94 lines
4.8 KiB
Markdown
94 lines
4.8 KiB
Markdown
# Documentation
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- Class name: TwoAdvancedSamplersForMask
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- Category: ImpactPack/Sampler
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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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TwoAdvancedSamplersForMask节点旨在使用两个不同的高级采样器对潜在图像执行高级采样操作。它应用掩膜腐蚀技术来细化掩膜，并使用两个独立的高级采样器处理图像的掩膜和未掩膜区域。该节点旨在通过利用两个采样器的优势来提高生成图像的质量。
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# Input types
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## Required
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- seed
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- 种子参数对于随机数生成过程至关重要，确保了采样的可重复性。它影响采样算法的初始状态，从而影响生成的潜在图像。
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- Comfy dtype: INT
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- Python dtype: int
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- steps
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- 步数参数定义了采样过程将经历的迭代次数。它是确定细节水平和最终图像质量的关键因素。
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- Comfy dtype: INT
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- Python dtype: int
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- denoise
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- 去噪参数控制采样过程中应用的噪声减少级别。它在生成图像的清晰度和锐度方面起着重要作用。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- samples
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- 样本参数保存将由采样器处理的初始潜在图像数据。它是输入的重要部分，因为它作为采样操作的起点。
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- Comfy dtype: LATENT
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- Python dtype: torch.Tensor
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- base_sampler
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- 基础采样器参数指定将用于处理图像未掩膜区域的高级采样器。它是实现所需采样结果的关键组件。
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- Comfy dtype: KSAMPLER_ADVANCED
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- Python dtype: KSamplerAdvancedWrapper
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- mask_sampler
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- 掩膜采样器参数标识专用于处理图像掩膜区域的高级采样器。它对于将特定的采样技术应用于掩膜区域至关重要。
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- Comfy dtype: KSAMPLER_ADVANCED
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- Python dtype: KSamplerAdvancedWrapper
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- mask
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- 掩膜参数提供了将应用于潜在图像以区分掩膜和未掩膜区域的掩膜。它对于指导采样过程很重要。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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## Optional
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- overlap_factor
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- 重叠因子参数确定掩膜腐蚀的范围，用于在掩膜和未掩膜区域之间创建缓冲区。它有助于最终图像中过渡的平滑度。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- latent_image
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- 潜在图像输出包含应用高级采样技术后处理的潜在图像。它代表了采样操作的最终结果。
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- Comfy dtype: LATENT
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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 TwoAdvancedSamplersForMask:
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'seed': ('INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}), 'steps': ('INT', {'default': 20, 'min': 1, 'max': 10000}), 'denoise': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'samples': ('LATENT',), 'base_sampler': ('KSAMPLER_ADVANCED',), 'mask_sampler': ('KSAMPLER_ADVANCED',), 'mask': ('MASK',), 'overlap_factor': ('INT', {'default': 10, 'min': 0, 'max': 10000})}}
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RETURN_TYPES = ('LATENT',)
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FUNCTION = 'doit'
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CATEGORY = 'ImpactPack/Sampler'
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@staticmethod
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def mask_erosion(samples, mask, grow_mask_by):
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mask = mask.clone()
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w = samples['samples'].shape[3]
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h = samples['samples'].shape[2]
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mask2 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(w, h), mode='bilinear')
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if grow_mask_by == 0:
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mask_erosion = mask2
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else:
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kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by))
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padding = math.ceil((grow_mask_by - 1) / 2)
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mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask2.round(), kernel_tensor, padding=padding), 0, 1)
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return mask_erosion[:, :, :w, :h].round()
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def doit(self, seed, steps, denoise, samples, base_sampler, mask_sampler, mask, overlap_factor):
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inv_mask = torch.where(mask != 1.0, torch.tensor(1.0), torch.tensor(0.0))
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adv_steps = int(steps / denoise)
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start_at_step = adv_steps - steps
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new_latent_image = samples.copy()
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mask_erosion = TwoAdvancedSamplersForMask.mask_erosion(samples, mask, overlap_factor)
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for i in range(start_at_step, adv_steps):
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add_noise = 'enable' if i == start_at_step else 'disable'
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return_with_leftover_noise = 'enable' if i + 1 != adv_steps else 'disable'
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new_latent_image['noise_mask'] = inv_mask
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new_latent_image = base_sampler.sample_advanced(add_noise, seed, adv_steps, new_latent_image, i, i + 1, 'enable', recovery_mode='ratio additional')
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new_latent_image['noise_mask'] = mask_erosion
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new_latent_image = mask_sampler.sample_advanced('disable', seed, adv_steps, new_latent_image, i, i + 1, return_with_leftover_noise, recovery_mode='ratio additional')
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del new_latent_image['noise_mask']
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return (new_latent_image,)
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``` |