CavinHuang-comfyui-nodes-docs/nodes-docs/en-US/ComfyUI-TiledDiffusion/docs/VAEEncodeTiled_TiledDiffusion.md

tags

tags
VAE

Tiled VAE Encode

Documentation

• Class name: VAEEncodeTiled_TiledDiffusion
• Category: _for_testing
• Output node: False

This node is designed for encoding images into a latent space representation using a tiled VAE approach, optimizing for efficiency and flexibility in handling various image sizes through tiling.

Input types

Required

• pixels
  • The input image to be encoded. This parameter is crucial for determining the content that will be transformed into a latent representation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• vae
  • The VAE model used for encoding. It defines the architecture and parameters of the variational autoencoder that processes the image.
  • Comfy dtype: VAE
  • Python dtype: VAE
• tile_size
  • Specifies the size of the tiles into which the image is divided for encoding. This affects the granularity of the encoding process and can be adjusted for performance or quality.
  • Comfy dtype: INT
  • Python dtype: int
• fast
  • A boolean flag that, when true, enables a faster but potentially less accurate encoding process.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool
• color_fix
  • A boolean flag that, when true, applies a color correction step to the image before encoding. This can be useful for maintaining color consistency across tiles.
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

Output types

• latent
  • Comfy dtype: LATENT
  • The encoded latent representation of the input image. This output captures the essential features of the image in a compressed form, suitable for various downstream tasks.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class VAEEncodeTiled_TiledDiffusion(TiledVAE):
    @classmethod
    def INPUT_TYPES(s):
        fast = True
        tile_size = get_rcmd_enc_tsize()
        return {"required": {"pixels": ("IMAGE", ),
                                "vae": ("VAE", ),
                                "tile_size": ("INT", {"default": tile_size, "min": 256, "max": 4096, "step": 16}),
                                "fast": ("BOOLEAN", {"default": fast}),
                                "color_fix": ("BOOLEAN", {"default": fast}),
                            }}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "process"
    CATEGORY = "_for_testing"

    def __init__(self):
        self.is_decoder = False
        super().__init__()