[ONNX] Don't download ONNX model by default

src/diffusers/pipelines/pipeline_utils.py

@@ -494,6 +494,7 @@ class DiffusionPipeline(ConfigMixin):
     _optional_components = []
     _exclude_from_cpu_offload = []
     _load_connected_pipes = False
+    _is_onnx = False
 
     def register_modules(self, **kwargs):
         # import it here to avoid circular import
@@ -839,6 +840,11 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 If set to `None`, the safetensors weights are downloaded if they're available **and** if the
                 safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
                 weights. If set to `False`, safetensors weights are not loaded.
+            use_onnx (`bool`, *optional*, defaults to `None`):
+                If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
+                will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
+                `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
+                with `.onnx` and `.pb`.
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
                 class). The overwritten components are passed directly to the pipelines `__init__` method. See example
@@ -1268,6 +1274,15 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
             variant (`str`, *optional*):
                 Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
                 loading `from_flax`.
+            use_safetensors (`bool`, *optional*, defaults to `None`):
+                If set to `None`, the safetensors weights are downloaded if they're available **and** if the
+                safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
+                weights. If set to `False`, safetensors weights are not loaded.
+            use_onnx (`bool`, *optional*, defaults to `False`):
+                If set to `True`, ONNX weights will always be downloaded if present. If set to `False`, ONNX weights
+                will never be downloaded. By default `use_onnx` defaults to the `_is_onnx` class attribute which is
+                `False` for non-ONNX pipelines and `True` for ONNX pipelines. ONNX weights include both files ending
+                with `.onnx` and `.pb`.
 
         Returns:
             `os.PathLike`:
@@ -1293,6 +1308,7 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
         custom_revision = kwargs.pop("custom_revision", None)
         variant = kwargs.pop("variant", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
+        use_onnx = kwargs.pop("use_onnx", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
 
         if use_safetensors and not is_safetensors_available():
@@ -1364,7 +1380,7 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
                     pretrained_model_name, use_auth_token, variant, revision, model_filenames
                 )
 
-            model_folder_names = {os.path.split(f)[0] for f in model_filenames}
+            model_folder_names = {os.path.split(f)[0] for f in model_filenames if os.path.split(f)[0] in folder_names}
 
             # all filenames compatible with variant will be added
             allow_patterns = list(model_filenames)
@@ -1411,6 +1427,10 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
             ):
                 ignore_patterns = ["*.bin", "*.msgpack"]
 
+                use_onnx = use_onnx if use_onnx is not None else pipeline_class._is_onnx
+                if not use_onnx:
+                    ignore_patterns += ["*.onnx", "*.pb"]
+
                 safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(".safetensors")}
                 safetensors_model_filenames = {f for f in model_filenames if f.endswith(".safetensors")}
                 if (
@@ -1423,6 +1443,10 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
             else:
                 ignore_patterns = ["*.safetensors", "*.msgpack"]
 
+                use_onnx = use_onnx if use_onnx is not None else pipeline_class._is_onnx
+                if not use_onnx:
+                    ignore_patterns += ["*.onnx", "*.pb"]
+
                 bin_variant_filenames = {f for f in variant_filenames if f.endswith(".bin")}
                 bin_model_filenames = {f for f in model_filenames if f.endswith(".bin")}
                 if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames:

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py

@@ -41,6 +41,7 @@ class OnnxStableDiffusionPipeline(DiffusionPipeline):
     feature_extractor: CLIPImageProcessor
 
     _optional_components = ["safety_checker", "feature_extractor"]
+    _is_onnx = True
 
     def __init__(
         self,

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -98,6 +98,7 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
     feature_extractor: CLIPImageProcessor
 
     _optional_components = ["safety_checker", "feature_extractor"]
+    _is_onnx = True
 
     def __init__(
         self,

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py

@@ -90,6 +90,7 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):
     feature_extractor: CLIPImageProcessor
 
     _optional_components = ["safety_checker", "feature_extractor"]
+    _is_onnx = True
 
     def __init__(
         self,

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -67,6 +67,7 @@ class OnnxStableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
     _optional_components = ["safety_checker", "feature_extractor"]
+    _is_onnx = True
 
     vae_encoder: OnnxRuntimeModel
     vae_decoder: OnnxRuntimeModel

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py

@@ -46,6 +46,8 @@ def preprocess(image):
 
 
 class OnnxStableDiffusionUpscalePipeline(StableDiffusionUpscalePipeline):
+    _is_onnx = True
+
     def __init__(
         self,
         vae: OnnxRuntimeModel,

tests/pipelines/test_pipelines.py

@@ -310,6 +310,49 @@ def test_download_bin_index(self):
                 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_openvino_by_default(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            tmpdirname = DiffusionPipeline.download(
+                "hf-internal-testing/tiny-stable-diffusion-open-vino",
+                cache_dir=tmpdirname,
+            )
+
+            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]
+
+            # make sure that by default no openvino weights are downloaded
+            assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
+            assert not any("openvino_" in f for f in files)
+
+    def test_download_no_onnx_by_default(self):
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            tmpdirname = DiffusionPipeline.download(
+                "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline",
+                cache_dir=tmpdirname,
+            )
+
+            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]
+
+            # make sure that by default no onnx weights are downloaded
+            assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
+            assert not any((f.endswith(".onnx") or f.endswith(".pb")) for f in files)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            tmpdirname = DiffusionPipeline.download(
+                "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline",
+                cache_dir=tmpdirname,
+                use_onnx=True,
+            )
+
+            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]
+
+            # if `use_onnx` is specified make sure weights are downloaded
+            assert any((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files)
+            assert any((f.endswith(".onnx")) for f in files)
+            assert any((f.endswith(".pb")) for f in files)
+
     def test_download_no_safety_checker(self):
         prompt = "hello"
         pipe = StableDiffusionPipeline.from_pretrained(