Fauvist Mona Lisa
Technical Sheet

Overview

Fauvist Mona Lisa is the inaugural collection by 1K Gallery. The series presents 1,000 digital reinterpretations of Leonardo da Vinci's masterpiece through a visual language inspired by Fauvism and Henri Matisse: intense colors, expressive contrasts, simplified forms, and broad chromatic planes.

Each NFT features a unique combination of environment, long-sleeve shirt, short-sleeve tunic, mantle, and necklace. The collection was built through the deterministic composition of artistic layers that were created, refined, and approved in advance. The final works were not produced as random prompt variations: each image results from a controlled combination of the collection's official traits.

Core specifications

Creation and curation process

1. The artistic layers for each trait category were created and approved.
2. The system calculated all 24,010 mathematically possible combinations.
3. Chromatic compatibility rules eliminated 12,530 combinations in which identical or excessively similar clothing colors weakened the visual reading.
4. Exactly 1,000 combinations were selected from the 11,480 chromatically valid options while preserving the approved frequency of every variant.
5. All combinations underwent automated validation and visual review.
6. Approved images were recomposed from the master files at their native 2,000 × 2,000-pixel resolution.
7. Final files were checked for quantity, dimensions, sequence, metadata correspondence, and cryptographic uniqueness.

Trait distribution

The percentages below represent the exact frequency of each trait within the 1,000-NFT supply.

T1 — Background

T2 — Long-Sleeve Shirt

T3 — Short-Sleeve Tunic

T4 — Mantle

T5 — Necklace

How to interpret rarity

The collection does not include a Rarity Tier trait and does not pre-classify NFTs as Common, Rare, or Legendary. Rarity is a mathematical consequence of trait frequencies and the specific combination present in each NFT.

A less frequent trait may contribute to an artwork's rarity, but overall rarity depends on the complete set of attributes. Marketplaces and independent tools can therefore calculate rankings from the actual metadata without a promotional category inserted by the collection.

Rarity gradient by token number — open disclosure

In this collection the artwork-to-token assignment was not shuffled: numbering follows the order of generation. This produced a measurable effect on how rarity is distributed across token IDs, and the gallery would rather disclose it than let third parties discover it.

Using a rarity score based on the sum of the inverse frequency of each trait, where position 1 is the rarest and 1,000 the most common:

The cause is the distribution of items with no necklace. The absence of a necklace is the most frequent value in the Necklace category, present in 40% of the collection, and therefore contributes least to rarity. Those 400 items cluster at the end of the numbering: 132 of them fall between IDs 801 and 1000, against 58 between IDs 1 and 200.

The gradient is not uniform. Between IDs 1 and 600 the average position hovers around 430 with no clear trend — the 101–200 range is in fact the strongest average in the collection. The slope appears from ID 601 onward and steepens in the final block. The individually rarest items are scattered: of the ten rarest in the collection, only two fall between IDs 1 and 100.

This is public from day one. The reveal is immediate and metadata is available at the moment of mint, so anyone can reproduce this calculation with standard rarity tools. The gallery publishes it for transparency, not as a selling point.

The 100 items reserved for the gallery are minted in the presale stage, before the public opening, and therefore correspond to tokens #1 through #100 — a range whose average position is 416, against 500 for the collection as a whole. The gallery states this explicitly.

From the second 1K Gallery collection onward, the artwork-to-token assignment will be shuffled before metadata generation, with the seed recorded in the provenance file. The gradient described above is specific to this inaugural collection and will not recur.

File integrity and provenance

Each of the 1,000 images has a unique trait combination and a distinct SHA-256 hash. The commitments below document the version prepared before publication without revealing the individual manifest or the association between images and Token IDs.

After permanent storage is established, the gallery may also disclose the permanent CIDs or URIs for the images and metadata. The complete individual image-hash manifest will be disclosed only after the reveal.

Images and metadata

• All images are 2,000 × 2,000 pixels with a square composition.
• The upload version uses palette-optimized PNG files to preserve the collection's flat-color language while reducing transfer size.
• Each optimized PNG is approximately 1.0 to 1.34 MB.
• RGB master files were preserved separately for archiving and conservation.
• The metadata contains 1,000 UTF-8 records, one for each NFT.
• The sequence 1.png through 1000.png corresponds directly to Fauvist Mona Lisa #1 through Fauvist Mona Lisa #1000.
• The metadata uses five public categories: Background, Long-Sleeve Shirt, Short-Sleeve Tunic, Mantle, and Necklace.

Information to publish at launch

To complete the technical transparency record after deployment, 1K Gallery will add data verified directly from the contract and permanent storage:

• official contract address;
• token standard;
• network and chain identifier;
• creator or deployer wallet address;
• image CID or base URI;
• metadata CID or base URI;
• deployment date and block;
• reveal mechanism, if applicable;
• marketplace or contract royalties;
• license granted to holders;
• official collection and marketplace links.