text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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"])
... | 703 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
... | 704 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])... | 705 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
@classme... | 706 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
@class... | 707 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
@cl... | 708 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
... | 709 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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", "transfo... | 710 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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", "tra... | 711 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
... | 712 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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", "transf... | 713 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
... | 714 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
@cl... | 715 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
... | 716 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
... | 717 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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"])
... | 718 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_objects.py |
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_... | 719 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/hub_utils.py |
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:
"""
... | 719 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/hub_utils.py |
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.
commi... | 719 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/hub_utils.py |
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 s... | 719 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/hub_utils.py |
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")
... | 719 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/hub_utils.py |
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... | 719 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/hub_utils.py |
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,... | 720 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_note_seq_objects.py |
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,... | 721 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_sentencepiece_objects.py |
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, ["t... | 722 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_sentencepiece_objects.py |
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, ["torc... | 723 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_sentencepiece_objects.py |
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", "transform... | 724 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py |
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... | 725 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py |
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... | 726 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py |
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"])
... | 727 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py |
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"])
... | 728 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_and_transformers_objects.py |
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, ["... | 729 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py |
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_conn... | 730 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py |
class OptionalDependencyNotAvailable(BaseException):
"""
An error indicating that an optional dependency of Diffusers was not found in the environment.
""" | 731 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py |
class _LazyModule(ModuleType):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
""" | 732 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py |
# 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_structur... | 732 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py |
# 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... | 732 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py |
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"
... | 732 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/import_utils.py |
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 fr... | 733 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_scipy_objects.py |
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, **kwa... | 734 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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):... | 735 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, ... | 736 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, **kwarg... | 737 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, **k... | 738 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, **kwarg... | 739 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, **kwarg... | 740 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, ... | 741 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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... | 742 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, **k... | 743 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, ... | 744 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, **kwarg... | 745 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, **kwar... | 746 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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, *... | 747 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_flax_objects.py |
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... | 748 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/outputs.py |
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:
... | 748 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/outputs.py |
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
... | 748 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/outputs.py |
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:
... | 748 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/outputs.py |
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()) | 748 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/outputs.py |
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 fo... | 749 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_outputs.py |
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 o... | 750 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/modeling_outputs.py |
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 `F... | 751 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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.sh... | 751 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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 `F... | 752 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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... | 752 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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... | 752 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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 ... | 752 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
# 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 = hid... | 752 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
# 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_f... | 752 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
# 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 | 752 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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`, ... | 753 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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... | 753 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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`):
... | 753 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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 ker... | 753 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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.shap... | 753 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
inverse_conv = F.conv_transpose2d(
hidden_states,
weight,
stride=stride,
output_padding=output_padding,
padding=0,
)
output = upfirdn2d_native(
inverse_conv,
torch.tensor(kernel, devi... | 753 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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_... | 753 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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(... | 754 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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]... | 754 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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 th... | 755 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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:
... | 755 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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_... | 755 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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 | 755 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/upsampling.py |
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):
... | 756 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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:
... | 756 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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):
... | 757 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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()... | 757 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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)
# ... | 757 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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... | 757 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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`, option... | 758 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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... | 758 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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 perf... | 758 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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... | 758 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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... | 758 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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),
... | 758 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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:
... | 758 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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.ten... | 759 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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],
... | 759 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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 ... | 760 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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
# (b... | 760 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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.sh... | 760 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/downsampling.py |
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