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README.md

@@ -4,4 +4,56 @@ tags:
 - pytorch
 - diffusers
 - unconditional-image-generation
----
+---
+
+# Denoising Diffusion Probabilistic Models (DDPM)
+
+**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.*
+
+## 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 DDPMPipeline, DDIMPipeline, PNDMPipeline
+
+model_id = "google/ddpm-cifar10-32"
+
+# load model and scheduler
+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()["sample"]
+
+
+# save image
+image[0].save("ddpm_generated_image.png")
+```
+
+For more in-detail information, please have a look at the [official inference example](_) # <- TODO(PVP) add link
+
+## Training
+
+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-church-256/resolve/main/generated_image_0.png)
+2. ![sample_2](https://huggingface.co/google/ddpm-church-256/resolve/main/generated_image_1.png)
+3. ![sample_3](https://huggingface.co/google/ddpm-church-256/resolve/main/generated_image_2.png)
+4. ![sample_4](https://huggingface.co/google/ddpm-church-256/resolve/main/generated_image_3.png)