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