
# Denoising Diffusion Probabilistic Models (DDPM)

**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)

**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

```python
# !pip install diffusers
from diffusers import DiffusionPipeline
import PIL.Image
import numpy as np

model_id = "fusing/ddpm-lsun-church-ema"

# load model and scheduler
ddpm = DiffusionPipeline.from_pretrained(model_id)

# run pipeline in inference (sample random noise and denoise)
image = ddpm()

# 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")
```

## Samples

1. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-lsun-church-ema/image_0.png)
2. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-lsun-church-ema/image_1.png)
3. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-lsun-church-ema/image_2.png)
4. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-lsun-church-ema/image_3.png)

{"tags": ["ddpm_diffusion"]}
transformers ddpm_diffusion arxiv:2006.11239 endpoints_compatible region:us
TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us 


# Denoising Diffusion Probabilistic Models (DDPM)

Paper: Denoising Diffusion Probabilistic Models

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



## Samples

1. !sample_1
2. !sample_1
3. !sample_1
4. !sample_1

# Denoising Diffusion Probabilistic Models (DDPM)

Paper: Denoising Diffusion Probabilistic Models

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 ## Samples

1. !sample_1
2. !sample_1
3. !sample_1
4. !sample_1
TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us 
 # Denoising Diffusion Probabilistic Models (DDPM)

Paper: Denoising Diffusion Probabilistic Models

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 ## Samples

1. !sample_1
2. !sample_1
3. !sample_1
4. !sample_1
passage: TAGS
#transformers #ddpm_diffusion #arxiv-2006.11239 #endpoints_compatible #region-us 
# Denoising Diffusion Probabilistic Models (DDPM)

Paper: Denoising Diffusion Probabilistic Models

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## Samples

1. !sample_1
2. !sample_1
3. !sample_1
4. !sample_1