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import copy
from typing import Optional
import PIL
import torch
from torch import Tensor
from torch.nn import Conv2d
from torch.nn import functional as F
from torch.nn.modules.utils import _pair
import comfy.samplers
import nodes
from typing import Optional
class SeamlessTile:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"tiling": (["enable", "x_only", "y_only", "disable"],),
"copy_model": (["Make a copy", "Modify in place"],),
},
}
CATEGORY = "SeamlessTile"
RETURN_TYPES = ("MODEL",)
FUNCTION = "run"
def run(self, model, copy_model, tiling):
if copy_model == "Modify in place":
model_copy = model
else:
model_copy = copy.deepcopy(model)
if tiling == "enable":
make_circular_asymm(model_copy.model, True, True)
elif tiling == "x_only":
make_circular_asymm(model_copy.model, True, False)
elif tiling == "y_only":
make_circular_asymm(model_copy.model, False, True)
else:
make_circular_asymm(model_copy.model, False, False)
return (model_copy,)
# asymmetric tiling from https://github.com/tjm35/asymmetric-tiling-sd-webui/blob/main/scripts/asymmetric_tiling.py
def make_circular_asymm(model, tileX: bool, tileY: bool):
for layer in [
layer for layer in model.modules() if isinstance(layer, torch.nn.Conv2d)
]:
layer.padding_modeX = 'circular' if tileX else 'constant'
layer.padding_modeY = 'circular' if tileY else 'constant'
layer.paddingX = (layer._reversed_padding_repeated_twice[0], layer._reversed_padding_repeated_twice[1], 0, 0)
layer.paddingY = (0, 0, layer._reversed_padding_repeated_twice[2], layer._reversed_padding_repeated_twice[3])
layer._conv_forward = __replacementConv2DConvForward.__get__(layer, Conv2d)
return model
def __replacementConv2DConvForward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
working = F.pad(input, self.paddingX, mode=self.padding_modeX)
working = F.pad(working, self.paddingY, mode=self.padding_modeY)
return F.conv2d(working, weight, bias, self.stride, _pair(0), self.dilation, self.groups)
class CircularVAEDecode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"samples": ("LATENT",),
"vae": ("VAE",),
"tiling": (["enable", "x_only", "y_only", "disable"],)
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "decode"
CATEGORY = "SeamlessTile"
def decode(self, samples, vae, tiling):
vae_copy = copy.deepcopy(vae)
if tiling == "enable":
make_circular_asymm(vae_copy.first_stage_model, True, True)
elif tiling == "x_only":
make_circular_asymm(vae_copy.first_stage_model, True, False)
elif tiling == "y_only":
make_circular_asymm(vae_copy.first_stage_model, False, True)
else:
make_circular_asymm(vae_copy.first_stage_model, False, False)
result = (vae_copy.decode(samples["samples"]),)
return result
class MakeCircularVAE:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"vae": ("VAE",),
"tiling": (["enable", "x_only", "y_only", "disable"],),
"copy_vae": (["Make a copy", "Modify in place"],),
}
}
RETURN_TYPES = ("VAE",)
FUNCTION = "run"
CATEGORY = "SeamlessTile"
def run(self, vae, tiling, copy_vae):
if copy_vae == "Modify in place":
vae_copy = vae
else:
vae_copy = copy.deepcopy(vae)
if tiling == "enable":
make_circular_asymm(vae_copy.first_stage_model, True, True)
elif tiling == "x_only":
make_circular_asymm(vae_copy.first_stage_model, True, False)
elif tiling == "y_only":
make_circular_asymm(vae_copy.first_stage_model, False, True)
else:
make_circular_asymm(vae_copy.first_stage_model, False, False)
return (vae_copy,)
class OffsetImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pixels": ("IMAGE",),
"x_percent": (
"FLOAT",
{"default": 50.0, "min": 0.0, "max": 100.0, "step": 1},
),
"y_percent": (
"FLOAT",
{"default": 50.0, "min": 0.0, "max": 100.0, "step": 1},
),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "run"
CATEGORY = "SeamlessTile"
def run(self, pixels, x_percent, y_percent):
n, y, x, c = pixels.size()
y = round(y * y_percent / 100)
x = round(x * x_percent / 100)
return (pixels.roll((y, x), (1, 2)),)
class TiledKSampler:
