Merge branch 'main' into chroma-docs

docs/source/en/optimization/memory.md

@@ -302,6 +302,13 @@ compute-bound, [group-offloading](#group-offloading) tends to be better. Group o
 
 </Tip>
 
+### Offloading to disk
+
+Group offloading can consume significant system RAM depending on the model size. In limited RAM environments,
+it can be useful to offload to the second memory, instead. You can do this by setting the `offload_to_disk_path`
+argument in either of [`~ModelMixin.enable_group_offload`] or [`~hooks.apply_group_offloading`]. Refer [here](https://github.com/huggingface/diffusers/pull/11682#issue-3129365363) and
+[here](https://github.com/huggingface/diffusers/pull/11682#issuecomment-2955715126) for the expected speed-memory trade-offs with this option enabled.
+
 ## Layerwise casting
 
 Layerwise casting stores weights in a smaller data format (for example, `torch.float8_e4m3fn` and `torch.float8_e5m2`) to use less memory and upcasts those weights to a higher precision like `torch.float16` or `torch.bfloat16` for computation. Certain layers (normalization and modulation related weights) are skipped because storing them in fp8 can degrade generation quality.

src/diffusers/hooks/group_offloading.py

@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 from contextlib import contextmanager, nullcontext
 from typing import Dict, List, Optional, Set, Tuple, Union
 
+import safetensors.torch
 import torch
 
 from ..utils import get_logger, is_accelerate_available
@@ -59,6 +61,7 @@ class ModuleGroup:
         record_stream: Optional[bool] = False,
         low_cpu_mem_usage: bool = False,
         onload_self: bool = True,
+        offload_to_disk_path: Optional[str] = None,
     ) -> None:
         self.modules = modules
         self.offload_device = offload_device
@@ -72,7 +75,26 @@ class ModuleGroup:
         self.record_stream = record_stream
         self.onload_self = onload_self
         self.low_cpu_mem_usage = low_cpu_mem_usage
-        self.cpu_param_dict = self._init_cpu_param_dict()
+
+        self.offload_to_disk_path = offload_to_disk_path
+        self._is_offloaded_to_disk = False
+
+        if self.offload_to_disk_path:
+            self.safetensors_file_path = os.path.join(self.offload_to_disk_path, f"group_{id(self)}.safetensors")
+
+            all_tensors = []
+            for module in self.modules:
+                all_tensors.extend(list(module.parameters()))
+                all_tensors.extend(list(module.buffers()))
+            all_tensors.extend(self.parameters)
+            all_tensors.extend(self.buffers)
+            all_tensors = list(dict.fromkeys(all_tensors))  # Remove duplicates
+
+            self.tensor_to_key = {tensor: f"tensor_{i}" for i, tensor in enumerate(all_tensors)}
+            self.key_to_tensor = {v: k for k, v in self.tensor_to_key.items()}
+            self.cpu_param_dict = {}
+        else:
+            self.cpu_param_dict = self._init_cpu_param_dict()
 
         if self.stream is None and self.record_stream:
             raise ValueError("`record_stream` cannot be True when `stream` is None.")
@@ -124,6 +146,30 @@ class ModuleGroup:
         context = nullcontext() if self.stream is None else torch_accelerator_module.stream(self.stream)
         current_stream = torch_accelerator_module.current_stream() if self.record_stream else None
 
