SD Class Butterflies 32

📦 Model Details

• Model type: Denoising Diffusion Probabilistic Model (DDPM)
• Library: 🤗 Diffusers
• Framework: PyTorch
• Training objective: Predict noise (ε) added in the forward process
• Usage: Unconditional image generation (no text prompt required)

📸 Example Usage

from diffusers import DDPMPipeline

pipeline = DDPMPipeline.from_pretrained('larryliu002/sd-class-butterflies-32')
image = pipeline().images[0]
image.show()  # or display(image) in notebooks

--- 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 ```