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ddpm-ema-butterflies-64

Model description

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

Intended uses & limitations

How to use

from diffusers import DDPMPipeline

model_id = "ceyda/ddpm-ema-butterflies-64"

# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference

# run pipeline in inference (sample random noise and denoise)
image = ddpm()["sample"]

# save image
image[0].save("ddpm_generated_image.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:

Training results

📈 TensorBoard logs