clean incomplete pages (#1008)

docs/source/api/schedulers.mdx

@@ -89,7 +89,7 @@ Original implementation can be found [here](https://github.com/crowsonkb/k-diffu
 
 [[autodoc]] PNDMScheduler
 
-#### variance exploding stochastic differential equation (SDE) scheduler
+#### variance exploding stochastic differential equation (VE-SDE) scheduler
 
 Original paper can be found [here](https://arxiv.org/abs/2011.13456).
 
@@ -99,7 +99,9 @@ Original paper can be found [here](https://arxiv.org/abs/2011.13456).
 
 Original implementation can be found [here](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296).
 
-#### variance preserving stochastic differential equation (SDE) scheduler
+[[autodoc]] IPNDMScheduler
+
+#### variance preserving stochastic differential equation (VP-SDE) scheduler
 
 Original paper can be found [here](https://arxiv.org/abs/2011.13456).
 

docs/source/conceptual/stable_diffusion.mdx

@@ -12,6 +12,4 @@ specific language governing permissions and limitations under the License.
 
 # Stable Diffusion
 
-Under construction 🚧
-
-For now please visit this [very in-detail blog post](https://huggingface.co/blog/stable_diffusion)
+Please visit this [very in-detail blog post](https://huggingface.co/blog/stable_diffusion) on Stable Diffusion!

docs/source/installation.mdx

@@ -53,7 +53,7 @@ The `main` version is useful for staying up-to-date with the latest developments
 For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet.
 However, this means the `main` version may not always be stable.
 We strive to keep the `main` version operational, and most issues are usually resolved within a few hours or a day.
-If you run into a problem, please open an [Issue](https://github.com/huggingface/transformers/issues) so we can fix it even sooner!
+If you run into a problem, please open an [Issue](https://github.com/huggingface/transformers/issues), so we can fix it even sooner!
 
 ## Editable install
 

src/diffusers/models/README.md

 # Models
 
-- Models: Neural network that models $p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t)$ (see image below) and is trained end-to-end to denoise a noisy input to an image. Examples: UNet, Conditioned UNet, 3D UNet, Transformer UNet
-
+For more detail on the models, please refer to the [docs](https://huggingface.co/docs/diffusers/api/models).
\ No newline at end of file
-## API
-
-TODO(Suraj, Patrick)
-
-## Examples
-
-TODO(Suraj, Patrick)

src/diffusers/pipelines/README.md

@@ -30,11 +30,12 @@ If you are looking for *official* training examples, please have a look at [exam
 The following table summarizes all officially supported pipelines, their corresponding paper, and if 
 available a colab notebook to directly try them out.
 
-| Pipeline | Paper | Tasks | Colab
-|---|---|:---:|:---:|
+| Pipeline                                                                                                                      | Source                                                                                                                       | Tasks | Colab
+|-------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|:---:|:---:|
+| [dance diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dance_diffusion)                 | [**Dance Diffusion**](https://github.com/Harmonai-org/sample-generator)                                                      | *Unconditional Audio Generation* |
 | [ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm)                                       | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239)                                             | *Unconditional Image Generation* |
 | [ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddim)                                       | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502)                                                  | *Unconditional Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [latent_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| *Text-to-Image Generation* | 
+| [latent_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion)               | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)                         | *Text-to-Image Generation* | 
 | [latent_diffusion_uncond](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)                         | *Unconditional Image Generation* | 
 | [pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pndm)                                       | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778)                           | *Unconditional Image Generation* | 
 | [score_sde_ve](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/score_sde_ve)                       | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | *Unconditional Image Generation* | 

src/diffusers/schedulers/README.md

 # Schedulers
 
-- Schedulers are the algorithms to use diffusion models in inference as well as for training. They include the noise schedules and define algorithm-specific diffusion steps.
-- Schedulers can be used interchangeable between diffusion models in inference to find the preferred trade-off between speed and generation quality.
+For more information on the schedulers, please refer to the [docs](https://huggingface.co/docs/diffusers/api/schedulers).
\ No newline at end of file
-- Schedulers are available in PyTorch and Jax.
-
-## API
-
-- Schedulers should provide one or more `def step(...)` functions that should be called iteratively to unroll the diffusion loop during 
-the forward pass.
-- Schedulers should be framework specific.
-
-## Examples
-
-- The DDPM scheduler was proposed in [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) and can be found in [scheduling_ddpm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py). An example of how to use this scheduler can be found in [pipeline_ddpm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm/pipeline_ddpm.py).
-- The DDIM scheduler was proposed in [Denoising Diffusion Implicit Models](https://arxiv.org/abs/2010.02502) and can be found in [scheduling_ddim.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py). An example of how to use this scheduler can be found in [pipeline_ddim.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddim/pipeline_ddim.py).
-- The PNDM scheduler was proposed in [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778) and can be found in [scheduling_pndm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py). An example of how to use this scheduler can be found in [pipeline_pndm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pndm/pipeline_pndm.py).