326 lines
13 KiB
Markdown
326 lines
13 KiB
Markdown
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# Documentation
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- Class name: ttN pipeLoader
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- Category: ttN/legacy
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- Output node: False
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ttN pipeLoader 节点旨在初始化和管理 tinyterraNodes 框架中各种数据管道的加载过程。它抽象了设置和配置数据流所涉及的复杂性，确保了数据管道不同阶段之间的无缝集成和高效处理。
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# Input types
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## Required
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- ckpt_name
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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- config_name
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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- vae_name
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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- clip_skip
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- 未知
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- Comfy dtype: INT
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- Python dtype: unknown
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- lora1_name
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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- lora1_model_strength
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- 未知
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- Comfy dtype: FLOAT
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- Python dtype: unknown
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- lora1_clip_strength
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- 未知
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- Comfy dtype: FLOAT
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- Python dtype: unknown
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- lora2_name
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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- lora2_model_strength
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- 未知
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- Comfy dtype: FLOAT
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- Python dtype: unknown
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- lora2_clip_strength
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- 未知
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- Comfy dtype: FLOAT
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- Python dtype: unknown
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- lora3_name
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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- lora3_model_strength
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- 未知
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- Comfy dtype: FLOAT
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- Python dtype: unknown
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- lora3_clip_strength
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- 未知
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- Comfy dtype: FLOAT
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- Python dtype: unknown
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- positive
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- 定义管道的正面条件输入，影响数据处理的方向和性质。
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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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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- positive_weight_interpretation
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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- negative
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- 设置负面条件输入，为正面条件提供平衡，进一步细化管道输出。
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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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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- negative_weight_interpretation
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- 未知
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- Comfy dtype: COMBO[STRING]
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- Python dtype: unknown
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- empty_latent_width
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- 未知
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- Comfy dtype: INT
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- Python dtype: unknown
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- empty_latent_height
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- 未知
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- Comfy dtype: INT
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- Python dtype: unknown
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- batch_size
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- 未知
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- Comfy dtype: INT
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- Python dtype: unknown
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- seed
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- 设置随机数生成的种子，确保管道操作的可重现性和一致性。
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- Comfy dtype: INT
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- Python dtype: int
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## Optional
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- model_override
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- 未知
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- Comfy dtype: MODEL
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- Python dtype: unknown
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- clip_override
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- 未知
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- Comfy dtype: CLIP
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- Python dtype: unknown
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- optional_lora_stack
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- 未知
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- Comfy dtype: LORA_STACK
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- Python dtype: unknown
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# Output types
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- pipe
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- Comfy dtype: PIPE_LINE
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- 输出配置好的管道，可用于进一步的处理或分析。
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- Python dtype: Dict[str, Any]
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- model
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- Comfy dtype: MODEL
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- 输出管道的模型组件。
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- Python dtype: str
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- positive
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- Comfy dtype: CONDITIONING
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- 输出管道的正面条件组件。
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- Python dtype: str
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- negative
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- Comfy dtype: CONDITIONING
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- 输出管道的负面条件组件。
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- Python dtype: str
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- latent
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- Comfy dtype: LATENT
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- 输出管道中使用的潜在空间表示。
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- Python dtype: str
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- vae
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- Comfy dtype: VAE
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- 输出集成到管道中的 VAE 模型。
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- Python dtype: str
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- clip
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- Comfy dtype: CLIP
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- 输出管道的 CLIP 模型组件。
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- Python dtype: str
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- seed
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- Comfy dtype: INT
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- 输出管道中用于随机数生成的种子。
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- Python dtype: int
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## Usage tips
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- Infra type: `CPU`
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- Common nodes:
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- [ttN pipeKSampler](../../ComfyUI_tinyterraNodes/Nodes/ttN pipeKSampler.md)
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- [ControlNetApply](../../Comfy/Nodes/ControlNetApply.md)
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## Source code
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```python
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class ttN_TSC_pipeLoader:
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version = '1.1.2'
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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"config_name": (["Default",] + folder_paths.get_filename_list("configs"), {"default": "Default"} ),
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"vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
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"clip_skip": ("INT", {"default": -1, "min": -24, "max": 0, "step": 1}),
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"lora1_name": (["None"] + folder_paths.get_filename_list("loras"),),
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"lora1_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora1_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora2_name": (["None"] + folder_paths.get_filename_list("loras"),),
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"lora2_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora2_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora3_name": (["None"] + folder_paths.get_filename_list("loras"),),
