[Download] Smart downloading

src/diffusers/onnx_utils.py

@@ -38,8 +38,6 @@
 
 
 class OnnxRuntimeModel:
-    base_model_prefix = "onnx_model"
-
     def __init__(self, model=None, **kwargs):
         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")
         self.model = model

src/diffusers/pipeline_utils.py

@@ -30,7 +30,10 @@
 from tqdm.auto import tqdm
 
 from .configuration_utils import ConfigMixin
-from .utils import DIFFUSERS_CACHE, BaseOutput, logging
+from .modeling_utils import WEIGHTS_NAME
+from .onnx_utils import ONNX_WEIGHTS_NAME
+from .schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
+from .utils import CONFIG_NAME, DIFFUSERS_CACHE, BaseOutput, logging
 
 
 INDEX_FILE = "diffusion_pytorch_model.bin"
@@ -285,6 +288,21 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         # 1. Download the checkpoints and configs
         # use snapshot download here to get it working from from_pretrained
         if not os.path.isdir(pretrained_model_name_or_path):
+            config_dict = cls.get_config_dict(
+                pretrained_model_name_or_path,
+                cache_dir=cache_dir,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                use_auth_token=use_auth_token,
+                revision=revision,
+            )
+            # make sure we only download sub-folders and `diffusers` filenames
+            folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
+            allow_patterns = [os.path.join(k, "*") for k in folder_names]
+            allow_patterns += [WEIGHTS_NAME, SCHEDULER_CONFIG_NAME, CONFIG_NAME, ONNX_WEIGHTS_NAME, cls.config_name]
+
+            # download all allow_patterns
             cached_folder = snapshot_download(
                 pretrained_model_name_or_path,
                 cache_dir=cache_dir,
@@ -293,6 +311,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 local_files_only=local_files_only,
                 use_auth_token=use_auth_token,
                 revision=revision,
+                allow_patterns=allow_patterns,
             )
         else:
             cached_folder = pretrained_model_name_or_path

tests/test_pipelines.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 import gc
+import os
 import random
 import tempfile
 import unittest
@@ -45,8 +46,11 @@
     UNet2DModel,
     VQModel,
 )
+from diffusers.modeling_utils import WEIGHTS_NAME
 from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.testing_utils import floats_tensor, load_image, slow, torch_device
+from diffusers.utils import CONFIG_NAME
 from PIL import Image
 from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
@@ -707,6 +711,27 @@ def tearDown(self):
         gc.collect()
         torch.cuda.empty_cache()
 
+    def test_smart_download(self):
+        model_id = "hf-internal-testing/unet-pipeline-dummy"
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            _ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True)
+            local_repo_name = "--".join(["models"] + model_id.split("/"))
+            snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots")
+            snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])
+
+            # inspect all downloaded files to make sure that everything is included
+            assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name))
+            assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME))
+            assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME))
+            assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME))
+            assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME))
+            assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
+            assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
+            # let's make sure the super large numpy file:
+            # https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy
+            # is not downloaded, but all the expected ones
+            assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy"))
+
     @property
     def dummy_safety_checker(self):
         def check(images, *args, **kwargs):