@classmethod
def INPUT_TYPES(cls):
return {"required":
{"model": ("MODEL", ),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"tiling": (["enable", "x_only", "y_only", "disable"],),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "SeamlessTile"
def apply_circular(self, model, enable):
for layer in [layer for layer in model.modules() if isinstance(layer, torch.nn.Conv2d)]:
layer.padding_mode = 'circular' if enable else 'zeros'
def sample(self, model, seed, tiling, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
self.apply_circular(model.model, tiling in ["enable", "x_only", "y_only"])
return nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
class Asymmetric_Tiled_KSampler:
@classmethod
def INPUT_TYPES(cls):
return {"required":
{"model": ("MODEL", ),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"tileX": ("INT", {"default": 1, "min": 0, "max": 1}),
"tileY": ("INT", {"default": 1, "min": 0, "max": 1}),
"startStep": ("INT", {"default": 0, "min": 0, "max": 10000}),
"stopStep": ("INT", {"default": -1, "min": -1, "max": 10000}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"latent_image": ("LATENT", ),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "SeamlessTile"
def apply_asymmetric_tiling(self, model, tileX, tileY):
for layer in [layer for layer in model.modules() if isinstance(layer, torch.nn.Conv2d)]:
layer.padding_modeX = 'circular' if tileX else 'constant'
layer.padding_modeY = 'circular' if tileY else 'constant'
layer.paddingX = (layer._reversed_padding_repeated_twice[0], layer._reversed_padding_repeated_twice[1], 0, 0)
layer.paddingY = (0, 0, layer._reversed_padding_repeated_twice[2], layer._reversed_padding_repeated_twice[3])
print(layer.paddingX, layer.paddingY)
def __hijackConv2DMethods(self, model, tileX: bool, tileY: bool, startStep: int, stopStep: int):
for layer in [l for l in model.modules() if isinstance(l, torch.nn.Conv2d)]:
layer.padding_modeX = 'circular' if tileX else 'constant'
layer.padding_modeY = 'circular' if tileY else 'constant'
layer.paddingX = (layer._reversed_padding_repeated_twice[0], layer._reversed_padding_repeated_twice[1], 0, 0)
layer.paddingY = (0, 0, layer._reversed_padding_repeated_twice[2], layer._reversed_padding_repeated_twice[3])
layer.paddingStartStep = startStep
layer.paddingStopStep = stopStep
def make_bound_method(method, current_layer):
def bound_method(self, *args, **kwargs): # Add 'self' here
return method(current_layer, *args, **kwargs)
return bound_method
bound_method = make_bound_method(self.__replacementConv2DConvForward, layer)
layer._conv_forward = bound_method.__get__(layer, type(layer))
def __replacementConv2DConvForward(self, layer, input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor]):
step = nodes.common_ksampler.current_step # Assuming there's a way to get the current step
if ((layer.paddingStartStep < 0 or step >= layer.paddingStartStep) and (layer.paddingStopStep < 0 or step <= layer.paddingStopStep)):
working = torch.nn.functional.pad(input, layer.paddingX, mode=layer.padding_modeX)
working = torch.nn.functional.pad(working, layer.paddingY, mode=layer.padding_modeY)
else:
working = torch.nn.functional.pad(input, layer.paddingX, mode='constant')
working = torch.nn.functional.pad(working, layer.paddingY, mode='constant')
return torch.nn.functional.conv2d(working, weight, bias, layer.stride, (0, 0), layer.dilation, layer.groups)
def __restoreConv2DMethods(self, model):
for layer in [l for l in model.modules() if isinstance(l, torch.nn.Conv2d)]:
layer._conv_forward = torch.nn.Conv2d._conv_forward.__get__(layer, torch.nn.Conv2d)
def sample(self, model, seed, tileX, tileY, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, startStep=0, stopStep=-1):
self.__hijackConv2DMethods(model.model, tileX == 1, tileY == 1, startStep, stopStep)
result = nodes.common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
self.__restoreConv2DMethods(model.model)
return result
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