[From pretrained] Speed-up loading from cache (#2515)

* [From pretrained] Speed-up loading from cache

* up

* Fix more

* fix one more bug

* make style

* bigger refactor

* factor out function

* Improve more

* better

* deprecate return cache folder

* clean up

* improve tests

* up

* upload

* add nice tests

* simplify

* finish

* correct

* fix version

* rename

* Apply suggestions from code review
Co-authored-by: Lucain <lucainp@gmail.com>

* rename

* correct doc string

* correct more

* Apply suggestions from code review
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* apply code suggestions

* finish

---------
Co-authored-by: Lucain <lucainp@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

scripts/convert_original_stable_diffusion_to_diffusers.py

@@ -16,7 +16,7 @@
 
 import argparse
 
-from diffusers.pipelines.stable_diffusion.convert_from_ckpt import load_pipeline_from_original_stable_diffusion_ckpt
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
 
 
 if __name__ == "__main__":
@@ -125,7 +125,7 @@ if __name__ == "__main__":
     )
     args = parser.parse_args()
 
-    pipe = load_pipeline_from_original_stable_diffusion_ckpt(
+    pipe = download_from_original_stable_diffusion_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,

setup.py

@@ -86,7 +86,8 @@ _deps = [
     "filelock",
     "flax>=0.4.1",
     "hf-doc-builder>=0.3.0",
-    "huggingface-hub>=0.10.0",
+    "huggingface-hub>=0.13.0",
+    "requests-mock==1.10.0",
     "importlib_metadata",
     "isort>=5.5.4",
     "jax>=0.2.8,!=0.3.2",
@@ -192,6 +193,7 @@ extras["test"] = deps_list(
     "pytest",
     "pytest-timeout",
     "pytest-xdist",
+    "requests-mock",
     "safetensors",
     "sentencepiece",
     "scipy",

src/diffusers/configuration_utils.py

@@ -31,7 +31,15 @@ from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, R
 from requests import HTTPError
 
 from . import __version__
-from .utils import DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, DummyObject, deprecate, logging
+from .utils import (
+    DIFFUSERS_CACHE,
+    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
+    DummyObject,
+    deprecate,
+    extract_commit_hash,
+    http_user_agent,
+    logging,
+)
 
 
 logger = logging.get_logger(__name__)
@@ -231,7 +239,11 @@ class ConfigMixin:
 
     @classmethod
     def load_config(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs
+        cls,
+        pretrained_model_name_or_path: Union[str, os.PathLike],
+        return_unused_kwargs=False,
+        return_commit_hash=False,
+        **kwargs,
     ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
         r"""
         Instantiate a Python class from a config dictionary
@@ -271,6 +283,10 @@ class ConfigMixin:
             subfolder (`str`, *optional*, defaults to `""`):
                 In case the relevant files are located inside a subfolder of the model repo (either remote in
                 huggingface.co or downloaded locally), you can specify the folder name here.
+            return_unused_kwargs (`bool`, *optional*, defaults to `False):
+                Whether unused keyword arguments of the config shall be returned.
+            return_commit_hash (`bool`, *optional*, defaults to `False):
+                Whether the commit_hash of the loaded configuration shall be returned.
 
         <Tip>
 
@@ -295,8 +311,10 @@ class ConfigMixin:
         revision = kwargs.pop("revision", None)
         _ = kwargs.pop("mirror", None)
         subfolder = kwargs.pop("subfolder", None)
+        user_agent = kwargs.pop("user_agent", {})
 
-        user_agent = {"file_type": "config"}
+        user_agent = {**user_agent, "file_type": "config"}
+        user_agent = http_user_agent(user_agent)
 
         pretrained_model_name_or_path = str(pretrained_model_name_or_path)
 
@@ -336,7 +354,6 @@ class ConfigMixin:
                     subfolder=subfolder,
                     revision=revision,
                 )
-
             except RepositoryNotFoundError:
                 raise EnvironmentError(
                     f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier"
@@ -378,13 +395,23 @@ class ConfigMixin:
         try:
             # Load config dict
             config_dict = cls._dict_from_json_file(config_file)
+
+            commit_hash = extract_commit_hash(config_file)
         except (json.JSONDecodeError, UnicodeDecodeError):
             raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")
 
