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README.md

@@ -10,36 +10,50 @@ tags:
 
 **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
 
+**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
+
 **Abstract**:
 
 *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
 
-## Usage
+## Inference
+
+**DDPM** models can use *discrete noise schedulers* such as:
+
+- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
+- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
+- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
+
+for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
+For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
+
+See the following code:
 
 ```python
 # !pip install diffusers
-from diffusers import DiffusionPipeline
-import PIL.Image
-import numpy as np
+from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
 
-model_id = "google/ddpm-celeba-hq"
+model_id = "google/ddpm-celebahq-256"
 
 # load model and scheduler
-ddpm = DiffusionPipeline.from_pretrained(model_id)
+ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
 
 # run pipeline in inference (sample random noise and denoise)
-image = ddpm()
+image = ddpm()["sample"]
 
-# process image to PIL
-image_processed = image.cpu().permute(0, 2, 3, 1)
-image_processed = (image_processed + 1.0) * 127.5
-image_processed = image_processed.numpy().astype(np.uint8)
-image_pil = PIL.Image.fromarray(image_processed[0])
 
 # save image
-image_pil.save("test.png")
+image[0].save("ddpm_generated_image.png")
 ```
 
-## Samples
+For more in-detail information, please have a look at the [official inference example](_) # <- TODO(PVP) add link
+
+## Training
 
-TODO ...
+If you want to train your own model, please have a look at the [official training example]( ) # <- TODO(PVP) add link
+
+## Samples
+1. ![sample_1](https://huggingface.co/google/ddpm-celebahq-256/resolve/main/generated_image_0.png)
+2. ![sample_2](https://huggingface.co/google/ddpm-celebahq-256/resolve/main/generated_image_1.png)
+3. ![sample_3](https://huggingface.co/google/ddpm-celebahq-256/resolve/main/generated_image_2.png)
+4. ![sample_4](https://huggingface.co/google/ddpm-celebahq-256/resolve/main/generated_image_3.png)