+        if self.offload_to_disk_path:
+            if self.stream is not None:
+                # Wait for previous Host->Device transfer to complete
+                self.stream.synchronize()
+
+            with context:
+                if self.stream is not None:
+                    # Load to CPU, pin, and async copy to device for overlapping transfer and compute
+                    loaded_cpu_tensors = safetensors.torch.load_file(self.safetensors_file_path, device="cpu")
+                    for key, tensor_obj in self.key_to_tensor.items():
+                        pinned_tensor = loaded_cpu_tensors[key].pin_memory()
+                        tensor_obj.data = pinned_tensor.to(self.onload_device, non_blocking=self.non_blocking)
+                        if self.record_stream:
+                            tensor_obj.data.record_stream(current_stream)
+                else:
+                    # Load directly to the target device (synchronous)
+                    onload_device = (
+                        self.onload_device.type if isinstance(self.onload_device, torch.device) else self.onload_device
+                    )
+                    loaded_tensors = safetensors.torch.load_file(self.safetensors_file_path, device=onload_device)
+                    for key, tensor_obj in self.key_to_tensor.items():
+                        tensor_obj.data = loaded_tensors[key]
+            return
+
         if self.stream is not None:
             # Wait for previous Host->Device transfer to complete
             self.stream.synchronize()
@@ -169,6 +215,26 @@ class ModuleGroup:
     @torch.compiler.disable()
     def offload_(self):
         r"""Offloads the group of modules to the offload_device."""
+        if self.offload_to_disk_path:
+            # TODO: we can potentially optimize this code path by checking if the _all_ the desired
+            # safetensor files exist on the disk and if so, skip this step entirely, reducing IO
+            # overhead. Currently, we just check if the given `safetensors_file_path` exists and if not
+            # we perform a write.
+            # Check if the file has been saved in this session or if it already exists on disk.
+            if not self._is_offloaded_to_disk and not os.path.exists(self.safetensors_file_path):
+                os.makedirs(os.path.dirname(self.safetensors_file_path), exist_ok=True)
+                tensors_to_save = {
+                    key: tensor.data.to(self.offload_device) for tensor, key in self.tensor_to_key.items()
+                }
+                safetensors.torch.save_file(tensors_to_save, self.safetensors_file_path)
+
+            # The group is now considered offloaded to disk for the rest of the session.
+            self._is_offloaded_to_disk = True
+
+            # We do this to free up the RAM which is still holding the up tensor data.
+            for tensor_obj in self.tensor_to_key.keys():
+                tensor_obj.data = torch.empty_like(tensor_obj.data, device=self.offload_device)
+            return
 
         torch_accelerator_module = (
             getattr(torch, torch.accelerator.current_accelerator().type)
@@ -205,11 +271,7 @@ class GroupOffloadingHook(ModelHook):
 
     _is_stateful = False
 
-    def __init__(
-        self,
-        group: ModuleGroup,
-        next_group: Optional[ModuleGroup] = None,
-    ) -> None:
+    def __init__(self, group: ModuleGroup, next_group: Optional[ModuleGroup] = None) -> None:
         self.group = group
         self.next_group = next_group
 
