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---
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tags:
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- pytorch
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- diffusers
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- unconditional-image-generation
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- image-generation
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- denoising-diffusion
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- stable-diffusion
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license: apache-2.0
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library_name: diffusers
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model_name: ddpm-celebahq-256
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---
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# DDPM CelebAHQ 256 with Safetensors
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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.
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## Model Information
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- **Model Type**: `UNet2DModel`
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- **Diffusion Process**: DDPM (Denoising Diffusion Probabilistic Models)
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- **Training Data**: CelebA HQ dataset
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- **Resolution**: 256x256
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- **Format**: The model weights are available in `safetensors`.
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## Features
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- **Safetensors Support**: The model weights are stored in the `safetensors` format, a safer and more efficient.
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- **Pretrained Model**: This model is pretrained on the CelebA HQ dataset and is designed for high-quality image generation.
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- **Model Formats**: Available in safetensors formats for easy integration into your workflow.
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## Example Images
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Here are some sample images generated by the model at different diffusion steps:
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### DDPM:
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### DDIM:
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### PNDM:
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## How to Use
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To use this model, you can load it using the `diffusers` library from Hugging Face. You can load the model in `safetensors` format.
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### Requirements (2 ways to install)
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- Install the required dependencies:
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```bash
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pip install torch diffusers safetensors
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```
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- OR install same environment as me
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To recreate the same environment used for this project, you can use the provided `environment.yml` file.
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1. Download the environment.yml file from [here](https://huggingface.co/Mou11209203/ddpm-celebahq-256/resolve/main/environment.yml).
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2. Run the following command to create the environment:
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```bash
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conda env create --file environment.yml
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```
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3. Activate the environment:
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```bash
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conda activate inpaint
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```
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### Loading the Model
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To load the model and run inference, you can use the following code:
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```python
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import torch
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import numpy as np
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import PIL.Image
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from diffusers import UNet2DModel, DDPMScheduler
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import tqdm
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# 1. Initialize the model
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# Choose a model ID, use google's with use_safetensors=False, use Mou11209203's with use_safetensors=True
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repo_id = "google/ddpm-celebahq-256"
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repo_id1 = "Mou11209203/ddpm-celebahq-256"
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model = UNet2DModel.from_pretrained(repo_id1, use_safetensors=True)
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model.to("cuda") # Move the model to GPU
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print("model.config: ", model.config)
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# 2. Initialize the scheduler
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scheduler = DDPMScheduler.from_pretrained(repo_id1)
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print("scheduler.config: ", scheduler.config)
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# 3. Create an image with Gaussian noise
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torch.manual_seed(1733783271) # Fix the random seed for reproducibility
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noisy_sample = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size).to("cuda")
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print(f"Noisy sample shape: {noisy_sample.shape}")
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# 4. Define a function to display the image
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def display_sample(sample, i):
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image_processed = sample.cpu().permute(0, 2, 3, 1)
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image_processed = (image_processed + 1.0) * 127.5
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image_processed = image_processed.numpy().astype(np.uint8)
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image_pil = PIL.Image.fromarray(image_processed[0])
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print(f"Image at step {i}")
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image_pil.show()
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# 5. Reverse diffusion process
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sample = noisy_sample
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for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
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# 1. Predict the noise residual
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with torch.no_grad():
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residual = model(sample, t).sample
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# 2. Compute the less noisy image and move x_t -> x_t-1
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sample = scheduler.step(residual, t, sample).prev_sample
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# 3. Optionally display the image (every 50 steps)
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if (i + 1) % 50 == 0:
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display_sample(sample, i + 1)
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print("Denoising complete.")
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```
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## Scheduler
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**DDPM** models can use *discrete noise schedulers* such as:
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- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
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- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
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- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
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for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
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For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
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See the following code:
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```python
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# !pip install diffusers
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from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
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```
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## Training
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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)
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## Model Storage
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The following files are available for download:
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- **Model Weights (Safetensors format)**: `diffusion_pytorch_model.safetensors`
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- **Environment File**: `environment.yml`
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- **Model Configuration**: `config.json`
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- **Scheduler Configuration**: `scheduler_config.json`
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- **Generated Images**: Various steps from 50 to 1000
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- **README.md**: This document for usage and setup instructions
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## Citation
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If you use this model in your research or project, please cite the original `google/ddpm-celebahq-256` repository:
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```bibtex
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@misc{google/ddpm-celebahq-256,
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author = {Google Research},
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title = {DDPM CelebAHQ 256},
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year = {2022},
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url = {https://huggingface.co/google/ddpm-celebahq-256}
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}
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```
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## License
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This model is provided under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). |
