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---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- denoising-diffusion
- image-generation
- diffusion-models-class
---
# 🧨 Diffusion Models Class - Unit 1: Unconditional Image Generation
This is a diffusion-based generative model trained for **unconditional image generation**, released as part of the [Diffusion Models Class](https://github.com/huggingface/diffusion-models-class) by Hugging Face.
This model learns to generate images from random noise via a **Denoising Diffusion Probabilistic Model (DDPM)** framework. It was trained on a toy dataset of **cute 🦋 images** (or other illustrative data, modify as needed).
---
## 📦 Model Details
- **Model type**: Denoising Diffusion Probabilistic Model (DDPM)
- **Library**: [🤗 Diffusers](https://github.com/huggingface/diffusers)
- **Framework**: PyTorch
- **Training objective**: Predict noise (ε) added in the forward process
- **Usage**: Unconditional image generation (no text prompt required)
---
## 📸 Example Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('larryliu002/sd-class-butterflies-32')
image = pipeline().images[0]
image.show() # or display(image) in notebooks
```
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