@@ -363,6 +425,7 @@ def apply_group_offloading(
     use_stream: bool = False,
     record_stream: bool = False,
     low_cpu_mem_usage: bool = False,
+    offload_to_disk_path: Optional[str] = None,
 ) -> None:
     r"""
     Applies group offloading to the internal layers of a torch.nn.Module. To understand what group offloading is, and
@@ -401,6 +464,9 @@ def apply_group_offloading(
         offload_type (`str`, defaults to "block_level"):
             The type of offloading to be applied. Can be one of "block_level" or "leaf_level". Default is
             "block_level".
+        offload_to_disk_path (`str`, *optional*, defaults to `None`):
+            The path to the directory where parameters will be offloaded. Setting this option can be useful in limited
+            RAM environment settings where a reasonable speed-memory trade-off is desired.
         num_blocks_per_group (`int`, *optional*):
             The number of blocks per group when using offload_type="block_level". This is required when using
             offload_type="block_level".
@@ -458,6 +524,7 @@ def apply_group_offloading(
             num_blocks_per_group=num_blocks_per_group,
             offload_device=offload_device,
             onload_device=onload_device,
+            offload_to_disk_path=offload_to_disk_path,
             non_blocking=non_blocking,
             stream=stream,
             record_stream=record_stream,
@@ -468,6 +535,7 @@ def apply_group_offloading(
             module=module,
             offload_device=offload_device,
             onload_device=onload_device,
+            offload_to_disk_path=offload_to_disk_path,
             non_blocking=non_blocking,
             stream=stream,
             record_stream=record_stream,
@@ -486,6 +554,7 @@ def _apply_group_offloading_block_level(
     stream: Union[torch.cuda.Stream, torch.Stream, None] = None,
     record_stream: Optional[bool] = False,
     low_cpu_mem_usage: bool = False,
+    offload_to_disk_path: Optional[str] = None,
 ) -> None:
     r"""
     This function applies offloading to groups of torch.nn.ModuleList or torch.nn.Sequential blocks. In comparison to
@@ -496,6 +565,9 @@ def _apply_group_offloading_block_level(
             The module to which group offloading is applied.
         offload_device (`torch.device`):
             The device to which the group of modules are offloaded. This should typically be the CPU.
+        offload_to_disk_path (`str`, *optional*, defaults to `None`):
+            The path to the directory where parameters will be offloaded. Setting this option can be useful in limited
+            RAM environment settings where a reasonable speed-memory trade-off is desired.
         onload_device (`torch.device`):
             The device to which the group of modules are onloaded.
         non_blocking (`bool`):
@@ -535,6 +607,7 @@ def _apply_group_offloading_block_level(
                 modules=current_modules,
                 offload_device=offload_device,
                 onload_device=onload_device,
+                offload_to_disk_path=offload_to_disk_path,
                 offload_leader=current_modules[-1],
                 onload_leader=current_modules[0],
                 non_blocking=non_blocking,
@@ -567,6 +640,7 @@ def _apply_group_offloading_block_level(
         modules=unmatched_modules,
         offload_device=offload_device,
         onload_device=onload_device,
+        offload_to_disk_path=offload_to_disk_path,
         offload_leader=module,
         onload_leader=module,
         parameters=parameters,
@@ -590,6 +664,7 @@ def _apply_group_offloading_leaf_level(
     stream: Union[torch.cuda.Stream, torch.Stream, None] = None,
     record_stream: Optional[bool] = False,
     low_cpu_mem_usage: bool = False,
+    offload_to_disk_path: Optional[str] = None,
 ) -> None:
     r"""
     This function applies offloading to groups of leaf modules in a torch.nn.Module. This method has minimal memory
@@ -604,6 +679,9 @@ def _apply_group_offloading_leaf_level(
             The device to which the group of modules are offloaded. This should typically be the CPU.
         onload_device (`torch.device`):
             The device to which the group of modules are onloaded.
+        offload_to_disk_path (`str`, *optional*, defaults to `None`):
+            The path to the directory where parameters will be offloaded. Setting this option can be useful in limited
+            RAM environment settings where a reasonable speed-memory trade-off is desired.
         non_blocking (`bool`):
             If True, offloading and onloading is done asynchronously. This can be useful for overlapping computation
             and data transfer.
@@ -629,6 +707,7 @@ def _apply_group_offloading_leaf_level(
             modules=[submodule],
             offload_device=offload_device,
             onload_device=onload_device,
+            offload_to_disk_path=offload_to_disk_path,
             offload_leader=submodule,
             onload_leader=submodule,
             non_blocking=non_blocking,
@@ -675,6 +754,7 @@ def _apply_group_offloading_leaf_level(
             onload_device=onload_device,
             offload_leader=parent_module,
             onload_leader=parent_module,
+            offload_to_disk_path=offload_to_disk_path,
             parameters=parameters,
             buffers=buffers,
             non_blocking=non_blocking,
@@ -693,6 +773,7 @@ def _apply_group_offloading_leaf_level(
             modules=[],
             offload_device=offload_device,
             onload_device=onload_device,
+            offload_to_disk_path=offload_to_disk_path,
             offload_leader=module,
             onload_leader=module,
             parameters=None,

src/diffusers/models/modeling_utils.py

@@ -548,6 +548,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         use_stream: bool = False,
         record_stream: bool = False,
         low_cpu_mem_usage=False,
+        offload_to_disk_path: Optional[str] = None,
     ) -> None:
         r"""
         Activates group offloading for the current model.
@@ -588,15 +589,16 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                 f"open an issue at https://github.com/huggingface/diffusers/issues."
             )
         apply_group_offloading(
-            self,
-            onload_device,
-            offload_device,
-            offload_type,
-            num_blocks_per_group,
-            non_blocking,
-            use_stream,
-            record_stream,
+            module=self,
+            onload_device=onload_device,
+            offload_device=offload_device,
+            offload_type=offload_type,
+            num_blocks_per_group=num_blocks_per_group,
+            non_blocking=non_blocking,
+            use_stream=use_stream,
+            record_stream=record_stream,
             low_cpu_mem_usage=low_cpu_mem_usage,
+            offload_to_disk_path=offload_to_disk_path,
         )
 
