import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Forbu14/meteolibre", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Model Card for Model ID
This model is used to do weather forecasting using deep learning.
Model Details
Model Description
- Developed by: Adrien Bufort
- Model type: VAE / video generation model
- License: Apache 2.0
Model Sources [optional]
- Repository: https://github.com/Forbu/meteolibre_model
- Paper [optional]: in the future
- Demo [optional]: in the future
Uses
Use to do weather forecasting
Bias, Risks, and Limitations
THIS IS NOT A CLIMATE MODEL FORECAST
Training Data
Firstly we use the openclimatefix/nimrod-uk-1km dataset from openclimatefix
Evaluation
TO BE DONE IN THE FUTURE
Model Architecture and Objective
Here we will use the classic autoencoder encoder => transformer => decoder architecture.
Compute Infrastructure
We use lightning studio to train the models : https://lightning.ai/
Model Card Authors [optional]
Adrien Bufort
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