---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
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# ddpm-ema-butterflies-64

## Model description

This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library 
on the `huggan/smithsonian_butterflies_subset` dataset. Using this [script](https://github.com/huggingface/diffusers/blob/cde0ed162a127b17f1b4d4b16ff7f736cf04e690/examples/train_unconditional.py)

## Intended uses & limitations

#### How to use

```python
from diffusers import DDPMPipeline

model_id = "ceyda/ddpm-ema-butterflies-64"

# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference

# run pipeline in inference (sample random noise and denoise)
image = ddpm()["sample"]

# save image
image[0].save("ddpm_generated_image.png")
```

#### Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

## Training data

[TODO: describe the data used to train the model]

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: cosine
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: no

### Training results

📈 [TensorBoard logs](https://huggingface.co/ceyda/ddpm-ema-butterflies-64/tensorboard?#scalars)