143 lines
4.1 KiB
Python
143 lines
4.1 KiB
Python
"""
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LLS / Latent
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============
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功能域：Latent 空间操作（对应功能分类总览第 4 节）
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CATEGORY = "LLS/Latent"
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"""
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from __future__ import annotations
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# ---------- 防御性导入 ----------
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try:
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import torch
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except Exception as exc:
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torch = None
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_TORCH_ERR = exc
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else:
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_TORCH_ERR = None
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try:
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import comfy.model_management as model_management
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except Exception:
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model_management = None
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# ---------- 工具函数 ----------
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_SIZE_PRESETS = [
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"Custom",
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# SD1.5 推荐尺寸
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"512x512",
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"512x768",
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"768x512",
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# SDXL 推荐尺寸
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"1024x1024",
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"832x1216",
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"1216x832",
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"896x1152",
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"1152x896",
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"1024x576",
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"576x1024",
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]
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def _round_to_8(value: int) -> int:
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"""将整数修正为最近的 8 的倍数。"""
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return max(8, round(value / 8) * 8)
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# ---------- 节点类 ----------
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class LLSSimpleEmptyLatent:
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"""
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生成空白 Latent，作为 txt2img 推理的起点。
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支持常用分辨率预设，自动修正宿高为 8 的倍数。
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"""
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CATEGORY = "LLS/Latent"
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FUNCTION = "create_empty_latent"
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RETURN_TYPES = ("LATENT", "INT", "INT", "STRING")
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RETURN_NAMES = ("latent", "width", "height", "latent_info")
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DESCRIPTION = (
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"Create an empty latent image as the starting point for txt2img. "
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"Select a preset or enter custom width/height. "
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"Width and height are automatically rounded to multiples of 8. "
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"SD1.5 recommended: 512x512; SDXL recommended: 1024x1024 or 896x1152."
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)
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"size_preset": (_SIZE_PRESETS, {"default": "512x512"}),
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"width": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 8}),
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"height": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 8}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
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}
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}
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def create_empty_latent(self, size_preset: str, width: int, height: int, batch_size: int):
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if torch is None:
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raise RuntimeError(
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"[LLS] PyTorch is not available. "
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"Make sure this node runs inside a ComfyUI environment."
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) from _TORCH_ERR
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notes = []
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# 如果选择了预设，覆盖 width / height
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if size_preset != "Custom":
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try:
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w_str, h_str = size_preset.split("x")
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width = int(w_str)
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height = int(h_str)
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except Exception:
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notes.append(f"preset parse failed, using custom {width}x{height}")
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# 修正为 8 的倍数
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corrected_w = _round_to_8(width)
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corrected_h = _round_to_8(height)
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if corrected_w != width or corrected_h != height:
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notes.append(f"size corrected {width}x{height} -> {corrected_w}x{corrected_h}")
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width, height = corrected_w, corrected_h
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device = "cpu"
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dtype = torch.float32
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if model_management is not None:
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try:
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device = model_management.intermediate_device()
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except Exception:
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device = "cpu"
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try:
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dtype = model_management.intermediate_dtype()
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except Exception:
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dtype = torch.float32
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# 生成空白 Latent（与 ComfyUI EmptyLatentImage 格式一致）
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# Latent 尺寸是图像尺寸的 1/8
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latent = torch.zeros(
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[batch_size, 4, height // 8, width // 8],
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device=device,
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dtype=dtype,
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)
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note_str = " | ".join(notes) if notes else "ok"
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latent_info = (
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f"size={width}x{height} | batch={batch_size} "
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f"| preset={size_preset} | {note_str}"
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)
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return ({"samples": latent, "downscale_ratio_spacial": 8}, width, height, latent_info)
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# ---------- 注册表 ----------
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NODE_CLASS_MAPPINGS: dict[str, type] = {
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"LLSSimpleEmptyLatent": LLSSimpleEmptyLatent,
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}
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NODE_DISPLAY_NAME_MAPPINGS: dict[str, str] = {
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"LLSSimpleEmptyLatent": "LLS Simple Empty Latent",
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}
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