64 lines
1.8 KiB
Markdown
64 lines
1.8 KiB
Markdown
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# Documentation
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- Class name: VAEEncodeBatch
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- Category: Bmad
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- Output node: False
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VAEEncodeBatch节点旨在使用指定的VAE模型对图像进行批量处理，将其编码为潜在空间表示。它允许同时编码多个图像，优化了处理效率和可扩展性。
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# Input types
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## Required
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- inputs_len
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- 指定要在批次中编码的图像数量。这个参数控制图像的迭代和后续编码过程，影响节点的执行和输出潜在表示的大小。
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- Comfy dtype: INT
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- Python dtype: int
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- vae
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- 用于编码图像的VAE模型。这个参数对于决定如何将图像转换为潜在空间表示至关重要。
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- Comfy dtype: VAE
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- Python dtype: torch.nn.Module
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# Output types
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- latent
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- 批量图像的编码潜在空间表示。这个输出封装了输入图像转换成适合进一步处理或分析的格式。
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- Comfy dtype: LATENT
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- Python dtype: Dict[str, 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 VAEEncodeBatch:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"inputs_len": ("INT", {"default": 3, "min": 2, "max": 32, "step": 1}),
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"vae": ("VAE",)
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "encode"
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CATEGORY = "Bmad"
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def encode(self, inputs_len, vae, **kwargs):
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vae_encoder = nodes.VAEEncode()
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def get_latent(input_name):
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pixels = kwargs[input_name]
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pixels = vae_encoder.vae_encode_crop_pixels(pixels)
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return vae.encode(pixels[:, :, :, :3])
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latent = get_latent("image_1")
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for r in range(1, inputs_len):
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latent = torch.cat([latent, get_latent(f"image_{r + 1}")], dim=0)
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return ({"samples": latent},)
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```
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