计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
interactive-colorization【效率与协作】Guides agents using interactive-deep-colorization local-hints colorization APIs, GUI semantics, masks, color suggestions, and saved outputs.
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Use this sub-skill when a task is about the repository's local-hints interactive colorization workflow: explaining the GUI, reproducing the notebook-style API flow, preparing user hint tensors, choosing PyTorch versus Caffe wrappers, interpreting recommended colors, or handling saved local-hints artifacts.
input_ab, mask, Lab arrays, recommendation distributions, or saved .npy/.png artifacts.--repo-root to check safe imports, Lab/gamut helpers, wrapper image preparation, and tiny PyTorch architecture forwards without loading model weights or GUI/Caffe modules.input_ab with shape 2 x Xd x Xd and input_mask with shape 1 x Xd x Xd before wrapper-specific normalization.ColorizeImageBase(Xd=256, Xfullres_max=10000), ColorizeImageTorch(Xd=256, maskcent=False), ColorizeImageTorchDist(Xd=256, maskcent=False), ColorizeImageCaffe(Xd=256), ColorizeImageCaffeDist(Xd=256), ColorizeImageCaffeGlobDist(Xd=256), ColorizeImageTorch.prep_net(self, gpu_id=None, path='', dist=False), SIGGRAPHGenerator(dist=False), and SIGGRAPHGenerator.forward(self, input_A, input_B, mask_B, maskcent=0).--dist_model stores into dest='color_model', so there is no independent args.dist_model; both PyTorch wrapper initializations read args.color_model.