apply_faster_cache -> apply_fastercache

Author: a-r-r-o-w

src/diffusers/__init__.py

@@ -423,7 +423,7 @@
             "WuerstchenCombinedPipeline",
             "WuerstchenDecoderPipeline",
             "WuerstchenPriorPipeline",
-            "apply_faster_cache",
+            "apply_fastercache",
         ]
     )
 
@@ -912,7 +912,7 @@
             WuerstchenCombinedPipeline,
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
-            apply_faster_cache,
+            apply_fastercache,
         )
 
     try:

src/diffusers/pipelines/__init__.py

@@ -58,7 +58,7 @@
         "StableDiffusionMixin",
         "ImagePipelineOutput",
     ]
-    _import_structure["faster_cache_utils"] = ["FasterCacheConfig", "apply_faster_cache"]
+    _import_structure["faster_cache_utils"] = ["FasterCacheConfig", "apply_fastercache"]
     _import_structure["deprecated"].extend(
         [
             "PNDMPipeline",
@@ -450,7 +450,7 @@
         from .ddpm import DDPMPipeline
         from .deprecated import KarrasVePipeline, LDMPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline
         from .dit import DiTPipeline
-        from .faster_cache_utils import FasterCacheConfig, apply_faster_cache
+        from .fastercache_utils import FasterCacheConfig, apply_fastercache
         from .latent_diffusion import LDMSuperResolutionPipeline
         from .pipeline_utils import (
             AudioPipelineOutput,

src/diffusers/pipelines/faster_cache_utils.py renamed to src/diffusers/pipelines/fastercache_utils.py

@@ -225,7 +225,7 @@ def reset(self):
         self.is_guidance_distilled = None
 
 
-def apply_faster_cache(
+def apply_fastercache(
     pipeline: DiffusionPipeline,
     config: Optional[FasterCacheConfig] = None,
 ) -> None:
@@ -241,7 +241,7 @@ def apply_faster_cache(
     Example:
     ```python
     >>> import torch
-    >>> from diffusers import CogVideoXPipeline, FasterCacheConfig, apply_faster_cache
+    >>> from diffusers import CogVideoXPipeline, FasterCacheConfig, apply_fastercache
 
     >>> pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
     >>> pipe.to("cuda")
@@ -255,7 +255,7 @@ def apply_faster_cache(
     ...     attention_weight_callback=lambda _: 0.3,
     ...     tensor_format="BFCHW",
     ... )
-    >>> apply_faster_cache(pipe, config)
+    >>> apply_fastercache(pipe, config)
     ```
     """
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -2552,5 +2552,5 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch", "transformers"])
 
 
-def apply_faster_cache(*args, **kwargs):
-    requires_backends(apply_faster_cache, ["torch", "transformers"])
+def apply_fastercache(*args, **kwargs):
+    requires_backends(apply_fastercache, ["torch", "transformers"])

tests/pipelines/test_pipelines_common.py

@@ -28,7 +28,7 @@
     StableDiffusionPipeline,
     StableDiffusionXLPipeline,
     UNet2DConditionModel,
-    apply_faster_cache,
+    apply_fastercache,
 )
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.loaders import FluxIPAdapterMixin, IPAdapterMixin
@@ -37,7 +37,7 @@
 from diffusers.models.unets.unet_3d_condition import UNet3DConditionModel
 from diffusers.models.unets.unet_i2vgen_xl import I2VGenXLUNet
 from diffusers.models.unets.unet_motion_model import UNetMotionModel
-from diffusers.pipelines.faster_cache_utils import FasterCacheBlockHook, FasterCacheDenoiserHook
+from diffusers.pipelines.fastercache_utils import FasterCacheBlockHook, FasterCacheDenoiserHook
 from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.utils import logging
@@ -2291,21 +2291,21 @@ def test_fastercache_basic_warning_or_errors_raised(self):
         # Check if warning is raised when no FasterCacheConfig is provided
         pipe = self.pipeline_class(**components)
         with CaptureLogger(logger) as cap_logger:
-            apply_faster_cache(pipe)
+            apply_fastercache(pipe)
         self.assertTrue("No FasterCacheConfig provided" in cap_logger.out)
 
         # Check if warning is raise when no attention_weight_callback is provided
         pipe = self.pipeline_class(**components)
         with CaptureLogger(logger) as cap_logger:
             config = FasterCacheConfig(spatial_attention_block_skip_range=2, attention_weight_callback=None)
-            apply_faster_cache(pipe, config)
+            apply_fastercache(pipe, config)
         self.assertTrue("No `attention_weight_callback` provided when enabling FasterCache" in cap_logger.out)
 
         # Check if error raised when unsupported tensor format used
         pipe = self.pipeline_class(**components)
         with self.assertRaises(ValueError):
             config = FasterCacheConfig(spatial_attention_block_skip_range=2, tensor_format="BFHWC")
-            apply_faster_cache(pipe, config)
+            apply_fastercache(pipe, config)
 
     def test_fastercache_inference(self, expected_atol: float = 0.1):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
@@ -2321,7 +2321,7 @@ def test_fastercache_inference(self, expected_atol: float = 0.1):
         original_image_slice = output.flatten()
         original_image_slice = np.concatenate((original_image_slice[:8], original_image_slice[-8:]))
 
-        apply_faster_cache(pipe, self.fastercache_config)
+        apply_fastercache(pipe, self.fastercache_config)
 
         inputs = self.get_dummy_inputs(device)
         inputs["num_inference_steps"] = 4
@@ -2353,7 +2353,7 @@ def test_fastercache_state(self):
         pipe = self.pipeline_class(**components)
         pipe.set_progress_bar_config(disable=None)
 
-        apply_faster_cache(pipe, self.fastercache_config)
+        apply_fastercache(pipe, self.fastercache_config)
 
         expected_hooks = 0
         if self.fastercache_config.spatial_attention_block_skip_range is not None: