# Documentation - Class name: samplerSettingsNoiseIn - Category: EasyUse/PreSampling - Output node: True - Repo Ref: https://github.com/yolain/ComfyUI-Easy-Use.git 该节点旨在为采样过程配置和应用噪声设置，这可以增强生成样本的多样性和质量。它允许调整噪声因素和调度，有助于实现更精细和可控的生成工作流程。 # Input types ## Required - pipe - ‘pipe’参数是必要的，因为它携带了正在处理的模型和样本的必要信息。它是节点操作的支柱，促进了应用噪声和后续采样步骤所需的数据流。 - Comfy dtype: PIPE_LINE - Python dtype: Dict[str, Any] - factor - ‘factor’参数在确定采样过程中引入的噪声程度方面至关重要。它直接影响原始噪声和变化噪声之间的平衡，从而影响最终输出的多样性和一致性。 - Comfy dtype: FLOAT - Python dtype: float - steps - ‘steps’参数定义了采样过程将要经历的迭代次数。它很重要，因为它决定了采样的复杂性和深度，更多的步骤可能导致更精细的结果。 - Comfy dtype: INT - Python dtype: int - cfg - ‘cfg’参数用于微调采样过程的配置。它在调整节点的整体行为方面发挥作用，确保采样按照期望的规格进行。 - Comfy dtype: FLOAT - Python dtype: float - sampler_name - ‘sampler_name’参数在选择合适的采样方法方面至关重要。它影响生成样本时采用的策略，不同的采样器提供不同的噪声应用和样本多样性方法。 - Comfy dtype: COMBO - Python dtype: str - scheduler - ‘scheduler’参数在管理采样步骤中噪声的应用方面很重要。它有助于控制引入噪声的速度，确保从采样过程的一个阶段平稳过渡到下一个阶段。 - Comfy dtype: COMBO - Python dtype: str - denoise - ‘denoise’参数负责指定要应用的去噪水平。它影响生成样本的清晰度和锐度，较高的值导致更清晰的输出。 - Comfy dtype: FLOAT - Python dtype: float - seed - ‘seed’参数对于确保采样过程中的可重复性至关重要。它初始化随机数生成器，允许在不同运行中使用相同设置时获得一致的结果。 - Comfy dtype: INT - Python dtype: int # Output types - pipe - ‘pipe’输出是输入‘pipe’的修改版本，现在包含调整后的噪声设置和生成的样本。它是节点处理的成果，封装了增强的采样数据以供进一步使用。 - Comfy dtype: PIPE_LINE - Python dtype: Dict[str, Any] # Usage tips - Infra type: CPU # Source code ``` class samplerSettingsNoiseIn: def __init__(self): pass @classmethod def INPUT_TYPES(cls): return {'required': {'pipe': ('PIPE_LINE',), 'factor': ('FLOAT', {'default': 0.1, 'min': 0.0, 'max': 1.0, 'step': 0.01, 'round': 0.01}), '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': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'seed': ('INT', {'default': 0, 'min': 0, 'max': MAX_SEED_NUM})}, 'optional': {'optional_noise_seed': ('INT', {'forceInput': True}), 'optional_latent': ('LATENT',)}, 'hidden': {'prompt': 'PROMPT', 'extra_pnginfo': 'EXTRA_PNGINFO', 'my_unique_id': 'UNIQUE_ID'}} RETURN_TYPES = ('PIPE_LINE',) RETURN_NAMES = ('pipe',) OUTPUT_NODE = True FUNCTION = 'settings' CATEGORY = 'EasyUse/PreSampling' def slerp(self, val, low, high): dims = low.shape low = low.reshape(dims[0], -1) high = high.reshape(dims[0], -1) low_norm = low / torch.norm(low, dim=1, keepdim=True) high_norm = high / torch.norm(high, dim=1, keepdim=True) low_norm[low_norm != low_norm] = 0.0 high_norm[high_norm != high_norm] = 0.0 omega = torch.acos((low_norm * high_norm).sum(1)) so = torch.sin(omega) res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high return res.reshape(dims) def prepare_mask(self, mask, shape): mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[2], shape[3]), mode='bilinear') mask = mask.expand((-1, shape[1], -1, -1)) if mask.shape[0] < shape[0]: mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] return mask def expand_mask(self, mask, expand, tapered_corners): try: import numpy as np import scipy c = 0 if tapered_corners else 1 kernel = np.array([[c, 1, c], [1, 1, 1], [c, 1, c]]) mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) out = [] for m in mask: output = m.numpy() for _ in range(abs(expand)): if expand < 0: output = scipy.ndimage.grey_erosion(output, footprint=kernel) else: output = scipy.ndimage.grey_dilation(output, footprint=kernel) output = torch.from_numpy(output) out.append(output) return torch.stack(out, dim=0) except: return None def settings(self, pipe, factor, steps, cfg, sampler_name, scheduler, denoise, seed, optional_noise_seed=None, optional_latent=None, prompt=None, extra_pnginfo=None, my_unique_id=None): latent = optional_latent if optional_latent is not None else pipe['samples'] model = pipe['model'] (batch_size, _, height, width) = latent['samples'].shape generator = torch.manual_seed(seed) base_noise = torch.randn((1, 4, height, width), dtype=torch.float32, device='cpu', generator=generator).repeat(batch_size, 1, 1, 1).cpu() if optional_noise_seed is None or optional_noise_seed == seed: optional_noise_seed = seed + 1 generator = torch.manual_seed(optional_noise_seed) variation_noise = torch.randn((batch_size, 4, height, width), dtype=torch.float32, device='cpu', generator=generator).cpu() slerp_noise = self.slerp(factor, base_noise, variation_noise) end_at_step = steps start_at_step = round(end_at_step - end_at_step * denoise) device = comfy.model_management.get_torch_device() comfy.model_management.load_model_gpu(model) model_patcher = comfy.model_patcher.ModelPatcher(model.model, load_device=device, offload_device=comfy.model_management.unet_offload_device()) sampler = comfy.samplers.KSampler(model_patcher, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=1.0, model_options=model.model_options) sigmas = sampler.sigmas sigma = sigmas[start_at_step] - sigmas[end_at_step] sigma /= model.model.latent_format.scale_factor sigma = sigma.cpu().numpy() work_latent = latent.copy() work_latent['samples'] = latent['samples'].clone() + slerp_noise * sigma if 'noise_mask' in latent: noise_mask = self.prepare_mask(latent['noise_mask'], latent['samples'].shape) work_latent['samples'] = noise_mask * work_latent['samples'] + (1 - noise_mask) * latent['samples'] work_latent['noise_mask'] = self.expand_mask(latent['noise_mask'].clone(), 5, True) if pipe is None: pipe = {} new_pipe = {'model': pipe['model'], 'positive': pipe['positive'], 'negative': pipe['negative'], 'vae': pipe['vae'], 'clip': pipe['clip'], 'samples': work_latent, 'images': pipe['images'], 'seed': seed, 'loader_settings': {**pipe['loader_settings'], 'steps': steps, 'cfg': cfg, 'sampler_name': sampler_name, 'scheduler': scheduler, 'denoise': denoise, 'add_noise': 'disable'}} return (new_pipe,) ```