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| # Copyright 2024 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| PyTorch utilities: Utilities related to PyTorch | |
| """ | |
| from typing import List, Optional, Tuple, Union | |
| from . import logging | |
| from .import_utils import is_torch_available, is_torch_version | |
| if is_torch_available(): | |
| import torch | |
| from torch.fft import fftn, fftshift, ifftn, ifftshift | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| try: | |
| from torch._dynamo import allow_in_graph as maybe_allow_in_graph | |
| except (ImportError, ModuleNotFoundError): | |
| def maybe_allow_in_graph(cls): | |
| return cls | |
| def randn_tensor( | |
| shape: Union[Tuple, List], | |
| generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None, | |
| device: Optional["torch.device"] = None, | |
| dtype: Optional["torch.dtype"] = None, | |
| layout: Optional["torch.layout"] = None, | |
| ): | |
| """A helper function to create random tensors on the desired `device` with the desired `dtype`. When | |
| passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor | |
| is always created on the CPU. | |
| """ | |
| # device on which tensor is created defaults to device | |
| rand_device = device | |
| batch_size = shape[0] | |
| layout = layout or torch.strided | |
| device = device or torch.device("cpu") | |
| if generator is not None: | |
| gen_device_type = generator.device.type if not isinstance(generator, list) else generator[0].device.type | |
| if gen_device_type != device.type and gen_device_type == "cpu": | |
| rand_device = "cpu" | |
| if device != "mps": | |
| logger.info( | |
| f"The passed generator was created on 'cpu' even though a tensor on {device} was expected." | |
| f" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably" | |
| f" slighly speed up this function by passing a generator that was created on the {device} device." | |
| ) | |
| elif gen_device_type != device.type and gen_device_type == "cuda": | |
| raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}.") | |
| # make sure generator list of length 1 is treated like a non-list | |
| if isinstance(generator, list) and len(generator) == 1: | |
| generator = generator[0] | |
| if isinstance(generator, list): | |
| shape = (1,) + shape[1:] | |
| latents = [ | |
| torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype, layout=layout) | |
| for i in range(batch_size) | |
| ] | |
| latents = torch.cat(latents, dim=0).to(device) | |
| else: | |
| latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device) | |
| return latents | |
| def is_compiled_module(module) -> bool: | |
| """Check whether the module was compiled with torch.compile()""" | |
| if is_torch_version("<", "2.0.0") or not hasattr(torch, "_dynamo"): | |
| return False | |
| return isinstance(module, torch._dynamo.eval_frame.OptimizedModule) | |
| def fourier_filter(x_in: "torch.Tensor", threshold: int, scale: int) -> "torch.Tensor": | |
| """Fourier filter as introduced in FreeU (https://arxiv.org/abs/2309.11497). | |
| This version of the method comes from here: | |
| https://github.com/huggingface/diffusers/pull/5164#issuecomment-1732638706 | |
| """ | |
| x = x_in | |
| B, C, H, W = x.shape | |
| # Non-power of 2 images must be float32 | |
| if (W & (W - 1)) != 0 or (H & (H - 1)) != 0: | |
| x = x.to(dtype=torch.float32) | |
| # FFT | |
| x_freq = fftn(x, dim=(-2, -1)) | |
| x_freq = fftshift(x_freq, dim=(-2, -1)) | |
| B, C, H, W = x_freq.shape | |
| mask = torch.ones((B, C, H, W), device=x.device) | |
| crow, ccol = H // 2, W // 2 | |
| mask[..., crow - threshold : crow + threshold, ccol - threshold : ccol + threshold] = scale | |
| x_freq = x_freq * mask | |
| # IFFT | |
| x_freq = ifftshift(x_freq, dim=(-2, -1)) | |
| x_filtered = ifftn(x_freq, dim=(-2, -1)).real | |
| return x_filtered.to(dtype=x_in.dtype) | |
| def apply_freeu( | |
| resolution_idx: int, hidden_states: "torch.Tensor", res_hidden_states: "torch.Tensor", **freeu_kwargs | |
| ) -> Tuple["torch.Tensor", "torch.Tensor"]: | |
| """Applies the FreeU mechanism as introduced in https: | |
| //arxiv.org/abs/2309.11497. Adapted from the official code repository: https://github.com/ChenyangSi/FreeU. | |
| Args: | |
| resolution_idx (`int`): Integer denoting the UNet block where FreeU is being applied. | |
| hidden_states (`torch.Tensor`): Inputs to the underlying block. | |
| res_hidden_states (`torch.Tensor`): Features from the skip block corresponding to the underlying block. | |
| s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features. | |
| s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features. | |
| b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. | |
| b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. | |
| """ | |
| if resolution_idx == 0: | |
| num_half_channels = hidden_states.shape[1] // 2 | |
| hidden_states[:, :num_half_channels] = hidden_states[:, :num_half_channels] * freeu_kwargs["b1"] | |
| res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs["s1"]) | |
| if resolution_idx == 1: | |
| num_half_channels = hidden_states.shape[1] // 2 | |
| hidden_states[:, :num_half_channels] = hidden_states[:, :num_half_channels] * freeu_kwargs["b2"] | |
| res_hidden_states = fourier_filter(res_hidden_states, threshold=1, scale=freeu_kwargs["s2"]) | |
| return hidden_states, res_hidden_states | |