92 lines
3.7 KiB
Markdown
92 lines
3.7 KiB
Markdown
# Documentation
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- Class name: IterativeMixingKSampler
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- Category: test
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- Output node: False
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- Repo Ref: https://github.com/deroberon/demofusion-comfyui
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该节点通过逐步引入一组参考潜在表示的指导，来细化一批潜在表示，旨在通过迭代混合提高生成样本的质量。
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# Input types
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## Required
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- model
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- 用于去噪和细化潜在表示的生成模型。
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- Comfy dtype: MODEL
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- Python dtype: torch.nn.Module
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- seed
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- 用于初始化噪声生成的随机数生成器的种子值。
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- Comfy dtype: INT
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- Python dtype: int
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- cfg
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- 影响去噪过程的配置参数。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- sampler_name
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- 用于迭代去噪过程的采样器名称。
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- Comfy dtype: ENUM
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- Python dtype: str
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- scheduler
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- 用于随时间调整去噪过程的调度策略。
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- Comfy dtype: ENUM
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- Python dtype: str
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- step_increment
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- 在每次去噪过程迭代中增加的步数。
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- Comfy dtype: INT
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- Python dtype: int
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- positive
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- 用于指导去噪过程的正向调节数据。
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- Comfy dtype: CONDITIONING
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- Python dtype: dict
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- negative
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- 用于进一步细化去噪过程的负向调节数据。
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- Comfy dtype: CONDITIONING
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- Python dtype: dict
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- latent_image_batch
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- 需要被去噪和细化的潜在表示批次。
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- Comfy dtype: LATENT
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- Python dtype: torch.Tensor
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- denoise
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- 控制每一步应用的去噪程度的参数。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- alpha_1
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- 影响参考潜在表示与去噪样本之间混合速率的参数。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- reverse_batch
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- 一个标志，指示在处理之前是否应反转批次。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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# Output types
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- latent_image_batch
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- 经过迭代去噪过程后细化的潜在表示批次。
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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: CPU
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# Source code
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```
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class IterativeMixingKSampler:
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"""
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Take a batch of latents, z_prime, and progressively de-noise them
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step by step from z_prime[0] to z_prime[steps], mixing in a weighted
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fraction of z_prime[i] at each step so that de-noising is guided by
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the z_prime latents. This batch sampler assumes that the number of steps
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is just the length of z_prime, so there is no steps parameter. The parameter
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latent_image_batch should come from the Batch Unsampler node.
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'model': ('MODEL',), 'seed': ('INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}), 'cfg': ('FLOAT', {'default': 8.0, 'min': 0.0, 'max': 100.0, 'step': 0.1, 'round': 0.01}), 'sampler_name': (comfy.samplers.KSampler.SAMPLERS,), 'scheduler': (comfy.samplers.KSampler.SCHEDULERS,), 'step_increment': ('INT', {'default': 1, 'min': 1, 'max': 10000}), 'positive': ('CONDITIONING',), 'negative': ('CONDITIONING',), 'latent_image_batch': ('LATENT',), 'denoise': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'alpha_1': ('FLOAT', {'default': 3.0, 'min': 0.1, 'max': 10.0}), 'reverse_batch': ('BOOLEAN', {'default': True})}}
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RETURN_TYPES = ('LATENT',)
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FUNCTION = 'sample'
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CATEGORY = 'test'
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def sample(self, model, seed, cfg, sampler_name, scheduler, step_increment, positive, negative, latent_image_batch, denoise=1.0, alpha_1=3.0, reverse_batch=True):
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if reverse_batch:
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latent_image_batch['samples'] = torch.flip(latent_image_batch['samples'], [0])
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return batched_ksampler(model, seed, cfg, sampler_name, scheduler, step_increment, positive, negative, latent_image_batch, denoise=denoise, alpha_1=alpha_1)
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``` |