138 lines
9.3 KiB
Markdown
138 lines
9.3 KiB
Markdown
# Documentation
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- Class name: DetailerForEach
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- Category: ImpactPack/Detailer
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- Output node: False
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- Repo Ref: https://github.com/ltdrdata/ComfyUI-Impact-Pack.git
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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。
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# Input types
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## Required
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- image
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- 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。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- segs
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- 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。
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- Comfy dtype: SEGS
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- Python dtype: List[Segment]
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- model
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- 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。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- clip
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- 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。
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- Comfy dtype: CLIP
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- Python dtype: Any
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- vae
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- 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。
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- Comfy dtype: VAE
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- Python dtype: torch.nn.Module
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- guide_size
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- 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。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- steps
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- 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。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- enhanced_image
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- 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。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: GPU
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# Source code
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```
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class DetailerForEach:
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@classmethod
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def INPUT_TYPES(s):
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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})}}
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RETURN_TYPES = ('IMAGE',)
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FUNCTION = 'doit'
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CATEGORY = 'ImpactPack/Detailer'
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@staticmethod
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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):
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if len(image) > 1:
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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.')
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image = image.clone()
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enhanced_alpha_list = []
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enhanced_list = []
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cropped_list = []
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cnet_pil_list = []
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segs = core.segs_scale_match(segs, image.shape)
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new_segs = []
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wildcard_concat_mode = None
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if wildcard_opt is not None:
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if wildcard_opt.startswith('[CONCAT]'):
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wildcard_concat_mode = 'concat'
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wildcard_opt = wildcard_opt[8:]
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(wmode, wildcard_chooser) = wildcards.process_wildcard_for_segs(wildcard_opt)
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else:
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(wmode, wildcard_chooser) = (None, None)
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if wmode in ['ASC', 'DSC']:
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if wmode == 'ASC':
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ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1]))
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else:
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ordered_segs = sorted(segs[1], key=lambda x: (x.bbox[0], x.bbox[1]), reverse=True)
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else:
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ordered_segs = segs[1]
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for (i, seg) in enumerate(ordered_segs):
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cropped_image = crop_ndarray4(image.numpy(), seg.crop_region)
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cropped_image = to_tensor(cropped_image)
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mask = to_tensor(seg.cropped_mask)
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mask = tensor_gaussian_blur_mask(mask, feather)
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is_mask_all_zeros = (seg.cropped_mask == 0).all().item()
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if is_mask_all_zeros:
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print(f'Detailer: segment skip [empty mask]')
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continue
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if noise_mask:
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cropped_mask = seg.cropped_mask
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else:
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cropped_mask = None
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if wildcard_chooser is not None and wmode != 'LAB':
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(seg_seed, wildcard_item) = wildcard_chooser.get(seg)
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elif wildcard_chooser is not None and wmode == 'LAB':
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(seg_seed, wildcard_item) = (None, wildcard_chooser.get(seg))
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else:
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(seg_seed, wildcard_item) = (None, None)
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seg_seed = seed + i if seg_seed is None else seg_seed
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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]
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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]
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(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)
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if cnet_pils is not None:
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cnet_pil_list.extend(cnet_pils)
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if not enhanced_image is None:
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image = image.cpu()
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enhanced_image = enhanced_image.cpu()
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tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask)
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enhanced_list.append(enhanced_image)
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if detailer_hook is not None:
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detailer_hook.post_paste(image)
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if not enhanced_image is None:
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enhanced_image_alpha = tensor_convert_rgba(enhanced_image)
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new_seg_image = enhanced_image.numpy()
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mask = tensor_resize(mask, *tensor_get_size(enhanced_image))
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tensor_putalpha(enhanced_image_alpha, mask)
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enhanced_alpha_list.append(enhanced_image_alpha)
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else:
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new_seg_image = None
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cropped_list.append(cropped_image)
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new_seg = SEG(new_seg_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
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new_segs.append(new_seg)
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image_tensor = tensor_convert_rgb(image)
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cropped_list.sort(key=lambda x: x.shape, reverse=True)
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enhanced_list.sort(key=lambda x: x.shape, reverse=True)
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enhanced_alpha_list.sort(key=lambda x: x.shape, reverse=True)
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return (image_tensor, cropped_list, enhanced_list, enhanced_alpha_list, cnet_pil_list, (segs[0], new_segs))
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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):
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(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)
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return (enhanced_img,)
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``` |