126 lines
3.1 KiB
Python
126 lines
3.1 KiB
Python
import copy
|
|
|
|
import PIL
|
|
import torch
|
|
|
|
|
|
class SeamlessTile:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": ("MODEL",),
|
|
"tiling": (["enable", "disable"],),
|
|
"copy_model": (["Modify in place", "Make a copy"],),
|
|
},
|
|
}
|
|
|
|
CATEGORY = "conditioning"
|
|
|
|
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":
|
|
model_copy.model.apply(make_circular)
|
|
else:
|
|
model_copy.model.apply(unmake_circular)
|
|
return (model_copy,)
|
|
|
|
|
|
def make_circular(m):
|
|
if isinstance(m, torch.nn.Conv2d):
|
|
m.padding_mode = "circular"
|
|
|
|
|
|
def unmake_circular(m):
|
|
if isinstance(m, torch.nn.Conv2d):
|
|
m.padding_mode = "zeros"
|
|
|
|
|
|
class CircularVAEDecode:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": {"samples": ("LATENT",), "vae": ("VAE",)}}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
FUNCTION = "decode"
|
|
|
|
CATEGORY = "latent"
|
|
|
|
def decode(self, vae, samples):
|
|
for layer in [
|
|
layer
|
|
for layer in vae.first_stage_model.modules()
|
|
if isinstance(layer, torch.nn.Conv2d)
|
|
]:
|
|
layer.padding_mode = "circular"
|
|
result = (vae.decode(samples["samples"]),)
|
|
for layer in [
|
|
layer
|
|
for layer in vae.first_stage_model.modules()
|
|
if isinstance(layer, torch.nn.Conv2d)
|
|
]:
|
|
layer.padding_mode = "zeros"
|
|
return result
|
|
|
|
|
|
class MakeCircularVAE:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"vae": ("VAE",),
|
|
"tiling": (["enable", "disable"],),
|
|
"copy_vae": (["Modify in place", "Make a copy"],),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("VAE",)
|
|
FUNCTION = "run"
|
|
CATEGORY = "latent"
|
|
|
|
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":
|
|
vae_copy.first_stage_model.apply(make_circular)
|
|
else:
|
|
vae_copy.first_stage_model.apply(unmake_circular)
|
|
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 = "image"
|
|
|
|
def run(self, pixels, x_percent, y_percent):
|
|
print(pixels.size())
|
|
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)),)
|