[Scheduler] Add Variational Diffusion Models (VDM) scheduler

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

@@ -0,0 +1,41 @@
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+
+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
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+
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+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+-->
+
+# VDMScheduler
+
+[Variational Diffusion Models](https://arxiv.org/abs/2107.00630) (VDM) by Diederik P. Kingma, Tim Salimans, Ben 
+Poole and Jonathan Ho introduces a family of diffusion-based generative models that achieve state-of-the-art 
+log-likelihoods on standard image density estimation benchmarks by formulating diffusion as a continuous-time problem 
+in terms of the signal-to-noise ratio.
+
+The abstract from the paper is:
+
+*Diffusion-based generative models have demonstrated a capacity for perceptually impressive synthesis, but can they 
+also be great likelihood-based models? We answer this in the affirmative, and introduce a family of diffusion-based 
+generative models that obtain state-of-the-art likelihoods on standard image density estimation benchmarks. Unlike 
+other diffusion-based models, our method allows for efficient optimization of the noise schedule jointly with the 
+rest of the model. We show that the variational lower bound (VLB) simplifies to a remarkably short expression in terms
+of the signal-to-noise ratio of the diffused data, thereby improving our theoretical understanding of this model class.
+Using this insight, we prove an equivalence between several models proposed in the literature. In addition, we show that
+the continuous-time VLB is invariant to the noise schedule, except for the signal-to-noise ratio at its endpoints. This
+enables us to learn a noise schedule that minimizes the variance of the resulting VLB estimator, leading to faster
+optimization. Combining these advances with architectural improvements, we obtain state-of-the-art likelihoods on image
+density estimation benchmarks, outperforming autoregressive models that have dominated these benchmarks for many years,
+with often significantly faster optimization. In addition, we show how to use the model as part of a bits-back
+compression scheme, and demonstrate lossless compression rates close to the theoretical optimum. Code is available at
+[this https URL](https://github.com/google-research/vdm).*
+
+## VDMScheduler
+[[autodoc]] VDMScheduler
+
+## VDMSchedulerOutput
+[[autodoc]] schedulers.scheduling_vdm.VDMSchedulerOutput

src/diffusers/__init__.py

@@ -191,6 +191,7 @@
             "TCDScheduler",
             "UnCLIPScheduler",
             "UniPCMultistepScheduler",
+            "VDMScheduler",
             "VQDiffusionScheduler",
         ]
     )
@@ -662,6 +663,7 @@
             TCDScheduler,
             UnCLIPScheduler,
             UniPCMultistepScheduler,
+            VDMScheduler,
             VQDiffusionScheduler,
         )
         from .training_utils import EMAModel

src/diffusers/schedulers/__init__.py

@@ -72,6 +72,7 @@
     _import_structure["scheduling_tcd"] = ["TCDScheduler"]
     _import_structure["scheduling_unclip"] = ["UnCLIPScheduler"]
     _import_structure["scheduling_unipc_multistep"] = ["UniPCMultistepScheduler"]
+    _import_structure["scheduling_vdm"] = ["VDMScheduler"]
     _import_structure["scheduling_utils"] = ["AysSchedules", "KarrasDiffusionSchedulers", "SchedulerMixin"]
     _import_structure["scheduling_vq_diffusion"] = ["VQDiffusionScheduler"]
 
@@ -172,6 +173,7 @@
         from .scheduling_tcd import TCDScheduler
         from .scheduling_unclip import UnCLIPScheduler
         from .scheduling_unipc_multistep import UniPCMultistepScheduler
+        from .scheduling_vdm import VDMScheduler
         from .scheduling_utils import AysSchedules, KarrasDiffusionSchedulers, SchedulerMixin
         from .scheduling_vq_diffusion import VQDiffusionScheduler