Generating realistic machine learning-based product images for online catalogs

A non-transitory computer-readable storage medium containing instructions that when executed by one or more processors perform actions comprising:

• accessing a generative diffusion model that accepts a textual query as input and generates a synthetic image as output;
• for each of a plurality of categories from a taxonomy of item categories:
• generating a token corresponding to the category and not corresponding to any of the other item categories;
• generating a fine-tuned generative diffusion model for the categories using the generative diffusion model, sets of images corresponding to the categories, and the tokens corresponding to the categories, the fine-tuned generative diffusion model accepting a textual query as input and generating a synthetic image as output;
• identifying an item lacking a corresponding representative image in a database;
• generating a representative image for the item using the fine-tuned generative diffusion model by:
• identifying a category, from the plurality of categories arranged within a hierarchy of taxonomy, corresponding to the item in an order lacking the corresponding representative image,
• identifying a token associated with that category,
• generating a textual query for the category, wherein the textual query includes the token, and
• specifying the textual query as input to the fine-tuned generative diffusion model;
• storing the generated representative image in the database in association with the item;
• and
• transmitting, to a picker device associated with a picker fulfilling the order through obtainment of items in the order, the order inclusive of the generated representative image for the item for display of the generated representative image on the picker device.

A computer system comprising:

• one or more computer processors;
• and
• a computer-readable storage medium storing instructions that when executed by the one or more computer processors perform actions comprising:
• accessing a generative diffusion model that accepts a textual query as input and generates a synthetic image as output;
• for each of a plurality of categories from a taxonomy of item categories:
• generating a token corresponding to the category and not corresponding to any of the other item categories;
• generating a fine-tuned generative diffusion model for the categories using the generative diffusion model, sets of images corresponding to the categories, and the tokens corresponding to the categories, the fine-tuned generative diffusion model accepting a textual query as input and generating a synthetic image as output;
• identifying an item lacking a corresponding representative image in a database;
• generating a representative image for the item using the fine-tuned generative diffusion model by:
• identifying a category, from the plurality of categories arranged within a hierarchy of taxonomy, corresponding to the item in an order lacking the corresponding representative image,
• identifying a token associated with that category,
• generating a textual query for the category, wherein the textual query includes the token, and
• specifying the textual query as input to the fine-tuned generative diffusion model;
• storing the generated representative image in the database in association with the item;
• and
• transmitting, to a picker device associated with a picker fulfilling the order through obtainment of items in the order, the order inclusive of the generated representative image for them for display of the generated representative image on the picker device.