Update diffusiondb-pixelart.py

diffusiondb-pixelart.py

@@ -41,9 +41,7 @@ _VERSION = datasets.Version("0.9.1")
 # hf_hub_url() provides a more flexible way to resolve the file URLs
 # https://huggingface.co/datasets/jainr3/diffusiondb-pixelart/resolve/main/images/part-000001.zip
 _URLS = {}
-_URLS_LARGE = {}
 _PART_IDS = range(1, 2001)
-_PART_IDS_LARGE = range(1, 14001)
 
 for i in _PART_IDS:
     _URLS[i] = hf_hub_url(
@@ -52,31 +50,12 @@ for i in _PART_IDS:
         repo_type="dataset",
     )
 
-for i in _PART_IDS_LARGE:
-    if i < 10001:
-        _URLS_LARGE[i] = hf_hub_url(
-            "jainr3/diffusiondb-pixelart",
-            filename=f"diffusiondb-pixelart-large-part-1/part-{i:06}.zip",
-            repo_type="dataset",
-        )
-    else:
-        _URLS_LARGE[i] = hf_hub_url(
-            "jainr3/diffusiondb-pixelart",
-            filename=f"diffusiondb-pixelart-large-part-2/part-{i:06}.zip",
-            repo_type="dataset",
-        )
 
 # Add the metadata parquet URL as well
 _URLS["metadata"] = hf_hub_url(
     "jainr3/diffusiondb-pixelart", filename="metadata.parquet", repo_type="dataset"
 )
 
-_URLS_LARGE["metadata"] = hf_hub_url(
-    "jainr3/diffusiondb-pixelart",
-    filename="metadata-large.parquet",
-    repo_type="dataset",
-)
-
 _SAMPLER_DICT = {
     1: "ddim",
     2: "plms",
@@ -93,16 +72,14 @@ _SAMPLER_DICT = {
 class DiffusionDBConfig(datasets.BuilderConfig):
     """BuilderConfig for DiffusionDB."""
 
-    def __init__(self, part_ids, is_large, **kwargs):
+    def __init__(self, part_ids, **kwargs):
         """BuilderConfig for DiffusionDB.
         Args:
           part_ids([int]): A list of part_ids.
-          is_large(bool): If downloading data from DiffusionDB Large (14 million)
           **kwargs: keyword arguments forwarded to super.
         """
         super(DiffusionDBConfig, self).__init__(version=_VERSION, **kwargs)
         self.part_ids = part_ids
-        self.is_large = is_large
 
 
 class DiffusionDB(datasets.GeneratorBasedBuilder):
@@ -114,50 +91,8 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
     # as the config key)
     for num_k in [1, 5, 10, 50, 100, 500, 1000]:
         for sampling in ["first", "random"]:
-            for is_large in [False, True]:
-                num_k_str = f"{num_k}k" if num_k < 1000 else f"{num_k // 1000}m"
-                subset_str = "large_" if is_large else "2m_"
-
-                if sampling == "random":
-                    # Name the config
-                    cur_name = subset_str + "random_" + num_k_str
-
-                    # Add a short description for each config
-                    cur_description = (
-                        f"Random {num_k_str} images with their prompts and parameters"
-                    )
-
-                    # Sample part_ids
-                    total_part_ids = _PART_IDS_LARGE if is_large else _PART_IDS
-                    part_ids = np.random.choice(
-                        total_part_ids, num_k, replace=False
-                    ).tolist()
-                else:
-                    # Name the config
-                    cur_name = subset_str + "first_" + num_k_str
-
-                    # Add a short description for each config
-                    cur_description = f"The first {num_k_str} images in this dataset with their prompts and parameters"
-
-                    # Sample part_ids
-                    total_part_ids = _PART_IDS_LARGE if is_large else _PART_IDS
-                    part_ids = total_part_ids[1 : num_k + 1]
-
-                # Create configs
-                BUILDER_CONFIGS.append(
-                    DiffusionDBConfig(
-                        name=cur_name,
-                        part_ids=part_ids,
-                        is_large=is_large,
-                        description=cur_description,
-                    ),
-                )
-
-    # Add few more options for Large only
-    for num_k in [5000, 10000]:
-        for sampling in ["first", "random"]:
-            num_k_str = f"{num_k // 1000}m"
-            subset_str = "large_"
+            num_k_str = f"{num_k}k" if num_k < 1000 else f"{num_k // 1000}m"
+            subset_str = "2m_"
 
             if sampling == "random":
                 # Name the config
@@ -169,7 +104,7 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
                 )
 
                 # Sample part_ids
-                total_part_ids = _PART_IDS_LARGE
+                total_part_ids = _PART_IDS
                 part_ids = np.random.choice(
                     total_part_ids, num_k, replace=False
                 ).tolist()
@@ -181,7 +116,7 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
                 cur_description = f"The first {num_k_str} images in this dataset with their prompts and parameters"
 
                 # Sample part_ids
-                total_part_ids = _PART_IDS_LARGE
+                total_part_ids = _PART_IDS
                 part_ids = total_part_ids[1 : num_k + 1]
 
             # Create configs
@@ -189,26 +124,16 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
                 DiffusionDBConfig(
                     name=cur_name,
                     part_ids=part_ids,
-                    is_large=True,
                     description=cur_description,
                 ),
             )
 
-    # Need to manually add all (2m) and all (large)
+
+    # Need to manually add all (2m)
     BUILDER_CONFIGS.append(
         DiffusionDBConfig(
             name="2m_all",
             part_ids=_PART_IDS,
-            is_large=False,
-            description="All images with their prompts and parameters",
-        ),
-    )
-
-    BUILDER_CONFIGS.append(
-        DiffusionDBConfig(
-            name="large_all",
-            part_ids=_PART_IDS_LARGE,
-            is_large=True,
             description="All images with their prompts and parameters",
         ),
     )
@@ -218,19 +143,10 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
         DiffusionDBConfig(
             name="2m_text_only",
             part_ids=[],
-            is_large=False,
             description="Only include all prompts and parameters (no image)",
         ),
     )
 
-    BUILDER_CONFIGS.append(
-        DiffusionDBConfig(
-            name="large_text_only",
-            part_ids=[],
-            is_large=True,
-            description="Only include all prompts and parameters (no image)",
-        ),
-    )
 
     # Default to only load 1k random images
     DEFAULT_CONFIG_NAME = "2m_random_1k"
@@ -300,10 +216,7 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
         json_paths = []
 
         # Resolve the urls
-        if self.config.is_large:
-            urls = _URLS_LARGE
-        else:
-            urls = _URLS
+        urls = _URLS
 
         for cur_part_id in self.config.part_ids:
             cur_url = urls[cur_part_id]