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Update l2i invoke and seamless to support AutoencoderTiny, remove att… #5936

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Mar 12, 2024
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Update l2i invoke and seamless to support AutoencoderTiny, remove att… #5936

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7b39365
Update l2i invoke and seamless to support AutoencoderTiny, remove att…
brandonrising Mar 12, 2024
8d2a4db
Found another instance of expecting a mid_block on the decoder in a vae
brandonrising Mar 12, 2024
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5 changes: 3 additions & 2 deletions invokeai/app/invocations/latent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -837,14 +837,15 @@ def invoke(self, context: InvocationContext) -> ImageOutput:
latents = context.tensors.load(self.latents.latents_name)

vae_info = context.models.load(self.vae.vae)
assert isinstance(vae_info.model, (UNet2DConditionModel, AutoencoderKL))
assert isinstance(vae_info.model, (UNet2DConditionModel, AutoencoderKL, AutoencoderTiny))
with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
assert isinstance(vae, torch.nn.Module)
latents = latents.to(vae.device)
if self.fp32:
vae.to(dtype=torch.float32)

use_torch_2_0_or_xformers = isinstance(
# AutoencoderTiny doesn't contain a mid_block property or appear to accept attn processors
use_torch_2_0_or_xformers = hasattr(vae.decoder, "mid_block") and isinstance(
vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
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3 changes: 2 additions & 1 deletion invokeai/backend/stable_diffusion/seamless.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@

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


Expand All @@ -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:
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