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class StableUnCLIPPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 67,319 | 67,754 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 700 |
class StableVideoDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 67,757 | 68,200 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 701 |
class TextToVideoSDPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 68,203 | 68,639 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 702 |
class TextToVideoZeroPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 68,642 | 69,080 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 703 |
class TextToVideoZeroSDXLPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 69,083 | 69,525 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 704 |
class UnCLIPImageVariationPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 69,528 | 69,971 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 705 |
class UnCLIPPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 69,974 | 70,403 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 706 |
class UniDiffuserModel(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 70,406 | 70,837 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 707 |
class UniDiffuserPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 70,840 | 71,274 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 708 |
class UniDiffuserTextDecoder(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 71,277 | 71,714 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 709 |
class VersatileDiffusionDualGuidedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 71,717 | 72,168 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 710 |
class VersatileDiffusionImageVariationPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 72,171 | 72,626 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 711 |
class VersatileDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 72,629 | 73,070 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 712 |
class VersatileDiffusionTextToImagePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 73,073 | 73,525 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 713 |
class VideoToVideoSDPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 73,528 | 73,965 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 714 |
class VQDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 73,968 | 74,402 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 715 |
class WuerstchenCombinedPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 74,405 | 74,846 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 716 |
class WuerstchenDecoderPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 74,849 | 75,289 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 717 |
class WuerstchenPriorPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"]) | class_definition | 75,292 | 75,730 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py | null | 718 |
class PushToHubMixin:
"""
A Mixin to push a model, scheduler, or pipeline to the Hugging Face Hub.
"""
def _upload_folder(
self,
working_dir: Union[str, os.PathLike],
repo_id: str,
token: Optional[str] = None,
commit_message: Optional[str] = None,
create_pr: bool = False,
):
"""
Uploads all files in `working_dir` to `repo_id`.
"""
if commit_message is None:
if "Model" in self.__class__.__name__:
commit_message = "Upload model"
elif "Scheduler" in self.__class__.__name__:
commit_message = "Upload scheduler"
else:
commit_message = f"Upload {self.__class__.__name__}"
logger.info(f"Uploading the files of {working_dir} to {repo_id}.")
return upload_folder(
repo_id=repo_id, folder_path=working_dir, token=token, commit_message=commit_message, create_pr=create_pr
)
def push_to_hub(
self,
repo_id: str,
commit_message: Optional[str] = None,
private: Optional[bool] = None,
token: Optional[str] = None,
create_pr: bool = False,
safe_serialization: bool = True,
variant: Optional[str] = None,
) -> str:
"""
Upload model, scheduler, or pipeline files to the 🤗 Hugging Face Hub.
Parameters:
repo_id (`str`):
The name of the repository you want to push your model, scheduler, or pipeline files to. It should
contain your organization name when pushing to an organization. `repo_id` can also be a path to a local
directory.
commit_message (`str`, *optional*):
Message to commit while pushing. Default to `"Upload {object}"`.
private (`bool`, *optional*):
Whether to make the repo private. If `None` (default), the repo will be public unless the
organization's default is private. This value is ignored if the repo already exists.
token (`str`, *optional*):
The token to use as HTTP bearer authorization for remote files. The token generated when running
`huggingface-cli login` (stored in `~/.huggingface`).
create_pr (`bool`, *optional*, defaults to `False`):
Whether or not to create a PR with the uploaded files or directly commit.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether or not to convert the model weights to the `safetensors` format.
variant (`str`, *optional*):
If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
Examples:
```python
from diffusers import UNet2DConditionModel
unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="unet")
# Push the `unet` to your namespace with the name "my-finetuned-unet".
unet.push_to_hub("my-finetuned-unet")
# Push the `unet` to an organization with the name "my-finetuned-unet".
unet.push_to_hub("your-org/my-finetuned-unet")
```
"""
repo_id = create_repo(repo_id, private=private, token=token, exist_ok=True).repo_id
# Create a new empty model card and eventually tag it
model_card = load_or_create_model_card(repo_id, token=token)
model_card = populate_model_card(model_card)
# Save all files.
save_kwargs = {"safe_serialization": safe_serialization}
if "Scheduler" not in self.__class__.__name__:
save_kwargs.update({"variant": variant})
with tempfile.TemporaryDirectory() as tmpdir:
self.save_pretrained(tmpdir, **save_kwargs)
# Update model card if needed:
model_card.save(os.path.join(tmpdir, "README.md"))
return self._upload_folder(
tmpdir,
repo_id,
token=token,
commit_message=commit_message,
create_pr=create_pr,
) | class_definition | 23,125 | 27,270 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/hub_utils.py | null | 719 |
class MidiProcessor(metaclass=DummyObject):
_backends = ["note_seq"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["note_seq"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["note_seq"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["note_seq"]) | class_definition | 129 | 505 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_note_seq_objects.py | null | 720 |
class KolorsImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "sentencepiece"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers", "sentencepiece"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers", "sentencepiece"]) | class_definition | 129 | 633 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_sentencepiece_objects.py | null | 721 |
class KolorsPAGPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "sentencepiece"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers", "sentencepiece"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers", "sentencepiece"]) | class_definition | 636 | 1,136 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_sentencepiece_objects.py | null | 722 |
class KolorsPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "sentencepiece"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "sentencepiece"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers", "sentencepiece"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers", "sentencepiece"]) | class_definition | 1,139 | 1,636 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_sentencepiece_objects.py | null | 723 |
class FlaxStableDiffusionControlNetPipeline(metaclass=DummyObject):
_backends = ["flax", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"]) | class_definition | 129 | 577 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py | null | 724 |
class FlaxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):
_backends = ["flax", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"]) | class_definition | 580 | 1,025 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py | null | 725 |
class FlaxStableDiffusionInpaintPipeline(metaclass=DummyObject):
_backends = ["flax", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"]) | class_definition | 1,028 | 1,473 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py | null | 726 |
class FlaxStableDiffusionPipeline(metaclass=DummyObject):
_backends = ["flax", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"]) | class_definition | 1,476 | 1,914 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py | null | 727 |
class FlaxStableDiffusionXLPipeline(metaclass=DummyObject):
_backends = ["flax", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax", "transformers"]) | class_definition | 1,917 | 2,357 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py | null | 728 |
class SpectrogramDiffusionPipeline(metaclass=DummyObject):
_backends = ["transformers", "torch", "note_seq"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["transformers", "torch", "note_seq"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["transformers", "torch", "note_seq"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["transformers", "torch", "note_seq"]) | class_definition | 129 | 620 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py | null | 729 |
class DummyObject(type):
"""
Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by
`requires_backend` each time a user tries to access any method of that class.
"""
def __getattr__(cls, key):
if key.startswith("_") and key not in ["_load_connected_pipes", "_is_onnx"]:
return super().__getattr__(cls, key)
requires_backends(cls, cls._backends) | class_definition | 24,377 | 24,818 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py | null | 730 |
class OptionalDependencyNotAvailable(BaseException):
"""
An error indicating that an optional dependency of Diffusers was not found in the environment.
""" | class_definition | 30,619 | 30,786 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py | null | 731 |
class _LazyModule(ModuleType):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
"""
# Very heavily inspired by optuna.integration._IntegrationModule
# https://github.com/optuna/optuna/blob/master/optuna/integration/__init__.py
def __init__(self, name, module_file, import_structure, module_spec=None, extra_objects=None):
super().__init__(name)
self._modules = set(import_structure.keys())
self._class_to_module = {}
for key, values in import_structure.items():
for value in values:
self._class_to_module[value] = key
# Needed for autocompletion in an IDE
self.__all__ = list(import_structure.keys()) + list(chain(*import_structure.values()))
self.__file__ = module_file
self.__spec__ = module_spec
self.__path__ = [os.path.dirname(module_file)]
self._objects = {} if extra_objects is None else extra_objects
self._name = name
self._import_structure = import_structure
# Needed for autocompletion in an IDE
def __dir__(self):
result = super().__dir__()
# The elements of self.__all__ that are submodules may or may not be in the dir already, depending on whether
# they have been accessed or not. So we only add the elements of self.__all__ that are not already in the dir.
for attr in self.__all__:
if attr not in result:
result.append(attr)
return result
def __getattr__(self, name: str) -> Any:
if name in self._objects:
return self._objects[name]
if name in self._modules:
value = self._get_module(name)
elif name in self._class_to_module.keys():
module = self._get_module(self._class_to_module[name])
value = getattr(module, name)
else:
raise AttributeError(f"module {self.__name__} has no attribute {name}")
setattr(self, name, value)
return value
def _get_module(self, module_name: str):
try:
return importlib.import_module("." + module_name, self.__name__)
except Exception as e:
raise RuntimeError(
f"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its"
f" traceback):\n{e}"
) from e
def __reduce__(self):
return (self.__class__, (self._name, self.__file__, self._import_structure)) | class_definition | 30,789 | 33,331 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py | null | 732 |
class LMSDiscreteScheduler(metaclass=DummyObject):
_backends = ["torch", "scipy"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "scipy"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "scipy"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "scipy"]) | class_definition | 129 | 536 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py | null | 733 |
class FlaxControlNetModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 129 | 495 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 734 |
class FlaxModelMixin(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 498 | 859 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 735 |
class FlaxUNet2DConditionModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 862 | 1,233 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 736 |
class FlaxAutoencoderKL(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 1,236 | 1,600 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 737 |
class FlaxDiffusionPipeline(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 1,603 | 1,971 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 738 |
class FlaxDDIMScheduler(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 1,974 | 2,338 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 739 |
class FlaxDDPMScheduler(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 2,341 | 2,705 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 740 |
class FlaxDPMSolverMultistepScheduler(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 2,708 | 3,086 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 741 |
class FlaxEulerDiscreteScheduler(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 3,089 | 3,462 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 742 |
class FlaxKarrasVeScheduler(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 3,465 | 3,833 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 743 |
class FlaxLMSDiscreteScheduler(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 3,836 | 4,207 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 744 |
class FlaxPNDMScheduler(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 4,210 | 4,574 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 745 |
class FlaxSchedulerMixin(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 4,577 | 4,942 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 746 |
class FlaxScoreSdeVeScheduler(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["flax"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["flax"]) | class_definition | 4,945 | 5,315 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py | null | 747 |
class BaseOutput(OrderedDict):
"""
Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a
tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular
Python dictionary.
<Tip warning={true}>
You can't unpack a [`BaseOutput`] directly. Use the [`~utils.BaseOutput.to_tuple`] method to convert it to a tuple
first.
</Tip>
"""
def __init_subclass__(cls) -> None:
"""Register subclasses as pytree nodes.
This is necessary to synchronize gradients when using `torch.nn.parallel.DistributedDataParallel` with
`static_graph=True` with modules that output `ModelOutput` subclasses.
"""
if is_torch_available():
import torch.utils._pytree
if is_torch_version("<", "2.2"):
torch.utils._pytree._register_pytree_node(
cls,
torch.utils._pytree._dict_flatten,
lambda values, context: cls(**torch.utils._pytree._dict_unflatten(values, context)),
)
else:
torch.utils._pytree.register_pytree_node(
cls,
torch.utils._pytree._dict_flatten,
lambda values, context: cls(**torch.utils._pytree._dict_unflatten(values, context)),
)
def __post_init__(self) -> None:
class_fields = fields(self)
# Safety and consistency checks
if not len(class_fields):
raise ValueError(f"{self.__class__.__name__} has no fields.")
first_field = getattr(self, class_fields[0].name)
other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and isinstance(first_field, dict):
for key, value in first_field.items():
self[key] = value
else:
for field in class_fields:
v = getattr(self, field.name)
if v is not None:
self[field.name] = v
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def setdefault(self, *args, **kwargs):
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def pop(self, *args, **kwargs):
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def update(self, *args, **kwargs):
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__(self, k: Any) -> Any:
if isinstance(k, str):
inner_dict = dict(self.items())
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__(self, name: Any, value: Any) -> None:
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(name, value)
super().__setattr__(name, value)
def __setitem__(self, key, value):
# Will raise a KeyException if needed
super().__setitem__(key, value)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(key, value)
def __reduce__(self):
if not is_dataclass(self):
return super().__reduce__()
callable, _args, *remaining = super().__reduce__()
args = tuple(getattr(self, field.name) for field in fields(self))
return callable, args, *remaining
def to_tuple(self) -> Tuple[Any, ...]:
"""
Convert self to a tuple containing all the attributes/keys that are not `None`.
"""
return tuple(self[k] for k in self.keys()) | class_definition | 1,081 | 4,952 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/outputs.py | null | 748 |
class AutoencoderKLOutput(BaseOutput):
"""
Output of AutoencoderKL encoding method.
Args:
latent_dist (`DiagonalGaussianDistribution`):
Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
`DiagonalGaussianDistribution` allows for sampling latents from the distribution.
"""
latent_dist: "DiagonalGaussianDistribution" # noqa: F821 | class_definition | 79 | 511 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_outputs.py | null | 749 |
class Transformer2DModelOutput(BaseOutput):
"""
The output of [`Transformer2DModel`].
Args:
sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
distributions for the unnoised latent pixels.
"""
sample: "torch.Tensor" # noqa: F821 | class_definition | 525 | 1,041 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_outputs.py | null | 750 |
class Upsample1D(nn.Module):
"""A 1D upsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
use_conv_transpose (`bool`, default `False`):
option to use a convolution transpose.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
name (`str`, default `conv`):
name of the upsampling 1D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
use_conv_transpose: bool = False,
out_channels: Optional[int] = None,
name: str = "conv",
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_conv_transpose = use_conv_transpose
self.name = name
self.conv = None
if use_conv_transpose:
self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
elif use_conv:
self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
assert inputs.shape[1] == self.channels
if self.use_conv_transpose:
return self.conv(inputs)
outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest")
if self.use_conv:
outputs = self.conv(outputs)
return outputs | class_definition | 828 | 2,426 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py | null | 751 |
class Upsample2D(nn.Module):
"""A 2D upsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
use_conv_transpose (`bool`, default `False`):
option to use a convolution transpose.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
name (`str`, default `conv`):
name of the upsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
use_conv_transpose: bool = False,
out_channels: Optional[int] = None,
name: str = "conv",
kernel_size: Optional[int] = None,
padding=1,
norm_type=None,
eps=None,
elementwise_affine=None,
bias=True,
interpolate=True,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_conv_transpose = use_conv_transpose
self.name = name
self.interpolate = interpolate
if norm_type == "ln_norm":
self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
elif norm_type == "rms_norm":
self.norm = RMSNorm(channels, eps, elementwise_affine)
elif norm_type is None:
self.norm = None
else:
raise ValueError(f"unknown norm_type: {norm_type}")
conv = None
if use_conv_transpose:
if kernel_size is None:
kernel_size = 4
conv = nn.ConvTranspose2d(
channels, self.out_channels, kernel_size=kernel_size, stride=2, padding=padding, bias=bias
)
elif use_conv:
if kernel_size is None:
kernel_size = 3
conv = nn.Conv2d(self.channels, self.out_channels, kernel_size=kernel_size, padding=padding, bias=bias)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if name == "conv":
self.conv = conv
else:
self.Conv2d_0 = conv
def forward(self, hidden_states: torch.Tensor, output_size: Optional[int] = None, *args, **kwargs) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
assert hidden_states.shape[1] == self.channels
if self.norm is not None:
hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
if self.use_conv_transpose:
return self.conv(hidden_states)
# Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 until PyTorch 2.1
# https://github.com/pytorch/pytorch/issues/86679#issuecomment-1783978767
dtype = hidden_states.dtype
if dtype == torch.bfloat16 and is_torch_version("<", "2.1"):
hidden_states = hidden_states.to(torch.float32)
# upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
if hidden_states.shape[0] >= 64:
hidden_states = hidden_states.contiguous()
# if `output_size` is passed we force the interpolation output
# size and do not make use of `scale_factor=2`
if self.interpolate:
# upsample_nearest_nhwc also fails when the number of output elements is large
# https://github.com/pytorch/pytorch/issues/141831
scale_factor = (
2 if output_size is None else max([f / s for f, s in zip(output_size, hidden_states.shape[-2:])])
)
if hidden_states.numel() * scale_factor > pow(2, 31):
hidden_states = hidden_states.contiguous()
if output_size is None:
hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
else:
hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
# Cast back to original dtype
if dtype == torch.bfloat16 and is_torch_version("<", "2.1"):
hidden_states = hidden_states.to(dtype)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if self.use_conv:
if self.name == "conv":
hidden_states = self.conv(hidden_states)
else:
hidden_states = self.Conv2d_0(hidden_states)
return hidden_states | class_definition | 2,429 | 7,354 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py | null | 752 |
class FirUpsample2D(nn.Module):
"""A 2D FIR upsampling layer with an optional convolution.
