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

Documentation

• Class name: LatentSender
• Category: ImpactPack/Util
• Output node: True
• Repo Ref: https://github.com/ltdrdata/ComfyUI-Impact-Pack.git

LatentSender节点旨在处理和管理潜在数据，将其转换为适合进一步使用或存储的格式。它在工作流程中扮演着关键角色，确保潜在信息得到正确处理并为下游任务做好准备。

Input types

Required

• samples
  • 'samples'参数至关重要，因为它包含了节点操作的潜在数据。它是主要的输入，决定了节点的处理和输出生成。
  • Comfy dtype: LATENT
  • Python dtype: torch.Tensor

Optional

• filename_prefix
  • 'filename_prefix'参数指定输出文件的前缀，这对于组织和识别保存的潜在数据文件很重要。
  • Comfy dtype: STRING
  • Python dtype: str
• link_id
  • 'link_id'参数用于将潜在数据与特定链接或过程关联起来，这对于跟踪和管理不同阶段的数据至关重要。
  • Comfy dtype: INT
  • Python dtype: int
• preview_method
  • 'preview_method'参数决定了在保存之前如何预览潜在数据。它影响数据的视觉表示，并且可以影响用户的理解和交互。
  • Comfy dtype: COMBO['Latent2RGB-SDXL', 'Latent2RGB-SD15', 'TAESDXL', 'TAESD15']
  • Python dtype: str
• prompt
  • 'prompt'参数提供了可以用于指导节点内潜在数据处理的附加上下文或指令。
  • Comfy dtype: PROMPT
  • Python dtype: str
• extra_pnginfo
  • 'extra_pnginfo'参数允许在潜在数据中包含额外的元数据，丰富与输出文件关联的信息。
  • Comfy dtype: EXTRA_PNGINFO
  • Python dtype: Dict[str, Any]

Output types

• ui
  • 'ui'输出提供用户界面元素，可用于显示或与节点操作的结果进行交互。
  • Comfy dtype: DICT
  • Python dtype: Dict[str, Any]

Usage tips

• Infra type: CPU

Source code

class LatentSender(nodes.SaveLatent):

    def __init__(self):
        super().__init__()
        self.output_dir = folder_paths.get_temp_directory()
        self.type = 'temp'

    @classmethod
    def INPUT_TYPES(s):
        return {'required': {'samples': ('LATENT',), 'filename_prefix': ('STRING', {'default': 'latents/LatentSender'}), 'link_id': ('INT', {'default': 0, 'min': 0, 'max': sys.maxsize, 'step': 1}), 'preview_method': (['Latent2RGB-SDXL', 'Latent2RGB-SD15', 'TAESDXL', 'TAESD15'],)}, 'hidden': {'prompt': 'PROMPT', 'extra_pnginfo': 'EXTRA_PNGINFO'}}
    OUTPUT_NODE = True
    RETURN_TYPES = ()
    FUNCTION = 'doit'
    CATEGORY = 'ImpactPack/Util'

    @staticmethod
    def save_to_file(tensor_bytes, prompt, extra_pnginfo, image, image_path):
        compressed_data = BytesIO()
        with zipfile.ZipFile(compressed_data, mode='w') as archive:
            archive.writestr('latent', tensor_bytes)
        image = image.copy()
        exif_data = {'Exif': {piexif.ExifIFD.UserComment: compressed_data.getvalue()}}
        metadata = PngInfo()
        if prompt is not None:
            metadata.add_text('prompt', json.dumps(prompt))
        if extra_pnginfo is not None:
            for x in extra_pnginfo:
                metadata.add_text(x, json.dumps(extra_pnginfo[x]))
        exif_bytes = piexif.dump(exif_data)
        image.save(image_path, format='png', exif=exif_bytes, pnginfo=metadata, optimize=True)

    @staticmethod
    def prepare_preview(latent_tensor, preview_method):
        from comfy.cli_args import LatentPreviewMethod
        import comfy.latent_formats as latent_formats
        lower_bound = 128
        upper_bound = 256
        if preview_method == 'Latent2RGB-SD15':
            latent_format = latent_formats.SD15()
            method = LatentPreviewMethod.Latent2RGB
        elif preview_method == 'TAESD15':
            latent_format = latent_formats.SD15()
            method = LatentPreviewMethod.TAESD
        elif preview_method == 'TAESDXL':
            latent_format = latent_formats.SDXL()
            method = LatentPreviewMethod.TAESD
        else:
            latent_format = latent_formats.SDXL()
            method = LatentPreviewMethod.Latent2RGB
        previewer = core.get_previewer('cpu', latent_format=latent_format, force=True, method=method)
        image = previewer.decode_latent_to_preview(latent_tensor)
        min_size = min(image.size[0], image.size[1])
        max_size = max(image.size[0], image.size[1])
        scale_factor = 1
        if max_size > upper_bound:
            scale_factor = upper_bound / max_size
        if min_size * scale_factor < lower_bound:
            scale_factor = lower_bound / min_size
        w = int(image.size[0] * scale_factor)
        h = int(image.size[1] * scale_factor)
        image = image.resize((w, h), resample=Image.NEAREST)
        return LatentSender.attach_format_text(image)

    @staticmethod
    def attach_format_text(image):
        (width_a, height_a) = image.size
        letter_image = Image.open(latent_letter_path)
        (width_b, height_b) = letter_image.size
        new_width = max(width_a, width_b)
        new_height = height_a + height_b
        new_image = Image.new('RGB', (new_width, new_height), (0, 0, 0))
        offset_x = (new_width - width_b) // 2
        offset_y = height_a + (new_height - height_a - height_b) // 2
        new_image.paste(letter_image, (offset_x, offset_y))
        new_image.paste(image, (0, 0))
        return new_image

    def doit(self, samples, filename_prefix='latents/LatentSender', link_id=0, preview_method='Latent2RGB-SDXL', prompt=None, extra_pnginfo=None):
        (full_output_folder, filename, counter, subfolder, filename_prefix) = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
        preview = LatentSender.prepare_preview(samples['samples'], preview_method)
        file = f'{filename}_{counter:05}_.latent.png'
        fullpath = os.path.join(full_output_folder, file)
        output = {'latent_tensor': samples['samples']}
        tensor_bytes = safetensors.torch.save(output)
        LatentSender.save_to_file(tensor_bytes, prompt, extra_pnginfo, preview, fullpath)
        latent_path = {'filename': file, 'subfolder': subfolder, 'type': self.type}
        PromptServer.instance.send_sync('latent-send', {'link_id': link_id, 'images': [latent_path]})
        return {'ui': {'images': [latent_path]}}