Sampler › Denoiser
Noised latents
×tensor
The latents at diffusion time .
- shape
- [B batch, T positions, 768 d]
Sources
Contextual evidence from Gaussian prior
The prior is a standard Gaussian.
The implementation, pinned at revision e18c42acdfb7, not yet public
Contextual evidence from Gaussian prior
Sampling starts from the prior with a zero self-conditioning estimate, uses the averaged denoiser, takes the adaptive grid when it is requested and the sampler has been updated and a uniform grid otherwise, steps N times carrying the state and the clean estimate, and returns the last step’s mean.
The implementation, pinned at revision e18c42acdfb7, not yet public
Contextual evidence from Join along features
The time embedding passes its MLP and a SiLU; the noised latents are concatenated with the previous clean estimate, zeros when there is none, and projected; the stack runs in bfloat16.
The implementation, pinned at revision e18c42acdfb7, not yet public
Contextual evidence from Join along features
Diffusion: self-conditioning on, `t_min` 0.05 and `eps` 10⁻⁵; the adaptive time sampler has 100 bins, a minimum weight of 0.0001, an EMA of 0.9 and updates every 5,000 steps; the denoiser’s EMA decay is 0.9999; scheduler `tanh` with d = 3, solver `euler`, `ode_sampling` false, N = 128 by default; the denoiser has 12 blocks of width 768 with 12 heads, time conditioning `adaln`, a standard MLP, softcap 50 and self-conditioning by concatenation; the timestep embedding’s `max_period` is 10.
The implementation, pinned at revision e18c42acdfb7, not yet public