---
tags:
  - pytorch
  - diffusers
  - unconditional-image-generation
  - image-generation
  - denoising-diffusion
  - stable-diffusion
license: apache-2.0
library_name: diffusers
model_name: ddpm-celebahq-256
---

# DDPM CelebAHQ 256 with Safetensors

This repository contains a **denoising diffusion probabilistic model (DDPM)** trained on the CelebA HQ dataset at a resolution of 256x256. The model is based on the original `google/ddpm-celebahq-256` implementation and has been updated to support **safetensors** for model storage.

## Model Information

- **Model Type**: `UNet2DModel`
- **Diffusion Process**: DDPM (Denoising Diffusion Probabilistic Models)
- **Training Data**: CelebA HQ dataset
- **Resolution**: 256x256
- **Format**: The model weights are available in both `safetensors` and standard PyTorch (`.pth`) formats.

## Features

- **Safetensors Support**: The model weights are stored in the `safetensors` format, a safer and more efficient alternative to regular PyTorch `.pth` files. It ensures better compatibility, security, and serialization of model weights.
- **Pretrained Model**: This model is pretrained on the CelebA HQ dataset and is designed for high-quality image generation.
- **Model Formats**: Available in both standard PyTorch and safetensors formats for easy integration into your workflow.

## Example Images

Here are some sample images generated by the model at different diffusion steps:

![Step 50](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_50.png)
![Step 100](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_100.png)
![Step 150](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_150.png)
![Step 200](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_200.png)
![Step 250](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_250.png)
![Step 300](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_300.png)
![Step 350](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_350.png)
![Step 400](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_400.png)
![Step 450](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_450.png)
![Step 500](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_500.png)
![Step 550](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_550.png)
![Step 600](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_600.png)
![Step 650](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_650.png)
![Step 700](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_700.png)
![Step 750](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_750.png)
![Step 800](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_800.png)
![Step 850](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_850.png)
![Step 900](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_900.png)
![Step 950](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_950.png)
![Step 1000](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/images/image_step_1000.png)


## How to Use

To use this model, you can load it using the `diffusers` library from Hugging Face. You can load the model in either `safetensors` format or the traditional `.pth` format.

### Requirements

- Install the required dependencies:
    ```bash
    pip install torch diffusers safetensors
    ```

### Loading the Model

To load the model and run inference, you can use the following code:

```python
import torch
import numpy as np
import PIL.Image
from diffusers import UNet2DModel, DDPMScheduler
import tqdm

# 1. Initialize the model
# Choose a model ID, use google's with use_safetensors=False, use Mou11209203's with use_safetensors=True
repo_id = "google/ddpm-celebahq-256"
repo_id1 = "Mou11209203/ddpm-celebahq-256"
model = UNet2DModel.from_pretrained(repo_id1, use_safetensors=True)
model.to("cuda")  # Move the model to GPU
print("model.config: ", model.config)

# 2. Initialize the scheduler
scheduler = DDPMScheduler.from_pretrained(repo_id1)
print("scheduler.config: ", scheduler.config)

# 3. Create an image with Gaussian noise
torch.manual_seed(1733783271)  # Fix the random seed for reproducibility
noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size).to("cuda")
print(f"Noisy sample shape: {noisy_sample.shape}")

# 4. Define a function to display the image
def display_sample(sample, i):
    image_processed = sample.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])
    print(f"Image at step {i}")
    image_pil.show()

# 5. Reverse diffusion process
sample = noisy_sample
for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
    # 1. Predict the noise residual
    with torch.no_grad():
        residual = model(sample, t).sample

    # 2. Compute the less noisy image and move x_t -> x_t-1
    sample = scheduler.step(residual, t, sample).prev_sample

    # 3. Optionally display the image (every 50 steps)
    if (i + 1) % 50 == 0:
        display_sample(sample, i + 1)

print("Denoising complete.")
```

## Training

If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)

## Model Storage

The following files are available for download:

- **Model Weights (PyTorch format)**: `diffusion_pytorch_model.pth`
- **Model Weights (Safetensors format)**: `diffusion_pytorch_model.safetensors`
- **Generated Images**: Various steps from 50 to 1000
- **README.md**: This document for usage and setup instructions

## Citation

If you use this model in your research or project, please cite the original `google/ddpm-celebahq-256` repository:

```bibtex
@misc{google/ddpm-celebahq-256,
  author = {Google Research},
  title = {DDPM CelebAHQ 256},
  year = {2022},
  url = {https://huggingface.co/google/ddpm-celebahq-256}
}
```

## License

This model is provided under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).