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gamma_cum_end (float) — |
The ending cumulative gamma value. |
The VQ-diffusion transformer outputs predicted probabilities of the initial unnoised image. |
The VQ-diffusion scheduler converts the transformer’s output into a sample for the unnoised image at the previous |
diffusion timestep. |
~ConfigMixin takes care of storing all config attributes that are passed in the scheduler’s __init__ |
function, such as num_train_timesteps. They can be accessed via scheduler.config.num_train_timesteps. |
SchedulerMixin provides general loading and saving functionality via the SchedulerMixin.save_pretrained() and |
from_pretrained() functions. |
For more details, see the original paper: https://arxiv.org/abs/2111.14822 |
log_Q_t_transitioning_to_known_class |
< |
source |
> |
( |
t: torch.int32 |
x_t: LongTensor |
log_onehot_x_t: FloatTensor |
cumulative: bool |
) |
→ |
torch.FloatTensor of shape (batch size, num classes - 1, num latent pixels) |
Parameters |
t (torch.Long) — |
The timestep that determines which transition matrix is used. |
x_t (torch.LongTensor of shape (batch size, num latent pixels)) — |
The classes of each latent pixel at time t. |
log_onehot_x_t (torch.FloatTensor of shape (batch size, num classes, num latent pixels)) — |
The log one-hot vectors of x_t |
cumulative (bool) — |
If cumulative is False, we use the single step transition matrix t-1->t. If cumulative is True, |
we use the cumulative transition matrix 0->t. |
Returns |
torch.FloatTensor of shape (batch size, num classes - 1, num latent pixels) |
Each column of the returned matrix is a row of log probabilities of the complete probability |
transition matrix. |
When non cumulative, returns self.num_classes - 1 rows because the initial latent pixel cannot be |
masked. |
Where: |
q_n is the probability distribution for the forward process of the nth latent pixel. |
C_0 is a class of a latent pixel embedding |
C_k is the class of the masked latent pixel |
non-cumulative result (omitting logarithms): |
_0(x_t | x_{t-1\} = C_0) ... q_n(x_t | x_{t-1\} = C_0) |
. . . |
. . . |
. . . |
q_0(x_t | x_{t-1\} = C_k) ... q_n(x_t | x_{t-1\} = C_k)`} |
/> |
cumulative result (omitting logarithms): |
_0_cumulative(x_t | x_0 = C_0) ... q_n_cumulative(x_t | x_0 = C_0) |
. . . |
. . . |
. . . |
q_0_cumulative(x_t | x_0 = C_{k-1\}) ... q_n_cumulative(x_t | x_0 = C_{k-1\})`} |
/> |
Returns the log probabilities of the rows from the (cumulative or non-cumulative) transition matrix for each |
latent pixel in x_t. |
See equation (7) for the complete non-cumulative transition matrix. The complete cumulative transition matrix |
is the same structure except the parameters (alpha, beta, gamma) are the cumulative analogs. |
q_posterior |
< |
source |
> |
( |
log_p_x_0 |
x_t |
t |
) |
→ |
torch.FloatTensor of shape (batch size, num classes, num latent pixels) |
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