Parameters:
channels (`int`, optional):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
kernel for the FIR filter.
"""
def __init__(
self,
channels: Optional[int] = None,
out_channels: Optional[int] = None,
use_conv: bool = False,
fir_kernel: Tuple[int, int, int, int] = (1, 3, 3, 1),
):
super().__init__()
out_channels = out_channels if out_channels else channels
if use_conv:
self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
self.use_conv = use_conv
self.fir_kernel = fir_kernel
self.out_channels = out_channels
def _upsample_2d(
self,
hidden_states: torch.Tensor,
weight: Optional[torch.Tensor] = None,
kernel: Optional[torch.Tensor] = None,
factor: int = 2,
gain: float = 1,
) -> torch.Tensor:
"""Fused `upsample_2d()` followed by `Conv2d()`.
Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
arbitrary order.
Args:
hidden_states (`torch.Tensor`):
Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
weight (`torch.Tensor`, *optional*):
Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
performed by `inChannels = x.shape[0] // numGroups`.
kernel (`torch.Tensor`, *optional*):
FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which
corresponds to nearest-neighbor upsampling.
factor (`int`, *optional*): Integer upsampling factor (default: 2).
gain (`float`, *optional*): Scaling factor for signal magnitude (default: 1.0).
Returns:
output (`torch.Tensor`):
Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same
datatype as `hidden_states`.
"""
assert isinstance(factor, int) and factor >= 1
# Setup filter kernel.
if kernel is None:
kernel = [1] * factor
# setup kernel
kernel = torch.tensor(kernel, dtype=torch.float32)
if kernel.ndim == 1:
kernel = torch.outer(kernel, kernel)
kernel /= torch.sum(kernel)
kernel = kernel * (gain * (factor**2))
if self.use_conv:
convH = weight.shape[2]
convW = weight.shape[3]
inC = weight.shape[1]
pad_value = (kernel.shape[0] - factor) - (convW - 1)
stride = (factor, factor)
# Determine data dimensions.
output_shape = (
(hidden_states.shape[2] - 1) * factor + convH,
(hidden_states.shape[3] - 1) * factor + convW,
)
output_padding = (
output_shape[0] - (hidden_states.shape[2] - 1) * stride[0] - convH,
output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,
)
assert output_padding[0] >= 0 and output_padding[1] >= 0
num_groups = hidden_states.shape[1] // inC
# Transpose weights.
weight = torch.reshape(weight, (num_groups, -1, inC, convH, convW))
weight = torch.flip(weight, dims=[3, 4]).permute(0, 2, 1, 3, 4)
weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
inverse_conv = F.conv_transpose2d(
hidden_states,
weight,
stride=stride,
output_padding=output_padding,
padding=0,
)
output = upfirdn2d_native(
inverse_conv,
torch.tensor(kernel, device=inverse_conv.device),
pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2 + 1),
)
else:
pad_value = kernel.shape[0] - factor
output = upfirdn2d_native(
hidden_states,
torch.tensor(kernel, device=hidden_states.device),
up=factor,
pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
)
return output
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if self.use_conv:
height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)
height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
else:
height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
return height | class_definition | 7,357 | 12,567 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py | null | 753 |
class KUpsample2D(nn.Module):
r"""A 2D K-upsampling layer.
Parameters:
pad_mode (`str`, *optional*, default to `"reflect"`): the padding mode to use.
"""
def __init__(self, pad_mode: str = "reflect"):
super().__init__()
self.pad_mode = pad_mode
kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]]) * 2
self.pad = kernel_1d.shape[1] // 2 - 1
self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)
weight = inputs.new_zeros(
[
inputs.shape[1],
inputs.shape[1],
self.kernel.shape[0],
self.kernel.shape[1],
]
)
indices = torch.arange(inputs.shape[1], device=inputs.device)
kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
weight[indices, indices] = kernel
return F.conv_transpose2d(inputs, weight, stride=2, padding=self.pad * 2 + 1) | class_definition | 12,570 | 13,684 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py | null | 754 |
class CogVideoXUpsample3D(nn.Module):
r"""
A 3D Upsample layer using in CogVideoX by Tsinghua University & ZhipuAI # Todo: Wait for paper relase.
Args:
in_channels (`int`):
Number of channels in the input image.
out_channels (`int`):
Number of channels produced by the convolution.
kernel_size (`int`, defaults to `3`):
Size of the convolving kernel.
stride (`int`, defaults to `1`):
Stride of the convolution.
padding (`int`, defaults to `1`):
Padding added to all four sides of the input.
compress_time (`bool`, defaults to `False`):
Whether or not to compress the time dimension.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
padding: int = 1,
compress_time: bool = False,
) -> None:
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
self.compress_time = compress_time
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
if self.compress_time:
if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1:
# split first frame
x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:]
x_first = F.interpolate(x_first, scale_factor=2.0)
x_rest = F.interpolate(x_rest, scale_factor=2.0)
x_first = x_first[:, :, None, :, :]
inputs = torch.cat([x_first, x_rest], dim=2)
elif inputs.shape[2] > 1:
inputs = F.interpolate(inputs, scale_factor=2.0)
else:
inputs = inputs.squeeze(2)
inputs = F.interpolate(inputs, scale_factor=2.0)
inputs = inputs[:, :, None, :, :]
else:
# only interpolate 2D
b, c, t, h, w = inputs.shape
inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
inputs = F.interpolate(inputs, scale_factor=2.0)
inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4)
b, c, t, h, w = inputs.shape
inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
inputs = self.conv(inputs)
inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4)
return inputs | class_definition | 13,687 | 16,160 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py | null | 755 |
class Downsample1D(nn.Module):
"""A 1D downsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
padding (`int`, default `1`):
padding for the convolution.
name (`str`, default `conv`):
name of the downsampling 1D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
out_channels: Optional[int] = None,
padding: int = 1,
name: str = "conv",
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = 2
self.name = name
if use_conv:
self.conv = nn.Conv1d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
else:
assert self.channels == self.out_channels
self.conv = nn.AvgPool1d(kernel_size=stride, stride=stride)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
assert inputs.shape[1] == self.channels
return self.conv(inputs) | class_definition | 819 | 2,193 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py | null | 756 |
class Downsample2D(nn.Module):
"""A 2D downsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
padding (`int`, default `1`):
padding for the convolution.
name (`str`, default `conv`):
name of the downsampling 2D layer.
"""
def __init__(
self,
channels: int,
use_conv: bool = False,
out_channels: Optional[int] = None,
padding: int = 1,
name: str = "conv",
kernel_size=3,
norm_type=None,
eps=None,
elementwise_affine=None,
bias=True,
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.padding = padding
stride = 2
self.name = name
if norm_type == "ln_norm":
self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
elif norm_type == "rms_norm":
self.norm = RMSNorm(channels, eps, elementwise_affine)
elif norm_type is None:
self.norm = None
else:
raise ValueError(f"unknown norm_type: {norm_type}")
if use_conv:
conv = nn.Conv2d(
self.channels, self.out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias
)
else:
assert self.channels == self.out_channels
conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
# TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
if name == "conv":
self.Conv2d_0 = conv
self.conv = conv
elif name == "Conv2d_0":
self.conv = conv
else:
self.conv = conv
def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
assert hidden_states.shape[1] == self.channels
if self.norm is not None:
hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
if self.use_conv and self.padding == 0:
pad = (0, 1, 0, 1)
hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
assert hidden_states.shape[1] == self.channels
hidden_states = self.conv(hidden_states)
return hidden_states | class_definition | 2,196 | 5,197 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py | null | 757 |
class FirDownsample2D(nn.Module):
"""A 2D FIR downsampling layer with an optional convolution.
Parameters:
channels (`int`):
number of channels in the inputs and outputs.
use_conv (`bool`, default `False`):
option to use a convolution.
out_channels (`int`, optional):
number of output channels. Defaults to `channels`.
fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
kernel for the FIR filter.
"""
def __init__(
self,
channels: Optional[int] = None,
out_channels: Optional[int] = None,
use_conv: bool = False,
fir_kernel: Tuple[int, int, int, int] = (1, 3, 3, 1),
):
super().__init__()
out_channels = out_channels if out_channels else channels
if use_conv:
self.Conv2d_0 = nn.Conv2d(channels, out_channels, kernel_size=3, stride=1, padding=1)
self.fir_kernel = fir_kernel
self.use_conv = use_conv
self.out_channels = out_channels
def _downsample_2d(
self,
hidden_states: torch.Tensor,
weight: Optional[torch.Tensor] = None,
kernel: Optional[torch.Tensor] = None,
factor: int = 2,
gain: float = 1,
) -> torch.Tensor:
"""Fused `Conv2d()` followed by `downsample_2d()`.
Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
arbitrary order.
Args:
hidden_states (`torch.Tensor`):
Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
weight (`torch.Tensor`, *optional*):
Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
performed by `inChannels = x.shape[0] // numGroups`.
kernel (`torch.Tensor`, *optional*):
FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] * factor`, which
corresponds to average pooling.
factor (`int`, *optional*, default to `2`):
Integer downsampling factor.
gain (`float`, *optional*, default to `1.0`):
Scaling factor for signal magnitude.
Returns:
output (`torch.Tensor`):
Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same
datatype as `x`.
"""
assert isinstance(factor, int) and factor >= 1
if kernel is None:
kernel = [1] * factor
# setup kernel
kernel = torch.tensor(kernel, dtype=torch.float32)
if kernel.ndim == 1:
kernel = torch.outer(kernel, kernel)
kernel /= torch.sum(kernel)
kernel = kernel * gain
if self.use_conv:
_, _, convH, convW = weight.shape
pad_value = (kernel.shape[0] - factor) + (convW - 1)
stride_value = [factor, factor]
upfirdn_input = upfirdn2d_native(
hidden_states,
torch.tensor(kernel, device=hidden_states.device),
pad=((pad_value + 1) // 2, pad_value // 2),
)
output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
else:
pad_value = kernel.shape[0] - factor
output = upfirdn2d_native(
hidden_states,
torch.tensor(kernel, device=hidden_states.device),
down=factor,
pad=((pad_value + 1) // 2, pad_value // 2),
)
return output
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if self.use_conv:
downsample_input = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
hidden_states = downsample_input + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
else:
hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
return hidden_states | class_definition | 5,200 | 9,400 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py | null | 758 |
class KDownsample2D(nn.Module):
r"""A 2D K-downsampling layer.
Parameters:
pad_mode (`str`, *optional*, default to `"reflect"`): the padding mode to use.
"""
def __init__(self, pad_mode: str = "reflect"):
super().__init__()
self.pad_mode = pad_mode
kernel_1d = torch.tensor([[1 / 8, 3 / 8, 3 / 8, 1 / 8]])
self.pad = kernel_1d.shape[1] // 2 - 1
self.register_buffer("kernel", kernel_1d.T @ kernel_1d, persistent=False)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)
weight = inputs.new_zeros(
[
inputs.shape[1],
inputs.shape[1],
self.kernel.shape[0],
self.kernel.shape[1],
]
)
indices = torch.arange(inputs.shape[1], device=inputs.device)
kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
weight[indices, indices] = kernel
return F.conv2d(inputs, weight, stride=2) | class_definition | 9,510 | 10,577 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py | null | 759 |
class CogVideoXDownsample3D(nn.Module):
# Todo: Wait for paper relase.
r"""
A 3D Downsampling layer using in [CogVideoX]() by Tsinghua University & ZhipuAI
Args:
in_channels (`int`):
Number of channels in the input image.
out_channels (`int`):
Number of channels produced by the convolution.
kernel_size (`int`, defaults to `3`):
Size of the convolving kernel.
stride (`int`, defaults to `2`):
Stride of the convolution.
padding (`int`, defaults to `0`):
Padding added to all four sides of the input.
compress_time (`bool`, defaults to `False`):
Whether or not to compress the time dimension.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 2,
padding: int = 0,
compress_time: bool = False,
):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
self.compress_time = compress_time
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.compress_time:
batch_size, channels, frames, height, width = x.shape
# (batch_size, channels, frames, height, width) -> (batch_size, height, width, channels, frames) -> (batch_size * height * width, channels, frames)
x = x.permute(0, 3, 4, 1, 2).reshape(batch_size * height * width, channels, frames)
if x.shape[-1] % 2 == 1:
x_first, x_rest = x[..., 0], x[..., 1:]
if x_rest.shape[-1] > 0:
# (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2)
x_rest = F.avg_pool1d(x_rest, kernel_size=2, stride=2)
x = torch.cat([x_first[..., None], x_rest], dim=-1)
# (batch_size * height * width, channels, (frames // 2) + 1) -> (batch_size, height, width, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, height, width)
x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
else:
# (batch_size * height * width, channels, frames) -> (batch_size * height * width, channels, frames // 2)
x = F.avg_pool1d(x, kernel_size=2, stride=2)
# (batch_size * height * width, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width)
x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
# Pad the tensor
pad = (0, 1, 0, 1)
x = F.pad(x, pad, mode="constant", value=0)
batch_size, channels, frames, height, width = x.shape
# (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width)
x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channels, height, width)
x = self.conv(x)
# (batch_size * frames, channels, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size, channels, frames, height, width)
x = x.reshape(batch_size, frames, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4)
return x | class_definition | 10,580 | 14,033 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py | null | 760 |
class GatedSelfAttentionDense(nn.Module):
r"""
A gated self-attention dense layer that combines visual features and object features.
Parameters:
query_dim (`int`): The number of channels in the query.
context_dim (`int`): The number of channels in the context.
n_heads (`int`): The number of heads to use for attention.
d_head (`int`): The number of channels in each head.
"""
def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
super().__init__()
# we need a linear projection since we need cat visual feature and obj feature
self.linear = nn.Linear(context_dim, query_dim)
self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
self.ff = FeedForward(query_dim, activation_fn="geglu")
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))
self.enabled = True
def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
if not self.enabled:
return x
n_visual = x.shape[1]
objs = self.linear(objs)
x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]
x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))
return x | class_definition | 1,921 | 3,415 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py | null | 761 |
class JointTransformerBlock(nn.Module):
r"""
A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
Reference: https://arxiv.org/abs/2403.03206
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
processing of `context` conditions.
"""
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
context_pre_only: bool = False,
qk_norm: Optional[str] = None,
use_dual_attention: bool = False,
):
super().__init__()
self.use_dual_attention = use_dual_attention
self.context_pre_only = context_pre_only
context_norm_type = "ada_norm_continous" if context_pre_only else "ada_norm_zero"
if use_dual_attention:
self.norm1 = SD35AdaLayerNormZeroX(dim)
else:
self.norm1 = AdaLayerNormZero(dim)
if context_norm_type == "ada_norm_continous":
self.norm1_context = AdaLayerNormContinuous(
dim, dim, elementwise_affine=False, eps=1e-6, bias=True, norm_type="layer_norm"
)
elif context_norm_type == "ada_norm_zero":
self.norm1_context = AdaLayerNormZero(dim)
else:
raise ValueError(
f"Unknown context_norm_type: {context_norm_type}, currently only support `ada_norm_continous`, `ada_norm_zero`"
)
if hasattr(F, "scaled_dot_product_attention"):
processor = JointAttnProcessor2_0()
else:
raise ValueError(
"The current PyTorch version does not support the `scaled_dot_product_attention` function."
)
self.attn = Attention(
query_dim=dim,
cross_attention_dim=None,
added_kv_proj_dim=dim,
dim_head=attention_head_dim,
heads=num_attention_heads,
out_dim=dim,
context_pre_only=context_pre_only,
bias=True,
processor=processor,
qk_norm=qk_norm,
eps=1e-6,
)
if use_dual_attention:
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=None,
dim_head=attention_head_dim,
heads=num_attention_heads,
out_dim=dim,
bias=True,
processor=processor,
qk_norm=qk_norm,
eps=1e-6,
)
else:
self.attn2 = None
self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
if not context_pre_only:
self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
else:
self.norm2_context = None
self.ff_context = None
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
# Copied from diffusers.models.attention.BasicTransformerBlock.set_chunk_feed_forward
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor,
temb: torch.FloatTensor,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
):
joint_attention_kwargs = joint_attention_kwargs or {}
if self.use_dual_attention:
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2 = self.norm1(
hidden_states, emb=temb
)
else:
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
if self.context_pre_only:
norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
else:
norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)
# Attention.
attn_output, context_attn_output = self.attn(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
**joint_attention_kwargs,
)
# Process attention outputs for the `hidden_states`.
attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = hidden_states + attn_output
if self.use_dual_attention:
attn_output2 = self.attn2(hidden_states=norm_hidden_states2, **joint_attention_kwargs)
attn_output2 = gate_msa2.unsqueeze(1) * attn_output2
hidden_states = hidden_states + attn_output2
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
else:
ff_output = self.ff(norm_hidden_states)
ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = hidden_states + ff_output
# Process attention outputs for the `encoder_hidden_states`.
if self.context_pre_only:
encoder_hidden_states = None
else:
context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
encoder_hidden_states = encoder_hidden_states + context_attn_output
norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
context_ff_output = _chunked_feed_forward(
self.ff_context, norm_encoder_hidden_states, self._chunk_dim, self._chunk_size
)
else:
context_ff_output = self.ff_context(norm_encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
return encoder_hidden_states, hidden_states | class_definition | 3,440 | 10,325 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py | null | 762 |
class BasicTransformerBlock(nn.Module):
r"""
A basic Transformer block.
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
num_embeds_ada_norm (:
obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
attention_bias (:
obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
only_cross_attention (`bool`, *optional*):
Whether to use only cross-attention layers. In this case two cross attention layers are used.
double_self_attention (`bool`, *optional*):
Whether to use two self-attention layers. In this case no cross attention layers are used.
upcast_attention (`bool`, *optional*):
Whether to upcast the attention computation to float32. This is useful for mixed precision training.
norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
Whether to use learnable elementwise affine parameters for normalization.
norm_type (`str`, *optional*, defaults to `"layer_norm"`):
The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
final_dropout (`bool` *optional*, defaults to False):
Whether to apply a final dropout after the last feed-forward layer.
attention_type (`str`, *optional*, defaults to `"default"`):
The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
positional_embeddings (`str`, *optional*, defaults to `None`):
The type of positional embeddings to apply to.
num_positional_embeddings (`int`, *optional*, defaults to `None`):
The maximum number of positional embeddings to apply.
"""
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,
only_cross_attention: bool = False,
double_self_attention: bool = False,
upcast_attention: bool = False,
norm_elementwise_affine: bool = True,
norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
norm_eps: float = 1e-5,
final_dropout: bool = False,
attention_type: str = "default",
positional_embeddings: Optional[str] = None,
num_positional_embeddings: Optional[int] = None,
ada_norm_continous_conditioning_embedding_dim: Optional[int] = None,
ada_norm_bias: Optional[int] = None,
ff_inner_dim: Optional[int] = None,
ff_bias: bool = True,
attention_out_bias: bool = True,
):
super().__init__()
self.dim = dim
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
self.dropout = dropout
self.cross_attention_dim = cross_attention_dim
self.activation_fn = activation_fn
self.attention_bias = attention_bias
self.double_self_attention = double_self_attention
self.norm_elementwise_affine = norm_elementwise_affine
self.positional_embeddings = positional_embeddings
self.num_positional_embeddings = num_positional_embeddings
self.only_cross_attention = only_cross_attention
# We keep these boolean flags for backward-compatibility.
self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
self.use_layer_norm = norm_type == "layer_norm"
self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous"
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
)
self.norm_type = norm_type
self.num_embeds_ada_norm = num_embeds_ada_norm
if positional_embeddings and (num_positional_embeddings is None):
raise ValueError(
"If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
)
if positional_embeddings == "sinusoidal":
self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
else:
self.pos_embed = None
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if norm_type == "ada_norm":
self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
elif norm_type == "ada_norm_zero":
self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
elif norm_type == "ada_norm_continuous":
self.norm1 = AdaLayerNormContinuous(
dim,
ada_norm_continous_conditioning_embedding_dim,
norm_elementwise_affine,
norm_eps,
ada_norm_bias,
"rms_norm",
)
else:
self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
upcast_attention=upcast_attention,
out_bias=attention_out_bias,
)
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
if norm_type == "ada_norm":
self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm)
elif norm_type == "ada_norm_continuous":
self.norm2 = AdaLayerNormContinuous(
dim,
ada_norm_continous_conditioning_embedding_dim,
norm_elementwise_affine,
norm_eps,
ada_norm_bias,
"rms_norm",
)
else:
self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim if not double_self_attention else None,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
out_bias=attention_out_bias,
) # is self-attn if encoder_hidden_states is none
else:
if norm_type == "ada_norm_single": # For Latte
self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
else:
self.norm2 = None
self.attn2 = None
# 3. Feed-forward
if norm_type == "ada_norm_continuous":
self.norm3 = AdaLayerNormContinuous(
dim,
ada_norm_continous_conditioning_embedding_dim,
norm_elementwise_affine,
norm_eps,
ada_norm_bias,
"layer_norm",
)
elif norm_type in ["ada_norm_zero", "ada_norm", "layer_norm"]:
self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
elif norm_type == "layer_norm_i2vgen":
self.norm3 = None
self.ff = FeedForward(
dim,
dropout=dropout,
activation_fn=activation_fn,
final_dropout=final_dropout,
inner_dim=ff_inner_dim,
bias=ff_bias,
)
# 4. Fuser
if attention_type == "gated" or attention_type == "gated-text-image":
self.fuser = GatedSelfAttentionDense(dim, cross_attention_dim, num_attention_heads, attention_head_dim)
# 5. Scale-shift for PixArt-Alpha.
if norm_type == "ada_norm_single":
self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
timestep: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
class_labels: Optional[torch.LongTensor] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
) -> torch.Tensor:
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None) is not None:
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
# Notice that normalization is always applied before the real computation in the following blocks.
# 0. Self-Attention
batch_size = hidden_states.shape[0]
if self.norm_type == "ada_norm":
norm_hidden_states = self.norm1(hidden_states, timestep)
elif self.norm_type == "ada_norm_zero":
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
)
elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
norm_hidden_states = self.norm1(hidden_states)
elif self.norm_type == "ada_norm_continuous":
norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
elif self.norm_type == "ada_norm_single":
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
).chunk(6, dim=1)
norm_hidden_states = self.norm1(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
else:
raise ValueError("Incorrect norm used")
if self.pos_embed is not None:
norm_hidden_states = self.pos_embed(norm_hidden_states)
# 1. Prepare GLIGEN inputs
cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
if self.norm_type == "ada_norm_zero":
attn_output = gate_msa.unsqueeze(1) * attn_output
elif self.norm_type == "ada_norm_single":
attn_output = gate_msa * attn_output
hidden_states = attn_output + hidden_states
if hidden_states.ndim == 4:
hidden_states = hidden_states.squeeze(1)
# 1.2 GLIGEN Control
if gligen_kwargs is not None:
hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
# 3. Cross-Attention
if self.attn2 is not None:
if self.norm_type == "ada_norm":
norm_hidden_states = self.norm2(hidden_states, timestep)
elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
norm_hidden_states = self.norm2(hidden_states)
elif self.norm_type == "ada_norm_single":
# For PixArt norm2 isn't applied here:
# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
norm_hidden_states = hidden_states
elif self.norm_type == "ada_norm_continuous":
norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
else:
raise ValueError("Incorrect norm")
if self.pos_embed is not None and self.norm_type != "ada_norm_single":
norm_hidden_states = self.pos_embed(norm_hidden_states)
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
# 4. Feed-forward
# i2vgen doesn't have this norm 🤷♂️
if self.norm_type == "ada_norm_continuous":
norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
elif not self.norm_type == "ada_norm_single":
norm_hidden_states = self.norm3(hidden_states)
if self.norm_type == "ada_norm_zero":
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self.norm_type == "ada_norm_single":
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
else:
ff_output = self.ff(norm_hidden_states)
if self.norm_type == "ada_norm_zero":
ff_output = gate_mlp.unsqueeze(1) * ff_output
elif self.norm_type == "ada_norm_single":
ff_output = gate_mlp * ff_output
hidden_states = ff_output + hidden_states
if hidden_states.ndim == 4:
hidden_states = hidden_states.squeeze(1)
return hidden_states | class_definition | 10,350 | 25,762 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py | null | 763 |
class LuminaFeedForward(nn.Module):
r"""
A feed-forward layer.
Parameters:
hidden_size (`int`):
The dimensionality of the hidden layers in the model. This parameter determines the width of the model's
hidden representations.
intermediate_size (`int`): The intermediate dimension of the feedforward layer.
multiple_of (`int`, *optional*): Value to ensure hidden dimension is a multiple
of this value.
ffn_dim_multiplier (float, *optional*): Custom multiplier for hidden
dimension. Defaults to None.
"""
def __init__(
self,
dim: int,
inner_dim: int,
multiple_of: Optional[int] = 256,
ffn_dim_multiplier: Optional[float] = None,
):
super().__init__()
inner_dim = int(2 * inner_dim / 3)
# custom hidden_size factor multiplier
if ffn_dim_multiplier is not None:
inner_dim = int(ffn_dim_multiplier * inner_dim)
inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of)
self.linear_1 = nn.Linear(
dim,
inner_dim,
bias=False,
)
self.linear_2 = nn.Linear(
inner_dim,
dim,
bias=False,
)
self.linear_3 = nn.Linear(
dim,
inner_dim,
bias=False,
)
self.silu = FP32SiLU()
def forward(self, x):
return self.linear_2(self.silu(self.linear_1(x)) * self.linear_3(x)) | class_definition | 25,765 | 27,303 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py | null | 764 |
class TemporalBasicTransformerBlock(nn.Module):
r"""
A basic Transformer block for video like data.
Parameters:
dim (`int`): The number of channels in the input and output.
time_mix_inner_dim (`int`): The number of channels for temporal attention.
num_attention_heads (`int`): The number of heads to use for multi-head attention.
attention_head_dim (`int`): The number of channels in each head.
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
"""
def __init__(
self,
dim: int,
time_mix_inner_dim: int,
num_attention_heads: int,
attention_head_dim: int,
cross_attention_dim: Optional[int] = None,
):
super().__init__()
self.is_res = dim == time_mix_inner_dim
self.norm_in = nn.LayerNorm(dim)
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
self.ff_in = FeedForward(
dim,
dim_out=time_mix_inner_dim,
activation_fn="geglu",
)
self.norm1 = nn.LayerNorm(time_mix_inner_dim)
self.attn1 = Attention(
query_dim=time_mix_inner_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
cross_attention_dim=None,
)
# 2. Cross-Attn
if cross_attention_dim is not None:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
self.norm2 = nn.LayerNorm(time_mix_inner_dim)
self.attn2 = Attention(
query_dim=time_mix_inner_dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
) # is self-attn if encoder_hidden_states is none
else:
self.norm2 = None
self.attn2 = None
# 3. Feed-forward
self.norm3 = nn.LayerNorm(time_mix_inner_dim)
self.ff = FeedForward(time_mix_inner_dim, activation_fn="geglu")
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = None
def set_chunk_feed_forward(self, chunk_size: Optional[int], **kwargs):
# Sets chunk feed-forward
self._chunk_size = chunk_size
# chunk dim should be hardcoded to 1 to have better speed vs. memory trade-off
self._chunk_dim = 1
def forward(
self,
hidden_states: torch.Tensor,
num_frames: int,
encoder_hidden_states: Optional[torch.Tensor] = None,
) -> torch.Tensor:
# Notice that normalization is always applied before the real computation in the following blocks.
# 0. Self-Attention
batch_size = hidden_states.shape[0]
batch_frames, seq_length, channels = hidden_states.shape
batch_size = batch_frames // num_frames
hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)
hidden_states = hidden_states.permute(0, 2, 1, 3)
hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)
residual = hidden_states
hidden_states = self.norm_in(hidden_states)
if self._chunk_size is not None:
hidden_states = _chunked_feed_forward(self.ff_in, hidden_states, self._chunk_dim, self._chunk_size)
else:
hidden_states = self.ff_in(hidden_states)
if self.is_res:
hidden_states = hidden_states + residual
norm_hidden_states = self.norm1(hidden_states)
attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
hidden_states = attn_output + hidden_states
# 3. Cross-Attention
if self.attn2 is not None:
norm_hidden_states = self.norm2(hidden_states)
attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
hidden_states = attn_output + hidden_states
# 4. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
if self._chunk_size is not None:
ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
else:
ff_output = self.ff(norm_hidden_states)
if self.is_res:
hidden_states = ff_output + hidden_states
else:
hidden_states = ff_output
hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels)
hidden_states = hidden_states.permute(0, 2, 1, 3)
hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels)
return hidden_states | class_definition | 27,328 | 32,297 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py | null | 765 |
class SkipFFTransformerBlock(nn.Module):
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
kv_input_dim: int,
kv_input_dim_proj_use_bias: bool,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
attention_bias: bool = False,
attention_out_bias: bool = True,
):
super().__init__()
if kv_input_dim != dim:
self.kv_mapper = nn.Linear(kv_input_dim, dim, kv_input_dim_proj_use_bias)
else:
self.kv_mapper = None
self.norm1 = RMSNorm(dim, 1e-06)
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim,
out_bias=attention_out_bias,
)
self.norm2 = RMSNorm(dim, 1e-06)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
out_bias=attention_out_bias,
)
def forward(self, hidden_states, encoder_hidden_states, cross_attention_kwargs):
cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
if self.kv_mapper is not None:
encoder_hidden_states = self.kv_mapper(F.silu(encoder_hidden_states))
norm_hidden_states = self.norm1(hidden_states)
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
norm_hidden_states = self.norm2(hidden_states)
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
return hidden_states | class_definition | 32,300 | 34,482 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py | null | 766 |
class FreeNoiseTransformerBlock(nn.Module):
r"""
A FreeNoise Transformer block.
