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Update l2i invoke and seamless to support AutoencoderTiny, remove attention processors if no mid_block is detected

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Brandon Rising
2024-03-12 12:00:24 -04:00
parent 43948e0758
commit 7b393656de
2 changed files with 5 additions and 3 deletions
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3
invokeai/backend/stable_diffusion/seamless.py
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@ -5,6 +5,7 @@ from typing import Callable, List, Union
import torch.nn as nn
from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
@ -26,7 +27,7 @@ def _conv_forward_asymmetric(self, input, weight, bias):
@contextmanager
def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]):
# Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
try: