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Remove old seamless class

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This commit is contained in:
Sergey Borisov
2024-07-23 18:04:33 +03:00
parent 62aa064e56
commit ca21996a97
4 changed files with 4 additions and 57 deletions
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2
invokeai/backend/stable_diffusion/__init__.py
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@ -7,11 +7,9 @@ from invokeai.backend.stable_diffusion.diffusers_pipeline import ( # noqa: F401
StableDiffusionGeneratorPipeline,
)
from invokeai.backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent # noqa: F401
from invokeai.backend.stable_diffusion.seamless import set_seamless # noqa: F401
__all__ = [
"PipelineIntermediateState",
"StableDiffusionGeneratorPipeline",
"InvokeAIDiffuserComponent",
"set_seamless",
]

51
invokeai/backend/stable_diffusion/seamless.py
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@ -1,51 +0,0 @@
from contextlib import contextmanager
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
from diffusers.models.lora import LoRACompatibleConv
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
@contextmanager
def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]):
if not seamless_axes:
yield
return
# override conv_forward
# https://github.com/huggingface/diffusers/issues/556#issuecomment-1993287019
def _conv_forward_asymmetric(self, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
self.paddingX = (self._reversed_padding_repeated_twice[0], self._reversed_padding_repeated_twice[1], 0, 0)
self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
working = torch.nn.functional.pad(input, self.paddingX, mode=x_mode)
working = torch.nn.functional.pad(working, self.paddingY, mode=y_mode)
return torch.nn.functional.conv2d(
working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups
)
original_layers: List[Tuple[nn.Conv2d, Callable]] = []
try:
x_mode = "circular" if "x" in seamless_axes else "constant"
y_mode = "circular" if "y" in seamless_axes else "constant"
conv_layers: List[torch.nn.Conv2d] = []
for module in model.modules():
if isinstance(module, torch.nn.Conv2d):
conv_layers.append(module)
for layer in conv_layers:
if isinstance(layer, LoRACompatibleConv) and layer.lora_layer is None:
layer.lora_layer = lambda *x: 0
original_layers.append((layer, layer._conv_forward))
layer._conv_forward = _conv_forward_asymmetric.__get__(layer, torch.nn.Conv2d)
yield
finally:
for layer, orig_conv_forward in original_layers:
layer._conv_forward = orig_conv_forward