Parameters:
dim (`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`):
The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability to use.
cross_attention_dim (`int`, *optional*):
The size of the encoder_hidden_states vector for cross attention.
activation_fn (`str`, *optional*, defaults to `"geglu"`):
Activation function to be used in feed-forward.
num_embeds_ada_norm (`int`, *optional*):
The number of diffusion steps used during training. See `Transformer2DModel`.
attention_bias (`bool`, defaults to `False`):
Configure if the attentions should contain a bias parameter.
only_cross_attention (`bool`, defaults to `False`):
Whether to use only cross-attention layers. In this case two cross attention layers are used.
double_self_attention (`bool`, defaults to `False`):
Whether to use two self-attention layers. In this case no cross attention layers are used.
upcast_attention (`bool`, defaults to `False`):
Whether to upcast the attention computation to float32. This is useful for mixed precision training.
norm_elementwise_affine (`bool`, defaults to `True`):
Whether to use learnable elementwise affine parameters for normalization.
norm_type (`str`, defaults to `"layer_norm"`):
The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
final_dropout (`bool` defaults to `False`):
Whether to apply a final dropout after the last feed-forward layer.
attention_type (`str`, defaults to `"default"`):
The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
positional_embeddings (`str`, *optional*):
The type of positional embeddings to apply to.
num_positional_embeddings (`int`, *optional*, defaults to `None`):
The maximum number of positional embeddings to apply.
ff_inner_dim (`int`, *optional*):
Hidden dimension of feed-forward MLP.
ff_bias (`bool`, defaults to `True`):
Whether or not to use bias in feed-forward MLP.
attention_out_bias (`bool`, defaults to `True`):
Whether or not to use bias in attention output project layer.
context_length (`int`, defaults to `16`):
The maximum number of frames that the FreeNoise block processes at once.
context_stride (`int`, defaults to `4`):
The number of frames to be skipped before starting to process a new batch of `context_length` frames.
weighting_scheme (`str`, defaults to `"pyramid"`):
The weighting scheme to use for weighting averaging of processed latent frames. As described in the
Equation 9. of the [FreeNoise](https://arxiv.org/abs/2310.15169) paper, "pyramid" is the default setting
used.
"""
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
dropout: float = 0.0,
cross_attention_dim: Optional[int] = None,
activation_fn: str = "geglu",
num_embeds_ada_norm: Optional[int] = None,
attention_bias: bool = False,
only_cross_attention: bool = False,
double_self_attention: bool = False,
upcast_attention: bool = False,
norm_elementwise_affine: bool = True,
norm_type: str = "layer_norm",
norm_eps: float = 1e-5,
final_dropout: bool = False,
positional_embeddings: Optional[str] = None,
num_positional_embeddings: Optional[int] = None,
ff_inner_dim: Optional[int] = None,
ff_bias: bool = True,
attention_out_bias: bool = True,
context_length: int = 16,
context_stride: int = 4,
weighting_scheme: str = "pyramid",
):
super().__init__()
self.dim = dim
self.num_attention_heads = num_attention_heads
self.attention_head_dim = attention_head_dim
self.dropout = dropout
self.cross_attention_dim = cross_attention_dim
self.activation_fn = activation_fn
self.attention_bias = attention_bias
self.double_self_attention = double_self_attention
self.norm_elementwise_affine = norm_elementwise_affine
self.positional_embeddings = positional_embeddings
self.num_positional_embeddings = num_positional_embeddings
self.only_cross_attention = only_cross_attention
self.set_free_noise_properties(context_length, context_stride, weighting_scheme)
# We keep these boolean flags for backward-compatibility.
self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
self.use_layer_norm = norm_type == "layer_norm"
self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous"
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
)
self.norm_type = norm_type
self.num_embeds_ada_norm = num_embeds_ada_norm
if positional_embeddings and (num_positional_embeddings is None):
raise ValueError(
"If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
)
if positional_embeddings == "sinusoidal":
self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
else:
self.pos_embed = None
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
upcast_attention=upcast_attention,
out_bias=attention_out_bias,
)
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim if not double_self_attention else None,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
out_bias=attention_out_bias,
) # is self-attn if encoder_hidden_states is none
# 3. Feed-forward
self.ff = FeedForward(
dim,
dropout=dropout,
activation_fn=activation_fn,
final_dropout=final_dropout,
inner_dim=ff_inner_dim,
bias=ff_bias,
)
self.norm3 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
def _get_frame_indices(self, num_frames: int) -> List[Tuple[int, int]]:
frame_indices = []
for i in range(0, num_frames - self.context_length + 1, self.context_stride):
window_start = i
window_end = min(num_frames, i + self.context_length)
frame_indices.append((window_start, window_end))
return frame_indices
def _get_frame_weights(self, num_frames: int, weighting_scheme: str = "pyramid") -> List[float]:
if weighting_scheme == "flat":
weights = [1.0] * num_frames
elif weighting_scheme == "pyramid":
if num_frames % 2 == 0:
# num_frames = 4 => [1, 2, 2, 1]
mid = num_frames // 2
weights = list(range(1, mid + 1))
weights = weights + weights[::-1]
else:
# num_frames = 5 => [1, 2, 3, 2, 1]
mid = (num_frames + 1) // 2
weights = list(range(1, mid))
weights = weights + [mid] + weights[::-1]
elif weighting_scheme == "delayed_reverse_sawtooth":
if num_frames % 2 == 0:
# num_frames = 4 => [0.01, 2, 2, 1]
mid = num_frames // 2
weights = [0.01] * (mid - 1) + [mid]
weights = weights + list(range(mid, 0, -1))
else:
# num_frames = 5 => [0.01, 0.01, 3, 2, 1]
mid = (num_frames + 1) // 2
weights = [0.01] * mid
weights = weights + list(range(mid, 0, -1))
else:
raise ValueError(f"Unsupported value for weighting_scheme={weighting_scheme}")
return weights
def set_free_noise_properties(
self, context_length: int, context_stride: int, weighting_scheme: str = "pyramid"
) -> None:
self.context_length = context_length
self.context_stride = context_stride
self.weighting_scheme = weighting_scheme
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0) -> None:
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
*args,
**kwargs,
) -> torch.Tensor:
if cross_attention_kwargs is not None:
if cross_attention_kwargs.get("scale", None) is not None:
logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
# hidden_states: [B x H x W, F, C]
device = hidden_states.device
dtype = hidden_states.dtype
num_frames = hidden_states.size(1)
frame_indices = self._get_frame_indices(num_frames)
frame_weights = self._get_frame_weights(self.context_length, self.weighting_scheme)
frame_weights = torch.tensor(frame_weights, device=device, dtype=dtype).unsqueeze(0).unsqueeze(-1)
is_last_frame_batch_complete = frame_indices[-1][1] == num_frames
# Handle out-of-bounds case if num_frames isn't perfectly divisible by context_length
# For example, num_frames=25, context_length=16, context_stride=4, then we expect the ranges:
# [(0, 16), (4, 20), (8, 24), (10, 26)]
if not is_last_frame_batch_complete:
if num_frames < self.context_length:
raise ValueError(f"Expected {num_frames=} to be greater or equal than {self.context_length=}")
last_frame_batch_length = num_frames - frame_indices[-1][1]
frame_indices.append((num_frames - self.context_length, num_frames))
num_times_accumulated = torch.zeros((1, num_frames, 1), device=device)
accumulated_values = torch.zeros_like(hidden_states)
for i, (frame_start, frame_end) in enumerate(frame_indices):
# The reason for slicing here is to ensure that if (frame_end - frame_start) is to handle
# cases like frame_indices=[(0, 16), (16, 20)], if the user provided a video with 19 frames, or
# essentially a non-multiple of `context_length`.
weights = torch.ones_like(num_times_accumulated[:, frame_start:frame_end])
weights *= frame_weights
hidden_states_chunk = hidden_states[:, frame_start:frame_end]
# Notice that normalization is always applied before the real computation in the following blocks.
# 1. Self-Attention
norm_hidden_states = self.norm1(hidden_states_chunk)
if self.pos_embed is not None:
norm_hidden_states = self.pos_embed(norm_hidden_states)
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
hidden_states_chunk = attn_output + hidden_states_chunk
if hidden_states_chunk.ndim == 4:
hidden_states_chunk = hidden_states_chunk.squeeze(1)
# 2. Cross-Attention
if self.attn2 is not None:
norm_hidden_states = self.norm2(hidden_states_chunk)
if self.pos_embed is not None and self.norm_type != "ada_norm_single":
norm_hidden_states = self.pos_embed(norm_hidden_states)
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
hidden_states_chunk = attn_output + hidden_states_chunk
if i == len(frame_indices) - 1 and not is_last_frame_batch_complete:
accumulated_values[:, -last_frame_batch_length:] += (
hidden_states_chunk[:, -last_frame_batch_length:] * weights[:, -last_frame_batch_length:]
)
num_times_accumulated[:, -last_frame_batch_length:] += weights[:, -last_frame_batch_length]
else:
accumulated_values[:, frame_start:frame_end] += hidden_states_chunk * weights
num_times_accumulated[:, frame_start:frame_end] += weights
# TODO(aryan): Maybe this could be done in a better way.
#
# Previously, this was:
# hidden_states = torch.where(
# num_times_accumulated > 0, accumulated_values / num_times_accumulated, accumulated_values
# )
#
# The reasoning for the change here is `torch.where` became a bottleneck at some point when golfing memory
# spikes. It is particularly noticeable when the number of frames is high. My understanding is that this comes
# from tensors being copied - which is why we resort to spliting and concatenating here. I've not particularly
# looked into this deeply because other memory optimizations led to more pronounced reductions.
hidden_states = torch.cat(
[
torch.where(num_times_split > 0, accumulated_split / num_times_split, accumulated_split)
for accumulated_split, num_times_split in zip(
accumulated_values.split(self.context_length, dim=1),
num_times_accumulated.split(self.context_length, dim=1),
)
],
dim=1,
).to(dtype)
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
if self._chunk_size is not None:
ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
else:
ff_output = self.ff(norm_hidden_states)
hidden_states = ff_output + hidden_states
if hidden_states.ndim == 4:
hidden_states = hidden_states.squeeze(1)
return hidden_states | class_definition | 34,507 | 50,766 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py | null | 767 |
class FeedForward(nn.Module):
r"""
A feed-forward layer.
Parameters:
dim (`int`): The number of channels in the input.
dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
"""
def __init__(
self,
dim: int,
dim_out: Optional[int] = None,
mult: int = 4,
dropout: float = 0.0,
activation_fn: str = "geglu",
final_dropout: bool = False,
inner_dim=None,
bias: bool = True,
):
super().__init__()
if inner_dim is None:
inner_dim = int(dim * mult)
dim_out = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
act_fn = GELU(dim, inner_dim, bias=bias)
if activation_fn == "gelu-approximate":
act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias)
elif activation_fn == "geglu":
act_fn = GEGLU(dim, inner_dim, bias=bias)
elif activation_fn == "geglu-approximate":
act_fn = ApproximateGELU(dim, inner_dim, bias=bias)
elif activation_fn == "swiglu":
act_fn = SwiGLU(dim, inner_dim, bias=bias)
elif activation_fn == "linear-silu":
act_fn = LinearActivation(dim, inner_dim, bias=bias, activation="silu")
self.net = nn.ModuleList([])
# project in
self.net.append(act_fn)
# project dropout
self.net.append(nn.Dropout(dropout))
# project out
self.net.append(nn.Linear(inner_dim, dim_out, bias=bias))
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(dropout))
def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
for module in self.net:
hidden_states = module(hidden_states)
return hidden_states | class_definition | 50,769 | 53,574 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention.py | null | 768 |
class Attention(nn.Module):
r"""
A cross attention layer.
Parameters:
query_dim (`int`):
The number of channels in the query.
cross_attention_dim (`int`, *optional*):
The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
heads (`int`, *optional*, defaults to 8):
The number of heads to use for multi-head attention.
kv_heads (`int`, *optional*, defaults to `None`):
The number of key and value heads to use for multi-head attention. Defaults to `heads`. If
`kv_heads=heads`, the model will use Multi Head Attention (MHA), if `kv_heads=1` the model will use Multi
Query Attention (MQA) otherwise GQA is used.
dim_head (`int`, *optional*, defaults to 64):
The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability to use.
bias (`bool`, *optional*, defaults to False):
Set to `True` for the query, key, and value linear layers to contain a bias parameter.
upcast_attention (`bool`, *optional*, defaults to False):
Set to `True` to upcast the attention computation to `float32`.
upcast_softmax (`bool`, *optional*, defaults to False):
Set to `True` to upcast the softmax computation to `float32`.
cross_attention_norm (`str`, *optional*, defaults to `None`):
The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
The number of groups to use for the group norm in the cross attention.
added_kv_proj_dim (`int`, *optional*, defaults to `None`):
The number of channels to use for the added key and value projections. If `None`, no projection is used.
norm_num_groups (`int`, *optional*, defaults to `None`):
The number of groups to use for the group norm in the attention.
spatial_norm_dim (`int`, *optional*, defaults to `None`):
The number of channels to use for the spatial normalization.
out_bias (`bool`, *optional*, defaults to `True`):
Set to `True` to use a bias in the output linear layer.
scale_qk (`bool`, *optional*, defaults to `True`):
Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
only_cross_attention (`bool`, *optional*, defaults to `False`):
Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
`added_kv_proj_dim` is not `None`.
eps (`float`, *optional*, defaults to 1e-5):
An additional value added to the denominator in group normalization that is used for numerical stability.
rescale_output_factor (`float`, *optional*, defaults to 1.0):
A factor to rescale the output by dividing it with this value.
residual_connection (`bool`, *optional*, defaults to `False`):
Set to `True` to add the residual connection to the output.
