60 lines
1.9 KiB
Markdown
60 lines
1.9 KiB
Markdown
# Documentation
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- Class name: LoadLatent
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- Category: _for_testing
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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该节点旨在从指定目录中检索和处理潜在数据，确保数据被正确缩放和格式化以便在系统中进一步使用。
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# Input types
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## Required
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- latent
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- ‘latent’参数至关重要，因为它指定了要加载的潜在文件，其中包含节点操作所需的数据。
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- Comfy dtype: COMBO[string]
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- Python dtype: List[str]
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# Output types
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- samples
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- ‘samples’输出提供了处理后的潜在数据，已经过缩放，准备在系统中使用。
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- Comfy dtype: dict
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- Python dtype: Dict[str, torch.Tensor]
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# Usage tips
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- Infra type: CPU
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# Source code
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```
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class LoadLatent:
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@classmethod
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def INPUT_TYPES(s):
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input_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith('.latent')]
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return {'required': {'latent': [sorted(files)]}}
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CATEGORY = '_for_testing'
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RETURN_TYPES = ('LATENT',)
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FUNCTION = 'load'
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def load(self, latent):
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latent_path = folder_paths.get_annotated_filepath(latent)
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latent = safetensors.torch.load_file(latent_path, device='cpu')
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multiplier = 1.0
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if 'latent_format_version_0' not in latent:
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multiplier = 1.0 / 0.18215
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samples = {'samples': latent['latent_tensor'].float() * multiplier}
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return (samples,)
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@classmethod
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def IS_CHANGED(s, latent):
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image_path = folder_paths.get_annotated_filepath(latent)
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(s, latent):
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if not folder_paths.exists_annotated_filepath(latent):
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return 'Invalid latent file: {}'.format(latent)
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return True
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``` |