Updated on:12/05/2025 05:18 PM 
Nvidia's research team has unveiled a groundbreaking AI image generation method that revolutionizes how customized text-to-image models can be deployed, dramatically reducing storage requirements while opening new possibilities for blockchain-based digital art and NFT creation.
According to a paper published on arXiv, the proposed method called “Perfusion” enables adding new visual concepts to an existing model using only 100KB of parameters per concept—a breakthrough efficiency that could transform how AI-generated art is minted and traded on blockchain platforms.
As the paper’s authors describe, Perfusion works by “making small updates to the internal representations of a text-to-image model,” a process that could enable more efficient NFT creation and verification on decentralized networks.
More specifically, it makes carefully calculated changes to the parts of the model that connect the text descriptions to the generated visual features. Applying minor, parameterized edits to the cross-attention layers allows Perfusion to modify how text inputs get translated into images—a capability that could revolutionize how artists create blockchain-compatible digital assets.
Therefore, Perfusion doesn’t totally retrain a text-to-image model from scratch. Instead, it slightly adjusts the mathematical transformations that turn words into pictures. This allows it to customize the model to produce new visual concepts without needing as much compute power or model retraining, making AI art generation more accessible to blockchain developers and NFT creators.
Perfusion achieved these results with two to five orders of magnitude fewer parameters than competing techniques, a breakthrough that could significantly reduce the computational resources required for blockchain-based AI art platforms.
While other methods may require hundreds of megabytes to gigabytes of storage per concept, Perfusion needs only 100KB – comparable to a small image, text, or WhatsApp message. This dramatic reduction could make deploying highly customized AI art models for NFT creation more feasible on blockchain networks.
According to co-author Gal Chechik, “The method allowed creative image generation, like a ‘teddy bear sailing in a teapot,’ using personalized concepts of ‘teddy bear’ and ‘teapot’ learned separately.” Such personalized AI creations could become valuable assets in blockchain-based digital art marketplaces.
Perfusion’s unique capability to enable the personalization of AI models using just 100KB per concept opens up a myriad of potential applications in the blockchain and NFT space:
This method paves the way for individuals to easily tailor text-to-image models with new objects, scenes, or styles, eliminating the need for expensive retraining. The efficiency of Perfusion’s 100KB parameter update per concept allows models that are customized with this technique to be implemented on consumer devices, enabling on-device image creation that could be directly minted as NFTs.
One of the most striking aspects of this technique is the potential it offers for sharing and collaboration around AI models. Users could share their personalized concepts as small add-on files on decentralized networks, circumventing the need to share cumbersome model checkpoints while maintaining provenance through blockchain technology.
In terms of distribution, models that are tailored to particular organizations could be more easily disseminated or deployed at the edge, with each creation verifiable on blockchain ledgers. As the practice of text-to-image generation continues to become more mainstream in the NFT space, the ability to achieve such significant size reductions without sacrificing functionality will be paramount for blockchain integration.
It’s important to note, however, that Perfusion primarily provides model personalization rather than full generative capability itself, though its efficiency makes it ideal for blockchain-based AI art platforms.
While promising, the technique does have some limitations. The authors note that critical choices during training can sometimes over-generalize a concept—a consideration that becomes even more important when creating blockchain-verified AI art. More research is still needed to seamlessly combine multiple personalized ideas within a single NFT.
The authors note that code for Perfusion will be made available on their project page, indicating an intention to release the method publicly in the future, likely pending peer review and an official research publication. This open approach could foster innovation in blockchain-based AI applications.
While Perfusion’s code is not yet accessible, the authors’ stated plan implies that this efficient, personalized AI system could find its way into the hands of developers, industries, and creators in due course, potentially revolutionizing how AI-generated art is created and verified on blockchain platforms.
As AI art platforms like MidJourney, DALL-E 2, and Stable Diffusion gain steam in the NFT marketplace, techniques that allow greater user control could prove critical for real-world deployment. With clever efficiency improvements like Perfusion, Nvidia appears determined to retain its edge in a rapidly evolving landscape where blockchain technology meets artificial intelligence.
