67 lines
2.5 KiB
Markdown
67 lines
2.5 KiB
Markdown
---
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tags:
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- VAE
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---
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# Tiled VAE Encode
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## Documentation
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- Class name: `VAEEncodeTiled_TiledDiffusion`
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- Category: `_for_testing`
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- Output node: `False`
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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.
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## Input types
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### Required
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- **`pixels`**
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- The input image to be encoded. This parameter is crucial for determining the content that will be transformed into a latent representation.
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- Comfy dtype: `IMAGE`
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- Python dtype: `torch.Tensor`
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- **`vae`**
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- The VAE model used for encoding. It defines the architecture and parameters of the variational autoencoder that processes the image.
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- Comfy dtype: `VAE`
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- Python dtype: `VAE`
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- **`tile_size`**
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- 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.
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- Comfy dtype: `INT`
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- Python dtype: `int`
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- **`fast`**
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- A boolean flag that, when true, enables a faster but potentially less accurate encoding process.
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- Comfy dtype: `BOOLEAN`
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- Python dtype: `bool`
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- **`color_fix`**
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- 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.
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- Comfy dtype: `BOOLEAN`
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- Python dtype: `bool`
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## Output types
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- **`latent`**
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- Comfy dtype: `LATENT`
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- 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.
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- Python dtype: `torch.Tensor`
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class VAEEncodeTiled_TiledDiffusion(TiledVAE):
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@classmethod
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def INPUT_TYPES(s):
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fast = True
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tile_size = get_rcmd_enc_tsize()
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return {"required": {"pixels": ("IMAGE", ),
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"vae": ("VAE", ),
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"tile_size": ("INT", {"default": tile_size, "min": 256, "max": 4096, "step": 16}),
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"fast": ("BOOLEAN", {"default": fast}),
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"color_fix": ("BOOLEAN", {"default": fast}),
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "process"
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CATEGORY = "_for_testing"
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def __init__(self):
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self.is_decoder = False
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super().__init__()
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```
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