# 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.