Add doc pages for the DiffusionGemma schedulers

docs/source/en/_toctree.yml

@@ -718,6 +718,8 @@
  title: DDPMScheduler
  - local: api/schedulers/deis
  title: DEISMultistepScheduler
+ - local: api/schedulers/discrete_ddim
+ title: DiscreteDDIMScheduler
  - local: api/schedulers/multistep_dpm_solver_inverse
  title: DPMSolverMultistepInverse
  - local: api/schedulers/multistep_dpm_solver
@@ -730,6 +732,8 @@
  title: EDMDPMSolverMultistepScheduler
  - local: api/schedulers/edm_euler
  title: EDMEulerScheduler
+ - local: api/schedulers/entropy_bound
+ title: EntropyBoundScheduler
  - local: api/schedulers/euler_ancestral
  title: EulerAncestralDiscreteScheduler
  - local: api/schedulers/euler

docs/source/en/api/pipelines/diffusion_gemma.md

@@ -76,11 +76,11 @@ the model's image inputs automatically.
 The scheduler is the sampler that denoises each canvas, and it is interchangeable: swap it to change the sampling
 strategy without touching anything else. Three schedulers are available:
 
-- `BlockRefinementScheduler` (default): commits the most confident tokens each step (above `threshold`, plus an even
+- [`BlockRefinementScheduler`] (default): commits the most confident tokens each step (above `threshold`, plus an even
  per-step quota) and renoises the rest. `editing_threshold` additionally lets it re-edit already committed tokens.
-- `DiscreteDDIMScheduler`: samples each position from the exact discrete posterior of the uniform corruption process
+- [`DiscreteDDIMScheduler`]: samples each position from the exact discrete posterior of the uniform corruption process
  (D3PM). It is parameter free, and the final step deterministically commits the predicted tokens.
-- `EntropyBoundScheduler`: commits the lowest-entropy positions whose joint entropy stays under `entropy_bound`, so
+- [`EntropyBoundScheduler`]: commits the lowest-entropy positions whose joint entropy stays under `entropy_bound`, so
  roughly independent tokens are accepted together. It anneals its sampling temperature from `t_max` (`0.8`) on the
  first step down to `t_min` (`0.4`) on the last, matching the released checkpoint's sampler.
 
@@ -108,7 +108,7 @@ greedy).
 
 ### Predictor-corrector sampling
 
-`DiscreteDDIMScheduler` supports the leave-one-out predictor-corrector of [Reparameterizing Uniform Diffusion Models](https://huggingface.co/papers/2605.22765). It refines the canvas with `corrector_steps` Gibbs sweeps that resample the least-confident positions from the one-coordinate conditional of the noisy marginal, which leaves that marginal invariant and improves generation at no extra training cost. It works directly on the released checkpoint: for uniform diffusion the denoiser and the leave-one-out posterior are interchangeable in closed form, so the corrector recovers the leave-one-out quantities it needs without any retraining.
+`DiscreteDDIMScheduler` supports the leave-one-out predictor-corrector of [Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation](https://huggingface.co/papers/2605.22765). It refines the canvas with `corrector_steps` Gibbs sweeps that resample the least-confident positions from the one-coordinate conditional of the noisy marginal, which leaves that marginal invariant and improves generation at no extra training cost. It works directly on the released checkpoint: for uniform diffusion the denoiser and the leave-one-out posterior are interchangeable in closed form, so the corrector recovers the leave-one-out quantities it needs without any retraining.
 
 The corrector sweeps are folded into the `num_inference_steps` budget rather than added on top: the pipeline runs fewer predictor steps and spends the freed forwards on correctors, so the total number of model forwards stays `num_inference_steps` and the predictor-corrector costs the same as plain ancestral sampling.
 

docs/source/en/api/schedulers/discrete_ddim.md

@@ -0,0 +1,27 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 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`].
+
+## DiscreteDDIMScheduler
+[[autodoc]] DiscreteDDIMScheduler
+
+## DiscreteDDIMSchedulerOutput
+[[autodoc]] schedulers.scheduling_discrete_ddim.DiscreteDDIMSchedulerOutput

docs/source/en/api/schedulers/entropy_bound.md

@@ -0,0 +1,26 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 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`].
+
+## EntropyBoundScheduler
+[[autodoc]] EntropyBoundScheduler
+
+## EntropyBoundSchedulerOutput
+[[autodoc]] schedulers.scheduling_entropy_bound.EntropyBoundSchedulerOutput

src/diffusers/schedulers/scheduling_discrete_ddim.py

@@ -57,11 +57,12 @@ class DiscreteDDIMScheduler(SchedulerMixin, ConfigMixin):
  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 "Reparameterizing Uniform Diffusion Models" 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 [`~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.
+ 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
+ [`~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.
 
  Args:
  num_inference_steps (`int`, defaults to 32):

src/diffusers/schedulers/scheduling_entropy_bound.py

@@ -57,7 +57,8 @@ class EntropyBoundScheduler(SchedulerMixin, ConfigMixin):
  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 "Beyond Next-Token Prediction" (https://huggingface.co/papers/2505.24857).
+ 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