_from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
Set to `True` if the attention block is loaded from a deprecated state dict.
processor (`AttnProcessor`, *optional*, defaults to `None`):
The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
`AttnProcessor` otherwise.
"""
def __init__(
self,
query_dim: int,
cross_attention_dim: Optional[int] = None,
heads: int = 8,
kv_heads: Optional[int] = None,
dim_head: int = 64,
dropout: float = 0.0,
bias: bool = False,
upcast_attention: bool = False,
upcast_softmax: bool = False,
cross_attention_norm: Optional[str] = None,
cross_attention_norm_num_groups: int = 32,
qk_norm: Optional[str] = None,
added_kv_proj_dim: Optional[int] = None,
added_proj_bias: Optional[bool] = True,
norm_num_groups: Optional[int] = None,
spatial_norm_dim: Optional[int] = None,
out_bias: bool = True,
scale_qk: bool = True,
only_cross_attention: bool = False,
eps: float = 1e-5,
rescale_output_factor: float = 1.0,
residual_connection: bool = False,
_from_deprecated_attn_block: bool = False,
processor: Optional["AttnProcessor"] = None,
out_dim: int = None,
out_context_dim: int = None,
context_pre_only=None,
pre_only=False,
elementwise_affine: bool = True,
is_causal: bool = False,
):
super().__init__()
# To prevent circular import.
from .normalization import FP32LayerNorm, LpNorm, RMSNorm
self.inner_dim = out_dim if out_dim is not None else dim_head * heads
self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
self.query_dim = query_dim
self.use_bias = bias
self.is_cross_attention = cross_attention_dim is not None
self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
self.upcast_attention = upcast_attention
self.upcast_softmax = upcast_softmax
self.rescale_output_factor = rescale_output_factor
self.residual_connection = residual_connection
self.dropout = dropout
self.fused_projections = False
self.out_dim = out_dim if out_dim is not None else query_dim
self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim
self.context_pre_only = context_pre_only
self.pre_only = pre_only
self.is_causal = is_causal
# we make use of this private variable to know whether this class is loaded
# with an deprecated state dict so that we can convert it on the fly
self._from_deprecated_attn_block = _from_deprecated_attn_block
self.scale_qk = scale_qk
self.scale = dim_head**-0.5 if self.scale_qk else 1.0
self.heads = out_dim // dim_head if out_dim is not None else heads
# for slice_size > 0 the attention score computation
# is split across the batch axis to save memory
# You can set slice_size with `set_attention_slice`
self.sliceable_head_dim = heads
self.added_kv_proj_dim = added_kv_proj_dim
self.only_cross_attention = only_cross_attention
if self.added_kv_proj_dim is None and self.only_cross_attention:
raise ValueError(
"`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
)
if norm_num_groups is not None:
self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)
else:
self.group_norm = None
if spatial_norm_dim is not None:
self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)
else:
self.spatial_norm = None
if qk_norm is None:
self.norm_q = None
self.norm_k = None
elif qk_norm == "layer_norm":
self.norm_q = nn.LayerNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
self.norm_k = nn.LayerNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
elif qk_norm == "fp32_layer_norm":
self.norm_q = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
self.norm_k = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
elif qk_norm == "layer_norm_across_heads":
# Lumina applies qk norm across all heads
self.norm_q = nn.LayerNorm(dim_head * heads, eps=eps)
self.norm_k = nn.LayerNorm(dim_head * kv_heads, eps=eps)
elif qk_norm == "rms_norm":
self.norm_q = RMSNorm(dim_head, eps=eps)
self.norm_k = RMSNorm(dim_head, eps=eps)
elif qk_norm == "rms_norm_across_heads":
# LTX applies qk norm across all heads
self.norm_q = RMSNorm(dim_head * heads, eps=eps)
self.norm_k = RMSNorm(dim_head * kv_heads, eps=eps)
elif qk_norm == "l2":
self.norm_q = LpNorm(p=2, dim=-1, eps=eps)
self.norm_k = LpNorm(p=2, dim=-1, eps=eps)
else:
raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None,'layer_norm','fp32_layer_norm','rms_norm'")
if cross_attention_norm is None:
self.norm_cross = None
elif cross_attention_norm == "layer_norm":
self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
elif cross_attention_norm == "group_norm":
if self.added_kv_proj_dim is not None:
# The given `encoder_hidden_states` are initially of shape
# (batch_size, seq_len, added_kv_proj_dim) before being projected
# to (batch_size, seq_len, cross_attention_dim). The norm is applied
# before the projection, so we need to use `added_kv_proj_dim` as
# the number of channels for the group norm.
norm_cross_num_channels = added_kv_proj_dim
else:
norm_cross_num_channels = self.cross_attention_dim
self.norm_cross = nn.GroupNorm(
num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
)
else:
raise ValueError(
f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
)
self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)
if not self.only_cross_attention:
# only relevant for the `AddedKVProcessor` classes
self.to_k = nn.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias)
self.to_v = nn.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias)
else:
self.to_k = None
self.to_v = None
self.added_proj_bias = added_proj_bias
if self.added_kv_proj_dim is not None:
self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias)
self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias)
if self.context_pre_only is not None:
self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
else:
self.add_q_proj = None
self.add_k_proj = None
self.add_v_proj = None
if not self.pre_only:
self.to_out = nn.ModuleList([])
self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
self.to_out.append(nn.Dropout(dropout))
else:
self.to_out = None
if self.context_pre_only is not None and not self.context_pre_only:
self.to_add_out = nn.Linear(self.inner_dim, self.out_context_dim, bias=out_bias)
else:
self.to_add_out = None
if qk_norm is not None and added_kv_proj_dim is not None:
if qk_norm == "fp32_layer_norm":
self.norm_added_q = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
self.norm_added_k = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
elif qk_norm == "rms_norm":
self.norm_added_q = RMSNorm(dim_head, eps=eps)
self.norm_added_k = RMSNorm(dim_head, eps=eps)
else:
raise ValueError(
f"unknown qk_norm: {qk_norm}. Should be one of `None,'layer_norm','fp32_layer_norm','rms_norm'`"
)
else:
self.norm_added_q = None
self.norm_added_k = None
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
if processor is None:
processor = (
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
)
self.set_processor(processor)
def set_use_xla_flash_attention(
self,
use_xla_flash_attention: bool,
partition_spec: Optional[Tuple[Optional[str], ...]] = None,
is_flux=False,
) -> None:
r"""
Set whether to use xla flash attention from `torch_xla` or not.
Args:
use_xla_flash_attention (`bool`):
Whether to use pallas flash attention kernel from `torch_xla` or not.
partition_spec (`Tuple[]`, *optional*):
Specify the partition specification if using SPMD. Otherwise None.
"""
if use_xla_flash_attention:
if not is_torch_xla_available:
raise "torch_xla is not available"
elif is_torch_xla_version("<", "2.3"):
raise "flash attention pallas kernel is supported from torch_xla version 2.3"
elif is_spmd() and is_torch_xla_version("<", "2.4"):
raise "flash attention pallas kernel using SPMD is supported from torch_xla version 2.4"
else:
if is_flux:
processor = XLAFluxFlashAttnProcessor2_0(partition_spec)
else:
processor = XLAFlashAttnProcessor2_0(partition_spec)
else:
processor = (
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
)
self.set_processor(processor)
def set_use_npu_flash_attention(self, use_npu_flash_attention: bool) -> None:
r"""
Set whether to use npu flash attention from `torch_npu` or not.
"""
if use_npu_flash_attention:
processor = AttnProcessorNPU()
else:
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
processor = (
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
)
self.set_processor(processor)
def set_use_memory_efficient_attention_xformers(
self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
) -> None:
r"""
Set whether to use memory efficient attention from `xformers` or not.
Args:
use_memory_efficient_attention_xformers (`bool`):
Whether to use memory efficient attention from `xformers` or not.
attention_op (`Callable`, *optional*):
The attention operation to use. Defaults to `None` which uses the default attention operation from
`xformers`.
"""
is_custom_diffusion = hasattr(self, "processor") and isinstance(
self.processor,
(CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0),
)
is_added_kv_processor = hasattr(self, "processor") and isinstance(
self.processor,
(
AttnAddedKVProcessor,
AttnAddedKVProcessor2_0,
SlicedAttnAddedKVProcessor,
XFormersAttnAddedKVProcessor,
),
)
is_ip_adapter = hasattr(self, "processor") and isinstance(
self.processor,
(IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor),
)
is_joint_processor = hasattr(self, "processor") and isinstance(
self.processor,
(
JointAttnProcessor2_0,
XFormersJointAttnProcessor,
),
)
if use_memory_efficient_attention_xformers:
if is_added_kv_processor and is_custom_diffusion:
raise NotImplementedError(
f"Memory efficient attention is currently not supported for custom diffusion for attention processor type {self.processor}"
)
if not is_xformers_available():
raise ModuleNotFoundError(
(
"Refer to https://github.com/facebookresearch/xformers for more information on how to install"
" xformers"
),
name="xformers",
)
elif not torch.cuda.is_available():
raise ValueError(
"torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
" only available for GPU "
)
else:
try:
# Make sure we can run the memory efficient attention
_ = xformers.ops.memory_efficient_attention(
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
torch.randn((1, 2, 40), device="cuda"),
)
except Exception as e:
raise e
if is_custom_diffusion:
processor = CustomDiffusionXFormersAttnProcessor(
train_kv=self.processor.train_kv,
train_q_out=self.processor.train_q_out,
hidden_size=self.processor.hidden_size,
cross_attention_dim=self.processor.cross_attention_dim,
attention_op=attention_op,
)
processor.load_state_dict(self.processor.state_dict())
if hasattr(self.processor, "to_k_custom_diffusion"):
processor.to(self.processor.to_k_custom_diffusion.weight.device)
elif is_added_kv_processor:
# TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP
# which uses this type of cross attention ONLY because the attention mask of format
# [0, ..., -10.000, ..., 0, ...,] is not supported
# throw warning
logger.info(
"Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
)
processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
elif is_ip_adapter:
processor = IPAdapterXFormersAttnProcessor(
hidden_size=self.processor.hidden_size,
cross_attention_dim=self.processor.cross_attention_dim,
num_tokens=self.processor.num_tokens,
scale=self.processor.scale,
attention_op=attention_op,
)
processor.load_state_dict(self.processor.state_dict())
if hasattr(self.processor, "to_k_ip"):
processor.to(
device=self.processor.to_k_ip[0].weight.device, dtype=self.processor.to_k_ip[0].weight.dtype
)
elif is_joint_processor:
processor = XFormersJointAttnProcessor(attention_op=attention_op)
else:
processor = XFormersAttnProcessor(attention_op=attention_op)
else:
if is_custom_diffusion:
attn_processor_class = (
CustomDiffusionAttnProcessor2_0
if hasattr(F, "scaled_dot_product_attention")
else CustomDiffusionAttnProcessor
)
processor = attn_processor_class(
train_kv=self.processor.train_kv,
train_q_out=self.processor.train_q_out,
hidden_size=self.processor.hidden_size,
cross_attention_dim=self.processor.cross_attention_dim,
)
processor.load_state_dict(self.processor.state_dict())
if hasattr(self.processor, "to_k_custom_diffusion"):
processor.to(self.processor.to_k_custom_diffusion.weight.device)
elif is_ip_adapter:
processor = IPAdapterAttnProcessor2_0(
hidden_size=self.processor.hidden_size,
cross_attention_dim=self.processor.cross_attention_dim,
num_tokens=self.processor.num_tokens,
scale=self.processor.scale,
)
processor.load_state_dict(self.processor.state_dict())
if hasattr(self.processor, "to_k_ip"):
processor.to(
device=self.processor.to_k_ip[0].weight.device, dtype=self.processor.to_k_ip[0].weight.dtype
)
else:
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
processor = (
AttnProcessor2_0()
if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
else AttnProcessor()
)
self.set_processor(processor)
def set_attention_slice(self, slice_size: int) -> None:
r"""
Set the slice size for attention computation.
Args:
slice_size (`int`):
The slice size for attention computation.
"""
if slice_size is not None and slice_size > self.sliceable_head_dim:
raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
if slice_size is not None and self.added_kv_proj_dim is not None:
processor = SlicedAttnAddedKVProcessor(slice_size)
elif slice_size is not None:
processor = SlicedAttnProcessor(slice_size)
elif self.added_kv_proj_dim is not None:
processor = AttnAddedKVProcessor()
else:
# set attention processor
# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
processor = (
AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
)
self.set_processor(processor)
def set_processor(self, processor: "AttnProcessor") -> None:
r"""
Set the attention processor to use.
Args:
processor (`AttnProcessor`):
The attention processor to use.
"""
# if current processor is in `self._modules` and if passed `processor` is not, we need to
# pop `processor` from `self._modules`
if (
hasattr(self, "processor")
and isinstance(self.processor, torch.nn.Module)
and not isinstance(processor, torch.nn.Module)
):
logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
self._modules.pop("processor")
self.processor = processor
def get_processor(self, return_deprecated_lora: bool = False) -> "AttentionProcessor":
r"""
Get the attention processor in use.
Args:
return_deprecated_lora (`bool`, *optional*, defaults to `False`):
Set to `True` to return the deprecated LoRA attention processor.
Returns:
"AttentionProcessor": The attention processor in use.
"""
if not return_deprecated_lora:
return self.processor
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**cross_attention_kwargs,
) -> torch.Tensor:
r"""
The forward method of the `Attention` class.
Args:
hidden_states (`torch.Tensor`):
The hidden states of the query.
encoder_hidden_states (`torch.Tensor`, *optional*):
The hidden states of the encoder.
attention_mask (`torch.Tensor`, *optional*):
The attention mask to use. If `None`, no mask is applied.
**cross_attention_kwargs:
Additional keyword arguments to pass along to the cross attention.
Returns:
`torch.Tensor`: The output of the attention layer.
"""
# The `Attention` class can call different attention processors / attention functions
# here we simply pass along all tensors to the selected processor class
# For standard processors that are defined here, `**cross_attention_kwargs` is empty
attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys())
quiet_attn_parameters = {"ip_adapter_masks", "ip_hidden_states"}
unused_kwargs = [
k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters and k not in quiet_attn_parameters
]
if len(unused_kwargs) > 0:
logger.warning(
f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
)
cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters}
return self.processor(
self,
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
is the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
head_size = self.heads
batch_size, seq_len, dim = tensor.shape
tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
return tensor
def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
r"""
Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
the number of heads initialized while constructing the `Attention` class.
Args:
tensor (`torch.Tensor`): The tensor to reshape.
out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
reshaped to `[batch_size * heads, seq_len, dim // heads]`.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
head_size = self.heads
if tensor.ndim == 3:
batch_size, seq_len, dim = tensor.shape
extra_dim = 1
else:
batch_size, extra_dim, seq_len, dim = tensor.shape
tensor = tensor.reshape(batch_size, seq_len * extra_dim, head_size, dim // head_size)
tensor = tensor.permute(0, 2, 1, 3)
if out_dim == 3:
tensor = tensor.reshape(batch_size * head_size, seq_len * extra_dim, dim // head_size)
return tensor
def get_attention_scores(
self, query: torch.Tensor, key: torch.Tensor, attention_mask: Optional[torch.Tensor] = None
) -> torch.Tensor:
r"""
Compute the attention scores.
