pipeLoader

Documentation

• Class name: ttN pipeLoader_v2
• Category: 🌏 tinyterra/pipe
• Output node: False

The ttN pipeLoader_v2 node is designed for advanced data loading and preprocessing within a pipeline. It focuses on efficiently handling and transforming data to prepare it for subsequent processing or analysis steps, leveraging enhanced capabilities over its predecessor for improved performance and flexibility.

Input types

Required

• ckpt_name
  • Specifies the checkpoint name for model loading, affecting the pipeline's configuration.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• config_name
  • Specifies the configuration name for the pipeline, affecting how data is processed and managed.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• vae_name
  • Specifies the VAE model name for loading, affecting the pipeline's configuration and capabilities.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• clip_skip
  • Determines the number of CLIP layers to skip, affecting the data preprocessing phase.
  • Comfy dtype: INT
  • Python dtype: int
• loras
  • Specifies the LoRA models to be used, affecting the pipeline's performance and output.
  • Comfy dtype: STRING
  • Python dtype: list
• positive
  • unknown
  • Comfy dtype: STRING
  • Python dtype: unknown
• positive_token_normalization
  • Indicates the method for normalizing positive tokens, affecting text processing.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• positive_weight_interpretation
  • Specifies how the weights for positive tokens are interpreted, affecting text processing.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• negative
  • unknown
  • Comfy dtype: STRING
  • Python dtype: unknown
• negative_token_normalization
  • Indicates the method for normalizing negative tokens, affecting text processing.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• negative_weight_interpretation
  • Specifies how the weights for negative tokens are interpreted, affecting text processing.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• empty_latent_aspect
  • Specifies the aspect ratio for empty latent images, affecting the pipeline's image processing capabilities.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• empty_latent_width
  • unknown
  • Comfy dtype: INT
  • Python dtype: unknown
• empty_latent_height
  • unknown
  • Comfy dtype: INT
  • Python dtype: unknown
• batch_size
  • Specifies the batch size for processing, affecting the pipeline's efficiency and throughput.
  • Comfy dtype: INT
  • Python dtype: int
• seed
  • Specifies the seed for random number generation in the pipeline, ensuring reproducibility.
  • Comfy dtype: INT
  • Python dtype: int

Optional

• model_override
  • Allows for overriding the default model, affecting the pipeline's flexibility and output.
  • Comfy dtype: MODEL
  • Python dtype: str
• clip_override
  • Allows for overriding the default CLIP model, affecting the pipeline's flexibility and output.
  • Comfy dtype: CLIP
  • Python dtype: str
• optional_lora_stack
  • Specifies an optional stack of LoRA models, enhancing the pipeline's adaptability and performance.
  • Comfy dtype: LORA_STACK
  • Python dtype: list
• optional_controlnet_stack
  • Specifies an optional stack of ControlNet models, enhancing the pipeline's adaptability and performance.
  • Comfy dtype: CONTROL_NET_STACK
  • Python dtype: list
• prepend_positive
  • Specifies text to prepend to positive conditioning, affecting text processing.
  • Comfy dtype: STRING
  • Python dtype: str
• prepend_negative
  • Specifies text to prepend to negative conditioning, affecting text processing.
  • Comfy dtype: STRING
  • Python dtype: str

Output types

• pipe
  • Comfy dtype: PIPE_LINE
  • Outputs a modified pipeline object, incorporating the loaded and preprocessed data ready for further stages.
  • Python dtype: dict
• model
  • Comfy dtype: MODEL
  • Outputs the model used in the pipeline after processing.
  • Python dtype: str
• positive
  • Comfy dtype: CONDITIONING
  • Outputs the positive conditioning used in the pipeline after processing.
  • Python dtype: str
• negative
  • Comfy dtype: CONDITIONING
  • Outputs the negative conditioning used in the pipeline after processing.
  • Python dtype: str
• latent
  • Comfy dtype: LATENT
  • Outputs the latent variables used in the pipeline after processing.
  • Python dtype: int
• vae
  • Comfy dtype: VAE
  • Outputs the VAE model used in the pipeline after processing.
  • Python dtype: str
• clip
  • Comfy dtype: CLIP
  • Outputs the CLIP model used in the pipeline after processing.
  • Python dtype: str
• seed
  • Comfy dtype: INT
  • Outputs the seed used for random number generation in the pipeline after processing.
  • Python dtype: int
• width
  • Comfy dtype: INT
  • unknown
  • Python dtype: unknown
• height
  • Comfy dtype: INT
  • unknown
  • Python dtype: unknown
• pos_string
  • Comfy dtype: STRING
  • Outputs the final positive conditioning string after processing.
  • Python dtype: str
• neg_string
  • Comfy dtype: STRING
  • Outputs the final negative conditioning string after processing.
  • Python dtype: str

