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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

Obama Model Card

DDPMObama is a latent noise-to-image diffusion model capable of generating images of obama. 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 cool, right?

Diffusers 

from diffusers import DiffusionPipeline

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

image = pipe().images[0]  

image.save("obama.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"

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: few-shot-obama

Developed by

  • Noa Linden Roggendorff

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