# cBottle

**Repository Path**: mirrors_NVlabs/cBottle

## Basic Information

- **Project Name**: cBottle
- **Description**: A generative foundation model for the kilometer-scale atmosphere
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-05-11
- **Last Updated**: 2026-10-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Climate in a Bottle
![](https://github.com/user-attachments/assets/a2cab939-48ce-421a-8008-00b17fd6fa9f)

cBottle is an diffusion model that generates atmospheric states at kilometer resolution using a cascaded diffusion architecture.

This model is for research and development only.

[📖 arXiv](https://arxiv.org/abs/2505.06474v1) [📦 Checkpoints](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/earth-2/models/cbottle) [📚 Documentation](https://nvlabs.github.io/cBottle/) 

## Setup

See [installation](docs/installation.md) instructions.


## Inference APIs

Inference APIs for the published checkpoints are provided in [Earth2Studio](https://github.com/NVIDIA/earth2studio).
For example, to start inferencing data from the coarse model (cBottle-3d):

```python
from datetime import datetime
from earth2studio.data import CBottle3D
package = CBottle3D.load_default_package()
ds = CBottle3D.load_model(package).to("cuda")
cbottle_da = ds([datetime(2022, 9, 5)], ["msl", "tcwv"])
```

See the Earth2Studio [install instructions](https://nvidia.github.io/earth2studio/userguide/about/install.html#diagnostics) and dedicated notebooks for more information:

- [CBottle Data Generation and Infilling](https://nvidia.github.io/earth2studio/examples/15_cbottle_generation.html)
- [CBottle Super Resolution](https://nvidia.github.io/earth2studio/examples/16_cbottle_super_resolution.html)

## Coarse Model (cBottle-3d)

### Training

```
python3 scripts/train_coarse.py --loop.noise_distribution log_uniform --loop.sigma_min 0.02 --loop.sigma_max 200 --loop.label_dropout 0.25 --loop.batch_gpu 4 --loop.batch_size 64 --loop.dataloader_num_workers 8 --loop.with_era5 --loop.use_labels  --loop.data_version 6 --loop.monthly_sst_input --name v6data  --loop.dataloader_prefetch_factor 100
```


## Coarse Video Model (cBottle-video)

###
Video training requires larger chunk sizes than image training.

```
python3 scripts/train_coarse.py \
    --name v6-video \
    --loop.time_length 12 \
    --loop.time_step 6 \
    --loop.icon_chunk_size 56 \
    --loop.era5_chunk_size 96 \
    --loop.use_labels \
    --loop.label_dropout 0.05 \
    --loop.with_era5 \
    --loop.monthly_sst_input \
    --loop.noise_distribution log_uniform \
    --loop.sigma_min 0.02 \
    --loop.sigma_max 1000 \
    --loop.network.model_channels 256 \
    --loop.snapshot_ticks 1 \
    --loop.state_dump_ticks 1 \
    --loop.steps_per_tick 4000 \
    --loop.batch_gpu 1 \
    --loop.batch_size 16 \
    --loop.valid_min_samples 64 \
    --loop.dataloader_prefetch_factor 10 \
    --loop.dataloader_num_workers 8 \
    --loop.lr_rampup_img 50000 \
    --loop.lr_flat_imgs 0 \
    --loop.lr_decay_imgs 3150000 \
    --loop.lr 0.0002 \
    --loop.lr_min 0 \
    --loop.bf16 \
    --loop.channels_last \
    --loop.compile
```

### Inference

Generate multi-step video rollouts as follows:
```
python scripts/inference_coarse_video.py \
    --dataset era5 \
    --sample.sigma_max 200 \
    --sample.frame_selection_strategy first_frame \
    --sample.bf16 \
    --sample.mode translate \
    --sample.autoregression_duration 60  \
    <CHECKPOINT> \
    <OUTPUT>
```

## Super-resolution model (cBottle-SR)

### Training
```
python3 scripts/train_multidiffusion.py --output-path OUTPUT 
```

### Inference

```
python3 scripts/inference_multidiffusion.py --input-path path/to/checkpoint output/
```


## Disclaimer

This project will download and install additional third-party open source
software projects. Review the license terms of these open source projects before
use.