SaveLatent

Documentation

• Class name: SaveLatent
• Category: _for_testing
• Output node: True

The SaveLatent node is designed for saving latent representations to a specified storage. It abstracts the process of persisting latent vectors or tensors, facilitating their reuse in future computations or analyses.

Input types

Required

• samples
  • The 'samples' parameter represents the latent representations to be saved. It is crucial for specifying the data that will be persisted for future use.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• filename_prefix
  • The 'filename_prefix' parameter allows for the specification of a prefix for the filename under which the latent representations will be saved. This aids in organizing and retrieving saved latents effectively.
  • Comfy dtype: STRING
  • Python dtype: str

Output types

The node doesn't have output types

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class SaveLatent:
    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT", ),
                              "filename_prefix": ("STRING", {"default": "latents/ComfyUI"})},
                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                }
    RETURN_TYPES = ()
    FUNCTION = "save"

    OUTPUT_NODE = True

    CATEGORY = "_for_testing"

    def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)

        # support save metadata for latent sharing
        prompt_info = ""
        if prompt is not None:
            prompt_info = json.dumps(prompt)

        metadata = None
        if not args.disable_metadata:
            metadata = {"prompt": prompt_info}
            if extra_pnginfo is not None:
                for x in extra_pnginfo:
                    metadata[x] = json.dumps(extra_pnginfo[x])

        file = f"{filename}_{counter:05}_.latent"

        results = list()
        results.append({
            "filename": file,
            "subfolder": subfolder,
            "type": "output"
        })

        file = os.path.join(full_output_folder, file)

        output = {}
        output["latent_tensor"] = samples["samples"]
        output["latent_format_version_0"] = torch.tensor([])

        comfy.utils.save_torch_file(output, file, metadata=metadata)
        return { "ui": { "latents": results } }