+        if not (return_unused_kwargs or return_commit_hash):
+            return config_dict
+
+        outputs = (config_dict,)
+
         if return_unused_kwargs:
-            return config_dict, kwargs
+            outputs += (kwargs,)
+
+        if return_commit_hash:
+            outputs += (commit_hash,)
 
-        return config_dict
+        return outputs
 
     @staticmethod
     def _get_init_keys(cls):

src/diffusers/dependency_versions_table.py

@@ -10,7 +10,8 @@ deps = {
     "filelock": "filelock",
     "flax": "flax>=0.4.1",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
-    "huggingface-hub": "huggingface-hub>=0.10.0",
+    "huggingface-hub": "huggingface-hub>=0.13.0",
+    "requests-mock": "requests-mock==1.10.0",
     "importlib_metadata": "importlib_metadata",
     "isort": "isort>=5.5.4",
     "jax": "jax>=0.2.8,!=0.3.2",

src/diffusers/models/modeling_utils.py

@@ -458,18 +458,34 @@ class ModelMixin(torch.nn.Module):
                 " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
             )
 
+        # Load config if we don't provide a configuration
+        config_path = pretrained_model_name_or_path
+
         user_agent = {
             "diffusers": __version__,
             "file_type": "model",
             "framework": "pytorch",
         }
 
-        # Load config if we don't provide a configuration
-        config_path = pretrained_model_name_or_path
-
-        # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
-        # Load model
-
+        # load config
+        config, unused_kwargs, commit_hash = cls.load_config(
+            config_path,
+            cache_dir=cache_dir,
+            return_unused_kwargs=True,
+            return_commit_hash=True,
+            force_download=force_download,
+            resume_download=resume_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            use_auth_token=use_auth_token,
+            revision=revision,
+            subfolder=subfolder,
+            device_map=device_map,
+            user_agent=user_agent,
+            **kwargs,
+        )
+
+        # load model
         model_file = None
         if from_flax:
             model_file = _get_model_file(
@@ -484,20 +500,7 @@ class ModelMixin(torch.nn.Module):
                 revision=revision,
                 subfolder=subfolder,
                 user_agent=user_agent,
-            )
-            config, unused_kwargs = cls.load_config(
-                config_path,
-                cache_dir=cache_dir,
-                return_unused_kwargs=True,
-                force_download=force_download,
-                resume_download=resume_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                revision=revision,
-                subfolder=subfolder,
-                device_map=device_map,
-                **kwargs,
+                commit_hash=commit_hash,
             )
             model = cls.from_config(config, **unused_kwargs)
 
@@ -520,6 +523,7 @@ class ModelMixin(torch.nn.Module):
                         revision=revision,
                         subfolder=subfolder,
                         user_agent=user_agent,
+                        commit_hash=commit_hash,
                     )
                 except:  # noqa: E722
                     pass
@@ -536,25 +540,12 @@ class ModelMixin(torch.nn.Module):
                     revision=revision,
                     subfolder=subfolder,
                     user_agent=user_agent,
+                    commit_hash=commit_hash,
                 )
 
             if low_cpu_mem_usage:
                 # Instantiate model with empty weights
                 with accelerate.init_empty_weights():
-                    config, unused_kwargs = cls.load_config(
-                        config_path,
-                        cache_dir=cache_dir,
-                        return_unused_kwargs=True,
-                        force_download=force_download,
-                        resume_download=resume_download,
-                        proxies=proxies,
-                        local_files_only=local_files_only,
-                        use_auth_token=use_auth_token,
-                        revision=revision,
-                        subfolder=subfolder,
-                        device_map=device_map,
-                        **kwargs,
-                    )
                     model = cls.from_config(config, **unused_kwargs)
 
