180 lines
7.4 KiB
Markdown
180 lines
7.4 KiB
Markdown
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# Documentation
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- Class name: ttN pipeEncodeConcat
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- Category: ttN/pipe
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- Output node: True
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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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。
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# Input types
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## Required
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- pipe
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- 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。
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- Comfy dtype: PIPE_LINE
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- Python dtype: dict
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- toggle
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- 一个布尔标志，用于决定是否执行编码和连接过程。
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- Comfy dtype: COMBO[BOOLEAN]
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- Python dtype: bool
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## Optional
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- positive
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- 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。
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- Comfy dtype: STRING
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- Python dtype: str
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- positive_token_normalization
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- 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。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- positive_weight_interpretation
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- 定义正向条件中权重的解释方式，影响模型的生成过程。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- negative
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- 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。
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- Comfy dtype: STRING
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- Python dtype: str
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- negative_token_normalization
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- 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。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- negative_weight_interpretation
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- 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。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- optional_positive_from
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- 可选的高级正向条件输入，允许更细致和具体的模型指导。
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- Comfy dtype: CONDITIONING
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- Python dtype: list of tuples
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- optional_negative_from
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- 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。
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- Comfy dtype: CONDITIONING
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- Python dtype: list of tuples
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- optional_clip
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- 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。
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- Comfy dtype: CLIP
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- Python dtype: object
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# Output types
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- pipe
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- 更新后的管道状态，包含新编码和连接的条件信息。
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- Comfy dtype: PIPE_LINE
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- Python dtype: dict
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- positive
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- 增强和连接后的正向条件输出，准备用于指导模型的生成。
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- Comfy dtype: CONDITIONING
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- Python dtype: list of tuples
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- negative
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- 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。
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- Comfy dtype: CONDITIONING
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- Python dtype: list of tuples
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- clip
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- 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。
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- Comfy dtype: CLIP
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- Python dtype: object
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- ui
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- 显示生成文本的UI组件，提供对编码和连接过程的洞察。
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class ttN_pipeEncodeConcat:
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version = '1.0.2'
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"pipe": ("PIPE_LINE",),
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"toggle": ([True, False],),
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},
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"optional": {
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"positive": ("STRING", {"default": "Positive","multiline": True}),
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"positive_token_normalization": (["none", "mean", "length", "length+mean"],),
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"positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
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"negative": ("STRING", {"default": "Negative","multiline": True}),
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"negative_token_normalization": (["none", "mean", "length", "length+mean"],),
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"negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
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"optional_positive_from": ("CONDITIONING",),
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"optional_negative_from": ("CONDITIONING",),
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"optional_clip": ("CLIP",),
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},
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"hidden": {
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"ttNnodeVersion": ttN_pipeEncodeConcat.version, "my_unique_id": "UNIQUE_ID"
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},
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}
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OUTPUT_NODE = True
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RETURN_TYPES = ("PIPE_LINE", "CONDITIONING", "CONDITIONING", "CLIP")
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RETURN_NAMES = ("pipe", "positive", "negative", "clip")
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FUNCTION = "concat"
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CATEGORY = "ttN/pipe"
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def concat(self, toggle, positive_token_normalization, positive_weight_interpretation,
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negative_token_normalization, negative_weight_interpretation,
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pipe=None, positive='', negative='', seed=None, my_unique_id=None, optional_positive_from=None, optional_negative_from=None, optional_clip=None):
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if toggle == False:
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return (pipe, pipe["positive"], pipe["negative"], pipe["clip"])
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positive_from = optional_positive_from if optional_positive_from is not None else pipe["positive"]
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negative_from = optional_negative_from if optional_negative_from is not None else pipe["negative"]
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samp_clip = optional_clip if optional_clip is not None else pipe["clip"]
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new_text = ''
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def enConcatConditioning(text, token_normalization, weight_interpretation, conditioning_from, new_text):
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out = []
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if "__" in text:
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text = loader.nsp_parse(text, pipe["seed"], title="encodeConcat", my_unique_id=my_unique_id)
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new_text += text
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conditioning_to, pooled = advanced_encode(samp_clip, text, token_normalization, weight_interpretation, w_max=1.0, apply_to_pooled='enable')
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conditioning_to = [[conditioning_to, {"pooled_output": pooled}]]
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if len(conditioning_from) > 1:
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ttNl.warn("encode and concat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to")
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cond_from = conditioning_from[0][0]
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for i in range(len(conditioning_to)):
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t1 = conditioning_to[i][0]
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tw = torch.cat((t1, cond_from),1)
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n = [tw, conditioning_to[i][1].copy()]
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out.append(n)
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return out
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pos, neg = None, None
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if positive not in ['', None, ' ']:
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pos = enConcatConditioning(positive, positive_token_normalization, positive_weight_interpretation, positive_from, new_text)
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if negative not in ['', None, ' ']:
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neg = enConcatConditioning(negative, negative_token_normalization, negative_weight_interpretation, negative_from, new_text)
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pos = pos if pos is not None else pipe["positive"]
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neg = neg if neg is not None else pipe["negative"]
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new_pipe = {
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"model": pipe["model"],
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"positive": pos,
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"negative": neg,
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"vae": pipe["vae"],
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"clip": samp_clip,
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"samples": pipe["samples"],
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"images": pipe["images"],
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"seed": pipe["seed"],
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"loader_settings": pipe["loader_settings"],
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}
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del pipe
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return (new_pipe, new_pipe["positive"], new_pipe["negative"], samp_clip, { "ui": { "string": new_text } } )
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```
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