Works for the first time
This commit is contained in:
Budi Hartono
+3
-5
@@ -1,10 +1,8 @@
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# __init__.py
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from .nodes import empty_latent_preset, empty_latent_by_axis
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from .nodes import EmptyLatentAspectPreset, EmptyLatentAspectByAxis
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NODE_CLASS_MAPPINGS = {
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"Empty Latent (Aspect Ratio Preset)": empty_latent_preset,
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"Empty Latent (Aspect Ratio by Axis)": empty_latent_by_axis,
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"Empty Latent (Aspect Ratio Preset)": EmptyLatentAspectPreset,
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"Empty Latent (Aspect Ratio by Axis)": EmptyLatentAspectByAxis,
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}
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NODE_DISPLAY_NAME = "ComfyUI Aspect Ratio Preset"
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@@ -1,130 +1,100 @@
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# nodes.py
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import numpy as np
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import torch
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from .presets import PRESETS
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# ─── Helpers ──────────────────────────────────────────────────────────
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def _validate_dim(v: int):
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if v <= 0:
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raise ValueError("Dimension must be > 0")
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if v % 8 != 0:
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raise ValueError("Dimension must be divisible by 8")
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# ─── Node 1: Preset ───────────────────────────────────────────────────
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def empty_latent_preset(
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preset: str,
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batch_size: int
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) -> np.ndarray:
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"""
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Empty Latent (Aspect Ratio Preset)
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preset: e.g. "512x512 - 1:1 SD 1.5"
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batch_size: how many latents to produce
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"""
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# find matching entry
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for model, lbl, W, H in PRESETS:
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key = f"{W}x{H} - {lbl.split()[0]} {model}"
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if key == preset:
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break
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else:
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raise ValueError(f"Unknown preset: {preset}")
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_validate_dim(W); _validate_dim(H)
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return np.zeros((batch_size, 4, H // 8, W // 8), dtype=np.float32)
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if v <= 0 or v % 8 != 0:
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raise ValueError("Dimension must be >0 and divisible by 8")
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def _preset_input_types():
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# Build the list of dropdown labels:
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choices = [
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f"{W}x{H} - {lbl.split()[0]} {model}"
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for model, lbl, W, H in PRESETS
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class EmptyLatentAspectPreset:
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"""Creates a blank latent using one of the predefined presets."""
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def __init__(self):
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# Build a mapping from the dropdown key to (W, H)
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self._map = {}
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for model, lbl, w, h in PRESETS:
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# "512x512 - 1:1 SD 1.5"
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key = f"{w}x{h} - {lbl.split()[0]} {model}"
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self._map[key] = (w, h)
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@classmethod
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def INPUT_TYPES(cls):
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choices = [f"{w}x{h} - {lbl.split()[0]} {model}" for model, lbl, w, h in PRESETS]
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return {
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"required": {
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"preset": (choices,),
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"batch_size": ("INT", {"default": 1, "min": 1})
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}
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}
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RETURN_TYPES = ("LATENT",)
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RETURN_NAMES = ("empty_latent",)
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FUNCTION = "generate"
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CATEGORY = "ComfyUI Aspect Ratio Preset"
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def generate(self, preset: str, batch_size: int):
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if preset not in self._map:
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raise ValueError(f"Unknown preset: {preset}")
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w, h = self._map[preset]
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_validate_dim(w)
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_validate_dim(h)
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latent = torch.zeros([batch_size, 4, h // 8, w // 8], dtype=torch.float32)
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# return a single‐element tuple of a dict
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return ({"samples": latent},)
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class EmptyLatentAspectByAxis:
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"""Creates a blank latent by fixing one axis and computing the other from an aspect ratio."""
