ddpm-butterflies-128

Model description

This diffusion model is trained with the 🤗 Diffusers library on the huggan/smithsonian_butterflies_subset dataset.

Intended uses & limitations

How to use

from diffusers import DDPMPipeline

model_id = "hjjeon/ddpm-butterflies-128"

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

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

# save image
image[0].save("butterfly.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=(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

hjjeon
/

ddpm-butterflies-128