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---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: imagefolder
metrics: []
---
# ddpm-apes-128
## 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)
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