diff --git a/README.md b/README.md
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+++ b/README.md
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----
-license: apache-2.0
----
+Demo dataset for our CVPR 2025 paper "ZoomLDM: Latent Diffusion Model for multi-scale image generation"
+
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diff --git a/tcga_dataset.py b/tcga_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..b41c0bdf3497d1aed68fa69d2fcc536da3473696
--- /dev/null
+++ b/tcga_dataset.py
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+import datasets
+import os
+import numpy as np
+from PIL import Image
+
+_DATASET_VERSION = datasets.Version("1.0.0")
+
+_N_EMBED = {
+    "20x": 1,
+    "10x": 4,
+    "5x": 16,
+    "2_5x": 64,
+    "1_25x": 256,
+}
+
+_MAG_DICT = {
+    "20x": 0,
+    "10x": 1,
+    "5x": 2,
+    "2_5x": 3,
+    "1_25x": 4,
+}
+
+_FIXED_SSL_FEATURE_DIM_1 = 1024
+
+def get_ssl_feat_shape(mag_level):
+    first_dim = _N_EMBED[mag_level]
+    return (first_dim, _FIXED_SSL_FEATURE_DIM_1)
+
+def preprocess_features(feat_array):
+    feat_array = feat_array.astype(np.float32)
+    mean = feat_array.mean(axis=0, keepdims=True)
+    std = feat_array.std(axis=0, keepdims=True)
+    processed_feat = (feat_array - mean) / (std + 1e-8)
+    return processed_feat
+
+class MagnificationConfig(datasets.BuilderConfig):
+    def __init__(self, mag_level=None, ssl_feat_shape=None, **kwargs):
+        super(MagnificationConfig, self).__init__(**kwargs)
+        self.mag_level = mag_level
+        self.ssl_feat_shape = ssl_feat_shape
+
+class TCGADataset(datasets.GeneratorBasedBuilder):
+    VERSION = _DATASET_VERSION
+    MAGNIFICATIONS = ["20x", "10x", "5x", "2_5x", "1_25x"]
+    BUILDER_CONFIGS = []
+    for mag_level in MAGNIFICATIONS:
+        feature_shape = get_ssl_feat_shape(mag_level)
+        
+        builder_config_instance = MagnificationConfig(
+            name=mag_level,
+            version=_DATASET_VERSION,
+            description=f"Dataset at {mag_level} mag. SSL feature shape: {feature_shape}",
+            data_dir=mag_level,
+            mag_level=mag_level,
+            ssl_feat_shape=feature_shape
+        )
+        BUILDER_CONFIGS.append(builder_config_instance)
+
+    DEFAULT_CONFIG_NAME = "20x"
+
+    def _info(self):
+
+        _CITATION = """\
+            @inproceedings{yellapragada2024zoomldm,
+            title={Learned representation-guided diffusion models for large-image generation},
+            author={Yellapragada, Srikar and Graikos, Alexandros and Triaridis, Kostas and Prasanna, Prateek and Gupta, Rajarsi R and Saltz, Joel and Samaras, Dimitris},
+            booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+            year={2025}
+            }
+        """
+
+        return datasets.DatasetInfo(
+            description=f"Dataset with images and SSL features. Configuration: {self.config.name}",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "ssl_feat": datasets.Array2D(shape=self.config.ssl_feat_shape, dtype="float16"),
+                    "filename_img": datasets.Value("string"),
+                    "filename_feat": datasets.Value("string"),
+                    "mag": datasets.Value("string"),
+                }
+            ),
+            homepage="https://github.com/cvlab-stonybrook/ZoomLDM",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        mag_folder_name = self.config.data_dir
+        mag_data_path = os.path.join("data", mag_folder_name)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "mag_folder_path": mag_data_path,
+                    "mag_level": self.config.mag_level,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, mag_folder_path, mag_level):
+        idx = 0
+        for i in range(16):
+            img_filename = f"{i}.jpg"
+            feat_filename = f"{i}_ssl_feat.npy"
+
+            img_path = os.path.join(mag_folder_path, img_filename)
+            feat_path = os.path.join(mag_folder_path, feat_filename)
+
+            if not (os.path.exists(img_path) and os.path.exists(feat_path)):
+                continue
+
+            ssl_feat_data = np.load(feat_path)
+
+            processed_feature = preprocess_features(ssl_feat_data)
+
+            yield idx, {
+                "image": img_path,
+                "ssl_feat": processed_feature.astype(np.float16),
+                "mag": _MAG_DICT[mag_level],
+            }
+            idx += 1
\ No newline at end of file