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---
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](https://huggingface.co/blog/stable_diffusion).
You can use this with the 🧨Diffusers library from [Hugging Face](https://huggingface.co).
### Diffusers
```py
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](https://huggingface.co/datasets/huggan/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](https://huggingface.co/runwayml/stable-diffusion-v1-5).* |