38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
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 {"required":
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{"model": ("MODEL",),
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"tiling": (["enable", "disable"],),
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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, tiling):
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for m in model.model.modules:
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if isinstance(m, torch.nn.Conv2d):
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if tiling == "enable":
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m.padding_mode = "circular"
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else:
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m.padding_mode = "zeros"
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return (model,)
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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 [layer for layer in vae.first_stage_model.modules() if isinstance(layer, torch.nn.Conv2d)]:
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layer.padding_mode = 'circular'
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return (vae.decode(samples["samples"]), ) |