# 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]]
#### diffusers.EntropyBoundScheduler[[diffusers.EntropyBoundScheduler]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_entropy_bound.py#L51)

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) 

stepdiffusers.EntropyBoundScheduler.stephttps://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_entropy_bound.py#L118[{"name": "model_output", "val": ": torch.Tensor"}, {"name": "timestep", "val": ": int | torch.Tensor"}, {"name": "sample", "val": ": torch.LongTensor"}, {"name": "entropy_bound", "val": ": float | None = None"}, {"name": "generator", "val": ": torch.Generator | None = None"}, {"name": "return_dict", "val": ": bool = True"}]- **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.0

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

**Parameters:**

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.

## EntropyBoundSchedulerOutput[[diffusers.EntropyBoundSchedulerOutput]]
#### diffusers.EntropyBoundSchedulerOutput[[diffusers.EntropyBoundSchedulerOutput]]

[Source](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_entropy_bound.py#L27)

Output class for the entropy bound scheduler.

**Parameters:**

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.