                 # if device_map is None, load the state dict and move the params from meta device to the cpu
@@ -593,20 +584,6 @@ class ModelMixin(torch.nn.Module):
                     "error_msgs": [],
                 }
             else:
-                config, unused_kwargs = cls.load_config(
-                    config_path,
-                    cache_dir=cache_dir,
-                    return_unused_kwargs=True,
-                    force_download=force_download,
-                    resume_download=resume_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    revision=revision,
-                    subfolder=subfolder,
-                    device_map=device_map,
-                    **kwargs,
-                )
                 model = cls.from_config(config, **unused_kwargs)
 
                 state_dict = load_state_dict(model_file, variant=variant)
@@ -803,6 +780,7 @@ def _get_model_file(
     use_auth_token,
     user_agent,
     revision,
+    commit_hash=None,
 ):
     pretrained_model_name_or_path = str(pretrained_model_name_or_path)
     if os.path.isfile(pretrained_model_name_or_path):
@@ -840,7 +818,7 @@ def _get_model_file(
                     use_auth_token=use_auth_token,
                     user_agent=user_agent,
                     subfolder=subfolder,
-                    revision=revision,
+                    revision=revision or commit_hash,
                 )
                 warnings.warn(
                     f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.",
@@ -865,7 +843,7 @@ def _get_model_file(
                 use_auth_token=use_auth_token,
                 user_agent=user_agent,
                 subfolder=subfolder,
-                revision=revision,
+                revision=revision or commit_hash,
             )
             return model_file
 

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -954,7 +954,7 @@ def stable_unclip_image_noising_components(
     return image_normalizer, image_noising_scheduler
 
 
-def load_pipeline_from_original_stable_diffusion_ckpt(
+def download_from_original_stable_diffusion_ckpt(
     checkpoint_path: str,
     original_config_file: str = None,
     image_size: int = 512,

src/diffusers/schedulers/scheduling_utils.py

@@ -136,10 +136,11 @@ class SchedulerMixin:
         </Tip>
 
         """
-        config, kwargs = cls.load_config(
+        config, kwargs, commit_hash = cls.load_config(
             pretrained_model_name_or_path=pretrained_model_name_or_path,
             subfolder=subfolder,
             return_unused_kwargs=True,
+            return_commit_hash=True,
             **kwargs,
         )
         return cls.from_config(config, return_unused_kwargs=return_unused_kwargs, **kwargs)

src/diffusers/utils/__init__.py

@@ -35,7 +35,11 @@ from .constants import (
 from .deprecation_utils import deprecate
 from .doc_utils import replace_example_docstring
 from .dynamic_modules_utils import get_class_from_dynamic_module
-from .hub_utils import HF_HUB_OFFLINE, http_user_agent
+from .hub_utils import (
+    HF_HUB_OFFLINE,
+    extract_commit_hash,
+    http_user_agent,
+)
 from .import_utils import (
     ENV_VARS_TRUE_AND_AUTO_VALUES,
     ENV_VARS_TRUE_VALUES,

src/diffusers/utils/hub_utils.py

@@ -15,6 +15,7 @@
 
 
 import os
+import re
 import sys
 import traceback
 from pathlib import Path
@@ -22,6 +23,7 @@ from typing import Dict, Optional, Union
 from uuid import uuid4
 
 from huggingface_hub import HfFolder, ModelCard, ModelCardData, whoami
+from huggingface_hub.file_download import REGEX_COMMIT_HASH
 from huggingface_hub.utils import is_jinja_available
 
 from .. import __version__
@@ -132,6 +134,20 @@ def create_model_card(args, model_name):
     model_card.save(card_path)
 
 
+def extract_commit_hash(resolved_file: Optional[str], commit_hash: Optional[str] = None):
+    """
+    Extracts the commit hash from a resolved filename toward a cache file.
+    """
+    if resolved_file is None or commit_hash is not None:
+        return commit_hash
+    resolved_file = str(Path(resolved_file).as_posix())
+    search = re.search(r"snapshots/([^/]+)/", resolved_file)
+    if search is None:
+        return None
+    commit_hash = search.groups()[0]
+    return commit_hash if REGEX_COMMIT_HASH.match(commit_hash) else None
+
+
 # Old default cache path, potentially to be migrated.
 # This logic was more or less taken from `transformers`, with the following differences:
 # - Diffusers doesn't use custom environment variables to specify the cache path.
@@ -150,7 +166,7 @@ def move_cache(old_cache_dir: Optional[str] = None, new_cache_dir: Optional[str]
 
     old_cache_dir = Path(old_cache_dir).expanduser()
     new_cache_dir = Path(new_cache_dir).expanduser()
-    for old_blob_path in old_cache_dir.glob("**/blobs/*"):  #  move file blob by blob
+    for old_blob_path in old_cache_dir.glob("**/blobs/*"):
         if old_blob_path.is_file() and not old_blob_path.is_symlink():
             new_blob_path = new_cache_dir / old_blob_path.relative_to(old_cache_dir)
             new_blob_path.parent.mkdir(parents=True, exist_ok=True)

tests/test_modeling_common.py

@@ -20,6 +20,7 @@ import unittest.mock as mock
 from typing import Dict, List, Tuple
 
 import numpy as np
+import requests_mock
 import torch
 from requests.exceptions import HTTPError
 
