46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
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import torch
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class PatchModelAddDownscale:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}),
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"downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}),
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
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"end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "patch"
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CATEGORY = "_for_testing"
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def patch(self, model, block_number, downscale_factor, start_percent, end_percent):
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sigma_start = model.model.model_sampling.percent_to_sigma(start_percent).item()
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sigma_end = model.model.model_sampling.percent_to_sigma(end_percent).item()
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def input_block_patch(h, transformer_options):
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if transformer_options["block"][1] == block_number:
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sigma = transformer_options["sigmas"][0].item()
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if sigma <= sigma_start and sigma >= sigma_end:
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h = torch.nn.functional.interpolate(h, scale_factor=(1.0 / downscale_factor), mode="bicubic", align_corners=False)
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return h
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def output_block_patch(h, hsp, transformer_options):
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if h.shape[2] != hsp.shape[2]:
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h = torch.nn.functional.interpolate(h, size=(hsp.shape[2], hsp.shape[3]), mode="bicubic", align_corners=False)
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return h, hsp
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m = model.clone()
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m.set_model_input_block_patch(input_block_patch)
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m.set_model_output_block_patch(output_block_patch)
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return (m, )
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NODE_CLASS_MAPPINGS = {
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"PatchModelAddDownscale": PatchModelAddDownscale,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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# Sampling
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"PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)",
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}
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