# Documentation - Class name: ttN pipeLoader - Category: ttN/legacy - Output node: False 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。 # Input types ## Required - ckpt_name - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - config_name - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - vae_name - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - clip_skip - 未知 - Comfy dtype: INT - Python dtype: unknown - lora1_name - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - lora1_model_strength - 未知 - Comfy dtype: FLOAT - Python dtype: unknown - lora1_clip_strength - 未知 - Comfy dtype: FLOAT - Python dtype: unknown - lora2_name - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - lora2_model_strength - 未知 - Comfy dtype: FLOAT - Python dtype: unknown - lora2_clip_strength - 未知 - Comfy dtype: FLOAT - Python dtype: unknown - lora3_name - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - lora3_model_strength - 未知 - Comfy dtype: FLOAT - Python dtype: unknown - lora3_clip_strength - 未知 - Comfy dtype: FLOAT - Python dtype: unknown - positive - 定义管道的正面条件输入，影响数据处理的方向和性质。 - Comfy dtype: STRING - Python dtype: str - positive_token_normalization - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - positive_weight_interpretation - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - negative - 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。 - Comfy dtype: STRING - Python dtype: str - negative_token_normalization - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - negative_weight_interpretation - 未知 - Comfy dtype: COMBO[STRING] - Python dtype: unknown - empty_latent_width - 未知 - Comfy dtype: INT - Python dtype: unknown - empty_latent_height - 未知 - Comfy dtype: INT - Python dtype: unknown - batch_size - 未知 - Comfy dtype: INT - Python dtype: unknown - seed - 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。 - Comfy dtype: INT - Python dtype: int ## Optional - model_override - 未知 - Comfy dtype: MODEL - Python dtype: unknown - clip_override - 未知 - Comfy dtype: CLIP - Python dtype: unknown - optional_lora_stack - 未知 - Comfy dtype: LORA_STACK - Python dtype: unknown # Output types - pipe - Comfy dtype: PIPE_LINE - 输出配置好的管道，可用于进一步的处理或分析。 - Python dtype: Dict[str, Any] - model - Comfy dtype: MODEL - 输出管道的模型组件。 - Python dtype: str - positive - Comfy dtype: CONDITIONING - 输出管道的正面条件组件。 - Python dtype: str - negative - Comfy dtype: CONDITIONING - 输出管道的负面条件组件。 - Python dtype: str - latent - Comfy dtype: LATENT - 输出管道中使用的潜在空间表示。 - Python dtype: str - vae - Comfy dtype: VAE - 输出集成到管道中的 VAE 模型。 - Python dtype: str - clip - Comfy dtype: CLIP - 输出管道的 CLIP 模型组件。 - Python dtype: str - seed - Comfy dtype: INT - 输出管道中用于随机数生成的种子。 - Python dtype: int ## Usage tips - Infra type: `CPU` - Common nodes: - [ttN pipeKSampler](../../ComfyUI_tinyterraNodes/Nodes/ttN pipeKSampler.md) - [ControlNetApply](../../Comfy/Nodes/ControlNetApply.md) ## Source code ```python class ttN_TSC_pipeLoader: version = '1.1.2' @classmethod def INPUT_TYPES(cls): return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ), "config_name": (["Default",] + folder_paths.get_filename_list("configs"), {"default": "Default"} ), "vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),), "clip_skip": ("INT", {"default": -1, "min": -24, "max": 0, "step": 1}), "lora1_name": (["None"] + folder_paths.get_filename_list("loras"),), "lora1_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora1_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora2_name": (["None"] + folder_paths.get_filename_list("loras"),), "lora2_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora2_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora3_name": (["None"] + folder_paths.get_filename_list("loras"),), "lora3_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora3_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "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"],), "empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), }, "optional": {"model_override": ("MODEL",), "clip_override": ("CLIP",), "optional_lora_stack": ("LORA_STACK",),}, "hidden": {"prompt": "PROMPT", "ttNnodeVersion": ttN_TSC_pipeLoader.version}, "my_unique_id": "UNIQUE_ID",} RETURN_TYPES = ("PIPE_LINE" ,"MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "INT",) RETURN_NAMES = ("pipe","model", "positive", "negative", "latent", "vae", "clip", "seed",) FUNCTION = "adv_pipeloader" CATEGORY = "ttN/legacy" def adv_pipeloader(self, ckpt_name, config_name, vae_name, clip_skip, lora1_name, lora1_model_strength, lora1_clip_strength, lora2_name, lora2_model_strength, lora2_clip_strength, lora3_name, lora3_model_strength, lora3_clip_strength, positive, positive_token_normalization, positive_weight_interpretation, negative, negative_token_normalization, negative_weight_interpretation, 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): model: ModelPatcher | None = None clip: CLIP | None = None vae: VAE | None = None # Create Empty Latent latent = sampler.emptyLatent(None, batch_size, empty_latent_width, empty_latent_height) samples = {"samples":latent} # Clean models from loaded_objects loader.update_loaded_objects(prompt) # Load models model, clip, vae = loader.load_checkpoint(ckpt_name, config_name) if model_override is not None: model = model_override if clip_override is not None: clip = clip_override if optional_lora_stack is not None: for lora in optional_lora_stack: model, clip = loader.load_lora(lora[0], model, clip, lora[1], lora[2]) if lora1_name != "None": model, clip = loader.load_lora(lora1_name, model, clip, lora1_model_strength, lora1_clip_strength) if lora2_name != "None": model, clip = loader.load_lora(lora2_name, model, clip, lora2_model_strength, lora2_clip_strength) if lora3_name != "None": model, clip = loader.load_lora(lora3_name, model, clip, lora3_model_strength, lora3_clip_strength) # Check for custom VAE if vae_name != "Baked VAE": vae = loader.load_vae(vae_name) # CLIP skip if not clip: raise Exception("No CLIP found") clipped = clip.clone() if clip_skip != 0: clipped.clip_layer(clip_skip) positive = loader.nsp_parse(positive, seed, title='pipeLoader Positive', my_unique_id=my_unique_id) positive_embeddings_final, positive_pooled = advanced_encode(clipped, positive, positive_token_normalization, positive_weight_interpretation, w_max=1.0, apply_to_pooled='enable') positive_embeddings_final = [[positive_embeddings_final, {"pooled_output": positive_pooled}]] negative = loader.nsp_parse(negative, seed, title='pipeLoader Negative', my_unique_id=my_unique_id) negative_embeddings_final, negative_pooled = advanced_encode(clipped, negative, negative_token_normalization, negative_weight_interpretation, w_max=1.0, apply_to_pooled='enable') negative_embeddings_final = [[negative_embeddings_final, {"pooled_output": negative_pooled}]] image = ttNsampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0))) pipe = {"model": model, "positive": positive_embeddings_final, "negative": negative_embeddings_final, "vae": vae, "clip": clip, "samples": samples, "images": image, "seed": seed, "loader_settings": {"ckpt_name": ckpt_name, "vae_name": vae_name, "lora1_name": lora1_name, "lora1_model_strength": lora1_model_strength, "lora1_clip_strength": lora1_clip_strength, "lora2_name": lora2_name, "lora2_model_strength": lora2_model_strength, "lora2_clip_strength": lora2_clip_strength, "lora3_name": lora3_name, "lora3_model_strength": lora3_model_strength, "lora3_clip_strength": lora3_clip_strength, "refiner_ckpt_name": None, "refiner_vae_name": None, "refiner_lora1_name": None, "refiner_lora1_model_strength": None, "refiner_lora1_clip_strength": None, "refiner_lora2_name": None, "refiner_lora2_model_strength": None, "refiner_lora2_clip_strength": None, "clip_skip": clip_skip, "positive": positive, "positive_l": None, "positive_g": None, "positive_token_normalization": positive_token_normalization, "positive_weight_interpretation": positive_weight_interpretation, "positive_balance": None, "negative": negative, "negative_l": None, "negative_g": None, "negative_token_normalization": negative_token_normalization, "negative_weight_interpretation": negative_weight_interpretation, "negative_balance": None, "empty_latent_width": empty_latent_width, "empty_latent_height": empty_latent_height, "batch_size": batch_size, "seed": seed, "empty_samples": samples,} } return (pipe, model, positive_embeddings_final, negative_embeddings_final, samples, vae, clip, seed) ```