Sligoville (hi-res in Unlockable) is a brand new Abstract artwork made by Alicia, our Artificial Intelligence system. In order to create it, Alicia studied thousands of paintings from renowned artists and learned their techniques.
The result is this one-of-a-kind artwork carefully selected and minted as a non-fungible token (NFT) on Ethereum's blockchain to certify its uniqueness and ownership. The owner receives an unlimited and exclusive license to use this artwork, including for commercial purposes.
With the purchase of this NFT the buyer will receive a physical copy of this artwork printed on museum-quality canvas and shipped worldwide at no extra cost.
In order to offset the carbon emissions this transaction generates, We have partnered with Sea-Trees to remove over 1 ton of CO2! For every NFT sold we will:
- Sequester 1/2 ton of CO2 with carbon credits from the Southern Cardamom REDD+ Project, Cambodia which provides 200+ jobs, education and healthcare benefits for 16,000+ people in the local community, and produces 3.5 million metric tons of carbon credits per year.
- Plant 2 mangrove trees in Indonesia, with Eden Reforestation Projects which has the potential to sequester an additional 1/2 metric tons of CO2
- Restore 1/2 sq-ft of kelp in Palos Verdes, California. This project provides habitat and food for over 700 species of algae, invertebrates, and fish. It also reminds us of My Octopus Teacher
• Edition: Abstract
• Size: Hi-Res, up to 100Mb image, stored on IPFS (your hash to the location on the interplanetary file system will be revealed as "unlockable content")
• 5% royalty fee for secondary sales [Learn more]
Physical copy specifications:
• Size: 12x12'' (30.48x30.48 cm). 1.5″ (3.81 cm) deep
• Acid-free, PH-neutral, poly-cotton base
• 20.5 mil (0.5 mm) thick canvas
• Canvas fabric weight: 13.9 oz/yd² (470 g/m²)
• Printed on textured and fade-resistant canvas (OBA-Free)
• Mounting brackets included
• Hand-glued solid wood stretcher bars
• Algorithms inspired by VQGAN+CLIP, Style Transfer libraries, and fan photo.
• Trained with over thousands of public domain paintings from publicly available/royalty free images on the Internet, the Met Museum and WikiArt as well as publicly available/royalty free images on the Internet.