[Pipeline download] Improve pipeline download for index and passed co…

src/diffusers/pipelines/pipeline_utils.py

@@ -134,7 +134,7 @@ class AudioPipelineOutput(BaseOutput):
     audios: np.ndarray
 
 
-def is_safetensors_compatible(filenames, variant=None) -> bool:
+def is_safetensors_compatible(filenames, variant=None, passed_components=None) -> bool:
     """
     Checking for safetensors compatibility:
     - By default, all models are saved with the default pytorch serialization, so we use the list of default pytorch
@@ -150,9 +150,14 @@ def is_safetensors_compatible(filenames, variant=None) -> bool:
 
     sf_filenames = set()
 
+    passed_components = passed_components or []
+
     for filename in filenames:
         _, extension = os.path.splitext(filename)
 
+        if len(filename.split("/")) == 2 and filename.split("/")[0] in passed_components:
+            continue
+
         if extension == ".bin":
             pt_filenames.append(filename)
         elif extension == ".safetensors":
@@ -163,10 +168,8 @@ def is_safetensors_compatible(filenames, variant=None) -> bool:
         path, filename = os.path.split(filename)
         filename, extension = os.path.splitext(filename)
 
-        if filename == "pytorch_model":
-            filename = "model"
-        elif filename == f"pytorch_model.{variant}":
-            filename = f"model.{variant}"
+        if filename.startswith("pytorch_model"):
+            filename = filename.replace("pytorch_model", "model")
         else:
             filename = filename
 
@@ -196,24 +199,51 @@ def variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLi
     weight_prefixes = [w.split(".")[0] for w in weight_names]
     # .bin, .safetensors, ...
     weight_suffixs = [w.split(".")[-1] for w in weight_names]
+    # -00001-of-00002
+    transformers_index_format = "\d{5}-of-\d{5}"
+
+    if variant is not None:
+        # `diffusion_pytorch_model.fp16.bin` as well as `model.fp16-00001-of-00002.safetenstors`
+        variant_file_re = re.compile(
+            f"({'|'.join(weight_prefixes)})\.({variant}|{variant}-{transformers_index_format})\.({'|'.join(weight_suffixs)})$"
+        )
+        # `text_encoder/pytorch_model.bin.index.fp16.json`
+        variant_index_re = re.compile(
+            f"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.{variant}\.json$"
+        )
 
-    variant_file_regex = (
-        re.compile(f"({'|'.join(weight_prefixes)})(.{variant}.)({'|'.join(weight_suffixs)})")
-        if variant is not None
-        else None
+    # `diffusion_pytorch_model.bin` as well as `model-00001-of-00002.safetenstors`
+    non_variant_file_re = re.compile(
+        f"({'|'.join(weight_prefixes)})(-{transformers_index_format})?\.({'|'.join(weight_suffixs)})$"
     )
-    non_variant_file_regex = re.compile(f"{'|'.join(weight_names)}")
+    # `text_encoder/pytorch_model.bin.index.json`
+    non_variant_index_re = re.compile(f"({'|'.join(weight_prefixes)})\.({'|'.join(weight_suffixs)})\.index\.json")
 
     if variant is not None:
-        variant_filenames = {f for f in filenames if variant_file_regex.match(f.split("/")[-1]) is not None}
+        variant_weights = {f for f in filenames if variant_file_re.match(f.split("/")[-1]) is not None}
+        variant_indexes = {f for f in filenames if variant_index_re.match(f.split("/")[-1]) is not None}
+        variant_filenames = variant_weights | variant_indexes
     else:
         variant_filenames = set()
 
-    non_variant_filenames = {f for f in filenames if non_variant_file_regex.match(f.split("/")[-1]) is not None}
+    non_variant_weights = {f for f in filenames if non_variant_file_re.match(f.split("/")[-1]) is not None}
+    non_variant_indexes = {f for f in filenames if non_variant_index_re.match(f.split("/")[-1]) is not None}
+    non_variant_filenames = non_variant_weights | non_variant_indexes
 
+    # all variant filenames will be used by default
     usable_filenames = set(variant_filenames)
+
+    def convert_to_variant(filename):
+        if "index" in filename:
+            variant_filename = filename.replace("index", f"index.{variant}")
+        elif re.compile(f"^(.*?){transformers_index_format}").match(filename) is not None:
+            variant_filename = f"{filename.split('-')[0]}.{variant}-{'-'.join(filename.split('-')[1:])}"
+        else:
+            variant_filename = f"{filename.split('.')[0]}.{variant}.{filename.split('.')[1]}"
+        return variant_filename
+
     for f in non_variant_filenames:
-        variant_filename = f"{f.split('.')[0]}.{variant}.{f.split('.')[1]}"
+        variant_filename = convert_to_variant(f)
         if variant_filename not in usable_filenames:
             usable_filenames.add(f)
 
@@ -292,6 +322,27 @@ def get_class_obj_and_candidates(library_name, class_name, importable_classes, p
     return class_obj, class_candidates
 
