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zzztzzzt/Lyra-2.1
Lyra-2.1 is a machine learning model from zzztzzzt. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This model takes a single color in OKLab color space as input and generates a palette of 9 harmonious colors by predicting color offsets.
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Updated Mar 23, 2026
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From the Hugging Face model README
This model takes a single color in OKLab color space as input and generates a palette of 9 harmonious colors by predicting color offsets.
| Property | Value |
|---|---|
| Type | Float32 |
| Shape | ℝ³ |
Shape :<br> Julia ( Lux ) : (3, 1)<br> PyTorch : (1, 3) or (N, 3)
Format :
[L, a, b]ᵀ
Channels :
| Channel | Description |
|---|---|
| L | Lightness |
| a | Green–Red axis |
| b | Blue–Yellow axis |
| Property | Value |
|---|---|
| Type | Float32 |
| Shape | ℝ^(9×3) |
Note : (3, 9) is equivalent to (9, 3) or flattened (27,) depending on the framework.
Shape :<br> Julia ( Lux ) : (3, 9)<br> PyTorch : (N, 27) => reshaped to (N, 9, 3)
Format :
Δ = [ δ₁ δ₂ ... δ₉ ]
where each δᵢ ∈ ℝ³
Each column δᵢ represents a predicted color offset in OKLab space.
The final palette is obtained by adding the input color to each predicted offset :
General form : Y = X + Δ
Where :
| Symbol | Description |
|---|---|
| X ∈ ℝ³ | Input color |
| Δ ∈ ℝ^(9×3) | Predicted offsets |
| Y ∈ ℝ^(9×3) | Final palette colors |
( Actual tensor layout depends on the framework implementation. )
Example implementations :
Julia : Y = Δ .+ repeat(X, 9)<br>
PyTorch : Y = X.repeat(1, 9) + Δ
| Property | Value |
|---|---|
| Shape | ℝ^(9×3) |
| Color space | OKLab |
Each color yᵢ ∈ ℝ³ is represented in OKLab space :
Y = [ y₁ y₂ ... y₉ ], yᵢ ∈ ℝ³
The result is a palette of 9 colors, each expressed as [L, a, b]ᵀ.