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class ttN_pipeLoader_v2:
    version = '2.1.0'
    @classmethod
    def INPUT_TYPES(cls):
        aspect_ratios = ["width x height [custom]",
                        "512 x 512 [S] 1:1",
                        "768 x 768 [S] 1:1",
                        "910 x 910 [S] 1:1",

                        "512 x 682 [P] 3:4",
                        "512 x 768 [P] 2:3",
                        "512 x 910 [P] 9:16",

                        "682 x 512 [L] 4:3",
                        "768 x 512 [L] 3:2",
                        "910 x 512 [L] 16:9",

                        "512 x 1024 [P] 1:2",
                        "1024 x 512 [L] 2:1",
                        "1024 x 1024 [S] 1:1",
                        ]

        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}),

                        "loras": ("STRING", {"placeholder": "<lora:loraName:weight:optClipWeight>", "multiline": True}),

                        "positive": ("STRING", {"default": "Positive","multiline": True, "dynamicPrompts": True}),
                        "positive_token_normalization": (["none", "mean", "length", "length+mean"],),
                        "positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),

                        "negative": ("STRING", {"default": "Negative", "multiline": True, "dynamicPrompts": True}),
                        "negative_token_normalization": (["none", "mean", "length", "length+mean"],),
                        "negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),

                        "empty_latent_aspect": (aspect_ratios, {"default":"512 x 512 [S] 1:1"}),
                        "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",),
                    "optional_controlnet_stack": ("CONTROL_NET_STACK",),
                    "prepend_positive": ("STRING", {"default": None, "forceInput": True}),
                    "prepend_negative": ("STRING", {"default": None, "forceInput": True}),
                    },
                "hidden": {"prompt": "PROMPT", "ttNnodeVersion": ttN_pipeLoader_v2.version, "my_unique_id": "UNIQUE_ID",}
                }

    RETURN_TYPES = ("PIPE_LINE" ,"MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "INT", "INT", "INT", "STRING", "STRING")
    RETURN_NAMES = ("pipe","model", "positive", "negative", "latent", "vae", "clip", "seed", "width", "height", "pos_string", "neg_string")

    FUNCTION = "adv_pipeloader"
    CATEGORY = "🌏 tinyterra/pipe"

    def adv_pipeloader(self, ckpt_name, config_name, vae_name, clip_skip,
                       loras,
                       positive, positive_token_normalization, positive_weight_interpretation, 
                       negative, negative_token_normalization, negative_weight_interpretation, 
                       empty_latent_aspect, empty_latent_width, empty_latent_height, batch_size, seed,
                       model_override=None, clip_override=None, optional_lora_stack=None, optional_controlnet_stack=None, prepend_positive=None, prepend_negative=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(empty_latent_aspect, batch_size, empty_latent_width, empty_latent_height)
        samples = {"samples":latent}

        loader.clear_cache(prompt)
        model, clip, vae = loader.load_main3(ckpt_name, config_name, vae_name, loras, clip_skip, model_override, clip_override, optional_lora_stack, my_unique_id)

        positive_embedding = loader.embedding_encode(positive, positive_token_normalization, positive_weight_interpretation, clip, seed=seed, title='pipeLoader Positive', my_unique_id=my_unique_id, prepend_text=prepend_positive)
        negative_embedding = loader.embedding_encode(negative, negative_token_normalization, negative_weight_interpretation, clip, seed=seed, title='pipeLoader Negative', my_unique_id=my_unique_id, prepend_text=prepend_negative)

        if optional_controlnet_stack is not None and len(optional_controlnet_stack) > 0:
            for cnt in optional_controlnet_stack:
                positive_embedding, negative_embedding = loader.load_controlNet(positive_embedding, negative_embedding, cnt[0], cnt[1], cnt[2], cnt[3], cnt[4])

        image = None

        pipe = {"model": model,
                "positive": positive_embedding,
                "negative": negative_embedding,
                "vae": vae,
                "clip": clip,

                "samples": samples,
                "images": image,
                "seed": seed,

                "loader_settings": None,
        }

        final_positive = (prepend_positive + ' ' if prepend_positive else '') + (positive + ' ' if positive else '')
        final_negative = (prepend_negative + ' ' if prepend_negative else '') + (negative + ' ' if negative else '')

        return (pipe, model, positive_embedding, negative_embedding, samples, vae, clip, seed, empty_latent_width, empty_latent_height, final_positive, final_negative)