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"lora3_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora3_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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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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"empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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},
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"optional": {"model_override": ("MODEL",), "clip_override": ("CLIP",), "optional_lora_stack": ("LORA_STACK",),},
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"hidden": {"prompt": "PROMPT", "ttNnodeVersion": ttN_TSC_pipeLoader.version}, "my_unique_id": "UNIQUE_ID",}
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RETURN_TYPES = ("PIPE_LINE" ,"MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "INT",)
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RETURN_NAMES = ("pipe","model", "positive", "negative", "latent", "vae", "clip", "seed",)
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FUNCTION = "adv_pipeloader"
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CATEGORY = "ttN/legacy"
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def adv_pipeloader(self, ckpt_name, config_name, vae_name, clip_skip,
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lora1_name, lora1_model_strength, lora1_clip_strength,
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lora2_name, lora2_model_strength, lora2_clip_strength,
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lora3_name, lora3_model_strength, lora3_clip_strength,
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positive, positive_token_normalization, positive_weight_interpretation,
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negative, negative_token_normalization, negative_weight_interpretation,
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empty_latent_width, empty_latent_height, batch_size, seed, model_override=None, clip_override=None, optional_lora_stack=None, prompt=None, my_unique_id=None):
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model: ModelPatcher | None = None
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clip: CLIP | None = None
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vae: VAE | None = None
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# Create Empty Latent
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latent = sampler.emptyLatent(None, batch_size, empty_latent_width, empty_latent_height)
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samples = {"samples":latent}
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# Clean models from loaded_objects
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loader.update_loaded_objects(prompt)
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# Load models
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model, clip, vae = loader.load_checkpoint(ckpt_name, config_name)
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if model_override is not None:
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model = model_override
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if clip_override is not None:
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clip = clip_override
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if optional_lora_stack is not None:
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for lora in optional_lora_stack:
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model, clip = loader.load_lora(lora[0], model, clip, lora[1], lora[2])
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if lora1_name != "None":
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model, clip = loader.load_lora(lora1_name, model, clip, lora1_model_strength, lora1_clip_strength)
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if lora2_name != "None":
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model, clip = loader.load_lora(lora2_name, model, clip, lora2_model_strength, lora2_clip_strength)
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if lora3_name != "None":
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model, clip = loader.load_lora(lora3_name, model, clip, lora3_model_strength, lora3_clip_strength)
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# Check for custom VAE
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if vae_name != "Baked VAE":
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vae = loader.load_vae(vae_name)
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# CLIP skip
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if not clip:
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raise Exception("No CLIP found")
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clipped = clip.clone()
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if clip_skip != 0:
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clipped.clip_layer(clip_skip)
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positive = loader.nsp_parse(positive, seed, title='pipeLoader Positive', my_unique_id=my_unique_id)
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positive_embeddings_final, positive_pooled = advanced_encode(clipped, positive, positive_token_normalization, positive_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
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positive_embeddings_final = [[positive_embeddings_final, {"pooled_output": positive_pooled}]]
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negative = loader.nsp_parse(negative, seed, title='pipeLoader Negative', my_unique_id=my_unique_id)
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negative_embeddings_final, negative_pooled = advanced_encode(clipped, negative, negative_token_normalization, negative_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
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negative_embeddings_final = [[negative_embeddings_final, {"pooled_output": negative_pooled}]]
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image = ttNsampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
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pipe = {"model": model,
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"positive": positive_embeddings_final,
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"negative": negative_embeddings_final,
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"vae": vae,
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"clip": clip,
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"samples": samples,
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"images": image,
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"seed": seed,
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"loader_settings": {"ckpt_name": ckpt_name,
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"vae_name": vae_name,
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"lora1_name": lora1_name,
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"lora1_model_strength": lora1_model_strength,
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"lora1_clip_strength": lora1_clip_strength,
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"lora2_name": lora2_name,
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"lora2_model_strength": lora2_model_strength,
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"lora2_clip_strength": lora2_clip_strength,
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"lora3_name": lora3_name,
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"lora3_model_strength": lora3_model_strength,
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"lora3_clip_strength": lora3_clip_strength,
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"refiner_ckpt_name": None,
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"refiner_vae_name": None,
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"refiner_lora1_name": None,
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"refiner_lora1_model_strength": None,
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"refiner_lora1_clip_strength": None,
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"refiner_lora2_name": None,
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"refiner_lora2_model_strength": None,
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"refiner_lora2_clip_strength": None,
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"clip_skip": clip_skip,
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"positive": positive,
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"positive_l": None,
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"positive_g": None,
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"positive_token_normalization": positive_token_normalization,
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"positive_weight_interpretation": positive_weight_interpretation,
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"positive_balance": None,
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"negative": negative,
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"negative_l": None,
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"negative_g": None,
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"negative_token_normalization": negative_token_normalization,
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"negative_weight_interpretation": negative_weight_interpretation,
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"negative_balance": None,
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"empty_latent_width": empty_latent_width,
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"empty_latent_height": empty_latent_height,
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"batch_size": batch_size,
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"seed": seed,
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"empty_samples": samples,}
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}
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return (pipe, model, positive_embeddings_final, negative_embeddings_final, samples, vae, clip, seed)
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```
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