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
| { | |
| "source_dataset": "Duyu/Time-Series-Forecasting-Benchmark-Datasets", | |
| "source_file": "Weather.csv", | |
| "window_length": 256, | |
| "stride": 4, | |
| "num_channels": 21, | |
| "channel_names": [ | |
| "feature_00", | |
| "feature_01", | |
| "feature_02", | |
| "feature_03", | |
| "feature_04", | |
| "feature_05", | |
| "feature_06", | |
| "feature_07", | |
| "feature_08", | |
| "feature_09", | |
| "feature_10", | |
| "feature_11", | |
| "feature_12", | |
| "feature_13", | |
| "feature_14", | |
| "feature_15", | |
| "feature_16", | |
| "feature_17", | |
| "feature_18", | |
| "feature_19", | |
| "feature_20" | |
| ], | |
| "split": "time_ordered_80_10_10", | |
| "normalization": "zscore_fit_on_train_split", | |
| "model_input": "concat(noisy_x, observed_x, observed_mask)", | |
| "model_output": "predicted_noise" | |
| } |