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README.md

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+---
+license: apache-2.0
+language:
+- en
+size_categories:
+- n<1K
+---
+Demo dataset for our CVPR 2025 paper "ZoomLDM: Latent Diffusion Model for multi-scale image generation". We extract patches from the Chesapeake land cover dataset.
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("StonyBrook-CVLab/ZoomLDM-demo-dataset-NAIP", name="3x", trust_remote_code=True, split='train')
+print(np.array(ds[0]['ssl_feat']).shape)
+>>> (1024, 4, 4)
+```
+
+
+## Citations
+
+
+```bibtex
+@inproceedings{yellapragada2025zoomldm,
+  title={ZoomLDM: Latent Diffusion Model for multi-scale image generation},
+  author={Yellapragada, Srikar and Graikos, Alexandros and Triaridis, Kostas and Prasanna, Prateek and Gupta, Rajarsi and Saltz, Joel and Samaras, Dimitris},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  pages={23453--23463},
+  year={2025}
+}
+```
+
+```
+@inproceedings{robinson2019large,
+  title={Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data},
+  author={Robinson, Caleb and Hou, Le and Malkin, Kolya and Soobitsky, Rachel and Czawlytko, Jacob and Dilkina, Bistra and Jojic, Nebojsa},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={12726--12735},
+  year={2019}
+}
+
+```

ZoomLDM-demo-dataset-NAIP.py

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+import datasets
+import numpy as np
+from pathlib import Path
+import torch
+import torch.nn.functional as F
+
+_DATASET_VERSION = datasets.Version("1.0.0")
+
+_N_EMBED = {
+    "1x": 1,
+    "2x": 4,
+    "3x": 16,
+    "4x": 64,
+}
+
+_MAG_DICT = {
+    "1x": 0,
+    "2x": 1,
+    "3x": 2,
+    "4x": 3,
+}
+
+_FIXED_SSL_FEATURE_DIM_1 = 1024
+
+def get_ssl_feat_shape(mag_level):
+    first_dim = _N_EMBED[mag_level]
+    h = int(np.sqrt(first_dim))
+    return (_FIXED_SSL_FEATURE_DIM_1, h, h)
+
+def preprocess_features(feat_array):
+
+    if len(feat_array.shape) == 1:
+        feat_array = feat_array[:, None]
+
+    mean = feat_array.mean(axis=0, keepdims=True)
+    std = feat_array.std(axis=0, keepdims=True)
+    feat_array = (feat_array - mean) / (std + 1e-8)
+
+    return feat_array
+
+class MagnificationConfig(datasets.BuilderConfig):
+    def __init__(self, mag_level=None, ssl_feat_shape=None, data_dir=None, **kwargs):
+        super(MagnificationConfig, self).__init__(**kwargs)
+        self.mag_level = mag_level
+        self.ssl_feat_shape = ssl_feat_shape
+        self.data_dir = data_dir
+
+class NAIPDataset(datasets.GeneratorBasedBuilder):
+    VERSION = _DATASET_VERSION
+    BUILDER_CONFIGS = []
+    for mag_level_str in _MAG_DICT.keys():
+
+        builder_config_instance = MagnificationConfig(
+            name=mag_level_str,
+            version=_DATASET_VERSION,
+            description=f"Dataset at {mag_level_str} mag",
+            data_dir=mag_level_str,
+            mag_level=mag_level_str,
+            ssl_feat_shape=get_ssl_feat_shape(mag_level_str),
+        )
+        BUILDER_CONFIGS.append(builder_config_instance)
+
+    DEFAULT_CONFIG_NAME = "1x"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=f"Dataset with images and SSL features. Configuration: {self.config.name}",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "ssl_feat": datasets.Array3D(shape=self.config.ssl_feat_shape, dtype="float32"),
+                    "mag": datasets.Value("int32"),
+                }
+            ),
+            homepage="https://github.com/cvlab-stonybrook/ZoomLDM",
+        )
+
+    def _split_generators(self, dl_manager):
+
+
+        original_script_dir = Path(self.base_path)
+        mag_folder_name = self.config.data_dir
+        
+        mag_data_abs_path = original_script_dir / "data" / mag_folder_name
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "mag_folder_abs_path": mag_data_abs_path, 
+                    "mag_level": self.config.mag_level,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, mag_folder_abs_path: Path, mag_level: str):
+        idx = 0
+        for i in range(16):
+            img_filename = f"{i}.jpg"
+            feat_filename = f"{i}_ssl_feat.npy"
+
+            img_path = mag_folder_abs_path / img_filename
+            feat_path = mag_folder_abs_path / feat_filename
+
+
+            ssl_feat_data = np.load(feat_path)
+            processed_feature = preprocess_features(ssl_feat_data)
+
+            h = np.sqrt(processed_feature.shape[1]).astype(int)
+            feat_array = torch.tensor(processed_feature.reshape((-1, h, h)))
+
+
+            yield idx, {
+                "image": str(img_path), 
+                "ssl_feat": feat_array,
+                "mag": _MAG_DICT[mag_level],
+            }
+            idx += 1

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