IPAdapter Tiled Batch

Documentation

• Class name: IPAdapterTiledBatch
• Category: ipadapter/tiled
• Output node: False

The IPAdapterTiledBatch node is designed to apply image processing adaptations in a batched manner to tiled images, leveraging the capabilities of an underlying IPAdapterTiled class. It focuses on enhancing or modifying images through a series of transformations, with an emphasis on batch processing for efficiency and scalability.

Input types

Required

• model
  • Specifies the model to which the image processing adaptations will be applied. It is central to the node's operation, determining the transformations that will be performed on the input images.
  • Comfy dtype: MODEL
  • Python dtype: MODEL
• ipadapter
  • Defines the specific IPAdapter to be used for the image processing adaptations. This parameter is crucial for determining the nature of the transformations applied to the images.
  • Comfy dtype: IPADAPTER
  • Python dtype: IPADAPTER
• image
  • The input image to be processed. This parameter is essential for the node's functionality, serving as the primary data on which the adaptations are applied.
  • Comfy dtype: IMAGE
  • Python dtype: IMAGE
• weight
  • Affects the intensity of the applied adaptations, allowing for fine-tuning of the transformation's impact on the image.
  • Comfy dtype: FLOAT
  • Python dtype: FLOAT
• weight_type
  • Determines the method of weighting the adaptations, influencing how the transformations are applied based on the specified type.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: WEIGHT_TYPES
• start_at
  • Defines the starting point of the adaptation process, enabling control over when the transformations begin to take effect.
  • Comfy dtype: FLOAT
  • Python dtype: FLOAT
• end_at
  • Specifies the endpoint of the adaptation process, allowing for precise control over the duration and extent of the transformations.
  • Comfy dtype: FLOAT
  • Python dtype: FLOAT
• sharpening
  • Controls the level of image sharpening applied during the adaptation process, enhancing the clarity and detail of the transformed images.
  • Comfy dtype: FLOAT
  • Python dtype: FLOAT
• embeds_scaling
  • Determines how the embeddings are scaled during the adaptation process, affecting the overall impact of the transformations on the image.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: List[str]
• encode_batch_size
  • Specifies the batch size for encoding operations, optimizing the efficiency of the adaptation process by adjusting the volume of data processed at once.
  • Comfy dtype: INT
  • Python dtype: int

Optional

• image_negative
  • An optional negative image input that can be used for contrastive adaptations, enhancing the effectiveness of certain transformations.
  • Comfy dtype: IMAGE
  • Python dtype: IMAGE
• attn_mask
  • An optional attention mask that can be applied to focus or restrict the adaptations to specific areas of the image.
  • Comfy dtype: MASK
  • Python dtype: MASK
• clip_vision
  • An optional parameter that, when provided, integrates CLIP vision features into the adaptation process, enriching the transformations with additional visual context.
  • Comfy dtype: CLIP_VISION
  • Python dtype: CLIP_VISION

Output types

• MODEL
  • Comfy dtype: MODEL
  • The model after the image processing adaptations have been applied, reflecting the changes made to the input images.
  • Python dtype: MODEL
• tiles
  • Comfy dtype: IMAGE
  • The processed tiles of the image, showcasing the results of the applied adaptations.
  • Python dtype: List[IMAGE]
• masks
  • Comfy dtype: MASK
  • The masks used or generated during the adaptation process, indicating areas of focus or alteration.
  • Python dtype: List[MASK]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class IPAdapterTiledBatch(IPAdapterTiled):
    def __init__(self):
        self.unfold_batch = True

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL", ),
                "ipadapter": ("IPADAPTER", ),
                "image": ("IMAGE",),
                "weight": ("FLOAT", { "default": 1.0, "min": -1, "max": 3, "step": 0.05 }),
                "weight_type": (WEIGHT_TYPES, ),
                "start_at": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001 }),
                "end_at": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001 }),
                "sharpening": ("FLOAT", { "default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05 }),
                "embeds_scaling": (['V only', 'K+V', 'K+V w/ C penalty', 'K+mean(V) w/ C penalty'], ),
                "encode_batch_size": ("INT", { "default": 0, "min": 0, "max": 4096 }),
            },
            "optional": {
                "image_negative": ("IMAGE",),
                "attn_mask": ("MASK",),
                "clip_vision": ("CLIP_VISION",),
            }
        }