# DiscreteDDIMScheduler The `DiscreteDDIMScheduler` samples each canvas position from the exact discrete posterior of the uniform corruption process (D3PM), following [Structured Denoising Diffusion Models in Discrete State-Spaces](https://huggingface.co/papers/2107.03006). It is parameter free, and the final step deterministically commits the predicted tokens. An optional predictor-corrector mode adds the leave-one-out Gibbs sweeps of [Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation](https://huggingface.co/papers/2605.22765) through `corrector_steps`. This scheduler is used by [DiffusionGemmaPipeline](/docs/diffusers/main/en/api/pipelines/diffusion_gemma#diffusers.DiffusionGemmaPipeline). ## DiscreteDDIMScheduler[[diffusers.DiscreteDDIMScheduler]] - **num_inference_steps** (`int`, defaults to 32) -- The number of denoising steps, defining the linear time grid the posterior is evaluated on. - **corrector_steps** (`int`, defaults to 0) -- Number of Gibbs corrector sweeps run after each predictor step. `0` recovers plain ancestral DDIM sampling. - **corrector_k** (`int`, defaults to 1) -- Number of positions resampled per corrector sweep. - **corrector_selection** (`str`, defaults to `"lowest_log_margin"`) -- How the resampled positions are chosen: `"lowest_log_margin"`, `"lowest_maxprob"`, `"lowest_current_prob"`, or `"random"`. - **corrector_selection_tau** (`float`, defaults to 1.0) -- Temperature of the Gumbel-top-k position selection (lower is greedier). Discrete DDIM scheduler for the uniform corruption process, following "Structured Denoising Diffusion Models in Discrete State-Spaces" (D3PM, https://huggingface.co/papers/2107.03006). On the linear schedule the survival probability of a clean token at time `t` is `alpha(t) = 1 - t`. One denoising step from time `t` to `s < t` samples every block position from the exact posterior `q(x_s | x_t, x0)`, which for the uniform kernel decomposes into three routes: jump to the predicted clean token `x0`, stay on the current token, or jump to a uniformly random token. Unlike masked diffusion, there is no mask token; uncommitted positions carry random tokens. An optional predictor-corrector mode follows "Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation" via the leave-one-out (LOO) denoiser (https://huggingface.co/papers/2605.22765). When `corrector_steps > 0`, the pipeline runs that many Gibbs corrector sweeps after each predictor step (see [step_correct()](/docs/diffusers/main/en/api/schedulers/discrete_ddim#diffusers.DiscreteDDIMScheduler.step_correct)), resampling the least-confident positions from the one-coordinate conditional `Cat(alpha_s * x0_loo + (1 - alpha_s) / K)` while holding the rest fixed, which leaves the marginal `p_s` invariant and improves generation at no training cost. - **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, in `[0, num_inference_steps - 1]`. - **sample** (`torch.LongTensor` of shape `(batch_size, block_length)`) -- Current block token IDs `x_t`. - **temperature** (`float`) -- Sampling temperature applied to the logits when drawing `x0`. - **generator** (`torch.Generator`, *optional*) -- RNG for sampling. - **return_dict** (`bool`) -- Whether to return a [DiscreteDDIMSchedulerOutput](/docs/diffusers/main/en/api/schedulers/discrete_ddim#diffusers.DiscreteDDIMSchedulerOutput) or a plain tuple. Sample the next block from the posterior `q(x_s | x_t, x0)` of the uniform corruption process. With `a = alpha_t / alpha_s` (survival probability from `s` to `t`) and `b = alpha_s`, the posterior mass of each route is clean: `b * (1 - a) / K + a * b * 1[x_t = x0]`, stay: `a * (1 - b) / K`, noise: `(1 - a) * (1 - b) / K`, so the last step (`b = 1`) deterministically commits the predicted clean tokens. - **model_output** (`torch.Tensor` of shape `(batch_size, block_length, vocab_size)`) -- Raw logits from the model recomputed on the current (post-predictor) `sample`. - **timestep** (`int` or `torch.Tensor`) -- The predictor step index just completed; the corrector runs at the following grid point `s`. - **sample** (`torch.LongTensor` of shape `(batch_size, block_length)`) -- Current block token IDs to refine. - **generator** (`torch.Generator`, *optional*) -- RNG for sampling. - **return_dict** (`bool`) -- Whether to return a [DiscreteDDIMSchedulerOutput](/docs/diffusers/main/en/api/schedulers/discrete_ddim#diffusers.DiscreteDDIMSchedulerOutput) or a plain tuple. Run one Gibbs corrector sweep at the post-predictor time `s`, following the leave-one-out predictor-corrector of https://huggingface.co/papers/2605.22765. The model logits (recomputed on the current `sample`) are converted to the LOO denoiser, the one-coordinate conditional `p_s(x^l | x^{-l}) = Cat(alpha_s * x0_loo + (1 - alpha_s) / K)` is formed, the least-confident `corrector_k` positions are selected, and those positions are resampled while the rest are held fixed. The sweep preserves `p_s`, so it refines the sample without changing its marginal and needs no extra training. ## DiscreteDDIMSchedulerOutput[[diffusers.DiscreteDDIMSchedulerOutput]] - **prev_sample** (`torch.LongTensor` of shape `(batch_size, block_length)`) -- Updated block tokens after the current denoising step. - **sampled_tokens** (`torch.LongTensor` of shape `(batch_size, block_length)`) -- Token IDs sampled from the model logits, i.e. the predicted clean tokens `x0`. - **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 denoiser logits, passed through for self-conditioning the next step. Output class for the discrete DDIM scheduler.