 
+def _get_pipeline_class(class_obj, config, custom_pipeline=None, cache_dir=None, revision=None):
+    if custom_pipeline is not None:
+        if custom_pipeline.endswith(".py"):
+            path = Path(custom_pipeline)
+            # decompose into folder & file
+            file_name = path.name
+            custom_pipeline = path.parent.absolute()
+        else:
+            file_name = CUSTOM_PIPELINE_FILE_NAME
+
+        return get_class_from_dynamic_module(
+            custom_pipeline, module_file=file_name, cache_dir=cache_dir, revision=revision
+        )
+
+    if class_obj != DiffusionPipeline:
+        return class_obj
+
+    diffusers_module = importlib.import_module(class_obj.__module__.split(".")[0])
+    return getattr(diffusers_module, config["_class_name"])
+
+
 def load_sub_model(
     library_name: str,
     class_name: str,
@@ -779,7 +830,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         device_map = kwargs.pop("device_map", None)
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
         variant = kwargs.pop("variant", None)
-        kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
+        use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
 
         # 1. Download the checkpoints and configs
         # use snapshot download here to get it working from from_pretrained
@@ -794,8 +845,11 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 use_auth_token=use_auth_token,
                 revision=revision,
                 from_flax=from_flax,
+                use_safetensors=use_safetensors,
                 custom_pipeline=custom_pipeline,
+                custom_revision=custom_revision,
                 variant=variant,
+                **kwargs,
             )
         else:
             cached_folder = pretrained_model_name_or_path
@@ -810,29 +864,17 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             for folder in os.listdir(cached_folder):
                 folder_path = os.path.join(cached_folder, folder)
                 is_folder = os.path.isdir(folder_path) and folder in config_dict
-                variant_exists = is_folder and any(path.split(".")[1] == variant for path in os.listdir(folder_path))
+                variant_exists = is_folder and any(
+                    p.split(".")[1].startswith(variant) for p in os.listdir(folder_path)
+                )
                 if variant_exists:
                     model_variants[folder] = variant
 
         # 3. Load the pipeline class, if using custom module then load it from the hub
         # if we load from explicit class, let's use it
-        if custom_pipeline is not None:
-            if custom_pipeline.endswith(".py"):
-                path = Path(custom_pipeline)
-                # decompose into folder & file
-                file_name = path.name
-                custom_pipeline = path.parent.absolute()
-            else:
-                file_name = CUSTOM_PIPELINE_FILE_NAME
-
-            pipeline_class = get_class_from_dynamic_module(
-                custom_pipeline, module_file=file_name, cache_dir=cache_dir, revision=custom_revision
-            )
-        elif cls != DiffusionPipeline:
-            pipeline_class = cls
-        else:
-            diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
-            pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
+        pipeline_class = _get_pipeline_class(
+            cls, config_dict, custom_pipeline=custom_pipeline, cache_dir=cache_dir, revision=custom_revision
+        )
 
         # DEPRECATED: To be removed in 1.0.0
         if pipeline_class.__name__ == "StableDiffusionInpaintPipeline" and version.parse(
@@ -1095,6 +1137,7 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
         revision = kwargs.pop("revision", None)
         from_flax = kwargs.pop("from_flax", False)
         custom_pipeline = kwargs.pop("custom_pipeline", None)
+        custom_revision = kwargs.pop("custom_revision", None)
         variant = kwargs.pop("variant", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
 
@@ -1153,7 +1196,7 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
             # this enables downloading schedulers, tokenizers, ...
             allow_patterns += [os.path.join(k, "*") for k in folder_names if k not in model_folder_names]
             # also allow downloading config.json files with the model
-            allow_patterns += [os.path.join(k, "*.json") for k in model_folder_names]
+            allow_patterns += [os.path.join(k, "config.json") for k in model_folder_names]
 
             allow_patterns += [
                 SCHEDULER_CONFIG_NAME,
@@ -1162,17 +1205,28 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
                 CUSTOM_PIPELINE_FILE_NAME,
             ]
 
+            # retrieve passed components that should not be downloaded
+            pipeline_class = _get_pipeline_class(
+                cls, config_dict, custom_pipeline=custom_pipeline, cache_dir=cache_dir, revision=custom_revision
+            )
+            expected_components, _ = cls._get_signature_keys(pipeline_class)
+            passed_components = [k for k in expected_components if k in kwargs]
+
             if (
                 use_safetensors
                 and not allow_pickle
-                and not is_safetensors_compatible(model_filenames, variant=variant)
+                and not is_safetensors_compatible(
+                    model_filenames, variant=variant, passed_components=passed_components
+                )
             ):
                 raise EnvironmentError(
                     f"Could not found the necessary `safetensors` weights in {model_filenames} (variant={variant})"
                 )
             if from_flax:
                 ignore_patterns = ["*.bin", "*.safetensors", "*.onnx", "*.pb"]
-            elif use_safetensors and is_safetensors_compatible(model_filenames, variant=variant):
+            elif use_safetensors and is_safetensors_compatible(
+                model_filenames, variant=variant, passed_components=passed_components
+            ):
                 ignore_patterns = ["*.bin", "*.msgpack"]
 
                 safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(".safetensors")}
@@ -1194,6 +1248,13 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
                         f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(bin_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\nIf this behavior is not expected, please check your folder structure."
                     )
 
