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

Documentation

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

DetailerForEach节点旨在增强图像中各个部分的细节。它通过对每个部分应用详细的增强过程来工作，包括放大、去噪和将增强的部分粘贴回原始图像。该节点特别适用于提高具有需要更详细表示的不同区域的图像的视觉质量。

Input types

Required

• image
  • 要应用细节增强过程的输入图像。它作为节点内所有后续操作的基础。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• segs
  • 在图像中定义的一系列段，每个段代表要增强的区域。这些段对于节点识别图像的哪些部分需要详细处理至关重要。
  • Comfy dtype: SEGS
  • Python dtype: List[Segment]
• model
  • 用于增强过程的模型，通常是一个能够理解和生成详细图像的深度学习模型。
  • Comfy dtype: MODEL
  • Python dtype: torch.nn.Module
• clip
  • CLIP模型或类似的模型，通过提供图像内容的文本描述，协助生成具有上下文感知能力的图像增强。
  • Comfy dtype: CLIP
  • Python dtype: Any
• vae
  • 变分自编码器，负责编码和解码图像数据，这对于细节增强过程至关重要。
  • Comfy dtype: VAE
  • Python dtype: torch.nn.Module
• guide_size
  • 引导尺寸参数决定了细节增强所关注的尺度。它是控制每个段应用的细节层次的关键因素。
  • Comfy dtype: FLOAT
  • Python dtype: float
• steps
  • 在采样过程中要执行的步数，这直接影响最终增强图像的质量和细节。
  • Comfy dtype: INT
  • Python dtype: int

Output types

• enhanced_image
  • 输出是具有改进细节的增强图像，其中每个段都经过单独处理，以获得更高的分辨率和清晰度。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU

Source code

class DetailerForEach:

    @classmethod
    def INPUT_TYPES(s):
        return {'required': {'image': ('IMAGE',), 'segs': ('SEGS',), 'model': ('MODEL',), 'clip': ('CLIP',), 'vae': ('VAE',), '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,), 'positive': ('CONDITIONING',), 'negative': ('CONDITIONING',), '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': True, 'label_on': 'enabled', 'label_off': 'disabled'}), 'wildcard': ('STRING', {'multiline': True, 'dynamicPrompts': False}), 'cycle': ('INT', {'default': 1, 'min': 1, 'max': 10, 'step': 1})}, 'optional': {'detailer_hook': ('DETAILER_HOOK',), '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',)
    FUNCTION = 'doit'
    CATEGORY = 'ImpactPack/Detailer'

    @staticmethod
    def do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard_opt=None, detailer_hook=None, refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, cycle=1, inpaint_model=False, noise_mask_feather=0):
        if len(image) > 1:
            raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
        image = image.clone()
        enhanced_alpha_list = []
        enhanced_list = []
        cropped_list = []
        cnet_pil_list = []
        segs = core.segs_scale_match(segs, image.shape)
        new_segs = []
        wildcard_concat_mode = None
        if wildcard_opt is not None:
            if wildcard_opt.startswith('[CONCAT]'):
                wildcard_concat_mode = 'concat'
                wildcard_opt = wildcard_opt[8:]
            (wmode, wildcard_chooser) = wildcards.process_wildcard_for_segs(wildcard_opt)
        else:
            (wmode, wildcard_chooser) = (None, None)
        if wmode in ['ASC', 'DSC']:
            if wmode == 'ASC':
                ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1]))
            else:
                ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1]), reverse=True)
        else:
            ordered_segs = segs[1]
        for (i, seg) in enumerate(ordered_segs):
            cropped_image = crop_ndarray4(image.numpy(), seg.crop_region)
            cropped_image = to_tensor(cropped_image)
            mask = to_tensor(seg.cropped_mask)
            mask = tensor_gaussian_blur_mask(mask, feather)
            is_mask_all_zeros = (seg.cropped_mask == 0).all().item()
            if is_mask_all_zeros:
                print(f'Detailer: segment skip [empty mask]')
                continue
            if noise_mask:
                cropped_mask = seg.cropped_mask
            else:
                cropped_mask = None
            if wildcard_chooser is not None and wmode != 'LAB':
                (seg_seed, wildcard_item) = wildcard_chooser.get(seg)
            elif wildcard_chooser is not None and wmode == 'LAB':
                (seg_seed, wildcard_item) = (None, wildcard_chooser.get(seg))
            else:
                (seg_seed, wildcard_item) = (None, None)
            seg_seed = seed + i if seg_seed is None else seg_seed
            cropped_positive = [[condition, {k: core.crop_condition_mask(v, image, seg.crop_region) if k == 'mask' else v for (k, v) in details.items()}] for (condition, details) in positive]
            cropped_negative = [[condition, {k: core.crop_condition_mask(v, image, seg.crop_region) if k == 'mask' else v for (k, v) in details.items()}] for (condition, details) in negative]
            (enhanced_image, cnet_pils) = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seg.bbox, seg_seed, steps, cfg, sampler_name, scheduler, cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint, wildcard_opt=wildcard_item, wildcard_opt_concat_mode=wildcard_concat_mode, detailer_hook=detailer_hook, refiner_ratio=refiner_ratio, refiner_model=refiner_model, refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper, cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
            if cnet_pils is not None:
                cnet_pil_list.extend(cnet_pils)
            if not enhanced_image is None:
                image = image.cpu()
                enhanced_image = enhanced_image.cpu()
                tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask)
                enhanced_list.append(enhanced_image)
                if detailer_hook is not None:
                    detailer_hook.post_paste(image)
            if not enhanced_image is None:
                enhanced_image_alpha = tensor_convert_rgba(enhanced_image)
                new_seg_image = enhanced_image.numpy()
                mask = tensor_resize(mask, *tensor_get_size(enhanced_image))
                tensor_putalpha(enhanced_image_alpha, mask)
                enhanced_alpha_list.append(enhanced_image_alpha)
            else:
                new_seg_image = None
            cropped_list.append(cropped_image)
            new_seg = SEG(new_seg_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
            new_segs.append(new_seg)
        image_tensor = tensor_convert_rgb(image)
        cropped_list.sort(key=lambda x: x.shape, reverse=True)
        enhanced_list.sort(key=lambda x: x.shape, reverse=True)
        enhanced_alpha_list.sort(key=lambda x: x.shape, reverse=True)
        return (image_tensor, cropped_list, enhanced_list, enhanced_alpha_list, cnet_pil_list, (segs[0], new_segs))

    def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, cycle=1, detailer_hook=None, inpaint_model=False, noise_mask_feather=0):
        (enhanced_img, *_) = DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, detailer_hook, cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather)
        return (enhanced_img,)