Temp Revert "[Core] better support offloading when side loading is enabled…

src/diffusers/loaders.py

@@ -45,7 +45,6 @@
 
 if is_accelerate_available():
     from accelerate import init_empty_weights
-    from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
     from accelerate.utils import set_module_tensor_to_device
 
 logger = logging.get_logger(__name__)
@@ -779,21 +778,6 @@ def load_textual_inversion(
                 f" `{self.load_textual_inversion.__name__}`"
             )
 
-        # Remove any existing hooks.
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-        recursive = False
-        for _, component in self.components.items():
-            if isinstance(component, nn.Module):
-                if hasattr(component, "_hf_hook"):
-                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
-                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_textual_inversion()`, the previous hooks will be first removed. Then the textual inversion parameters will be loaded and the hooks will be applied again."
-                    )
-                    recursive = is_sequential_cpu_offload
-                    remove_hook_from_module(component, recurse=recursive)
-
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
         force_download = kwargs.pop("force_download", False)
         resume_download = kwargs.pop("resume_download", False)
@@ -947,12 +931,6 @@ def load_textual_inversion(
         for token_id, embedding in token_ids_and_embeddings:
             text_encoder.get_input_embeddings().weight.data[token_id] = embedding
 
-        # offload back
-        if is_model_cpu_offload:
-            self.enable_model_cpu_offload()
-        elif is_sequential_cpu_offload:
-            self.enable_sequential_cpu_offload()
-
 
 class LoraLoaderMixin:
     r"""
@@ -984,21 +962,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
             kwargs (`dict`, *optional*):
                 See [`~loaders.LoraLoaderMixin.lora_state_dict`].
         """
-        # Remove any existing hooks.
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-        recurive = False
-        for _, component in self.components.items():
-            if isinstance(component, nn.Module):
-                if hasattr(component, "_hf_hook"):
-                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
-                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    recurive = is_sequential_cpu_offload
-                    remove_hook_from_module(component, recurse=recurive)
-
         state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
         self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
         self.load_lora_into_text_encoder(
@@ -1008,12 +971,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
             lora_scale=self.lora_scale,
         )
 
-        # Offload back.
-        if is_model_cpu_offload:
-            self.enable_model_cpu_offload()
-        elif is_sequential_cpu_offload:
-            self.enable_sequential_cpu_offload()
-
     @classmethod
     def lora_state_dict(
         cls,

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -1549,26 +1549,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL
         # pipeline.
-
-        # Remove any existing hooks.
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
-        else:
-            raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
-
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-        recursive = False
-        for _, component in self.components.items():
-            if isinstance(component, torch.nn.Module):
-                if hasattr(component, "_hf_hook"):
-                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
-                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    recursive = is_sequential_cpu_offload
-                    remove_hook_from_module(component, recurse=recursive)
         state_dict, network_alphas = self.lora_state_dict(
             pretrained_model_name_or_path_or_dict,
             unet_config=self.unet.config,
@@ -1596,12 +1576,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
                 lora_scale=self.lora_scale,
             )
 
-        # Offload back.
-        if is_model_cpu_offload:
-            self.enable_model_cpu_offload()
-        elif is_sequential_cpu_offload:
-            self.enable_sequential_cpu_offload()
-
     @classmethod
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
     def save_lora_weights(

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -1216,26 +1216,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL
         # pipeline.
-
-        # Remove any existing hooks.
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
-        else:
-            raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
-
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-        recursive = False
-        for _, component in self.components.items():
-            if isinstance(component, torch.nn.Module):
-                if hasattr(component, "_hf_hook"):
-                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
-                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    recursive = is_sequential_cpu_offload
-                    remove_hook_from_module(component, recurse=recursive)
         state_dict, network_alphas = self.lora_state_dict(
             pretrained_model_name_or_path_or_dict,
             unet_config=self.unet.config,
@@ -1263,12 +1243,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
                 lora_scale=self.lora_scale,
             )
 
-        # Offload back.
-        if is_model_cpu_offload:
-            self.enable_model_cpu_offload()
-        elif is_sequential_cpu_offload:
-            self.enable_sequential_cpu_offload()
-
     @classmethod
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
     def save_lora_weights(

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -922,26 +922,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL
         # pipeline.
-
-        # Remove any existing hooks.
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
-        else:
-            raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
-
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-        recursive = False
-        for _, component in self.components.items():
-            if isinstance(component, torch.nn.Module):
-                if hasattr(component, "_hf_hook"):
-                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
-                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    recursive = is_sequential_cpu_offload
-                    remove_hook_from_module(component, recurse=recursive)
         state_dict, network_alphas = self.lora_state_dict(
             pretrained_model_name_or_path_or_dict,
             unet_config=self.unet.config,
@@ -969,12 +949,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
                 lora_scale=self.lora_scale,
             )
 
-        # Offload back.
-        if is_model_cpu_offload:
-            self.enable_model_cpu_offload()
-        elif is_sequential_cpu_offload:
-            self.enable_sequential_cpu_offload()
-
     @classmethod
     def save_lora_weights(
         self,