@@ -29,6 +30,13 @@ from diffusers.utils import torch_device
 
 
 class ModelUtilsTest(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+
+        import diffusers
+
+        diffusers.utils.import_utils._safetensors_available = True
+
     def test_accelerate_loading_error_message(self):
         with self.assertRaises(ValueError) as error_context:
             UNet2DConditionModel.from_pretrained("hf-internal-testing/stable-diffusion-broken", subfolder="unet")
@@ -60,6 +68,37 @@ class ModelUtilsTest(unittest.TestCase):
             if p1.data.ne(p2.data).sum() > 0:
                 assert False, "Parameters not the same!"
 
+    def test_one_request_upon_cached(self):
+        # TODO: For some reason this test fails on MPS where no HEAD call is made.
+        if torch_device == "mps":
+            return
+
+        import diffusers
+
+        diffusers.utils.import_utils._safetensors_available = False
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with requests_mock.mock(real_http=True) as m:
+                UNet2DConditionModel.from_pretrained(
+                    "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet", cache_dir=tmpdirname
+                )
+
+            download_requests = [r.method for r in m.request_history]
+            assert download_requests.count("HEAD") == 2, "2 HEAD requests one for config, one for model"
+            assert download_requests.count("GET") == 2, "2 GET requests one for config, one for model"
+
+            with requests_mock.mock(real_http=True) as m:
+                UNet2DConditionModel.from_pretrained(
+                    "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet", cache_dir=tmpdirname
+                )
+
+            cache_requests = [r.method for r in m.request_history]
+            assert (
+                "HEAD" == cache_requests[0] and len(cache_requests) == 1
+            ), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
+
+        diffusers.utils.import_utils._safetensors_available = True
+
 
 class ModelTesterMixin:
     def test_from_save_pretrained(self):

tests/test_pipelines.py

@@ -25,6 +25,7 @@ import unittest.mock as mock
 
 import numpy as np
 import PIL
+import requests_mock
 import safetensors.torch
 import torch
 from parameterized import parameterized
@@ -61,14 +62,44 @@ torch.backends.cuda.matmul.allow_tf32 = False
 
 
 class DownloadTests(unittest.TestCase):
+    def test_one_request_upon_cached(self):
+        # TODO: For some reason this test fails on MPS where no HEAD call is made.
+        if torch_device == "mps":
+            return
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with requests_mock.mock(real_http=True) as m:
+                DiffusionPipeline.download(
+                    "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
+                )
+
+            download_requests = [r.method for r in m.request_history]
+            assert download_requests.count("HEAD") == 16, "15 calls to files + send_telemetry"
+            assert download_requests.count("GET") == 17, "15 calls to files + model_info + model_index.json"
+            assert (
+                len(download_requests) == 33
+            ), "2 calls per file (15 files) + send_telemetry, model_info and model_index.json"
+
+            with requests_mock.mock(real_http=True) as m:
+                DiffusionPipeline.download(
+                    "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
+                )
+
+            cache_requests = [r.method for r in m.request_history]
+            assert cache_requests.count("HEAD") == 1, "send_telemetry is only HEAD"
+            assert cache_requests.count("GET") == 1, "model info is only GET"
+            assert (
+                len(cache_requests) == 2
+            ), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
+
     def test_download_only_pytorch(self):
         with tempfile.TemporaryDirectory() as tmpdirname:
             # pipeline has Flax weights
-            _ = DiffusionPipeline.from_pretrained(
+            tmpdirname = DiffusionPipeline.download(
                 "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
             )
 
-            all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))]
+            all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
             files = [item for sublist in all_root_files for item in sublist]
 
