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# ddpm-ema-flower-64

## Model description

This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library 
on the `huggan/flowers-102-categories` dataset.

## Intended uses & limitations

#### How to use

```python
from diffusers import DDPMPipeline

model_id = "mrm8488/ddpm-ema-flower-64"

# load model and scheduler
pipeline = DDPMPipeline.from_pretrained(model_id)

# run pipeline in inference 
image = pipeline()["sample"]

# save image
image[0].save("flower.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: 256
- eval_batch_size: 128
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: fp16

### Training results

📈 [TensorBoard logs](https://huggingface.co/mrm8488/ddpm-ema-flower-64/tensorboard?#scalars)

> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) with the support of [Q Blocks](https://www.qblocks.cloud/)

{"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/flowers-102-categories", "metrics": []}
diffusers tensorboard en dataset:huggan/flowers-102-categories license:apache-2.0 has_space diffusers:DDPMPipeline region:us
TAGS
#diffusers #tensorboard #en #dataset-huggan/flowers-102-categories #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us 




# ddpm-ema-flower-64

## Model description

This diffusion model is trained with the  Diffusers library 
on the 'huggan/flowers-102-categories' 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: 256
- eval_batch_size: 128
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: fp16

### Training results

 TensorBoard logs

> Created by Manuel Romero/@mrm8488 with the support of Q Blocks

# ddpm-ema-flower-64 ## Model description

This diffusion model is trained with the  Diffusers library 
on the 'huggan/flowers-102-categories' 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: 256
- eval_batch_size: 128
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: fp16 ### Training results

 TensorBoard logs

> Created by Manuel Romero/@mrm8488 with the support of Q Blocks
TAGS
#diffusers #tensorboard #en #dataset-huggan/flowers-102-categories #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us 
 # ddpm-ema-flower-64 ## Model description

This diffusion model is trained with the  Diffusers library 
on the 'huggan/flowers-102-categories' 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: 256
- eval_batch_size: 128
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: fp16 ### Training results

 TensorBoard logs

> Created by Manuel Romero/@mrm8488 with the support of Q Blocks
passage: TAGS
#diffusers #tensorboard #en #dataset-huggan/flowers-102-categories #license-apache-2.0 #has_space #diffusers-DDPMPipeline #region-us 
# ddpm-ema-flower-64## Model description

This diffusion model is trained with the  Diffusers library 
on the 'huggan/flowers-102-categories' 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: 256
- eval_batch_size: 128
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(0.95, 0.999), weight_decay=1e-06 and epsilon=1e-08
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: 1.0
- ema_inv_gamma: 0.75
- ema_inv_gamma: 0.9999
- mixed_precision: fp16### Training results

 TensorBoard logs

> Created by Manuel Romero/@mrm8488 with the support of Q Blocks