[From pretrained] Allow returning local path (#1450)

Allow returning local path

src/diffusers/pipeline_utils.py

@@ -377,7 +377,8 @@ class DiffusionPipeline(ConfigMixin):
                 also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
                 model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
                 setting this argument to `True` will raise an error.
-
+            return_cached_folder (`bool`, *optional*, defaults to `False`):
+                If set to `True`, path to downloaded cached folder will be returned in addition to loaded pipeline.
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
                 specific pipeline class. The overwritten components are then directly passed to the pipelines
@@ -430,33 +431,7 @@ class DiffusionPipeline(ConfigMixin):
         sess_options = kwargs.pop("sess_options", None)
         device_map = kwargs.pop("device_map", None)
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
-
-        if low_cpu_mem_usage and not is_accelerate_available():
-            low_cpu_mem_usage = False
-            logger.warning(
-                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
-                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
-                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
-                " install accelerate\n```\n."
-            )
-
-        if device_map is not None and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `device_map=None`."
-            )
-
-        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
-            raise NotImplementedError(
-                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
-                " `low_cpu_mem_usage=False`."
-            )
-
-        if low_cpu_mem_usage is False and device_map is not None:
-            raise ValueError(
-                f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
-                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
-            )
+        return_cached_folder = kwargs.pop("return_cached_folder", False)
 
         # 1. Download the checkpoints and configs
         # use snapshot download here to get it working from from_pretrained
@@ -585,6 +560,33 @@ class DiffusionPipeline(ConfigMixin):
                 f"Keyword arguments {unused_kwargs} are not expected by {pipeline_class.__name__} and will be ignored."
             )
 
+        if low_cpu_mem_usage and not is_accelerate_available():
+            low_cpu_mem_usage = False
+            logger.warning(
+                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
+                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
+                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
+                " install accelerate\n```\n."
+            )
+
+        if device_map is not None and not is_torch_version(">=", "1.9.0"):
+            raise NotImplementedError(
+                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                " `device_map=None`."
+            )
+
+        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
+            raise NotImplementedError(
+                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
+                " `low_cpu_mem_usage=False`."
+            )
+
+        if low_cpu_mem_usage is False and device_map is not None:
+            raise ValueError(
+                f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
+                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
+            )
+
         # import it here to avoid circular import
         from diffusers import pipelines
 
@@ -704,6 +706,9 @@ class DiffusionPipeline(ConfigMixin):
 
         # 5. Instantiate the pipeline
         model = pipeline_class(**init_kwargs)
+
+        if return_cached_folder:
+            return model, cached_folder
         return model
 
     @staticmethod

tests/test_pipelines.py

@@ -95,6 +95,35 @@ class DownloadTests(unittest.TestCase):
             # We need to never convert this tiny model to safetensors for this test to pass
             assert not any(f.endswith(".safetensors") for f in files)
 
+    def test_returned_cached_folder(self):
+        prompt = "hello"
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
+        )
+        _, local_path = StableDiffusionPipeline.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None, return_cached_folder=True
+        )
+        pipe_2 = StableDiffusionPipeline.from_pretrained(local_path)
+
+        pipe = pipe.to(torch_device)
+        pipe_2 = pipe.to(torch_device)
+        if torch_device == "mps":
+            # device type MPS is not supported for torch.Generator() api.
+            generator = torch.manual_seed(0)
+        else:
+            generator = torch.Generator(device=torch_device).manual_seed(0)
+
+        out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
+
+        if torch_device == "mps":
+            # device type MPS is not supported for torch.Generator() api.
+            generator = torch.manual_seed(0)
+        else:
+            generator = torch.Generator(device=torch_device).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_download_safetensors(self):
         with tempfile.TemporaryDirectory() as tmpdirname:
             # pipeline has Flax weights