77 lines
3.0 KiB
Markdown
77 lines
3.0 KiB
Markdown
# Set Props on Cond 🎭🅐🅓
|
|
## Documentation
|
|
- Class name: ADE_ConditioningSetMask
|
|
- Category: Animate Diff 🎭🅐🅓/conditioning/single cond ops
|
|
- Output node: False
|
|
- Repo Ref: https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved.git
|
|
|
|
此节点旨在将掩码应用于条件数据，根据指定参数调整条件的区域和强度。它使得条件输入的动态修改成为可能，以更好地符合预期结果，如关注或排除数据中的特定区域。
|
|
|
|
## Input types
|
|
### Required
|
|
- cond_ADD
|
|
- 要添加或修改的条件数据。此参数对于在现有条件框架中引入新的上下文或内容至关重要。
|
|
- Comfy dtype: CONDITIONING
|
|
- Python dtype: Tensor
|
|
- strength
|
|
- 定义掩码对条件数据影响的强度，允许对修改影响进行精细控制。
|
|
- Comfy dtype: FLOAT
|
|
- Python dtype: float
|
|
- set_cond_area
|
|
- 确定掩码效果是应用于默认区域还是掩码的边界，提供在特定区域进行定位的灵活性。
|
|
- Comfy dtype: COMBO[STRING]
|
|
- Python dtype: str
|
|
|
|
### Optional
|
|
- opt_mask
|
|
- 一个可选掩码，指定要修改的条件数据区域，提供对条件影响的额外控制。
|
|
- Comfy dtype: MASK
|
|
- Python dtype: Tensor
|
|
- opt_lora_hook
|
|
- 一个可选参数，允许将Lora挂钩应用于条件，进一步定制条件过程。
|
|
- Comfy dtype: LORA_HOOK
|
|
- Python dtype: LoraHookGroup
|
|
- opt_timesteps
|
|
- 可选的时间步条件，允许对条件数据进行时间调整。
|
|
- Comfy dtype: TIMESTEPS_COND
|
|
- Python dtype: TimestepsCond
|
|
|
|
## Output types
|
|
- conditioning
|
|
- Comfy dtype: CONDITIONING
|
|
- 修改后的条件数据，反映了应用的掩码和调整，包括添加的任何额外条件数据。
|
|
- Python dtype: Tensor
|
|
|
|
## Usage tips
|
|
- Infra type: CPU
|
|
- Common nodes: unknown
|
|
|
|
## Source code
|
|
```python
|
|
class ConditioningSetMaskHooked:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"cond_ADD": ("CONDITIONING",),
|
|
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
|
|
"set_cond_area": (COND_CONST._LIST_COND_AREA,),
|
|
},
|
|
"optional": {
|
|
"opt_mask": ("MASK", ),
|
|
"opt_lora_hook": ("LORA_HOOK",),
|
|
"opt_timesteps": ("TIMESTEPS_COND",)
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CONDITIONING",)
|
|
CATEGORY = "Animate Diff 🎭🅐🅓/conditioning/single cond ops"
|
|
FUNCTION = "append_and_hook"
|
|
|
|
def append_and_hook(self, cond_ADD,
|
|
strength: float, set_cond_area: str,
|
|
opt_mask: Tensor=None, opt_lora_hook: LoraHookGroup=None, opt_timesteps: TimestepsCond=None):
|
|
(final_conditioning,) = set_mask_conds(conds=[cond_ADD],
|
|
strength=strength, set_cond_area=set_cond_area,
|
|
opt_mask=opt_mask, opt_lora_hook=opt_lora_hook, opt_timesteps=opt_timesteps)
|
|
return (final_conditioning,) |