---
license: cc-by-nc-4.0
pipeline_tag: image-classification
viewer: false
tags:
- ambient omni
- diffusion
- ambient diffusion
- imagenet
- low-quality data
---

# Ambient Diffusion Omni (Ambient-o): Training Good Models with Bad Data
![](https://huggingface.co/giannisdaras/ambient-o/resolve/main/ambient_logo.png)

## Dataset Description

Ambient Diffusion Omni (Ambient-o) is a framework for using low-quality, synthetic, and out-of-distribution images to improve the quality of diffusion models. Unlike traditional approaches that rely on highly curated datasets, Ambient-o extracts valuable signal from all available images during training, including data typically discarded as "low-quality."

This model card is for the noise classifier on cifar10 for blurring corruptions with `sigma_B=0.6`, used in our pixel diffusion experiments based on [EDM](https://github.com/NVlabs/edm). The architecture is a time-dependent half U-Net backbone + linear projection.

Given a noisy image `x_t` and a noise level `sigma_t`, it predicts the probabily that the un-noised image `x` is blurry or not blurry. We use this to "annotate" the blurry (low-quality) part of our dataset with the minimum noise level `sigma_tn` at which its safe to use.

## Visualization
For visualization purposes, we include here a figure from our paper explaining how it works:

![](https://github.com/giannisdaras/ambient-omni/raw/main/figs/classifier_probs.jpg)

As shown in the figure above, when fed the blurry image our annotation pipeline would output 1.5.

## Usage instructions

You can load this using the following code, after cloning [our repository](https://github.com/giannisdaras/ambient-omni) and running `cd pixel-diffusion`
```python
import dnnlib
from huggingface_hub import hf_hub_download
import json
from safetensors.torch import load_file

# Get network args from HF
options_path = hf_hub_download(repo_id=ckpt_file, filename="training_options.json")
options = json.load(open(options_path, "r", encoding="utf-8"))

interface_kwargs_path = hf_hub_download(repo_id=ckpt_file, filename="interface_kwargs.json")
interface_kwargs = json.load(open(interface_kwargs_path, "r", encoding="utf-8"))

# Construct architecture
net = dnnlib.util.construct_class_by_name(**options['network_kwargs'], **interface_kwargs)

# Load state dict
state_dict_path = hf_hub_download(repo_id=ckpt_file, filename="ema.safetensors")
state_dict = load_file(state_dict_path)
net.load_state_dict(state_dict)
net.eval()
```


## Citation

```bibtex
@article{daras2025ambient,
  title={Ambient Diffusion Omni: Training Good Models with Bad Data},
  author={Daras, Giannis and Rodriguez-Munoz, Adrian and Klivans, Adam and Torralba, Antonio and Daskalakis, Constantinos},
  journal={arXiv preprint},
  year={2025},
}
```