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# ddpm-butterflies-128

## Model description

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

## Intended uses & limitations

#### How to use

```python
# TODO: add an example code snippet for running this diffusion pipeline
```

#### 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=(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/Tian7/ddpm-butterflies-128/tensorboard?#scalars)


language:en license:apache-2.0 region:us
{"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "/content/drive/MyDrive/image_and_text", "metrics": []}
TAGS
#language-English #license-apache-2.0 #region-us 




# ddpm-butterflies-128

## Model description

This diffusion model is trained with the  Diffusers library 
on the '/content/drive/MyDrive/image_and_text' dataset.

## Intended uses & limitations

#### How to use



#### 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=(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


# ddpm-butterflies-128 ## Model description

This diffusion model is trained with the  Diffusers library 
on the '/content/drive/MyDrive/image_and_text' dataset. ## Intended uses & limitations #### How to use #### 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=(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
TAGS
#language-English #license-apache-2.0 #region-us 
 # ddpm-butterflies-128 ## Model description

This diffusion model is trained with the  Diffusers library 
on the '/content/drive/MyDrive/image_and_text' dataset. ## Intended uses & limitations #### How to use #### 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=(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
passage: TAGS
#language-English #license-apache-2.0 #region-us 
# ddpm-butterflies-128## Model description

This diffusion model is trained with the  Diffusers library 
on the '/content/drive/MyDrive/image_and_text' dataset.## Intended uses & limitations#### How to use#### 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=(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