add community pipeline docs; add minimal text to some empty doc pages

docs/source/_toctree.yml

@@ -12,9 +12,9 @@
       title: "Loading Pipelines, Models, and Schedulers"
     - local: using-diffusers/configuration
       title: "Configuring Pipelines, Models, and Schedulers"
-    - local: using-diffusers/custom_pipelines
-      title: "Loading and Creating Custom Pipelines"
-    title: "Loading"
+    - local: using-diffusers/custom_pipeline_overview
+      title: "Loading and Adding Custom Pipelines"
+    title: "Loading & Hub"
   - sections:
     - local: using-diffusers/unconditional_image_generation
       title: "Unconditional Image Generation"
@@ -24,8 +24,10 @@
       title: "Text-Guided Image-to-Image"
     - local: using-diffusers/inpaint
       title: "Text-Guided Image-Inpainting"
-    - local: using-diffusers/custom
-      title: "Create a custom pipeline"
+    - local: using-diffusers/custom_pipeline_examples
+      title: "Community Pipelines"
+    - local: using-diffusers/contribute_pipeline
+      title: "How to contribute a Pipeline"
     title: "Pipelines for Inference"
   title: "Using Diffusers"
 - sections:

docs/source/api/pipelines/ddim.mdx

@@ -1,3 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
 # DDIM
 
 ## Overview

docs/source/api/pipelines/ddpm.mdx

@@ -1,3 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
 # DDPM
 
 ## Overview

docs/source/api/pipelines/latent_diffusion.mdx

@@ -1,3 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
 # Latent Diffusion
 
 ## Overview

docs/source/api/pipelines/latent_diffusion_uncond.mdx

@@ -1,3 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
 # Unconditional Latent Diffusion
 
 ## Overview

docs/source/api/pipelines/pndm.mdx

@@ -1,3 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
 # PNDM
 
 ## Overview

docs/source/api/pipelines/score_sde_ve.mdx

@@ -1,3 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
 # Score SDE VE
 
 ## Overview

docs/source/api/pipelines/stable_diffusion.mdx

@@ -1,3 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
 # Stable diffusion pipelines
 
 Stable Diffusion is a text-to-image _latent diffusion_ model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs.

docs/source/api/pipelines/stochastic_karras_ve.mdx

@@ -1,3 +1,15 @@
+<!--Copyright 2022 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.
+-->
+
 # Stochastic Karras VE
 
 ## Overview

docs/source/using-diffusers/configuration.mdx

@@ -12,21 +12,10 @@ specific language governing permissions and limitations under the License.
 
 
 
-# Quicktour
+# Configuration
 
-Start using Diffusers🧨 quickly!
-To start, use the [`DiffusionPipeline`] for quick inference and sample generations!
-
-```
-pip install diffusers
-```
-
-## Main classes
-
-### Models
-
-### Schedulers
-
-### Pipelines
+The handling of configurations in Diffusers is with the `ConfigMixin` class.
 
+[[autodoc]] ConfigMixin
 
+Under further construction 🚧, open a [PR](https://github.com/huggingface/diffusers/compare) if you want to contribute!

