File size: 1,631 Bytes
c11c83e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
from functools import wraps
import torch
import diffusers # pylint: disable=import-error
# pylint: disable=protected-access, missing-function-docstring, line-too-long
# Diffusers FreeU
original_fourier_filter = diffusers.utils.torch_utils.fourier_filter
@wraps(diffusers.utils.torch_utils.fourier_filter)
def fourier_filter(x_in, threshold, scale):
return_dtype = x_in.dtype
return original_fourier_filter(x_in.to(dtype=torch.float32), threshold, scale).to(dtype=return_dtype)
# fp64 error
class FluxPosEmbed(torch.nn.Module):
def __init__(self, theta: int, axes_dim):
super().__init__()
self.theta = theta
self.axes_dim = axes_dim
def forward(self, ids: torch.Tensor) -> torch.Tensor:
n_axes = ids.shape[-1]
cos_out = []
sin_out = []
pos = ids.float()
for i in range(n_axes):
cos, sin = diffusers.models.embeddings.get_1d_rotary_pos_embed(
self.axes_dim[i],
pos[:, i],
theta=self.theta,
repeat_interleave_real=True,
use_real=True,
freqs_dtype=torch.float32,
)
cos_out.append(cos)
sin_out.append(sin)
freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
return freqs_cos, freqs_sin
def ipex_diffusers(device_supports_fp64=False, can_allocate_plus_4gb=False):
diffusers.utils.torch_utils.fourier_filter = fourier_filter
if not device_supports_fp64:
diffusers.models.embeddings.FluxPosEmbed = FluxPosEmbed
|