add more readmes

src/diffusers/models/README.md

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+# Models
+
+- Models: Neural network that models p_θ(x_t-1|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
+
+## API
+
+TODO(Suraj, Patrick)
+
+## Examples
+
+TODO(Suraj, Patrick)

src/diffusers/pipelines/+

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+# Pipelines
+
+- Pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box
+- Pipelines should stay as close as possible to their original implementation 
+- Pipelines can include components of other library, such as text-encoders. 
+
+## API
+
+TODO(Patrick, Anton, Suraj)
+
+## Examples
+
+- DDPM for unconditional image generation in [pipeline_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_ddpm.py).
+- DDIM for unconditional image generation in [pipeline_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_ddim.py).
+- PNDM for unconditional image generation in [pipeline_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py).
+- Latent diffusion for text to image generation / conditional image generation in [pipeline_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_bddm.py).
+- Glide for text to image generation / conditional image generation in [pipeline_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_bddm.py).
+- BDDM for spectrogram-to-sound vocoding in [pipeline_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_bddm.py).
+- Grad-TTS for text to audio generation / conditional audio generation in [pipeline_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_bddm.py).

src/diffusers/pipelines/README.md

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+# Pipelines
+
+- Pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box
+- Pipelines should stay as close as possible to their original implementation 
+- Pipelines can include components of other library, such as text-encoders. 
+
+## API
+
+TODO(Patrick, Anton, Suraj)
+
+## Examples
+
+- DDPM for unconditional image generation in [pipeline_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_ddpm.py).
+- DDIM for unconditional image generation in [pipeline_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_ddim.py).
+- PNDM for unconditional image generation in [pipeline_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py).
+- Latent diffusion for text to image generation / conditional image generation in [pipeline_latent_diffusion](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_latent_diffusion.py).
+- Glide for text to image generation / conditional image generation in [pipeline_glide](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_glide.py).
+- BDDM for spectrogram-to-sound vocoding in [pipeline_bddm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_bddm.py).
+- Grad-TTS for text to audio generation / conditional audio generation in [pipeline_grad_tts](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_grad_tts.py).

src/diffusers/schedulers/README.md

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+# 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 interchangable between diffusion models in inference to find the preferred tradef-off between speed and generation quality.
+- Schedulers are available in numpy, but can easily be transformed into PyTorch.
+
+## 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-agonstic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch 
+with a `set_format(...)` method.
+
+## 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/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/pipeline_ddim.py).
+- The PNMD 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/pipeline_pndm.py).