     def save_pretrained(

tests/models/test_modeling_common.py

@@ -15,6 +15,7 @@
 
 import copy
 import gc
+import glob
 import inspect
 import json
 import os
@@ -1693,6 +1694,35 @@ class ModelTesterMixin:
         model.enable_layerwise_casting(storage_dtype=storage_dtype, compute_dtype=compute_dtype)
         _ = model(**inputs_dict)[0]
 
+    @parameterized.expand([(False, "block_level"), (True, "leaf_level")])
+    @require_torch_accelerator
+    @torch.no_grad()
+    def test_group_offloading_with_disk(self, record_stream, offload_type):
+        torch.manual_seed(0)
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+
+        if not getattr(model, "_supports_group_offloading", True):
+            return
+
+        torch.manual_seed(0)
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+        model.eval()
+        additional_kwargs = {} if offload_type == "leaf_level" else {"num_blocks_per_group": 1}
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model.enable_group_offload(
+                torch_device,
+                offload_type=offload_type,
+                offload_to_disk_path=tmpdir,
+                use_stream=True,
+                record_stream=record_stream,
+                **additional_kwargs,
+            )
+            has_safetensors = glob.glob(f"{tmpdir}/*.safetensors")
+            assert has_safetensors, "No safetensors found in the directory."
+            _ = model(**inputs_dict)[0]
+
     def test_auto_model(self, expected_max_diff=5e-5):
         if self.forward_requires_fresh_args:
             model = self.model_class(**self.init_dict)

tests/pipelines/sana/test_sana_controlnet.py

@@ -30,6 +30,7 @@ from diffusers.utils.testing_utils import (
     enable_full_determinism,
     torch_device,
 )
+from diffusers.utils.torch_utils import randn_tensor
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin, to_np
@@ -151,7 +152,7 @@ class SanaControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         else:
             generator = torch.Generator(device=device).manual_seed(seed)
 
-        control_image = torch.randn(1, 3, 32, 32, generator=generator)
+        control_image = randn_tensor((1, 3, 32, 32), generator=generator, device=device)
         inputs = {
             "prompt": "",
             "negative_prompt": "",

tests/pipelines/sana/test_sana_sprint_img2img.py

@@ -24,6 +24,7 @@ from diffusers.utils.testing_utils import (
     enable_full_determinism,
     torch_device,
 )
+from diffusers.utils.torch_utils import randn_tensor
 
 from ..pipeline_params import (
     IMAGE_TO_IMAGE_IMAGE_PARAMS,
@@ -137,7 +138,7 @@ class SanaSprintImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase)
             generator = torch.manual_seed(seed)
         else:
             generator = torch.Generator(device=device).manual_seed(seed)
-        image = torch.randn(1, 3, 32, 32, generator=generator)
+        image = randn_tensor((1, 3, 32, 32), generator=generator, device=device)
         inputs = {
             "prompt": "",
             "image": image,

tests/pipelines/wan/test_wan_vace.py

@@ -199,3 +199,15 @@ class WanVACEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     @unittest.skip("Batching is not yet supported with this pipeline")
     def test_inference_batch_single_identical(self):
         return super().test_inference_batch_single_identical()
+
+    @unittest.skip(
+        "AutoencoderKLWan encoded latents are always in FP32. This test is not designed to handle mixed dtype inputs"
+    )
+    def test_float16_inference(self):
+        pass
+
+    @unittest.skip(
+        "AutoencoderKLWan encoded latents are always in FP32. This test is not designed to handle mixed dtype inputs"
+    )
+    def test_save_load_float16(self):
+        pass