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -1072,26 +1072,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL
         # pipeline.
-
-        # Remove any existing hooks.
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
-        else:
-            raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
-
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-        recursive = False
-        for _, component in self.components.items():
-            if isinstance(component, torch.nn.Module):
-                if hasattr(component, "_hf_hook"):
-                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
-                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    recursive = is_sequential_cpu_offload
-                    remove_hook_from_module(component, recurse=recursive)
         state_dict, network_alphas = self.lora_state_dict(
             pretrained_model_name_or_path_or_dict,
             unet_config=self.unet.config,
@@ -1119,12 +1099,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
                 lora_scale=self.lora_scale,
             )
 
-        # Offload back.
-        if is_model_cpu_offload:
-            self.enable_model_cpu_offload()
-        elif is_sequential_cpu_offload:
-            self.enable_sequential_cpu_offload()
-
     @classmethod
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
     def save_lora_weights(

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -1392,26 +1392,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
         # We could have accessed the unet config from `lora_state_dict()` too. We pass
         # it here explicitly to be able to tell that it's coming from an SDXL
         # pipeline.
-
-        # Remove any existing hooks.
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate.hooks import AlignDevicesHook, CpuOffload, remove_hook_from_module
-        else:
-            raise ImportError("Offloading requires `accelerate v0.17.0` or higher.")
-
-        is_model_cpu_offload = False
-        is_sequential_cpu_offload = False
-        recursive = False
-        for _, component in self.components.items():
-            if isinstance(component, torch.nn.Module):
-                if hasattr(component, "_hf_hook"):
-                    is_model_cpu_offload = isinstance(getattr(component, "_hf_hook"), CpuOffload)
-                    is_sequential_cpu_offload = isinstance(getattr(component, "_hf_hook"), AlignDevicesHook)
-                    logger.info(
-                        "Accelerate hooks detected. Since you have called `load_lora_weights()`, the previous hooks will be first removed. Then the LoRA parameters will be loaded and the hooks will be applied again."
-                    )
-                    recursive = is_sequential_cpu_offload
-                    remove_hook_from_module(component, recurse=recursive)
         state_dict, network_alphas = self.lora_state_dict(
             pretrained_model_name_or_path_or_dict,
             unet_config=self.unet.config,
@@ -1439,12 +1419,6 @@ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Di
                 lora_scale=self.lora_scale,
             )
 
-        # Offload back.
-        if is_model_cpu_offload:
-            self.enable_model_cpu_offload()
-        elif is_sequential_cpu_offload:
-            self.enable_sequential_cpu_offload()
-
     @classmethod
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
     def save_lora_weights(

tests/models/test_lora_layers.py

@@ -1081,42 +1081,6 @@ def test_a1111(self):
 
         self.assertTrue(np.allclose(images, expected, atol=1e-3))
 
-    def test_a1111_with_model_cpu_offload(self):
-        generator = torch.Generator().manual_seed(0)
-
-        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None)
-        pipe.enable_model_cpu_offload()
-        lora_model_id = "hf-internal-testing/civitai-light-shadow-lora"
-        lora_filename = "light_and_shadow.safetensors"
-        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
-
-        images = pipe(
-            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
-        ).images
-
-        images = images[0, -3:, -3:, -1].flatten()
-        expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])
-
-        self.assertTrue(np.allclose(images, expected, atol=1e-3))
-
-    def test_a1111_with_sequential_cpu_offload(self):
-        generator = torch.Generator().manual_seed(0)
-
-        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/Counterfeit-V2.5", safety_checker=None)
-        pipe.enable_sequential_cpu_offload()
-        lora_model_id = "hf-internal-testing/civitai-light-shadow-lora"
-        lora_filename = "light_and_shadow.safetensors"
-        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
-
-        images = pipe(
-            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
-        ).images
-
-        images = images[0, -3:, -3:, -1].flatten()
-        expected = np.array([0.3636, 0.3708, 0.3694, 0.3679, 0.3829, 0.3677, 0.3692, 0.3688, 0.3292])
-
-        self.assertTrue(np.allclose(images, expected, atol=1e-3))
-
     def test_kohya_sd_v15_with_higher_dimensions(self):
         generator = torch.Generator().manual_seed(0)
 
@@ -1293,10 +1257,10 @@ def test_sdxl_1_0_lora(self):
         generator = torch.Generator().manual_seed(0)
 
         pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
-        pipe.enable_model_cpu_offload()
         lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
         lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
         pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe.enable_model_cpu_offload()
 
         images = pipe(
             "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
@@ -1447,21 +1411,3 @@ def test_sdxl_1_0_fuse_unfuse_all(self):
         assert state_dicts_almost_equal(text_encoder_1_sd, pipe.text_encoder.state_dict())
         assert state_dicts_almost_equal(text_encoder_2_sd, pipe.text_encoder_2.state_dict())
         assert state_dicts_almost_equal(unet_sd, pipe.unet.state_dict())
-
-    def test_sdxl_1_0_lora_with_sequential_cpu_offloading(self):
-        generator = torch.Generator().manual_seed(0)
-
-        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
-        pipe.enable_sequential_cpu_offload()
-        lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
-        lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
-        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
-
-        images = pipe(
-            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
-        ).images
-
-        images = images[0, -3:, -3:, -1].flatten()
-        expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535])
-
-        self.assertTrue(np.allclose(images, expected, atol=1e-3))