Args:
query (`torch.Tensor`): The query tensor.
key (`torch.Tensor`): The key tensor.
attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
Returns:
`torch.Tensor`: The attention probabilities/scores.
"""
dtype = query.dtype
if self.upcast_attention:
query = query.float()
key = key.float()
if attention_mask is None:
baddbmm_input = torch.empty(
query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
)
beta = 0
else:
baddbmm_input = attention_mask
beta = 1
attention_scores = torch.baddbmm(
baddbmm_input,
query,
key.transpose(-1, -2),
beta=beta,
alpha=self.scale,
)
del baddbmm_input
if self.upcast_softmax:
attention_scores = attention_scores.float()
attention_probs = attention_scores.softmax(dim=-1)
del attention_scores
attention_probs = attention_probs.to(dtype)
return attention_probs
def prepare_attention_mask(
self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3
) -> torch.Tensor:
r"""
Prepare the attention mask for the attention computation.
Args:
attention_mask (`torch.Tensor`):
The attention mask to prepare.
target_length (`int`):
The target length of the attention mask. This is the length of the attention mask after padding.
batch_size (`int`):
The batch size, which is used to repeat the attention mask.
out_dim (`int`, *optional*, defaults to `3`):
The output dimension of the attention mask. Can be either `3` or `4`.
Returns:
`torch.Tensor`: The prepared attention mask.
"""
head_size = self.heads
if attention_mask is None:
return attention_mask
current_length: int = attention_mask.shape[-1]
if current_length != target_length:
if attention_mask.device.type == "mps":
# HACK: MPS: Does not support padding by greater than dimension of input tensor.
# Instead, we can manually construct the padding tensor.
padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)
padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)
attention_mask = torch.cat([attention_mask, padding], dim=2)
else:
# TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
# we want to instead pad by (0, remaining_length), where remaining_length is:
# remaining_length: int = target_length - current_length
# TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
if out_dim == 3:
if attention_mask.shape[0] < batch_size * head_size:
attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
elif out_dim == 4:
attention_mask = attention_mask.unsqueeze(1)
attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
return attention_mask
def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
r"""
Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
`Attention` class.
Args:
encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
Returns:
`torch.Tensor`: The normalized encoder hidden states.
"""
assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
if isinstance(self.norm_cross, nn.LayerNorm):
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
elif isinstance(self.norm_cross, nn.GroupNorm):
# Group norm norms along the channels dimension and expects
# input to be in the shape of (N, C, *). In this case, we want
# to norm along the hidden dimension, so we need to move
# (batch_size, sequence_length, hidden_size) ->
# (batch_size, hidden_size, sequence_length)
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
else:
assert False
return encoder_hidden_states
@torch.no_grad()
def fuse_projections(self, fuse=True):
device = self.to_q.weight.data.device
dtype = self.to_q.weight.data.dtype
if not self.is_cross_attention:
# fetch weight matrices.
concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
in_features = concatenated_weights.shape[1]
out_features = concatenated_weights.shape[0]
# create a new single projection layer and copy over the weights.
self.to_qkv = nn.Linear(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype)
self.to_qkv.weight.copy_(concatenated_weights)
if self.use_bias:
concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
self.to_qkv.bias.copy_(concatenated_bias)
else:
concatenated_weights = torch.cat([self.to_k.weight.data, self.to_v.weight.data])
in_features = concatenated_weights.shape[1]
out_features = concatenated_weights.shape[0]
self.to_kv = nn.Linear(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype)
self.to_kv.weight.copy_(concatenated_weights)
if self.use_bias:
concatenated_bias = torch.cat([self.to_k.bias.data, self.to_v.bias.data])
self.to_kv.bias.copy_(concatenated_bias)
# handle added projections for SD3 and others.
if (
getattr(self, "add_q_proj", None) is not None
and getattr(self, "add_k_proj", None) is not None
and getattr(self, "add_v_proj", None) is not None
):
concatenated_weights = torch.cat(
[self.add_q_proj.weight.data, self.add_k_proj.weight.data, self.add_v_proj.weight.data]
)
in_features = concatenated_weights.shape[1]
out_features = concatenated_weights.shape[0]
self.to_added_qkv = nn.Linear(
in_features, out_features, bias=self.added_proj_bias, device=device, dtype=dtype
)
self.to_added_qkv.weight.copy_(concatenated_weights)
if self.added_proj_bias:
concatenated_bias = torch.cat(
[self.add_q_proj.bias.data, self.add_k_proj.bias.data, self.add_v_proj.bias.data]
)
self.to_added_qkv.bias.copy_(concatenated_bias)
self.fused_projections = fuse | class_definition | 1,620 | 38,027 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 769 |
class SanaMultiscaleAttentionProjection(nn.Module):
def __init__(
self,
in_channels: int,
num_attention_heads: int,
kernel_size: int,
) -> None:
super().__init__()
channels = 3 * in_channels
self.proj_in = nn.Conv2d(
channels,
channels,
kernel_size,
padding=kernel_size // 2,
groups=channels,
bias=False,
)
self.proj_out = nn.Conv2d(channels, channels, 1, 1, 0, groups=3 * num_attention_heads, bias=False)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.proj_in(hidden_states)
hidden_states = self.proj_out(hidden_states)
return hidden_states | class_definition | 38,030 | 38,791 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 770 |
class SanaMultiscaleLinearAttention(nn.Module):
r"""Lightweight multi-scale linear attention"""
def __init__(
self,
in_channels: int,
out_channels: int,
num_attention_heads: Optional[int] = None,
attention_head_dim: int = 8,
mult: float = 1.0,
norm_type: str = "batch_norm",
kernel_sizes: Tuple[int, ...] = (5,),
eps: float = 1e-15,
residual_connection: bool = False,
):
super().__init__()
# To prevent circular import
from .normalization import get_normalization
self.eps = eps
self.attention_head_dim = attention_head_dim
self.norm_type = norm_type
self.residual_connection = residual_connection
num_attention_heads = (
int(in_channels // attention_head_dim * mult) if num_attention_heads is None else num_attention_heads
)
inner_dim = num_attention_heads * attention_head_dim
self.to_q = nn.Linear(in_channels, inner_dim, bias=False)
self.to_k = nn.Linear(in_channels, inner_dim, bias=False)
self.to_v = nn.Linear(in_channels, inner_dim, bias=False)
self.to_qkv_multiscale = nn.ModuleList()
for kernel_size in kernel_sizes:
self.to_qkv_multiscale.append(
SanaMultiscaleAttentionProjection(inner_dim, num_attention_heads, kernel_size)
)
self.nonlinearity = nn.ReLU()
self.to_out = nn.Linear(inner_dim * (1 + len(kernel_sizes)), out_channels, bias=False)
self.norm_out = get_normalization(norm_type, num_features=out_channels)
self.processor = SanaMultiscaleAttnProcessor2_0()
def apply_linear_attention(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
value = F.pad(value, (0, 0, 0, 1), mode="constant", value=1) # Adds padding
scores = torch.matmul(value, key.transpose(-1, -2))
hidden_states = torch.matmul(scores, query)
hidden_states = hidden_states.to(dtype=torch.float32)
hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps)
return hidden_states
def apply_quadratic_attention(self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
scores = torch.matmul(key.transpose(-1, -2), query)
scores = scores.to(dtype=torch.float32)
scores = scores / (torch.sum(scores, dim=2, keepdim=True) + self.eps)
hidden_states = torch.matmul(value, scores.to(value.dtype))
return hidden_states
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return self.processor(self, hidden_states) | class_definition | 38,794 | 41,491 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 771 |
class MochiAttention(nn.Module):
def __init__(
self,
query_dim: int,
added_kv_proj_dim: int,
processor: "MochiAttnProcessor2_0",
heads: int = 8,
dim_head: int = 64,
dropout: float = 0.0,
bias: bool = False,
added_proj_bias: bool = True,
out_dim: Optional[int] = None,
out_context_dim: Optional[int] = None,
out_bias: bool = True,
context_pre_only: bool = False,
eps: float = 1e-5,
):
super().__init__()
from .normalization import MochiRMSNorm
self.inner_dim = out_dim if out_dim is not None else dim_head * heads
self.out_dim = out_dim if out_dim is not None else query_dim
self.out_context_dim = out_context_dim if out_context_dim else query_dim
self.context_pre_only = context_pre_only
self.heads = out_dim // dim_head if out_dim is not None else heads
self.norm_q = MochiRMSNorm(dim_head, eps, True)
self.norm_k = MochiRMSNorm(dim_head, eps, True)
self.norm_added_q = MochiRMSNorm(dim_head, eps, True)
self.norm_added_k = MochiRMSNorm(dim_head, eps, True)
self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)
self.to_k = nn.Linear(query_dim, self.inner_dim, bias=bias)
self.to_v = nn.Linear(query_dim, self.inner_dim, bias=bias)
self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
if self.context_pre_only is not None:
self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
self.to_out = nn.ModuleList([])
self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
self.to_out.append(nn.Dropout(dropout))
if not self.context_pre_only:
self.to_add_out = nn.Linear(self.inner_dim, self.out_context_dim, bias=out_bias)
self.processor = processor
def forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
):
return self.processor(
self,
hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
**kwargs,
) | class_definition | 41,494 | 43,957 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 772 |
class MochiAttnProcessor2_0:
"""Attention processor used in Mochi."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("MochiAttnProcessor2_0 requires PyTorch 2.0. To use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: "MochiAttention",
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
query = query.unflatten(2, (attn.heads, -1))
key = key.unflatten(2, (attn.heads, -1))
value = value.unflatten(2, (attn.heads, -1))
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
encoder_query = attn.add_q_proj(encoder_hidden_states)
encoder_key = attn.add_k_proj(encoder_hidden_states)
encoder_value = attn.add_v_proj(encoder_hidden_states)
encoder_query = encoder_query.unflatten(2, (attn.heads, -1))
encoder_key = encoder_key.unflatten(2, (attn.heads, -1))
encoder_value = encoder_value.unflatten(2, (attn.heads, -1))
if attn.norm_added_q is not None:
encoder_query = attn.norm_added_q(encoder_query)
if attn.norm_added_k is not None:
encoder_key = attn.norm_added_k(encoder_key)
if image_rotary_emb is not None:
def apply_rotary_emb(x, freqs_cos, freqs_sin):
x_even = x[..., 0::2].float()
x_odd = x[..., 1::2].float()
cos = (x_even * freqs_cos - x_odd * freqs_sin).to(x.dtype)
sin = (x_even * freqs_sin + x_odd * freqs_cos).to(x.dtype)
return torch.stack([cos, sin], dim=-1).flatten(-2)
query = apply_rotary_emb(query, *image_rotary_emb)
key = apply_rotary_emb(key, *image_rotary_emb)
query, key, value = query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2)
encoder_query, encoder_key, encoder_value = (
encoder_query.transpose(1, 2),
encoder_key.transpose(1, 2),
encoder_value.transpose(1, 2),
)
sequence_length = query.size(2)
encoder_sequence_length = encoder_query.size(2)
total_length = sequence_length + encoder_sequence_length
batch_size, heads, _, dim = query.shape
attn_outputs = []
for idx in range(batch_size):
mask = attention_mask[idx][None, :]
valid_prompt_token_indices = torch.nonzero(mask.flatten(), as_tuple=False).flatten()
valid_encoder_query = encoder_query[idx : idx + 1, :, valid_prompt_token_indices, :]
valid_encoder_key = encoder_key[idx : idx + 1, :, valid_prompt_token_indices, :]
valid_encoder_value = encoder_value[idx : idx + 1, :, valid_prompt_token_indices, :]
valid_query = torch.cat([query[idx : idx + 1], valid_encoder_query], dim=2)
valid_key = torch.cat([key[idx : idx + 1], valid_encoder_key], dim=2)
valid_value = torch.cat([value[idx : idx + 1], valid_encoder_value], dim=2)
attn_output = F.scaled_dot_product_attention(
valid_query, valid_key, valid_value, dropout_p=0.0, is_causal=False
)
valid_sequence_length = attn_output.size(2)
attn_output = F.pad(attn_output, (0, 0, 0, total_length - valid_sequence_length))
attn_outputs.append(attn_output)
hidden_states = torch.cat(attn_outputs, dim=0)
hidden_states = hidden_states.transpose(1, 2).flatten(2, 3)
hidden_states, encoder_hidden_states = hidden_states.split_with_sizes(
(sequence_length, encoder_sequence_length), dim=1
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if hasattr(attn, "to_add_out"):
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states | class_definition | 43,960 | 48,254 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 773 |
class AttnProcessor:
r"""
Default processor for performing attention-related computations.
"""
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states | class_definition | 48,257 | 51,006 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 774 |
class CustomDiffusionAttnProcessor(nn.Module):
r"""
Processor for implementing attention for the Custom Diffusion method.
Args:
train_kv (`bool`, defaults to `True`):
Whether to newly train the key and value matrices corresponding to the text features.
train_q_out (`bool`, defaults to `True`):
Whether to newly train query matrices corresponding to the latent image features.
hidden_size (`int`, *optional*, defaults to `None`):
The hidden size of the attention layer.
cross_attention_dim (`int`, *optional*, defaults to `None`):
The number of channels in the `encoder_hidden_states`.
out_bias (`bool`, defaults to `True`):
Whether to include the bias parameter in `train_q_out`.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probability to use.
"""
def __init__(
self,
train_kv: bool = True,
train_q_out: bool = True,
hidden_size: Optional[int] = None,
cross_attention_dim: Optional[int] = None,
out_bias: bool = True,
dropout: float = 0.0,
):
super().__init__()
self.train_kv = train_kv
self.train_q_out = train_q_out
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
# `_custom_diffusion` id for easy serialization and loading.
if self.train_kv:
self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
if self.train_q_out:
self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
self.to_out_custom_diffusion = nn.ModuleList([])
self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
self.to_out_custom_diffusion.append(nn.Dropout(dropout))
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if self.train_q_out:
query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)
else:
query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))
if encoder_hidden_states is None:
crossattn = False
encoder_hidden_states = hidden_states
else:
crossattn = True
if attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
if self.train_kv:
key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
key = key.to(attn.to_q.weight.dtype)
value = value.to(attn.to_q.weight.dtype)
else:
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
if crossattn:
detach = torch.ones_like(key)
detach[:, :1, :] = detach[:, :1, :] * 0.0
key = detach * key + (1 - detach) * key.detach()
value = detach * value + (1 - detach) * value.detach()
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
if self.train_q_out:
# linear proj
hidden_states = self.to_out_custom_diffusion[0](hidden_states)
# dropout
hidden_states = self.to_out_custom_diffusion[1](hidden_states)
else:
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
return hidden_states | class_definition | 51,009 | 55,415 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 775 |
class AttnAddedKVProcessor:
r"""
Processor for performing attention-related computations with extra learnable key and value matrices for the text
encoder.
