CavinHuang-comfyui-nodes-docs/docs/GetLatentsFromBatchIndexed.md

Documentation

• Class name: GetLatentsFromBatchIndexed
• Category: KJNodes
• Output node: False

GetLatentsFromBatchIndexed节点旨在从给定的潜在变量批次中选择并返回特定的潜在变量。它便于提取潜在变量的子集以进行进一步处理或分析，是需要对潜在空间进行有针对性操作或检查的操作的重要组成部分。

Input types

Required

• latents
  • latents参数表示可供选择的潜在变量批次。它对确定可供选择的潜在变量范围至关重要。
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• indexes
  • indexes参数指定要从批次中选择的潜在变量的索引。它在识别要提取和处理的潜在变量方面起着关键作用。
  • Comfy dtype: STRING
  • Python dtype: str

Output types

• latent
  • 输出是输入潜在变量批次的修改版本，仅包含指定索引处的潜在变量。
  • Comfy dtype: LATENT
  • Python dtype: Tuple[Dict[str, torch.Tensor]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class GetLatentsFromBatchIndexed:
    
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "indexedlatentsfrombatch"
    CATEGORY = "KJNodes"
    DESCRIPTION = """
Selects and returns the latents at the specified indices as an latent batch.
"""

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                 "latents": ("LATENT",),
                 "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
        },
    } 
    
    def indexedlatentsfrombatch(self, latents, indexes):
        
        samples = latents.copy()
        latent_samples = samples["samples"] 

        # Parse the indexes string into a list of integers
        index_list = [int(index.strip()) for index in indexes.split(',')]
        
        # Convert list of indices to a PyTorch tensor
        indices_tensor = torch.tensor(index_list, dtype=torch.long)
        
        # Select the latents at the specified indices
        chosen_latents = latent_samples[indices_tensor]

        samples["samples"] = chosen_latents
        return (samples,)