+            # Don't download any objects that are passed
+            allow_patterns = [
+                p for p in allow_patterns if not (len(p.split("/")) == 2 and p.split("/")[0] in passed_components)
+            ]
+            # Don't download index files of forbidden patterns either
+            ignore_patterns = ignore_patterns + [f"{i}.index.*json" for i in ignore_patterns]
+
             re_ignore_pattern = [re.compile(fnmatch.translate(p)) for p in ignore_patterns]
             re_allow_pattern = [re.compile(fnmatch.translate(p)) for p in allow_patterns]
 

tests/test_pipelines.py

@@ -78,9 +78,7 @@ def test_one_request_upon_cached(self):
 
         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
-                )
+                DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-pipe", cache_dir=tmpdirname)
 
             download_requests = [r.method for r in m.request_history]
             assert download_requests.count("HEAD") == 15, "15 calls to files"
@@ -101,6 +99,55 @@ def test_one_request_upon_cached(self):
                 len(cache_requests) == 2
             ), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
 
+    def test_less_downloads_passed_object(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            cached_folder = DiffusionPipeline.download(
+                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
+            )
+
+            # make sure safety checker is not downloaded
+            assert "safety_checker" not in os.listdir(cached_folder)
+
+            # make sure rest is downloaded
+            assert "unet" in os.listdir(cached_folder)
+            assert "tokenizer" in os.listdir(cached_folder)
+            assert "vae" in os.listdir(cached_folder)
+            assert "model_index.json" in os.listdir(cached_folder)
+            assert "scheduler" in os.listdir(cached_folder)
+            assert "feature_extractor" in os.listdir(cached_folder)
+
+    def test_less_downloads_passed_object_calls(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]
+            # 15 - 2 because no call to config or model file for `safety_checker`
+            assert download_requests.count("HEAD") == 13, "13 calls to files"
+            # 17 - 2 because no call to config or model file for `safety_checker`
+            assert download_requests.count("GET") == 15, "13 calls to files + model_info + model_index.json"
+            assert (
+                len(download_requests) == 28
+            ), "2 calls per file (13 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, "model_index.json 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
@@ -165,6 +212,54 @@ def test_download_safetensors(self):
             # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
             assert not any(f.endswith(".bin") for f in files)
 
+    def test_download_safetensors_index(self):
+        for variant in ["fp16", None]:
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                tmpdirname = DiffusionPipeline.download(
+                    "hf-internal-testing/tiny-stable-diffusion-pipe-indexes",
+                    cache_dir=tmpdirname,
+                    use_safetensors=True,
+                    variant=variant,
+                )
+
+                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 safetensors file even if we have some here:
+                # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder
+                if variant is None:
+                    assert not any("fp16" in f for f in files)
+                else:
+                    model_files = [f for f in files if "safetensors" in f]
+                    assert all("fp16" in f for f in model_files)
+
+                assert len([f for f in files if ".safetensors" in f]) == 8
+                assert not any(".bin" in f for f in files)
+
+    def test_download_bin_index(self):
+        for variant in ["fp16", None]:
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                tmpdirname = DiffusionPipeline.download(
+                    "hf-internal-testing/tiny-stable-diffusion-pipe-indexes",
+                    cache_dir=tmpdirname,
+                    use_safetensors=False,
+                    variant=variant,
+                )
+
+                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 safetensors file even if we have some here:
+                # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder
+                if variant is None:
+                    assert not any("fp16" in f for f in files)
+                else:
+                    model_files = [f for f in files if "bin" in f]
+                    assert all("fp16" in f for f in model_files)
+
+                assert len([f for f in files if ".bin" in f]) == 8
+                assert not any(".safetensors" in f for f in files)
+
     def test_download_no_safety_checker(self):
         prompt = "hello"
         pipe = StableDiffusionPipeline.from_pretrained(
@@ -362,6 +457,33 @@ def test_download_broken_variant(self):
 
         diffusers.utils.import_utils._safetensors_available = True
 
+    def test_local_save_load_index(self):
+        prompt = "hello"
+        for variant in [None, "fp16"]:
+            for use_safe in [True, False]:
+                pipe = StableDiffusionPipeline.from_pretrained(
+                    "hf-internal-testing/tiny-stable-diffusion-pipe-indexes",
+                    variant=variant,
+                    use_safetensors=use_safe,
+                    safety_checker=None,
+                )
+                pipe = pipe.to(torch_device)
+                generator = torch.manual_seed(0)
+                out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
+
+                with tempfile.TemporaryDirectory() as tmpdirname:
+                    pipe.save_pretrained(tmpdirname)
+                    pipe_2 = StableDiffusionPipeline.from_pretrained(
+                        tmpdirname, safe_serialization=use_safe, variant=variant
+                    )
+                    pipe_2 = pipe_2.to(torch_device)
+
+                generator = torch.manual_seed(0)
+
+                out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
+
+                assert np.max(np.abs(out - out_2)) < 1e-3
+
     def test_text_inversion_download(self):
         pipe = StableDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None