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---
license: mit
tags:
- downscaling
- ERA5 - COSMO-REA6
- reanalysis data
- wind velocities
- diffusion
- superresolution
library_name: diffusers
model_type: ddim
datasets:
- your-dataset-name
---
# DDIM-DSC: 4× Downscaling of Wind Velocities
**DDIM-DSC** is a custom-trained [Denoising Diffusion Implicit Model (DDIM)](https://github.com/huggingface/diffusers) designed for the **downscaling of wind velocity fields** from coarse- to high-resolution using reanalysis data.
It performs **4× spatial downscaling** on 2-channel wind fields (u and v components), using **ERA5** as low-resolution input and **COSMO-REA6** as the high-resolution target.
---
## 📊 Data
- **Input**: ERA5 100 m wind components (u, v), 2 channels
- **Target**: COSMO-REA6 100 m wind components (u, v), 2 channels
- **Sequence length**: 3 (with temporal context across 3 timesteps)
- **Total input channels**: 8 (2 channels × 3 timesteps + 2 static channels)
---
## 🧠 Model Architecture
- **Model type**: DDIM (using `diffusers`)
- **Scheduler**: DDIMScheduler
- **Conditioning**: Concatenated temporal sequences
- **Latent noise sampling**: 10 per input
- **Scale factor**: 4×
- **Input channels**: 8
- **Output channels**: 2
- **Note**: The low-resolution input must be **resized to high-resolution shape using bilinear interpolation** before being passed into the model.
## 🚀 Usage
```python
import torch
from diffusers import DiffusionPipeline
# load the custom DDIM pipeline
pipe = DiffusionPipeline.from_pretrained(
"lschmidt/ddim-dsc",
custom_pipeline="cond_ddim_pipeline",
trust_remote_code=True
)
# create a sample low-resolution input --> shape: (sequence_length, channels, height, width)
lres_image = torch.randn((3, 2, 32, 32)).to(pipe.device)
# interpolate to match high-resolution
# run inference
outputs = pipe(image=lres_image)
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