7.4 KiB
7.4 KiB
Documentation
- Class name: ttN pipeEncodeConcat
- Category: ttN/pipe
- Output node: True
- Repo Ref: https://github.com/comfyanonymous/ComfyUI
ttN pipeEncodeConcat节点旨在增强和连接管道内的条件信息，利用高级编码技术来优化和合并文本条件输入，从而提供更好的模型指导。该节点在处理和整合各种条件输入方面发挥关键作用，为生成过程提供更精细和有针对性的控制。
Input types
Required
- pipe
- 代表管道状态，包括模型、条件设置和其他相关参数，作为编码和连接过程的基础。
- Comfy dtype: PIPE_LINE
- Python dtype: dict
- toggle
- 一个布尔标志，用于决定是否执行编码和连接过程。
- Comfy dtype: COMBO[BOOLEAN]
- Python dtype: bool
Optional
- positive
- 可选的正向文本输入，用于条件设置，旨在引导模型生成与指定属性一致的内容。
- Comfy dtype: STRING
- Python dtype: str
- positive_token_normalization
- 指定正向条件中标记归一化的方法，影响模型对这些标记的解释和权重分配。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- positive_weight_interpretation
- 定义正向条件中权重的解释方式，影响模型的生成过程。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- negative
- 可选的负向文本输入，用于条件设置，引导模型避免生成具有指定属性的内容。
- Comfy dtype: STRING
- Python dtype: str
- negative_token_normalization
- 指定负向条件中标记归一化的方法，影响模型对这些标记的解释和权重分配。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- negative_weight_interpretation
- 定义负向条件中权重的解释方式，与正向条件相对，影响模型的生成过程。
- Comfy dtype: COMBO[STRING]
- Python dtype: str
- optional_positive_from
- 可选的高级正向条件输入，允许更细致和具体的模型指导。
- Comfy dtype: CONDITIONING
- Python dtype: list of tuples
- optional_negative_from
- 可选的高级负向条件输入，实现更详细和有针对性的模型影响。
- Comfy dtype: CONDITIONING
- Python dtype: list of tuples
- optional_clip
- 可选的CLIP模型参数，可用于通过视觉指导进一步优化条件设置过程。
- Comfy dtype: CLIP
- Python dtype: object
Output types
- pipe
- 更新后的管道状态，包含新编码和连接的条件信息。
- Comfy dtype: PIPE_LINE
- Python dtype: dict
- positive
- 增强和连接后的正向条件输出，准备用于指导模型的生成。
- Comfy dtype: CONDITIONING
- Python dtype: list of tuples
- negative
- 增强和连接后的负向条件输出，为模型提供与正向条件相对的精细指导。
- Comfy dtype: CONDITIONING
- Python dtype: list of tuples
- clip
- CLIP模型参数，可能在编码和连接过程中更新，用于进一步优化视觉指导。
- Comfy dtype: CLIP
- Python dtype: object
- ui
- 显示生成文本的UI组件，提供对编码和连接过程的洞察。
Usage tips
- Infra type:
CPU - Common nodes: unknown
Source code
class ttN_pipeEncodeConcat:
version = '1.0.2'
@classmethod
def INPUT_TYPES(s):
return {"required": {
"pipe": ("PIPE_LINE",),
"toggle": ([True, False],),
},
"optional": {
"positive": ("STRING", {"default": "Positive","multiline": True}),
"positive_token_normalization": (["none", "mean", "length", "length+mean"],),
"positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"negative": ("STRING", {"default": "Negative","multiline": True}),
"negative_token_normalization": (["none", "mean", "length", "length+mean"],),
"negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"optional_positive_from": ("CONDITIONING",),
"optional_negative_from": ("CONDITIONING",),
"optional_clip": ("CLIP",),
},
"hidden": {
"ttNnodeVersion": ttN_pipeEncodeConcat.version, "my_unique_id": "UNIQUE_ID"
},
}
OUTPUT_NODE = True
RETURN_TYPES = ("PIPE_LINE", "CONDITIONING", "CONDITIONING", "CLIP")
RETURN_NAMES = ("pipe", "positive", "negative", "clip")
FUNCTION = "concat"
CATEGORY = "ttN/pipe"
def concat(self, toggle, positive_token_normalization, positive_weight_interpretation,
negative_token_normalization, negative_weight_interpretation,
pipe=None, positive='', negative='', seed=None, my_unique_id=None, optional_positive_from=None, optional_negative_from=None, optional_clip=None):
if toggle == False:
return (pipe, pipe["positive"], pipe["negative"], pipe["clip"])
positive_from = optional_positive_from if optional_positive_from is not None else pipe["positive"]
negative_from = optional_negative_from if optional_negative_from is not None else pipe["negative"]
samp_clip = optional_clip if optional_clip is not None else pipe["clip"]
new_text = ''
def enConcatConditioning(text, token_normalization, weight_interpretation, conditioning_from, new_text):
out = []
if "__" in text:
text = loader.nsp_parse(text, pipe["seed"], title="encodeConcat", my_unique_id=my_unique_id)
new_text += text
conditioning_to, pooled = advanced_encode(samp_clip, text, token_normalization, weight_interpretation, w_max=1.0, apply_to_pooled='enable')
conditioning_to = [[conditioning_to, {"pooled_output": pooled}]]
if len(conditioning_from) > 1:
ttNl.warn("encode and concat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to")
cond_from = conditioning_from[0][0]
for i in range(len(conditioning_to)):
t1 = conditioning_to[i][0]
tw = torch.cat((t1, cond_from),1)
n = [tw, conditioning_to[i][1].copy()]
out.append(n)
return out
pos, neg = None, None
if positive not in ['', None, ' ']:
pos = enConcatConditioning(positive, positive_token_normalization, positive_weight_interpretation, positive_from, new_text)
if negative not in ['', None, ' ']:
neg = enConcatConditioning(negative, negative_token_normalization, negative_weight_interpretation, negative_from, new_text)
pos = pos if pos is not None else pipe["positive"]
neg = neg if neg is not None else pipe["negative"]
new_pipe = {
"model": pipe["model"],
"positive": pos,
"negative": neg,
"vae": pipe["vae"],
"clip": samp_clip,
"samples": pipe["samples"],
"images": pipe["images"],
"seed": pipe["seed"],
"loader_settings": pipe["loader_settings"],
}
del pipe
return (new_pipe, new_pipe["positive"], new_pipe["negative"], samp_clip, { "ui": { "string": new_text } } )