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Unconditional Image Generation
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DDPMPipeline
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metadata
license: mit
metrics:
  - mse
library_name: diffusers
tags:
  - diffusion
pipeline_tag: unconditional-image-generation

Cursedte kitty cats Model Card

DDPMCats is a latent noise-to-image diffusion model capable of generating images of cats. For more information about how Stable Diffusion functions, please have a look at 🤗's Stable Diffusion blog.

You can use this with the 🧨Diffusers library from Hugging Face.

So cute, right?

Diffusers 

from diffusers import DiffusionPipeline

pipeline = DiffusionPipeline.from_pretrained("nroggendorff/cats")
pipe = pipeline.to("cuda")

image = pipe().images[0]  

image.save("cat.png")

Model Details

  • train_batch_size: 16
  • eval_batch_size: 16
  • num_epochs: 50
  • gradient_accumulation_steps: 1
  • learning_rate: 1e-4
  • lr_warmup_steps: 500
  • mixed_precision: "fp16"
  • eval_metric: "mean_squared_error"

Bias

  • This model may exhibit biases due to its training data. It will not display images of abused or sick cats, as it prioritizes the well-being of animals.

Limitations

  • The model does not achieve perfect photorealism
  • The model cannot render legible text
  • The model was trained on a medium-to-large-scale dataset: huggan/few-shot-cat

Developed by

  • Noa Linden Roggendorff

This model card was written by Noa Roggendorff and is based on the Stable Diffusion v1-5 Model Card.