsequent time steps is a result image feature generated in a previous time step;
• obtaining a second cross-attention feature of a second image feature of the reference image and the text feature;
• editing the first cross-attention feature based on the second cross-attention feature to obtain a third cross-attention feature;
• and
• generating a result image feature of the time step based on the third cross-attention feature and the text feature;
• and
• decoding a result image feature of a last time step to generate the target image.

A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to enable a computer to perform operations comprising:

• obtaining a reference image and a description text, wherein the description text comprises a content description text describing content of the reference image and a style description text describing a style of a target image to be generated;
• extracting a text feature of the description text;
• and
• performing the following operations based on a pre-trained diffusion model to generate the target image:
• in each time step of the diffusion model:
• calculating a first cross-attention feature of a first image feature and the text feature, wherein the first image feature in a first time step is an image feature of an initial image, and the first image feature in each of a second time step and subsequent time steps is a result image feature generated in a previous time step;
• obtaining a second cross-attention feature of a second image feature of the reference image and the text feature;
• editing the first cross-attention feature based on the second cross-attention feature to obtain a third cross-attention feature;
• and
• generating a result image feature of the time step based on the third cross-attention feature and the text feature;
• and
• decoding a result image feature of a last time step to generate the target image.