121 lines
4.8 KiB
Python
121 lines
4.8 KiB
Python
# layerstyle advance
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'''
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推理部分代码来自https://github.com/hustvl/EVF-SAM
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'''
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import sys
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from .imagefunc import *
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sys.path.append(os.path.join(os.path.dirname(__file__), 'evf_sam'))
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from evf_sam.evf_sam_inference import evf_sam_main
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class EVF_SAM_Ultra:
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def __init__(self):
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self.NODE_NAME = 'EVF_SAM Ultra'
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pass
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@classmethod
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def INPUT_TYPES(cls):
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# model_list = ["evf-sam2","evf-sam", "evf-sam2-multitask", "evf-sam-multitask"]
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model_list = ["evf-sam2", "evf-sam"]
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precision_list = ["fp16", "bf16", "fp32"]
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load_in_bit_list = ["full", "8", "4"]
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method_list = ['VITMatte', 'VITMatte(local)', 'vitmatte-base-composition-1k', 'PyMatting', 'GuidedFilter', ]
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device_list = ['cuda', 'cpu']
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return {"required":
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{
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"image": ("IMAGE",),
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"model": (model_list,),
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"precision": (precision_list,),
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"load_in_bit": (load_in_bit_list,),
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"prompt": ("STRING", {"default": "subject"}),
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"detail_method": (method_list,),
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"detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
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"detail_dilate": ("INT", {"default": 4, "min": 1, "max": 255, "step": 1}),
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"black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}),
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"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}),
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"process_detail": ("BOOLEAN", {"default": True}),
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"device": (device_list,),
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"max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask",)
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FUNCTION = "evf_sam_ultra"
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CATEGORY = '😺dzNodes/LayerMask'
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def evf_sam_ultra(self, image, model, precision, load_in_bit, prompt,
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detail_method, detail_erode, detail_dilate, black_point, white_point,
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process_detail, device, max_megapixels,
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):
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ret_images = []
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ret_masks = []
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if detail_method == 'VITMatte(local)':
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local_files_only = True
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else:
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local_files_only = False
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if model == 'evf-sam2' or model == 'evf-sam2-multitask':
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model_type = 'sam2'
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elif model == 'evf-sam' or model == 'evf-sam-multitask':
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model_type = 'ori'
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else:
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model_type = 'effi'
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if load_in_bit == 'full':
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load_in_bit = 16
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else:
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load_in_bit = int(load_in_bit)
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model_path = ""
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model_folder_name = 'EVF-SAM'
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try:
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model_path = os.path.join(
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os.path.normpath(folder_paths.folder_names_and_paths[model_folder_name][0][0]), model)
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except:
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pass
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if not os.path.exists(model_path):
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model_path = os.path.join(folder_paths.models_dir, model_folder_name, model)
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for i in image:
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i = torch.unsqueeze(i, 0)
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orig_image = tensor2pil(i).convert('RGB')
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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mask_image = evf_sam_main(model_path, model_type, precision, load_in_bit, orig_image, prompt)
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_mask = pil2tensor(mask_image)
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detail_range = detail_erode + detail_dilate
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if process_detail:
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if detail_method == 'GuidedFilter':
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_mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
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_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
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elif detail_method == 'PyMatting':
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_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
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else:
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_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
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_mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device,
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max_megapixels=max_megapixels, method=detail_method)
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_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
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else:
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_mask = mask2image(_mask)
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ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
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ret_images.append(pil2tensor(ret_image))
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ret_masks.append(image2mask(_mask))
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerMask: EVFSAMUltra": EVF_SAM_Ultra
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: EVFSAMUltra": "LayerMask: EVF-SAM Ultra(Advance)"
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}
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