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