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
Add Hub task metadata
Browse files
README.md
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---
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library_name: diffusers
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tags:
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- time-series
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- diffusion
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- scenario-generation
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- weather
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- multivariate-time-series
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---
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# WeatherScenarioDiffusion-1D
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library_name: diffusers
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pipeline_tag: time-series-forecasting
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datasets:
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- Duyu/Time-Series-Forecasting-Benchmark-Datasets
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metrics:
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- mse
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tags:
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- time-series
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- diffusion
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- scenario-generation
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- weather
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- multivariate-time-series
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model-index:
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- name: WeatherScenarioDiffusion-1D
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results:
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- task:
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type: time-series-forecasting
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name: Time Series Forecasting
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dataset:
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type: Duyu/Time-Series-Forecasting-Benchmark-Datasets
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name: Weather.csv
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metrics:
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- type: mse
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name: Future-mask MSE (z-space)
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value: 0.16154611110687256
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- type: mse
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name: Channel-inpainting MSE (z-space)
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value: 0.10761465132236481
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---
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# WeatherScenarioDiffusion-1D
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