Train WeatherScenarioDiffusion-1D

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+sample_plots/future_mask_sample.png filter=lfs diff=lfs merge=lfs -text
+sample_plots/real_vs_generated.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,463 @@
+---
+library_name: diffusers
+tags:
+- time-series
+- diffusion
+- scenario-generation
+- weather
+- multivariate-time-series
+---
+
+# WeatherScenarioDiffusion-1D
+
+WeatherScenarioDiffusion-1D is a conditional 1D diffusion model for multivariate weather time-series scenario generation.
+
+The model is trained on [`Duyu/Time-Series-Forecasting-Benchmark-Datasets`](https://huggingface.co/datasets/Duyu/Time-Series-Forecasting-Benchmark-Datasets), file `Weather.csv`.
+
+## What The Model Does
+
+This is a single conditional diffusion model with three usage modes:
+
+1. **Unconditional scenario generation**: sample realistic multivariate weather trajectories from noise.
+2. **Future-mask generation**: condition on the first part of a window and generate the missing future segment.
+3. **Channel inpainting**: condition on known weather variables and generate missing variables.
+
+The model uses:
+
+- `diffusers.UNet1DModel`
+- `diffusers.DDPMScheduler`
+- mask conditioning through concatenated input channels: `noisy_x`, `observed_x`, and `observed_mask`
+
+## Data
+
+- Source dataset: `Duyu/Time-Series-Forecasting-Benchmark-Datasets`
+- Source file: `Weather.csv`
+- Numeric channels detected: `21`
+- Window length: `256`
+- Stride: `4`
+- Split: time-ordered 80% train / 10% validation / 10% test
+- Normalization: z-score fitted only on the train split
+
+Detected channels:
+
+```json
+[
+  "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"
+]
+```
+
+## Training
+
+```json
+{
+  "dataset_repo": "Duyu/Time-Series-Forecasting-Benchmark-Datasets",
+  "dataset_file": "Weather.csv",
+  "model_repo_id": "kyLELEng/weather-scenario-diffusion-1d",
+  "output_dir": "/tmp/weather-scenario-diffusion-1d",
+  "window_length": 256,
+  "stride": 4,
+  "max_train_steps": 8000,
+  "train_batch_size": 128,
+  "eval_batch_size": 128,
+  "num_workers": 8,
+  "learning_rate": 0.0002,
+  "weight_decay": 0.01,
+  "grad_clip_norm": 1.0,
+  "num_train_timesteps": 1000,
+  "eval_every": 1000,
+  "save_every": 2000,
+  "num_eval_batches": 12,
+  "sample_inference_steps": 80,
+  "sample_count": 24,
+  "mixed_precision": "bf16",
+  "seed": 42,
+  "model_size": "large",
+  "smoke_test": false
+}
+```
+
+The training objective is noise prediction:
+
+```text
+MSE(predicted_noise, true_noise)
+```
+
+Known observed regions are provided as conditioning input. The loss is weighted toward unknown/masked regions so the model learns conditional reconstruction as well as unconditional generation.
+
+## Evaluation
+
+```json
+{
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+  "channel_inpainting_mse_zspace": 0.10761465132236481,
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+  "best_validation_denoising_loss": 0.05256535982092222,
+  "final_step": 8000
+}
+```
+
+Evaluation is based on held-out windows and includes:
+
+- validation denoising loss
+- future-mask inpainting MSE
+- channel-inpainting MSE
+- generated-vs-real distribution statistics
+- cross-channel correlation matrix error
+- absolute-value autocorrelation error
+
+## Intended Use
+
+This model is for research and demonstration of multivariate time-series diffusion. It is not a production forecasting system.
+
+## Files
+
+- `config.json`: 1D U-Net model configuration
+- `diffusion_pytorch_model.safetensors`: model weights
+- `scheduler/scheduler_config.json`: DDPM scheduler configuration
+- `preprocess_config.json`: dataset, split, normalization, window, and channel metadata
+- `normalization_stats.json`: train-split mean and standard deviation
+- `evaluation_report.json`: held-out evaluation metrics
+- `sample_plots/`: generated examples and conditional samples

config.json

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+{
+  "_class_name": "UNet1DModel",
+  "_diffusers_version": "0.37.1",
+  "act_fn": "silu",
+  "block_out_channels": [
+    64,
+    128,
+    256,
+    512
+  ],
+  "down_block_types": [
+    "DownBlock1DNoSkip",
+    "DownBlock1D",
+    "AttnDownBlock1D",
+    "AttnDownBlock1D"
+  ],
+  "downsample_each_block": false,
+  "extra_in_channels": 128,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0.0,
+  "in_channels": 63,
+  "layers_per_block": 2,
+  "mid_block_type": "UNetMidBlock1D",
+  "norm_num_groups": 8,
+  "out_block_type": null,
+  "out_channels": 21,
+  "sample_rate": null,
+  "sample_size": 256,
+  "time_embedding_dim": null,
+  "time_embedding_type": "fourier",
+  "up_block_types": [
+    "AttnUpBlock1D",
+    "AttnUpBlock1D",
+    "UpBlock1D",
+    "UpBlock1DNoSkip"
+  ],
+  "use_timestep_embedding": false
+}

diffusion_pytorch_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e666b5ba8aa70e199fa07f8b3e1c5374662960cfd24a05cd01b363f7e9c6e6ec
+size 166272740

evaluation_report.json

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preprocess_config.json

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training_config.json

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