             # None of the downloaded files should be a flax file even if we have some here:
@@ -101,13 +132,13 @@ class DownloadTests(unittest.TestCase):
     def test_download_safetensors(self):
         with tempfile.TemporaryDirectory() as tmpdirname:
             # pipeline has Flax weights
-            _ = DiffusionPipeline.from_pretrained(
+            tmpdirname = DiffusionPipeline.download(
                 "hf-internal-testing/tiny-stable-diffusion-pipe-safetensors",
                 safety_checker=None,
                 cache_dir=tmpdirname,
             )
 
-            all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))]
+            all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))]
             files = [item for sublist in all_root_files for item in sublist]
 
             # None of the downloaded files should be a pytorch file even if we have some here:
@@ -204,12 +235,10 @@ class DownloadTests(unittest.TestCase):
 
             other_format = ".bin" if safe_avail else ".safetensors"
             with tempfile.TemporaryDirectory() as tmpdirname:
-                StableDiffusionPipeline.from_pretrained(
+                tmpdirname = StableDiffusionPipeline.download(
                     "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname
                 )
-                all_root_files = [
-                    t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))
-                ]
+                all_root_files = [t[-1] for t in os.walk(tmpdirname)]
                 files = [item for sublist in all_root_files for item in sublist]
 
                 # None of the downloaded files should be a variant file even if we have some here:
@@ -232,12 +261,10 @@ class DownloadTests(unittest.TestCase):
             variant = "fp16"
 
             with tempfile.TemporaryDirectory() as tmpdirname:
-                StableDiffusionPipeline.from_pretrained(
+                tmpdirname = StableDiffusionPipeline.download(
                     "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant
                 )
-                all_root_files = [
-                    t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))
-                ]
+                all_root_files = [t[-1] for t in os.walk(tmpdirname)]
                 files = [item for sublist in all_root_files for item in sublist]
 
                 # None of the downloaded files should be a non-variant file even if we have some here:
@@ -262,14 +289,13 @@ class DownloadTests(unittest.TestCase):
             variant = "no_ema"
 
             with tempfile.TemporaryDirectory() as tmpdirname:
-                StableDiffusionPipeline.from_pretrained(
+                tmpdirname = StableDiffusionPipeline.download(
                     "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant
                 )
-                snapshots = os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots")
-                all_root_files = [t[-1] for t in os.walk(snapshots)]
+                all_root_files = [t[-1] for t in os.walk(tmpdirname)]
                 files = [item for sublist in all_root_files for item in sublist]
 
-                unet_files = os.listdir(os.path.join(snapshots, os.listdir(snapshots)[0], "unet"))
+                unet_files = os.listdir(os.path.join(tmpdirname, "unet"))
 
                 # Some of the downloaded files should be a non-variant file, check:
                 # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet
@@ -292,7 +318,7 @@ class DownloadTests(unittest.TestCase):
             for variant in [None, "no_ema"]:
                 with self.assertRaises(OSError) as error_context:
                     with tempfile.TemporaryDirectory() as tmpdirname:
-                        StableDiffusionPipeline.from_pretrained(
+                        tmpdirname = StableDiffusionPipeline.from_pretrained(
                             "hf-internal-testing/stable-diffusion-broken-variants",
                             cache_dir=tmpdirname,
                             variant=variant,
@@ -302,13 +328,11 @@ class DownloadTests(unittest.TestCase):
 
             # text encoder has fp16 variants so we can load it
             with tempfile.TemporaryDirectory() as tmpdirname:
-                pipe = StableDiffusionPipeline.from_pretrained(
+                tmpdirname = StableDiffusionPipeline.download(
                     "hf-internal-testing/stable-diffusion-broken-variants", cache_dir=tmpdirname, variant="fp16"
                 )
-                assert pipe is not None
 
-                snapshots = os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots")
-                all_root_files = [t[-1] for t in os.walk(snapshots)]
+                all_root_files = [t[-1] for t in os.walk(tmpdirname)]
                 files = [item for sublist in all_root_files for item in sublist]
 
                 # None of the downloaded files should be a non-variant file even if we have some here:
@@ -395,7 +419,7 @@ class CustomPipelineTests(unittest.TestCase):
 
     @slow
     @require_torch_gpu
-    def test_load_pipeline_from_git(self):
+    def test_download_from_git(self):
         clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
 
         feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)