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---
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 `safetensors`.
## Features
- **Safetensors Support**: The model weights are stored in the `safetensors` format, a safer and more efficient.
- **Pretrained Model**: This model is pretrained on the CelebA HQ dataset and is designed for high-quality image generation.
- **Model Formats**: Available in safetensors formats for easy integration into your workflow.
## Example Images
Here are some sample images generated by the model at different diffusion steps:
### DDPM:
### DDIM:
### PNDM:
## How to Use
To use this model, you can load it using the `diffusers` library from Hugging Face. You can load the model in `safetensors` format.
### Requirements (2 ways to install)
- Install the required dependencies:
```bash
pip install torch diffusers safetensors
```
- OR install same environment as me
To recreate the same environment used for this project, you can use the provided `environment.yml` file.
1. Download the environment.yml file from [here](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/environment.yml).
2. Run the following command to create the environment:
```bash
conda env create --file environment.yml
```
3. Activate the environment:
```bash
conda activate inpaint
```
### 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.")
```
## Scheduler
**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
```
## 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 (Safetensors format)**: `diffusion_pytorch_model.safetensors`
- **Environment File**: `environment.yml`
- **Model Configuration**: `config.json`
- **Scheduler Configuration**: `scheduler_config.json`
- **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). |