Instructions to use dataymeric/ArchesWeatherSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dataymeric/ArchesWeatherSR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dataymeric/ArchesWeatherSR", 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
| cluster: | |
| wandb_mode: offline | |
| use_custom_requeue: false | |
| precision: bf16-mixed | |
| batch_size: 1 | |
| gpus: 4 | |
| num_workers: 8 | |
| blosc_nthreads: 3 | |
| num_nodes: 1 | |
| dataloader: | |
| dataset: | |
| _target_: archesweathersr.dataloaders.era5_hdf5.ERA5Downscaling | |
| lowres_path: era5/240x121/weatherbench2/yearly | |
| highres_path: era5/1440x721/weatherbench2/yearly | |
| lead_time_hours: 24 | |
| norm_scheme: era5 | |
| domain: train | |
| load_prev: false | |
| multistep: 0 | |
| validation_args: | |
| domain: val_z0012 | |
| test_args: | |
| domain: test_z0012 | |
| module: | |
| metrics: | |
| era5_ensemble_metrics: | |
| _target_: geoarches.metrics.ensemble_metrics.Era5EnsembleMetrics | |
| lead_time_hours: 24 | |
| project: super-resolution | |
| module: | |
| _target_: archesweathersr.lightning_modules.sr_flow_matching.DownscalingDiffusionModule | |
| name: archesweathersr-1 | |
| cond_dim: 256 | |
| scheduler: euler | |
| prediction_type: sample | |
| conditional: '' | |
| interp_args: | |
| mode: bicubic | |
| align_corners: false | |
| state_normalization: residual | |
| pow: 2.0 | |
| lr: 0.0003 | |
| betas: | |
| - 0.9 | |
| - 0.98 | |
| weight_decay: 0.01 | |
| num_warmup_steps: 5000 | |
| num_training_steps: 75000 | |
| val: | |
| sample_every_n_epochs: 5 | |
| num_members: 2 | |
| metrics: | |
| - _target_: geoarches.metrics.ensemble_metrics.Era5EnsembleMetrics | |
| lead_time_hours: 24 | |
| metrics_kwargs: | |
| save_memory: true | |
| inference: | |
| num_steps: 25 | |
| num_members: 2 | |
| cf_guidance: 1 | |
| s_churn: 0.0 | |
| save_test_outputs: false | |
| metrics: | |
| era5_ensemble_metrics: | |
| _target_: geoarches.metrics.ensemble_metrics.Era5EnsembleMetrics | |
| lead_time_hours: 24 | |
| metrics_kwargs: | |
| save_memory: true | |
| backbone: | |
| _target_: archesweathersr.backbones.archesweather.ArchesWeatherCondBackbone | |
| tensor_size: | |
| - 8 | |
| - 180 | |
| - 360 | |
| emb_dim: 192 | |
| cond_dim: 256 | |
| window_size: | |
| - 1 | |
| - 6 | |
| - 12 | |
| droppath_coeff: 0.2 | |
| dropout: 0 | |
| depth_multiplier: 1 | |
| use_skip: true | |
| first_interaction_layer: linear | |
| axis_attn: true | |
| mlp_layer: swiglu | |
| mlp_ratio: 4.0 | |
| gradient_checkpointing: true | |
| embedder: | |
| _target_: archesweathersr.backbones.archesweather.WeatherEncodeDecodeLayer | |
| img_size: | |
| - 13 | |
| - 721 | |
| - 1440 | |
| emb_dim: 192 | |
| out_emb_dim: 384 | |
| patch_size: | |
| - 2 | |
| - 4 | |
| - 4 | |
| surface_ch: 4 | |
| level_ch: 6 | |
| n_concatenated_states: 1 | |
| log: true | |
| entity: null | |
| project: super-resolution | |
| name: archesweathersr-1 | |
| exp_dir: runs/archesweathersr-1 | |
| resume: true | |
| seed: None | |
| max_steps: 75000 | |
| batch_size: 1 | |
| save_step_frequency: 2500 | |
| log_freq: 100 | |
| limit_val_batches: 1.0 | |
| accumulate_grad_batches: 1 | |
| mode: train | |