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from ..utils import common_annotator_call, annotator_ckpts_path, ANIFACESEG_MODEL_NAME, create_node_input_types |
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import comfy.model_management as model_management |
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import torch |
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from einops import rearrange |
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class AnimeFace_SemSegPreprocessor: |
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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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"image": ("IMAGE",) |
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}, |
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"optional": { |
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"remove_background_using_abg": ("BOOLEAN", {"default": True}), |
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"resolution": ("INT", {"default": 512, "min": 512, "max": 512, "step": 64}) |
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} |
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} |
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RETURN_TYPES = ("IMAGE", "MASK") |
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RETURN_NAMES = ("IMAGE", "ABG_CHARACTER_MASK (MASK)") |
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FUNCTION = "execute" |
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CATEGORY = "ControlNet Preprocessors/Semantic Segmentation" |
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def execute(self, image, remove_background_using_abg=True, resolution=512, **kwargs): |
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from controlnet_aux.anime_face_segment import AnimeFaceSegmentor |
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model = AnimeFaceSegmentor.from_pretrained(ANIFACESEG_MODEL_NAME, cache_dir=annotator_ckpts_path).to(model_management.get_torch_device()) |
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if remove_background_using_abg: |
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out_image_with_mask = common_annotator_call(model, image, resolution=resolution, remove_background=True) |
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out_image = out_image_with_mask[..., :3] |
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mask = out_image_with_mask[..., 3:] |
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mask = rearrange(mask, "n h w c -> n c h w") |
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else: |
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out_image = common_annotator_call(model, image, resolution=resolution, remove_background=False) |
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N, H, W, C = out_image.shape |
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mask = torch.ones(N, C, H, W) |
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del model |
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return (out_image, mask) |
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NODE_CLASS_MAPPINGS = { |
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"AnimeFace_SemSegPreprocessor": AnimeFace_SemSegPreprocessor |
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} |
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NODE_DISPLAY_NAME_MAPPINGS = { |
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"AnimeFace_SemSegPreprocessor": "Anime Face Segmentor" |
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} |