"""
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
query = attn.head_to_batch_dim(query)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
if not attn.only_cross_attention:
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
else:
key = encoder_hidden_states_key_proj
value = encoder_hidden_states_value_proj
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
hidden_states = hidden_states + residual
return hidden_states | class_definition | 55,418 | 58,327 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 776 |
class AttnAddedKVProcessor2_0:
r"""
Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra
learnable key and value matrices for the text encoder.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
query = attn.head_to_batch_dim(query, out_dim=4)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4)
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4)
if not attn.only_cross_attention:
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
key = attn.head_to_batch_dim(key, out_dim=4)
value = attn.head_to_batch_dim(value, out_dim=4)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
else:
key = encoder_hidden_states_key_proj
value = encoder_hidden_states_value_proj
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1])
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
hidden_states = hidden_states + residual
return hidden_states | class_definition | 58,330 | 61,779 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 777 |
class JointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
batch_size = hidden_states.shape[0]
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# `context` projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
query = torch.cat([query, encoder_hidden_states_query_proj], dim=2)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=2)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=2)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
# Split the attention outputs.
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 61,782 | 65,402 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 778 |
class PAGJointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"PAGJointAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
residual = hidden_states
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
context_input_ndim = encoder_hidden_states.ndim
if context_input_ndim == 4:
batch_size, channel, height, width = encoder_hidden_states.shape
encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
# store the length of image patch sequences to create a mask that prevents interaction between patches
# similar to making the self-attention map an identity matrix
identity_block_size = hidden_states.shape[1]
# chunk
hidden_states_org, hidden_states_ptb = hidden_states.chunk(2)
encoder_hidden_states_org, encoder_hidden_states_ptb = encoder_hidden_states.chunk(2)
################## original path ##################
batch_size = encoder_hidden_states_org.shape[0]
# `sample` projections.
query_org = attn.to_q(hidden_states_org)
key_org = attn.to_k(hidden_states_org)
value_org = attn.to_v(hidden_states_org)
# `context` projections.
encoder_hidden_states_org_query_proj = attn.add_q_proj(encoder_hidden_states_org)
encoder_hidden_states_org_key_proj = attn.add_k_proj(encoder_hidden_states_org)
encoder_hidden_states_org_value_proj = attn.add_v_proj(encoder_hidden_states_org)
# attention
query_org = torch.cat([query_org, encoder_hidden_states_org_query_proj], dim=1)
key_org = torch.cat([key_org, encoder_hidden_states_org_key_proj], dim=1)
value_org = torch.cat([value_org, encoder_hidden_states_org_value_proj], dim=1)
inner_dim = key_org.shape[-1]
head_dim = inner_dim // attn.heads
query_org = query_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_org = key_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_org = value_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
hidden_states_org = F.scaled_dot_product_attention(
query_org, key_org, value_org, dropout_p=0.0, is_causal=False
)
hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states_org = hidden_states_org.to(query_org.dtype)
# Split the attention outputs.
hidden_states_org, encoder_hidden_states_org = (
hidden_states_org[:, : residual.shape[1]],
hidden_states_org[:, residual.shape[1] :],
)
# linear proj
hidden_states_org = attn.to_out[0](hidden_states_org)
# dropout
hidden_states_org = attn.to_out[1](hidden_states_org)
if not attn.context_pre_only:
encoder_hidden_states_org = attn.to_add_out(encoder_hidden_states_org)
if input_ndim == 4:
hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states_org = encoder_hidden_states_org.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
################## perturbed path ##################
batch_size = encoder_hidden_states_ptb.shape[0]
# `sample` projections.
query_ptb = attn.to_q(hidden_states_ptb)
key_ptb = attn.to_k(hidden_states_ptb)
value_ptb = attn.to_v(hidden_states_ptb)
# `context` projections.
encoder_hidden_states_ptb_query_proj = attn.add_q_proj(encoder_hidden_states_ptb)
encoder_hidden_states_ptb_key_proj = attn.add_k_proj(encoder_hidden_states_ptb)
encoder_hidden_states_ptb_value_proj = attn.add_v_proj(encoder_hidden_states_ptb)
# attention
query_ptb = torch.cat([query_ptb, encoder_hidden_states_ptb_query_proj], dim=1)
key_ptb = torch.cat([key_ptb, encoder_hidden_states_ptb_key_proj], dim=1)
value_ptb = torch.cat([value_ptb, encoder_hidden_states_ptb_value_proj], dim=1)
inner_dim = key_ptb.shape[-1]
head_dim = inner_dim // attn.heads
query_ptb = query_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_ptb = key_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_ptb = value_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# create a full mask with all entries set to 0
seq_len = query_ptb.size(2)
full_mask = torch.zeros((seq_len, seq_len), device=query_ptb.device, dtype=query_ptb.dtype)
# set the attention value between image patches to -inf
full_mask[:identity_block_size, :identity_block_size] = float("-inf")
# set the diagonal of the attention value between image patches to 0
full_mask[:identity_block_size, :identity_block_size].fill_diagonal_(0)
# expand the mask to match the attention weights shape
full_mask = full_mask.unsqueeze(0).unsqueeze(0) # Add batch and num_heads dimensions
hidden_states_ptb = F.scaled_dot_product_attention(
query_ptb, key_ptb, value_ptb, attn_mask=full_mask, dropout_p=0.0, is_causal=False
)
hidden_states_ptb = hidden_states_ptb.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states_ptb = hidden_states_ptb.to(query_ptb.dtype)
# split the attention outputs.
hidden_states_ptb, encoder_hidden_states_ptb = (
hidden_states_ptb[:, : residual.shape[1]],
hidden_states_ptb[:, residual.shape[1] :],
)
# linear proj
hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
# dropout
hidden_states_ptb = attn.to_out[1](hidden_states_ptb)
if not attn.context_pre_only:
encoder_hidden_states_ptb = attn.to_add_out(encoder_hidden_states_ptb)
if input_ndim == 4:
hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states_ptb = encoder_hidden_states_ptb.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
################ concat ###############
hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])
encoder_hidden_states = torch.cat([encoder_hidden_states_org, encoder_hidden_states_ptb])
return hidden_states, encoder_hidden_states | class_definition | 65,405 | 72,695 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 779 |
class PAGCFGJointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"PAGCFGJointAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
context_input_ndim = encoder_hidden_states.ndim
if context_input_ndim == 4:
batch_size, channel, height, width = encoder_hidden_states.shape
encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
identity_block_size = hidden_states.shape[
1
] # patch embeddings width * height (correspond to self-attention map width or height)
# chunk
hidden_states_uncond, hidden_states_org, hidden_states_ptb = hidden_states.chunk(3)
hidden_states_org = torch.cat([hidden_states_uncond, hidden_states_org])
(
encoder_hidden_states_uncond,
encoder_hidden_states_org,
encoder_hidden_states_ptb,
) = encoder_hidden_states.chunk(3)
encoder_hidden_states_org = torch.cat([encoder_hidden_states_uncond, encoder_hidden_states_org])
################## original path ##################
batch_size = encoder_hidden_states_org.shape[0]
# `sample` projections.
query_org = attn.to_q(hidden_states_org)
key_org = attn.to_k(hidden_states_org)
value_org = attn.to_v(hidden_states_org)
# `context` projections.
encoder_hidden_states_org_query_proj = attn.add_q_proj(encoder_hidden_states_org)
encoder_hidden_states_org_key_proj = attn.add_k_proj(encoder_hidden_states_org)
encoder_hidden_states_org_value_proj = attn.add_v_proj(encoder_hidden_states_org)
# attention
query_org = torch.cat([query_org, encoder_hidden_states_org_query_proj], dim=1)
key_org = torch.cat([key_org, encoder_hidden_states_org_key_proj], dim=1)
value_org = torch.cat([value_org, encoder_hidden_states_org_value_proj], dim=1)
inner_dim = key_org.shape[-1]
head_dim = inner_dim // attn.heads
query_org = query_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_org = key_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_org = value_org.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
hidden_states_org = F.scaled_dot_product_attention(
query_org, key_org, value_org, dropout_p=0.0, is_causal=False
)
hidden_states_org = hidden_states_org.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states_org = hidden_states_org.to(query_org.dtype)
# Split the attention outputs.
hidden_states_org, encoder_hidden_states_org = (
hidden_states_org[:, : residual.shape[1]],
hidden_states_org[:, residual.shape[1] :],
)
# linear proj
hidden_states_org = attn.to_out[0](hidden_states_org)
# dropout
hidden_states_org = attn.to_out[1](hidden_states_org)
if not attn.context_pre_only:
encoder_hidden_states_org = attn.to_add_out(encoder_hidden_states_org)
if input_ndim == 4:
hidden_states_org = hidden_states_org.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states_org = encoder_hidden_states_org.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
################## perturbed path ##################
batch_size = encoder_hidden_states_ptb.shape[0]
# `sample` projections.
query_ptb = attn.to_q(hidden_states_ptb)
key_ptb = attn.to_k(hidden_states_ptb)
value_ptb = attn.to_v(hidden_states_ptb)
# `context` projections.
encoder_hidden_states_ptb_query_proj = attn.add_q_proj(encoder_hidden_states_ptb)
encoder_hidden_states_ptb_key_proj = attn.add_k_proj(encoder_hidden_states_ptb)
encoder_hidden_states_ptb_value_proj = attn.add_v_proj(encoder_hidden_states_ptb)
# attention
query_ptb = torch.cat([query_ptb, encoder_hidden_states_ptb_query_proj], dim=1)
key_ptb = torch.cat([key_ptb, encoder_hidden_states_ptb_key_proj], dim=1)
value_ptb = torch.cat([value_ptb, encoder_hidden_states_ptb_value_proj], dim=1)
inner_dim = key_ptb.shape[-1]
head_dim = inner_dim // attn.heads
query_ptb = query_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key_ptb = key_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value_ptb = value_ptb.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# create a full mask with all entries set to 0
seq_len = query_ptb.size(2)
full_mask = torch.zeros((seq_len, seq_len), device=query_ptb.device, dtype=query_ptb.dtype)
# set the attention value between image patches to -inf
full_mask[:identity_block_size, :identity_block_size] = float("-inf")
# set the diagonal of the attention value between image patches to 0
full_mask[:identity_block_size, :identity_block_size].fill_diagonal_(0)
# expand the mask to match the attention weights shape
full_mask = full_mask.unsqueeze(0).unsqueeze(0) # Add batch and num_heads dimensions
hidden_states_ptb = F.scaled_dot_product_attention(
query_ptb, key_ptb, value_ptb, attn_mask=full_mask, dropout_p=0.0, is_causal=False
)
hidden_states_ptb = hidden_states_ptb.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states_ptb = hidden_states_ptb.to(query_ptb.dtype)
# split the attention outputs.
hidden_states_ptb, encoder_hidden_states_ptb = (
hidden_states_ptb[:, : residual.shape[1]],
hidden_states_ptb[:, residual.shape[1] :],
)
# linear proj
hidden_states_ptb = attn.to_out[0](hidden_states_ptb)
# dropout
hidden_states_ptb = attn.to_out[1](hidden_states_ptb)
if not attn.context_pre_only:
encoder_hidden_states_ptb = attn.to_add_out(encoder_hidden_states_ptb)
if input_ndim == 4:
hidden_states_ptb = hidden_states_ptb.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states_ptb = encoder_hidden_states_ptb.transpose(-1, -2).reshape(
batch_size, channel, height, width
)
################ concat ###############
hidden_states = torch.cat([hidden_states_org, hidden_states_ptb])
encoder_hidden_states = torch.cat([encoder_hidden_states_org, encoder_hidden_states_ptb])
return hidden_states, encoder_hidden_states | class_definition | 72,698 | 80,242 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 780 |
class FusedJointAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
context_input_ndim = encoder_hidden_states.ndim
if context_input_ndim == 4:
batch_size, channel, height, width = encoder_hidden_states.shape
encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size = encoder_hidden_states.shape[0]
# `sample` projections.
qkv = attn.to_qkv(hidden_states)
split_size = qkv.shape[-1] // 3
query, key, value = torch.split(qkv, split_size, dim=-1)
# `context` projections.
encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
split_size = encoder_qkv.shape[-1] // 3
(
encoder_hidden_states_query_proj,
encoder_hidden_states_key_proj,
encoder_hidden_states_value_proj,
) = torch.split(encoder_qkv, split_size, dim=-1)
# attention
query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# Split the attention outputs.
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if context_input_ndim == 4:
encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
return hidden_states, encoder_hidden_states | class_definition | 80,245 | 83,617 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 781 |
class XFormersJointAttnProcessor:
r"""
Processor for implementing memory efficient attention using xFormers.
Args:
attention_op (`Callable`, *optional*, defaults to `None`):
The base
[operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
operator.
"""
def __init__(self, attention_op: Optional[Callable] = None):
self.attention_op = attention_op
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
*args,
**kwargs,
) -> torch.FloatTensor:
residual = hidden_states
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# `context` projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = attn.head_to_batch_dim(encoder_hidden_states_query_proj).contiguous()
encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj).contiguous()
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj).contiguous()
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)
key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)
value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)
hidden_states = xformers.ops.memory_efficient_attention(
query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
)
hidden_states = hidden_states.to(query.dtype)
hidden_states = attn.batch_to_head_dim(hidden_states)
if encoder_hidden_states is not None:
# Split the attention outputs.
hidden_states, encoder_hidden_states = (
hidden_states[:, : residual.shape[1]],
hidden_states[:, residual.shape[1] :],
)
if not attn.context_pre_only:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 83,620 | 87,184 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 782 |
class AllegroAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
used in the Allegro model. It applies a normalization layer and rotary embedding on the query and key vector.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"AllegroAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# Apply RoPE if needed
if image_rotary_emb is not None and not attn.is_cross_attention:
from .embeddings import apply_rotary_emb_allegro
query = apply_rotary_emb_allegro(query, image_rotary_emb[0], image_rotary_emb[1])
key = apply_rotary_emb_allegro(key, image_rotary_emb[0], image_rotary_emb[1])
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states | class_definition | 87,187 | 91,027 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 783 |
class AuraFlowAttnProcessor2_0:
"""Attention processor used typically in processing Aura Flow."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention") and is_torch_version("<", "2.1"):
raise ImportError(
"AuraFlowAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to at least 2.1 or above as we use `scale` in `F.scaled_dot_product_attention()`. "
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
*args,
**kwargs,
) -> torch.FloatTensor:
batch_size = hidden_states.shape[0]
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
# `context` projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
# Reshape.
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim)
key = key.view(batch_size, -1, attn.heads, head_dim)
value = value.view(batch_size, -1, attn.heads, head_dim)
# Apply QK norm.
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# Concatenate the projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(batch_size, -1, attn.heads, head_dim)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_q(encoder_hidden_states_key_proj)
query = torch.cat([encoder_hidden_states_query_proj, query], dim=1)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
# Attention.
hidden_states = F.scaled_dot_product_attention(
query, key, value, dropout_p=0.0, scale=attn.scale, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# Split the attention outputs.
