diffusers/main/en/api/schedulers/entropy_bound.md

# EntropyBoundScheduler

The `EntropyBoundScheduler` commits the lowest-entropy positions whose joint entropy stays under `entropy_bound`, so
roughly independent tokens are accepted together and the rest are renoised. It anneals its sampling temperature from
`t_max` on the first step down to `t_min` on the last, matching the released checkpoint's sampler. Proposed in
[Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking](https://huggingface.co/papers/2505.24857).

This scheduler is used by [DiffusionGemmaPipeline](/docs/diffusers/main/en/api/pipelines/diffusion_gemma#diffusers.DiffusionGemmaPipeline).

## EntropyBoundScheduler[[diffusers.EntropyBoundScheduler]]

- **entropy_bound** (`float`, defaults to 0.1) --
  The maximum tolerated joint entropy of the accepted tokens. Larger values accept more tokens per step.
- **t_max** (`float`, defaults to 0.8) --
  Sampling temperature on the first denoising step.
- **t_min** (`float`, defaults to 0.4) --
  Sampling temperature on the last denoising step.
- **num_inference_steps** (`int`, defaults to 32) --
  The maximum number of denoising steps.

Entropy bound scheduler for the uniform corruption process.

At each step the scheduler samples a candidate token per position and accepts the `k` lowest-entropy positions such
that `sum_i^k entropy_i - max(entropy_1, ..., entropy_k) <= entropy_bound`. The left-hand side upper-bounds the
joint mutual information between the accepted tokens, so they are approximately independent. Accepted positions
keep their sampled token; the rest are renoised with uniformly random tokens (there is no mask token).

Proposed in "Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking"
(https://huggingface.co/papers/2505.24857).

The sampling temperature is annealed from `t_max` on the first step down to `t_min` on the last, matching the
released checkpoint's sampler (sharper sampling as denoising advances). It is applied to the logits before both the
candidate sampling and the entropy that drives acceptance.

- **model_output** (`torch.Tensor` of shape `(batch_size, block_length, vocab_size)`) --
  Raw logits from the model for the current block.
- **timestep** (`int` or `torch.Tensor`) --
  Current step index within the denoising schedule; sets the annealed sampling temperature.
- **sample** (`torch.LongTensor` of shape `(batch_size, block_length)`) --
  Current block token IDs.
- **entropy_bound** (`float`, *optional*) --
  Overrides the configured entropy bound for this step.
- **generator** (`torch.Generator`, *optional*) --
  RNG for sampling.
- **return_dict** (`bool`) --
  Whether to return an [EntropyBoundSchedulerOutput](/docs/diffusers/main/en/api/schedulers/entropy_bound#diffusers.EntropyBoundSchedulerOutput) or a plain tuple.

Accept the lowest-entropy positions under the entropy bound and renoise the rest.

## EntropyBoundSchedulerOutput[[diffusers.EntropyBoundSchedulerOutput]]

- **prev_sample** (`torch.LongTensor` of shape `(batch_size, block_length)`) --
  Updated block tokens after the current denoising step.
- **accepted_index** (`torch.BoolTensor` of shape `(batch_size, block_length)`) --
  Boolean mask of the positions accepted (committed) in this step.
- **sampled_tokens** (`torch.LongTensor` of shape `(batch_size, block_length)`) --
  Token IDs sampled from the model logits.
- **sampled_probs** (`torch.Tensor` of shape `(batch_size, block_length)`) --
  Probabilities of the sampled tokens.
- **pred_logits** (`torch.Tensor` of shape `(batch_size, block_length, vocab_size)`) --
  The temperature-scaled logits the candidates were drawn from, for self-conditioning the next step.

Output class for the entropy bound scheduler.