CavinHuang-comfyui-nodes-docs/docs/FaceDetailerPipe.md

Documentation

• Class name: FaceDetailerPipe
• Category: ImpactPack/Simple
• Output node: False
• Repo Ref: https://github.com/ltdrdata/ComfyUI-Impact-Pack.git

FaceDetailerPipe 节点旨在增强图像中面部的细节。它处理输入图像以细化面部特征并提高整体质量，利用一系列先进的图像处理技术和模型。其主要目标是提供更详细和精细的视觉输出，特别适用于需要高质量面部表示的应用。

Input types

Required

• image
  • 输入图像是节点执行面部细化所需的主要数据。它是所有后续处理和增强活动的基础。输入图像的质量和分辨率直接影响最终输出。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• detailer_pipe
  • detailer_pipe 是指导面部细化过程的模型和参数集合。它包括模型、clip 和 vae 等重要组件，这些对于实现所需的细节增强水平至关重要。
  • Comfy dtype: DETAILER_PIPE
  • Python dtype: Tuple[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]
• guide_size
  • guide_size 参数确定用于面部细化的引导图像的大小。这是一个关键因素，它影响最终输出中可以实现的细节水平。较大的引导尺寸通常允许更详细的增强。
  • Comfy dtype: FLOAT
  • Python dtype: float
• max_size
  • max_size 参数设置了输入图像的最大尺寸。这是一个重要的考虑因素，因为较大的图像可以显著增加处理时间和资源需求，对性能和内存使用有影响。
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• image
  • 增强的图像输出是 FaceDetailerPipe 节点的主要结果。它包含了从原始输入图像中提炼和改进的面部细节。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• cropped_refined
  • cropped_refined 输出包含从增强图像中裁剪出的一系列细化的面部图像。它们可以用于进一步处理或作为独立的详细图像。
  • Comfy dtype: LIST[IMAGE]
  • Python dtype: List[torch.Tensor]
• mask
  • mask 输出是一个二进制图像，它描绘了已经被增强的面部区域。这对于隔离详细区域以进行额外的处理或分析非常有用。
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU

Source code

class FaceDetailerPipe:

    @classmethod
    def INPUT_TYPES(s):
        return {'required': {'image': ('IMAGE',), 'detailer_pipe': ('DETAILER_PIPE',), 'guide_size': ('FLOAT', {'default': 384, 'min': 64, 'max': nodes.MAX_RESOLUTION, 'step': 8}), 'guide_size_for': ('BOOLEAN', {'default': True, 'label_on': 'bbox', 'label_off': 'crop_region'}), 'max_size': ('FLOAT', {'default': 1024, 'min': 64, 'max': nodes.MAX_RESOLUTION, 'step': 8}), 'seed': ('INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}), 'steps': ('INT', {'default': 20, 'min': 1, 'max': 10000}), 'cfg': ('FLOAT', {'default': 8.0, 'min': 0.0, 'max': 100.0}), 'sampler_name': (comfy.samplers.KSampler.SAMPLERS,), 'scheduler': (comfy.samplers.KSampler.SCHEDULERS,), 'denoise': ('FLOAT', {'default': 0.5, 'min': 0.0001, 'max': 1.0, 'step': 0.01}), 'feather': ('INT', {'default': 5, 'min': 0, 'max': 100, 'step': 1}), 'noise_mask': ('BOOLEAN', {'default': True, 'label_on': 'enabled', 'label_off': 'disabled'}), 'force_inpaint': ('BOOLEAN', {'default': False, 'label_on': 'enabled', 'label_off': 'disabled'}), 'bbox_threshold': ('FLOAT', {'default': 0.5, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'bbox_dilation': ('INT', {'default': 10, 'min': -512, 'max': 512, 'step': 1}), 'bbox_crop_factor': ('FLOAT', {'default': 3.0, 'min': 1.0, 'max': 10, 'step': 0.1}), 'sam_detection_hint': (['center-1', 'horizontal-2', 'vertical-2', 'rect-4', 'diamond-4', 'mask-area', 'mask-points', 'mask-point-bbox', 'none'],), 'sam_dilation': ('INT', {'default': 0, 'min': -512, 'max': 512, 'step': 1}), 'sam_threshold': ('FLOAT', {'default': 0.93, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'sam_bbox_expansion': ('INT', {'default': 0, 'min': 0, 'max': 1000, 'step': 1}), 'sam_mask_hint_threshold': ('FLOAT', {'default': 0.7, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'sam_mask_hint_use_negative': (['False', 'Small', 'Outter'],), 'drop_size': ('INT', {'min': 1, 'max': MAX_RESOLUTION, 'step': 1, 'default': 10}), 'refiner_ratio': ('FLOAT', {'default': 0.2, 'min': 0.0, 'max': 1.0}), 'cycle': ('INT', {'default': 1, 'min': 1, 'max': 10, 'step': 1})}, 'optional': {'inpaint_model': ('BOOLEAN', {'default': False, 'label_on': 'enabled', 'label_off': 'disabled'}), 'noise_mask_feather': ('INT', {'default': 20, 'min': 0, 'max': 100, 'step': 1})}}
    RETURN_TYPES = ('IMAGE', 'IMAGE', 'IMAGE', 'MASK', 'DETAILER_PIPE', 'IMAGE')
    RETURN_NAMES = ('image', 'cropped_refined', 'cropped_enhanced_alpha', 'mask', 'detailer_pipe', 'cnet_images')
    OUTPUT_IS_LIST = (False, True, True, False, False, True)
    FUNCTION = 'doit'
    CATEGORY = 'ImpactPack/Simple'

    def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, denoise, feather, noise_mask, force_inpaint, bbox_threshold, bbox_dilation, bbox_crop_factor, sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative, drop_size, refiner_ratio=None, cycle=1, inpaint_model=False, noise_mask_feather=0):
        result_img = None
        result_mask = None
        result_cropped_enhanced = []
        result_cropped_enhanced_alpha = []
        result_cnet_images = []
        if len(image) > 1:
            print(f'[Impact Pack] WARN: FaceDetailer is not a node designed for video detailing. If you intend to perform video detailing, please use Detailer For AnimateDiff.')
        (model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector, sam_model_opt, detailer_hook, refiner_model, refiner_clip, refiner_positive, refiner_negative) = detailer_pipe
        for (i, single_image) in enumerate(image):
            (enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list) = FaceDetailer.enhance_face(single_image.unsqueeze(0), model, clip, vae, guide_size, guide_size_for, max_size, seed + i, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, bbox_threshold, bbox_dilation, bbox_crop_factor, sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold, sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector, sam_model_opt, wildcard, detailer_hook, refiner_ratio=refiner_ratio, refiner_model=refiner_model, refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative, cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
            result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
            result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
            result_cropped_enhanced.extend(cropped_enhanced)
            result_cropped_enhanced_alpha.extend(cropped_enhanced_alpha)
            result_cnet_images.extend(cnet_pil_list)
        if len(result_cropped_enhanced) == 0:
            result_cropped_enhanced = [empty_pil_tensor()]
        if len(result_cropped_enhanced_alpha) == 0:
            result_cropped_enhanced_alpha = [empty_pil_tensor()]
        if len(result_cnet_images) == 0:
            result_cnet_images = [empty_pil_tensor()]
        return (result_img, result_cropped_enhanced, result_cropped_enhanced_alpha, result_mask, detailer_pipe, result_cnet_images)