if encoder_hidden_states is not None:
hidden_states, encoder_hidden_states = (
hidden_states[:, encoder_hidden_states.shape[1] :],
hidden_states[:, : encoder_hidden_states.shape[1]],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 91,030 | 94,871 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 784 |
class FusedAuraFlowAttnProcessor2_0:
"""Attention processor used typically in processing Aura Flow with fused projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention") and is_torch_version("<", "2.1"):
raise ImportError(
"FusedAuraFlowAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to at least 2.1 or above as we use `scale` in `F.scaled_dot_product_attention()`. "
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
*args,
**kwargs,
) -> torch.FloatTensor:
batch_size = hidden_states.shape[0]
# `sample` projections.
qkv = attn.to_qkv(hidden_states)
split_size = qkv.shape[-1] // 3
query, key, value = torch.split(qkv, split_size, dim=-1)
# `context` projections.
if encoder_hidden_states is not None:
encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
split_size = encoder_qkv.shape[-1] // 3
(
encoder_hidden_states_query_proj,
encoder_hidden_states_key_proj,
encoder_hidden_states_value_proj,
) = torch.split(encoder_qkv, split_size, dim=-1)
# Reshape.
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim)
key = key.view(batch_size, -1, attn.heads, head_dim)
value = value.view(batch_size, -1, attn.heads, head_dim)
# Apply QK norm.
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# Concatenate the projections.
if encoder_hidden_states is not None:
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(batch_size, -1, attn.heads, head_dim)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_q(encoder_hidden_states_key_proj)
query = torch.cat([encoder_hidden_states_query_proj, query], dim=1)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
query = query.transpose(1, 2)
key = key.transpose(1, 2)
value = value.transpose(1, 2)
# Attention.
hidden_states = F.scaled_dot_product_attention(
query, key, value, dropout_p=0.0, scale=attn.scale, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# Split the attention outputs.
if encoder_hidden_states is not None:
hidden_states, encoder_hidden_states = (
hidden_states[:, encoder_hidden_states.shape[1] :],
hidden_states[:, : encoder_hidden_states.shape[1]],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if encoder_hidden_states is not None:
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
if encoder_hidden_states is not None:
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 94,874 | 98,859 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 785 |
class FluxAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from .embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 98,862 | 102,788 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 786 |
class FluxAttnProcessor2_0_NPU:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"FluxAttnProcessor2_0_NPU requires PyTorch 2.0 and torch NPU, to use it, please upgrade PyTorch to 2.0 and install torch NPU"
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from .embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
if query.dtype in (torch.float16, torch.bfloat16):
hidden_states = torch_npu.npu_fusion_attention(
query,
key,
value,
attn.heads,
input_layout="BNSD",
pse=None,
scale=1.0 / math.sqrt(query.shape[-1]),
pre_tockens=65536,
next_tockens=65536,
keep_prob=1.0,
sync=False,
inner_precise=0,
)[0]
else:
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 102,791 | 107,319 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 787 |
class FusedFluxAttnProcessor2_0:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"FusedFluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
qkv = attn.to_qkv(hidden_states)
split_size = qkv.shape[-1] // 3
query, key, value = torch.split(qkv, split_size, dim=-1)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
# `context` projections.
if encoder_hidden_states is not None:
encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
split_size = encoder_qkv.shape[-1] // 3
(
encoder_hidden_states_query_proj,
encoder_hidden_states_key_proj,
encoder_hidden_states_value_proj,
) = torch.split(encoder_qkv, split_size, dim=-1)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from .embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 107,322 | 111,394 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 788 |
class FusedFluxAttnProcessor2_0_NPU:
"""Attention processor used typically in processing the SD3-like self-attention projections."""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"FluxAttnProcessor2_0_NPU requires PyTorch 2.0 and torch NPU, to use it, please upgrade PyTorch to 2.0, and install torch NPU"
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
qkv = attn.to_qkv(hidden_states)
split_size = qkv.shape[-1] // 3
query, key, value = torch.split(qkv, split_size, dim=-1)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
# `context` projections.
if encoder_hidden_states is not None:
encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
split_size = encoder_qkv.shape[-1] // 3
(
encoder_hidden_states_query_proj,
encoder_hidden_states_key_proj,
encoder_hidden_states_value_proj,
) = torch.split(encoder_qkv, split_size, dim=-1)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from .embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
if query.dtype in (torch.float16, torch.bfloat16):
hidden_states = torch_npu.npu_fusion_attention(
query,
key,
value,
attn.heads,
input_layout="BNSD",
pse=None,
scale=1.0 / math.sqrt(query.shape[-1]),
pre_tockens=65536,
next_tockens=65536,
keep_prob=1.0,
sync=False,
inner_precise=0,
)[0]
else:
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 111,397 | 116,039 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 789 |
class FluxIPAdapterJointAttnProcessor2_0(torch.nn.Module):
"""Flux Attention processor for IP-Adapter."""
def __init__(
self, hidden_size: int, cross_attention_dim: int, num_tokens=(4,), scale=1.0, device=None, dtype=None
):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
f"{self.__class__.__name__} requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
if not isinstance(num_tokens, (tuple, list)):
num_tokens = [num_tokens]
if not isinstance(scale, list):
scale = [scale] * len(num_tokens)
if len(scale) != len(num_tokens):
raise ValueError("`scale` should be a list of integers with the same length as `num_tokens`.")
self.scale = scale
self.to_k_ip = nn.ModuleList(
[
nn.Linear(cross_attention_dim, hidden_size, bias=True, device=device, dtype=dtype)
for _ in range(len(num_tokens))
]
)
self.to_v_ip = nn.ModuleList(
[
nn.Linear(cross_attention_dim, hidden_size, bias=True, device=device, dtype=dtype)
for _ in range(len(num_tokens))
]
)
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
ip_hidden_states: Optional[List[torch.Tensor]] = None,
ip_adapter_masks: Optional[torch.Tensor] = None,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
hidden_states_query_proj = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
hidden_states_query_proj = hidden_states_query_proj.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
hidden_states_query_proj = attn.norm_q(hidden_states_query_proj)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, hidden_states_query_proj], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from .embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
# IP-adapter
ip_query = hidden_states_query_proj
ip_attn_output = None
# for ip-adapter
# TODO: support for multiple adapters
for current_ip_hidden_states, scale, to_k_ip, to_v_ip in zip(
ip_hidden_states, self.scale, self.to_k_ip, self.to_v_ip
):
ip_key = to_k_ip(current_ip_hidden_states)
ip_value = to_v_ip(current_ip_hidden_states)
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
ip_attn_output = F.scaled_dot_product_attention(
ip_query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
ip_attn_output = ip_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
ip_attn_output = scale * ip_attn_output
ip_attn_output = ip_attn_output.to(ip_query.dtype)
return hidden_states, encoder_hidden_states, ip_attn_output
else:
return hidden_states | class_definition | 116,042 | 122,492 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 790 |
class CogVideoXAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
query and key vectors, but does not include spatial normalization.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
text_seq_length = encoder_hidden_states.size(1)
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# Apply RoPE if needed
if image_rotary_emb is not None:
from .embeddings import apply_rotary_emb
query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
if not attn.is_cross_attention:
key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states, hidden_states = hidden_states.split(
[text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
)
return hidden_states, encoder_hidden_states | class_definition | 122,495 | 125,340 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 791 |
class FusedCogVideoXAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention for the CogVideoX model. It applies a rotary embedding on
query and key vectors, but does not include spatial normalization.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("CogVideoXAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.Tensor:
text_seq_length = encoder_hidden_states.size(1)
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
qkv = attn.to_qkv(hidden_states)
split_size = qkv.shape[-1] // 3
query, key, value = torch.split(qkv, split_size, dim=-1)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# Apply RoPE if needed
if image_rotary_emb is not None:
from .embeddings import apply_rotary_emb
query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb)
if not attn.is_cross_attention:
key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb)
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states, hidden_states = hidden_states.split(
[text_seq_length, hidden_states.size(1) - text_seq_length], dim=1
)
return hidden_states, encoder_hidden_states | class_definition | 125,343 | 128,218 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 792 |
class XFormersAttnAddedKVProcessor:
r"""
Processor for implementing memory efficient attention using xFormers.
Args:
attention_op (`Callable`, *optional*, defaults to `None`):
The base
[operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
operator.
"""
def __init__(self, attention_op: Optional[Callable] = None):
self.attention_op = attention_op
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
residual = hidden_states
hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
batch_size, sequence_length, _ = hidden_states.shape
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
query = attn.head_to_batch_dim(query)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)
if not attn.only_cross_attention:
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
else:
key = encoder_hidden_states_key_proj
value = encoder_hidden_states_value_proj
hidden_states = xformers.ops.memory_efficient_attention(
query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
)
hidden_states = hidden_states.to(query.dtype)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
hidden_states = hidden_states + residual
return hidden_states | class_definition | 128,221 | 131,194 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 793 |
class XFormersAttnProcessor:
r"""
Processor for implementing memory efficient attention using xFormers.
Args:
attention_op (`Callable`, *optional*, defaults to `None`):
The base
[operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
operator.
"""
def __init__(self, attention_op: Optional[Callable] = None):
self.attention_op = attention_op
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, key_tokens, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
if attention_mask is not None:
# expand our mask's singleton query_tokens dimension:
# [batch*heads, 1, key_tokens] ->
# [batch*heads, query_tokens, key_tokens]
# so that it can be added as a bias onto the attention scores that xformers computes:
# [batch*heads, query_tokens, key_tokens]
# we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
_, query_tokens, _ = hidden_states.shape
attention_mask = attention_mask.expand(-1, query_tokens, -1)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query).contiguous()
key = attn.head_to_batch_dim(key).contiguous()
value = attn.head_to_batch_dim(value).contiguous()
hidden_states = xformers.ops.memory_efficient_attention(
query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
)
hidden_states = hidden_states.to(query.dtype)
hidden_states = attn.batch_to_head_dim(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states | class_definition | 131,197 | 135,144 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 794 |
class AttnProcessorNPU:
r"""
Processor for implementing flash attention using torch_npu. Torch_npu supports only fp16 and bf16 data types. If
fp32 is used, F.scaled_dot_product_attention will be used for computation, but the acceleration effect on NPU is
not significant.
"""
def __init__(self):
if not is_torch_npu_available():
raise ImportError("AttnProcessorNPU requires torch_npu extensions and is supported only on npu devices.")
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
if query.dtype in (torch.float16, torch.bfloat16):
hidden_states = torch_npu.npu_fusion_attention(
query,
key,
value,
attn.heads,
input_layout="BNSD",
pse=None,
atten_mask=attention_mask,
scale=1.0 / math.sqrt(query.shape[-1]),
pre_tockens=65536,
next_tockens=65536,
keep_prob=1.0,
sync=False,
inner_precise=0,
)[0]
else:
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states | class_definition | 135,147 | 139,570 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 795 |
class AttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
if len(args) > 0 or kwargs.get("scale", None) is not None:
deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
deprecate("scale", "1.0.0", deprecation_message)
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states | class_definition | 139,573 | 143,425 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 796 |
class XLAFlashAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention with pallas flash attention kernel if using `torch_xla`.
"""
def __init__(self, partition_spec: Optional[Tuple[Optional[str], ...]] = None):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"XLAFlashAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
if is_torch_xla_version("<", "2.3"):
raise ImportError("XLA flash attention requires torch_xla version >= 2.3.")
if is_spmd() and is_torch_xla_version("<", "2.4"):
raise ImportError("SPMD support for XLA flash attention needs torch_xla version >= 2.4.")
self.partition_spec = partition_spec
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
temb: Optional[torch.Tensor] = None,
*args,
**kwargs,
) -> torch.Tensor:
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
if all(tensor.shape[2] >= 4096 for tensor in [query, key, value]):
if attention_mask is not None:
attention_mask = attention_mask.view(batch_size, 1, 1, attention_mask.shape[-1])
# Convert mask to float and replace 0s with -inf and 1s with 0
attention_mask = (
attention_mask.float()
.masked_fill(attention_mask == 0, float("-inf"))
.masked_fill(attention_mask == 1, float(0.0))
)
# Apply attention mask to key
key = key + attention_mask
query /= math.sqrt(query.shape[3])
partition_spec = self.partition_spec if is_spmd() else None
hidden_states = flash_attention(query, key, value, causal=False, partition_spec=partition_spec)
else:
logger.warning(
"Unable to use the flash attention pallas kernel API call due to QKV sequence length < 4096."
)
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states | class_definition | 143,428 | 148,346 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 797 |
class XLAFluxFlashAttnProcessor2_0:
r"""
Processor for implementing scaled dot-product attention with pallas flash attention kernel if using `torch_xla`.
"""
def __init__(self, partition_spec: Optional[Tuple[Optional[str], ...]] = None):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
"XLAFlashAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
if is_torch_xla_version("<", "2.3"):
raise ImportError("XLA flash attention requires torch_xla version >= 2.3.")
if is_spmd() and is_torch_xla_version("<", "2.4"):
raise ImportError("SPMD support for XLA flash attention needs torch_xla version >= 2.4.")
self.partition_spec = partition_spec
def __call__(
self,
attn: Attention,
hidden_states: torch.FloatTensor,
encoder_hidden_states: torch.FloatTensor = None,
attention_mask: Optional[torch.FloatTensor] = None,
image_rotary_emb: Optional[torch.Tensor] = None,
) -> torch.FloatTensor:
batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
# `sample` projections.
query = attn.to_q(hidden_states)
key = attn.to_k(hidden_states)
value = attn.to_v(hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
if encoder_hidden_states is not None:
# `context` projections.
encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
batch_size, -1, attn.heads, head_dim
).transpose(1, 2)
if attn.norm_added_q is not None:
encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
if attn.norm_added_k is not None:
encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
# attention
query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
if image_rotary_emb is not None:
from .embeddings import apply_rotary_emb
query = apply_rotary_emb(query, image_rotary_emb)
key = apply_rotary_emb(key, image_rotary_emb)
query /= math.sqrt(head_dim)
hidden_states = flash_attention(query, key, value, causal=False)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
if encoder_hidden_states is not None:
encoder_hidden_states, hidden_states = (
hidden_states[:, : encoder_hidden_states.shape[1]],
hidden_states[:, encoder_hidden_states.shape[1] :],
)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
return hidden_states, encoder_hidden_states
else:
return hidden_states | class_definition | 148,349 | 152,754 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 798 |
class MochiVaeAttnProcessor2_0:
r"""
Attention processor used in Mochi VAE.
"""
def __init__(self):
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
def __call__(
self,
attn: Attention,
hidden_states: torch.Tensor,
encoder_hidden_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
residual = hidden_states
is_single_frame = hidden_states.shape[1] == 1
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if is_single_frame:
hidden_states = attn.to_v(hidden_states)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
if attn.norm_q is not None:
query = attn.norm_q(query)
if attn.norm_k is not None:
key = attn.norm_k(key)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=attn.is_causal
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states | class_definition | 152,757 | 155,845 | 0 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/attention_processor.py | null | 799 |
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