83 lines
2.9 KiB
Markdown
83 lines
2.9 KiB
Markdown
|
|
# Documentation
|
|
- Class name: CustomSigmas
|
|
- Category: KJNodes/noise
|
|
- Output node: False
|
|
|
|
CustomSigmas节点旨在将浮点数列表转换为sigma张量，从而便于在神经网络模型的噪声相关操作中操作和应用这些值。
|
|
|
|
# Input types
|
|
## Required
|
|
- sigmas_string
|
|
- 该参数表示一串逗号分隔的sigma值，用于转换成sigma张量。它对于定义后续操作中要使用的具体sigma值至关重要，直接影响噪声应用的行为和结果。
|
|
- Comfy dtype: STRING
|
|
- Python dtype: str
|
|
- interpolate_to_steps
|
|
- 指定sigma值应该插值到的步数。这个参数对于调整sigma张量的长度以匹配模型处理中所需的步数非常重要。
|
|
- Comfy dtype: INT
|
|
- Python dtype: int
|
|
|
|
# Output types
|
|
- SIGMAS
|
|
- 输出是从输入的浮点数列表派生的sigma张量。这个张量对于神经网络模型中的噪声操作和应用过程至关重要。
|
|
- Comfy dtype: SIGMAS
|
|
- Python dtype: torch.Tensor
|
|
|
|
|
|
## Usage tips
|
|
- Infra type: `GPU`
|
|
- Common nodes: unknown
|
|
|
|
|
|
## Source code
|
|
```python
|
|
class CustomSigmas:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required":
|
|
{
|
|
"sigmas_string" :("STRING", {"default": "14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029","multiline": True}),
|
|
"interpolate_to_steps": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}),
|
|
}
|
|
}
|
|
RETURN_TYPES = ("SIGMAS",)
|
|
RETURN_NAMES = ("SIGMAS",)
|
|
CATEGORY = "KJNodes/noise"
|
|
FUNCTION = "customsigmas"
|
|
DESCRIPTION = """
|
|
Creates a sigmas tensor from a string of comma separated values.
|
|
Examples:
|
|
|
|
Nvidia's optimized AYS 10 step schedule for SD 1.5:
|
|
14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029
|
|
SDXL:
|
|
14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029
|
|
SVD:
|
|
700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002
|
|
"""
|
|
def customsigmas(self, sigmas_string, interpolate_to_steps):
|
|
sigmas_list = sigmas_string.split(', ')
|
|
sigmas_float_list = [float(sigma) for sigma in sigmas_list]
|
|
sigmas_tensor = torch.tensor(sigmas_float_list)
|
|
if len(sigmas_tensor) != interpolate_to_steps:
|
|
sigmas_tensor = self.loglinear_interp(sigmas_tensor, interpolate_to_steps)
|
|
return (sigmas_tensor,)
|
|
|
|
def loglinear_interp(self, t_steps, num_steps):
|
|
"""
|
|
Performs log-linear interpolation of a given array of decreasing numbers.
|
|
"""
|
|
t_steps_np = t_steps.numpy()
|
|
|
|
xs = np.linspace(0, 1, len(t_steps_np))
|
|
ys = np.log(t_steps_np[::-1])
|
|
|
|
new_xs = np.linspace(0, 1, num_steps)
|
|
new_ys = np.interp(new_xs, xs, ys)
|
|
|
|
interped_ys = np.exp(new_ys)[::-1].copy()
|
|
interped_ys_tensor = torch.tensor(interped_ys)
|
|
return interped_ys_tensor
|
|
|
|
```
|