Time Series Forecasting
Diffusers
Safetensors
time-series
diffusion
scenario-generation
weather
multivariate-time-series
Eval Results (legacy)
Instructions to use kyLELEng/weather-scenario-diffusion-1d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kyLELEng/weather-scenario-diffusion-1d with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kyLELEng/weather-scenario-diffusion-1d", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download sample_generated_z.npy from kyLELEng/weather-scenario-diffusion-1d: direct link, hf CLI and curl.
- Browser
- Download file 516 kB
-
https://huggingface.co/kyLELEng/weather-scenario-diffusion-1d/resolve/main/sample_generated_z.npy
- Command line
-
hf download hf://kyLELEng/weather-scenario-diffusion-1d/sample_generated_z.npy
-
curl -L -o sample_generated_z.npy https://huggingface.co/kyLELEng/weather-scenario-diffusion-1d/resolve/main/sample_generated_z.npy
516 kB
- Xet hash:
- be903ada011335b9edc64c4f9c80296aca16c5f203373f051313ed4e0913e6ea
- Size of remote file:
- 516 kB
- SHA256:
- 63587114eaa4b9b4b73e36e64503961d8c512c53e6ffb72b2e4ed2f0d943b824
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