122 lines
6.7 KiB
Markdown
122 lines
6.7 KiB
Markdown
# Documentation
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- Class name: IPAdapterTiled
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- Category: ipadapter/tiled
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- Output node: False
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- Repo Ref: https://github.com/cubiq/ComfyUI_IPAdapter_plus.git
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IPAdapterTiled节点旨在通过平铺机制处理图像，这对于处理可能不适合作为整体处理时放入内存的大型图像特别有用。它对图像平铺应用一系列变换和调整，利用IPAdapter和CLIPVision模型的能力生成嵌入，这些嵌入可以用于进一步的处理或模型训练。
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# Input types
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## Required
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- model
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- 模型参数是必需的，因为它代表了将用于处理图像数据的深度学习模型。它是节点功能的核心组件，决定了图像平铺最终如何被分析和转换。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- ipadapter
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- ipadapter参数指定了适配器模型，该模型将与主模型一起使用以适配图像特征。它在节点修改和增强图像数据以提高模型性能的能力中起着关键作用。
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- Comfy dtype: IPADAPTER
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- Python dtype: Dict[str, Any]
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- image
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- 图像输入是节点的主要数据源。它是原始图像数据，将被分解成平铺并由节点处理。图像数据的质量和格式直接影响节点的输出和后续分析。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Optional
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- weight
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- 权重参数允许调整图像平铺对模型输出的影响。它可以微调每个平铺的贡献，以实现最终结果中所需的效果或平衡。
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- Comfy dtype: FLOAT
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- Python dtype: float
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# Output types
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- MODEL
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- 输出模型代表了应用平铺和图像处理技术后的处理模型。它封装了已经应用于输入图像平铺的学到的特征和转换。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- tiles
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- 平铺输出包含处理后的图像平铺。每个平铺都经历了节点的转换过程，并且可以用于进一步使用或分析。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- masks
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- 掩码输出提供了在处理图像平铺期间使用的关注掩码。这些掩码对于聚焦图像的特定区域或从分析中排除某些区域可能有用。
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- Comfy dtype: MASK
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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 IPAdapterTiled:
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def __init__(self):
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self.unfold_batch = False
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'model': ('MODEL',), 'ipadapter': ('IPADAPTER',), 'image': ('IMAGE',), 'weight': ('FLOAT', {'default': 1.0, 'min': -1, 'max': 3, 'step': 0.05}), 'weight_type': (WEIGHT_TYPES,), 'combine_embeds': (['concat', 'add', 'subtract', 'average', 'norm average'],), 'start_at': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 1.0, 'step': 0.001}), 'end_at': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.001}), 'sharpening': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 1.0, 'step': 0.05}), 'embeds_scaling': (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'],)}, 'optional': {'image_negative': ('IMAGE',), 'attn_mask': ('MASK',), 'clip_vision': ('CLIP_VISION',)}}
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RETURN_TYPES = ('MODEL', 'IMAGE', 'MASK')
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RETURN_NAMES = ('MODEL', 'tiles', 'masks')
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FUNCTION = 'apply_tiled'
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CATEGORY = 'ipadapter/tiled'
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def apply_tiled(self, model, ipadapter, image, weight, weight_type, start_at, end_at, sharpening, combine_embeds='concat', image_negative=None, attn_mask=None, clip_vision=None, embeds_scaling='V only'):
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if 'ipadapter' in ipadapter:
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ipadapter_model = ipadapter['ipadapter']['model']
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clip_vision = clip_vision if clip_vision is not None else ipadapter['clipvision']['model']
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else:
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ipadapter_model = ipadapter
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clip_vision = clip_vision
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if clip_vision is None:
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raise Exception('Missing CLIPVision model.')
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del ipadapter
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tile_size = 256
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(_, oh, ow, _) = image.shape
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if attn_mask is None:
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attn_mask = torch.ones([1, oh, ow], dtype=image.dtype, device=image.device)
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image = image.permute([0, 3, 1, 2])
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attn_mask = attn_mask.unsqueeze(1)
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attn_mask = T.Resize((oh, ow), interpolation=T.InterpolationMode.BICUBIC, antialias=True)(attn_mask)
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if oh / ow > 0.75 and oh / ow < 1.33:
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image = T.CenterCrop(min(oh, ow))(image)
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resize = (tile_size * 2, tile_size * 2)
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attn_mask = T.CenterCrop(min(oh, ow))(attn_mask)
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else:
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resize = (int(tile_size * ow / oh), tile_size) if oh < ow else (tile_size, int(tile_size * oh / ow))
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imgs = []
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for img in image:
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img = T.ToPILImage()(img)
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img = img.resize(resize, resample=Image.Resampling['LANCZOS'])
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imgs.append(T.ToTensor()(img))
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image = torch.stack(imgs)
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del imgs, img
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attn_mask = T.Resize(resize[::-1], interpolation=T.InterpolationMode.BICUBIC, antialias=True)(attn_mask)
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if oh / ow > 4 or oh / ow < 0.25:
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crop = (tile_size, tile_size * 4) if oh < ow else (tile_size * 4, tile_size)
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image = T.CenterCrop(crop)(image)
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attn_mask = T.CenterCrop(crop)(attn_mask)
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attn_mask = attn_mask.squeeze(1)
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if sharpening > 0:
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image = contrast_adaptive_sharpening(image, sharpening)
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image = image.permute([0, 2, 3, 1])
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(_, oh, ow, _) = image.shape
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tiles_x = math.ceil(ow / tile_size)
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tiles_y = math.ceil(oh / tile_size)
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overlap_x = max(0, (tiles_x * tile_size - ow) / (tiles_x - 1 if tiles_x > 1 else 1))
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overlap_y = max(0, (tiles_y * tile_size - oh) / (tiles_y - 1 if tiles_y > 1 else 1))
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base_mask = torch.zeros([attn_mask.shape[0], oh, ow], dtype=image.dtype, device=image.device)
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tiles = []
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masks = []
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for y in range(tiles_y):
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for x in range(tiles_x):
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start_x = int(x * (tile_size - overlap_x))
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start_y = int(y * (tile_size - overlap_y))
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tiles.append(image[:, start_y:start_y + tile_size, start_x:start_x + tile_size, :])
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mask = base_mask.clone()
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mask[:, start_y:start_y + tile_size, start_x:start_x + tile_size] = attn_mask[:, start_y:start_y + tile_size, start_x:start_x + tile_size]
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masks.append(mask)
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del mask
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model = model.clone()
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for i in range(len(tiles)):
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ipa_args = {'image': tiles[i], 'image_negative': image_negative, 'weight': weight, 'weight_type': weight_type, 'combine_embeds': combine_embeds, 'start_at': start_at, 'end_at': end_at, 'attn_mask': masks[i], 'unfold_batch': self.unfold_batch, 'embeds_scaling': embeds_scaling}
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model = ipadapter_execute(model, ipadapter_model, clip_vision, **ipa_args)
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return (model, torch.cat(tiles), torch.cat(masks))
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``` |