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final tidying before marking PR as ready for review

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- Replace AnyModelLoader with ModelLoaderRegistry
- Fix type check errors in multiple files
- Remove apparently unneeded `get_model_config_enum()` method from model manager
- Remove last vestiges of old model manager
- Updated tests and documentation

resolve conflict with seamless.py
This commit is contained in:
psychedelicious
2024-02-18 17:27:42 +11:00
parent 5d612ec095
commit 5a3195f757
74 changed files with 672 additions and 10362 deletions
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96
invokeai/backend/stable_diffusion/seamless.py
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@ -1,10 +1,11 @@
from __future__ import annotations
from contextlib import contextmanager
from typing import List, Union
from typing import Callable, List, Union
import torch.nn as nn
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
def _conv_forward_asymmetric(self, input, weight, bias):
@ -26,70 +27,51 @@ def _conv_forward_asymmetric(self, input, weight, bias):
@contextmanager
def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
# Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
try:
to_restore = []
# Hard coded to skip down block layers, allowing for seamless tiling at the expense of prompt adherence
skipped_layers = 1
for m_name, m in model.named_modules():
if isinstance(model, UNet2DConditionModel):
if ".attentions." in m_name:
if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
continue
if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
# down_blocks.1.resnets.1.conv1
_, block_num, _, resnet_num, submodule_name = m_name.split(".")
block_num = int(block_num)
resnet_num = int(resnet_num)
if block_num >= len(model.down_blocks) - skipped_layers:
continue
if ".resnets." in m_name:
if ".conv2" in m_name:
continue
if ".conv_shortcut" in m_name:
continue
"""
if isinstance(model, UNet2DConditionModel):
if False and ".upsamplers." in m_name:
# Skip the second resnet (could be configurable)
if resnet_num > 0:
continue
if False and ".downsamplers." in m_name:
# Skip Conv2d layers (could be configurable)
if submodule_name == "conv2":
continue
if True and ".resnets." in m_name:
if True and ".conv1" in m_name:
if False and "down_blocks" in m_name:
continue
if False and "mid_block" in m_name:
continue
if False and "up_blocks" in m_name:
continue
m.asymmetric_padding_mode = {}
m.asymmetric_padding = {}
m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
m.asymmetric_padding["x"] = (
m._reversed_padding_repeated_twice[0],
m._reversed_padding_repeated_twice[1],
0,
0,
)
m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
m.asymmetric_padding["y"] = (
0,
0,
m._reversed_padding_repeated_twice[2],
m._reversed_padding_repeated_twice[3],
)
if True and ".conv2" in m_name:
continue
if True and ".conv_shortcut" in m_name:
continue
if True and ".attentions." in m_name:
continue
if False and m_name in ["conv_in", "conv_out"]:
continue
"""
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
m.asymmetric_padding_mode = {}
m.asymmetric_padding = {}
m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
m.asymmetric_padding["x"] = (
m._reversed_padding_repeated_twice[0],
m._reversed_padding_repeated_twice[1],
0,
0,
)
m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
m.asymmetric_padding["y"] = (
0,
0,
m._reversed_padding_repeated_twice[2],
m._reversed_padding_repeated_twice[3],
)
to_restore.append((m, m._conv_forward))
m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
to_restore.append((m, m._conv_forward))
m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
yield