CavinHuang-comfyui-nodes-docs/docs/ttN pipeLoader.md

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

Source code

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)