---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---



# ddpm-apes-128

![example image](example.png)

## Model description

This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library 
on the `imagefolder` dataset.

## Intended uses & limitations

#### How to use

```python
from diffusers import DDPMPipeline
import torch

model_id = "dn-gh/ddpm-apes-128"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id).to(device) 

# run pipeline in inference
image = ddpm().images[0]

# save image
image.save("generated_image.png")
```

#### Limitations and bias

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

## Training data

This model is trained on 4866 images generated with [ykilcher/apes](https://huggingface.co/ykilcher/apes) for 30 epochs.

### 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=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16

### Training results

📈 [TensorBoard logs](https://huggingface.co/dn-gh/ddpm-apes-128/tensorboard?#scalars)