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cleanup: seamless unused older code cleanup

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blessedcoolant 2024-05-10 21:26:52 +05:30 committed by psychedelicious
parent 6c9fb617dc
commit da61396b1c
4 changed files with 1 additions and 63 deletions
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7
invokeai/app/invocations/latent.py
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@ -586,13 +586,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
unet: UNet2DConditionModel,
scheduler: Scheduler,
) -> StableDiffusionGeneratorPipeline:
# TODO:
# configure_model_padding(
# unet,
# self.seamless,
# self.seamless_axes,
# )
class FakeVae:
class FakeVaeConfig:
def __init__(self) -> None:

1
invokeai/backend/image_util/__init__.py
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@ -4,5 +4,4 @@ Initialization file for invokeai.backend.image_util methods.
from .infill_methods.patchmatch import PatchMatch # noqa: F401
from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata # noqa: F401
from .seamless import configure_model_padding # noqa: F401
from .util import InitImageResizer, make_grid # noqa: F401

52
invokeai/backend/image_util/seamless.py
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@ -1,52 +0,0 @@
import torch.nn as nn
def _conv_forward_asymmetric(self, input, weight, bias):
"""
Patch for Conv2d._conv_forward that supports asymmetric padding
"""
working = nn.functional.pad(input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"])
working = nn.functional.pad(working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"])
return nn.functional.conv2d(
working,
weight,
bias,
self.stride,
nn.modules.utils._pair(0),
self.dilation,
self.groups,
)
def configure_model_padding(model, seamless, seamless_axes):
"""
Modifies the 2D convolution layers to use a circular padding mode based on
the `seamless` and `seamless_axes` options.
"""
# TODO: get an explicit interface for this in diffusers: https://github.com/huggingface/diffusers/issues/556
for m in model.modules():
if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
if seamless:
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],
)
m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
else:
m._conv_forward = nn.Conv2d._conv_forward.__get__(m, nn.Conv2d)
if hasattr(m, "asymmetric_padding_mode"):
del m.asymmetric_padding_mode
if hasattr(m, "asymmetric_padding"):
del m.asymmetric_padding

4
invokeai/backend/stable_diffusion/seamless.py
View File

@ -1,5 +1,3 @@
from __future__ import annotations
from contextlib import contextmanager
from typing import Callable, List, Optional, Tuple, Union
@ -28,7 +26,7 @@ def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTi
working, weight, bias, self.stride, torch.nn.modules.utils._pair(0), self.dilation, self.groups
)
original_layers: List[Tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
original_layers: List[Tuple[nn.Conv2d, Callable]] = []
try:
x_mode = "circular" if "x" in seamless_axes else "constant"