Input Sequence:
- We have T tokens in our sequence
- Each token at position t is denoted as $\mathbf{x}_t$ where$t \in {1, 2, ..., T}$ 
- Each token embedding has dimension $d_{model}$ 
Input Sequence:
RoPE encodes positional information by rotating embedding vectors in a way that:
Discrete diffusion models have shown remarkable progress in generating complex data like natural language and DNA sequences. However, unlike their continuous counterparts that can produce high-quality samples in just a few denoising steps, discrete diffusion models require hundreds or even thousands of steps to perform well. A recent paper "Discrete Copula Diffusion" identifies the fundamental limitation causing this inefficiency and proposes an elegant solution.
In this blog post, we'll dive deep into understanding why discrete diffusion models struggle with few-step generation and how the proposed copula approach addresses this core limitation.
The key is applying Einstein's field equations to a homogeneous, isotropic universe.
The foundation is Einstein's field equation:
Where:
| ARRAY FUNCTION SYNTAX | ARRAY FUNCTION DESCRIPTION | 
|---|---|
| array_contains(column: Column, value: Any) | Check if a value presents in an array column. Return below values.true - Returns if value presents in an array.false - When valu eno presents |