docs/source/using-diffusers/contribute_pipeline.mdx

@@ -0,0 +1,169 @@
+<!--Copyright 2022 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.
+-->
+
+# How to build a community pipeline
+
+*Note*: this page was built from the GitHub Issue on Community Pipelines [#841](https://github.com/huggingface/diffusers/issues/841).
+
+Let's make an example!
+Say you want to define a pipeline that just does a single forward pass to a U-Net and then calls a scheduler only once (Note, this doesn't make any sense from a scientific point of view, but only represents an example of how things work under the hood).
+
+Cool! So you open your favorite IDE and start creating your pipeline 💻.
+First, what model weights and configurations do we need?
+We have a U-Net and a scheduler, so our pipeline should take a U-Net and a scheduler as an argument.
+Also, as stated above, you'd like to be able to load weights and the scheduler config for Hub and share your code with others, so we'll inherit from `DiffusionPipeline`:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+
+class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
+    def __init__(self, unet, scheduler):
+        super().__init__()
+```
+
+Now, we must save the `unet` and `scheduler` in a config file so that you can save your pipeline with `save_pretrained`.
+Therefore, make sure you add every component that is save-able to the `register_modules` function:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+
+class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+```
+
+Cool, the init is done! 🔥 Now, let's go into the forward pass, which we recommend defining as `__call__` . Here you're given all the creative freedom there is. For our amazing "one-step" pipeline, we simply create a random image and call the unet once and the scheduler once:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+
+class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    def __call__(self):
+        image = torch.randn(
+            (1, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+        )
+        timestep = 1
+
+        model_output = self.unet(image, timestep).sample
+        scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample
+
+        return scheduler_output
+```
+
+Cool, that's it! 🚀 You can now run this pipeline by passing a `unet` and a `scheduler` to the init:
+
+```python
+from diffusers import DDPMScheduler, Unet2DModel
+
+scheduler = DDPMScheduler()
+unet = UNet2DModel()
+
+pipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)
+
+output = pipeline()
+```
+
+But what's even better is that you can load pre-existing weights into the pipeline if they match exactly your pipeline structure. This is e.g. the case for [https://huggingface.co/google/ddpm-cifar10-32](https://huggingface.co/google/ddpm-cifar10-32) so that we can do the following:
+
+```python
+pipeline = UnetSchedulerOneForwardPipeline.from_pretrained("google/ddpm-cifar10-32")
+
+output = pipeline()
+```
+
+We want to share this amazing pipeline with the community, so we would open a PR request to add the following code under `one_step_unet.py` to [https://github.com/huggingface/diffusers/tree/main/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) .
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+
+class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
+    def __init__(self, unet, scheduler):
+        super().__init__()
+
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    def __call__(self):
+        image = torch.randn(
+            (1, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+        )
+        timestep = 1
+
+        model_output = self.unet(image, timestep).sample
+        scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample
+
+        return scheduler_output
+```
+
+Our amazing pipeline got merged here: [#840](https://github.com/huggingface/diffusers/pull/840).
+Now everybody that has `diffusers >= 0.4.0` installed can use our pipeline magically 🪄 as follows:
+
+```python
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="one_step_unet")
+pipe()
+```
+
+Another way to upload your custom_pipeline, besides sending a PR, is uploading the code that contains it to the Hugging Face Hub, [as exemplified here](https://huggingface.co/docs/diffusers/using-diffusers/custom_pipelines#loading-custom-pipelines-from-the-hub).
+
+**Try it out now - it works!**
+
+In general, you will want to create much more sophisticated pipelines, so we recommend looking at existing pipelines here: [https://github.com/huggingface/diffusers/tree/main/examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community).
+
+IMPORTANT:
+You can use whatever package you want in your community pipeline file - as long as the user has it installed, everything will work fine. Make sure you have one and only one pipeline class that inherits from `DiffusionPipeline` as this will be automatically detected.
+
+## How do community pipelines work?
+A community pipeline is a class that has to inherit from ['DiffusionPipeline']:
+and that has been added to `examples/community` [files](https://github.com/huggingface/diffusers/tree/main/examples/community).
+The community can load the pipeline code via the custom_pipeline argument from DiffusionPipeline. See docs [here](https://huggingface.co/docs/diffusers/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.custom_pipeline):
+
+This means:
+The model weights and configs of the pipeline should be loaded from the `pretrained_model_name_or_path` [argument](https://huggingface.co/docs/diffusers/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path):
+whereas the code that powers the community pipeline is defined in a file added in [`examples/community`](https://github.com/huggingface/diffusers/tree/main/examples/community).
+
+Now, it might very well be that only some of your pipeline components weights can be downloaded from an official repo.
+The other components should then be passed directly to init as is the case for the ClIP guidance notebook [here](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb#scrollTo=z9Kglma6hjki).
+
+The magic behind all of this is that we load the code directly from GitHub. You can check it out in more detail if you follow the functionality defined here:
+
+```python
+# 2. Load the pipeline class, if using custom module then load it from the hub
+# if we load from explicit class, let's use it
+if custom_pipeline is not None:
+    pipeline_class = get_class_from_dynamic_module(
+        custom_pipeline, module_file=CUSTOM_PIPELINE_FILE_NAME, cache_dir=custom_pipeline
+    )
+elif cls != DiffusionPipeline:
+    pipeline_class = cls
+else:
+    diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
+    pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
+```
+
+This is why a community pipeline merged to GitHub will be directly available to all `diffusers` packages.
+