Generating and editing images from text prompts using multimodal encoders

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Source arXiv

Computer Vision

Generating and editing images from text prompts using multimodal encoders

VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance

Generating and editing images from open domain text prompts is a challenging task that heretofore has required expensive and specially trained models.

We demonstrate a novel methodology for both tasks which is capable of producing images of high visual quality from text prompts of significant semanticcomplexity without any training by using a multimodal encoder to guide imagegenerations.

We demonstrate on a variety of tasks how using a multimodal encoder to guide vqgan [11] produces higher visual quality outputs than prior, less flexible approaches like dall-e [38], glide [33] and open-edit [24] despite not being trained for the tasks presented.

Authors

Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato, Edward Raff

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