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ddpm-ema-flower-64

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 = "mrm8488/ddpm-ema-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: 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

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Hosted inference API

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Dataset used to train mrm8488/ddpm-ema-butterflies-128

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