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# Precompute aspect choices
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ASPECT_CHOICES = [
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("1:1 Square", (1, 1)),
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("3:2 Landscape", (3, 2)),
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("2:3 Portrait", (2, 3)),
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("4:3 Landscape", (4, 3)),
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("3:4 Portrait", (3, 4)),
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("16:9 Landscape", (16, 9)),
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("9:16 Portrait", (9, 16)),
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("5:4 Landscape", (5, 4)),
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("4:5 Portrait", (4, 5)),
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("21:9 Widescreen",(21, 9)),
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("9:21 Portrait", (9, 21)),
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("7:5 Landscape", (7, 5)),
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("5:7 Portrait", (5, 7)),
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]
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return {
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"required": {
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# A true dropdown of strings:
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"preset": (choices,),
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"batch_size": ("INT", {"default": 1, "min": 1})
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REFERENCE_CHOICES = ["Width", "Height"]
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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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"primary_dim": ("INT", {"default": 512, "min": 8}),
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"reference": (cls.REFERENCE_CHOICES,),
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"aspect_ratio": ([lbl for lbl, _ in cls.ASPECT_CHOICES],),
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"batch_size": ("INT", {"default": 1, "min": 1})
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}
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}
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}
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empty_latent_preset.INPUT_TYPES = _preset_input_types
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empty_latent_preset.RETURN_TYPES = ("LATENT",)
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empty_latent_preset.CATEGORY = "ComfyUI Aspect Ratio Preset"
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RETURN_TYPES = ("LATENT",)
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RETURN_NAMES = ("empty_latent",)
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FUNCTION = "generate"
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CATEGORY = "ComfyUI Aspect Ratio Preset"
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def generate(self, primary_dim: int, reference: str, aspect_ratio: str, batch_size: int):
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# Lookup numeric aspect tuple
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ratio_map = dict(self.ASPECT_CHOICES)
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if aspect_ratio not in ratio_map:
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raise ValueError(f"Unknown aspect ratio: {aspect_ratio}")
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wr, hr = ratio_map[aspect_ratio]
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# ─── Node 2: By Axis ──────────────────────────────────────────────────
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_validate_dim(primary_dim)
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if reference == "Width":
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w, h = primary_dim, round(primary_dim * hr / wr)
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else:
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h, w = primary_dim, round(primary_dim * wr / hr)
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ASPECT_CHOICES = [
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("1:1 Square", (1, 1)),
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("3:2 Landscape", (3, 2)),
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("2:3 Portrait", (2, 3)),
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("4:3 Landscape", (4, 3)),
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("3:4 Portrait", (3, 4)),
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("16:9 Landscape", (16, 9)),
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("9:16 Portrait", (9, 16)),
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("5:4 Landscape", (5, 4)),
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("4:5 Portrait", (4, 5)),
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("21:9 Widescreen",(21, 9)),
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("9:21 Portrait", (9, 21)),
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("7:5 Landscape", (7, 5)),
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("5:7 Portrait", (5, 7)),
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]
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_validate_dim(w)
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_validate_dim(h)
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def empty_latent_by_axis(
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primary_dim: int,
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reference: str,
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aspect_ratio: str,
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batch_size: int
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) -> np.ndarray:
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"""
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Empty Latent (Aspect Ratio by Axis)
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primary_dim: numeric value for Width or Height
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reference: "Width" or "Height"
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aspect_ratio: e.g. "3:2 Landscape"
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batch_size: how many latents to produce
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"""
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# Look up numeric ratio
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for lbl, (wr, hr) in ASPECT_CHOICES:
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if lbl == aspect_ratio:
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break
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else:
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raise ValueError(f"Unknown aspect ratio: {aspect_ratio}")
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_validate_dim(primary_dim)
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if reference == "Width":
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W = primary_dim
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H = round(primary_dim * hr / wr)
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else:
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H = primary_dim
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W = round(primary_dim * wr / hr)
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_validate_dim(W); _validate_dim(H)
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return np.zeros((batch_size, 4, H // 8, W // 8), dtype=np.float32)
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def _byaxis_input_types():
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return {
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"required": {
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"primary_dim": ("INT", {"default": 512, "min": 8}),
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# A true dropdown of strings:
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"reference": (["Width", "Height"],),
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"aspect_ratio": ([lbl for lbl, _ in ASPECT_CHOICES],),
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"batch_size": ("INT", {"default": 1, "min": 1})
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}
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}
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empty_latent_by_axis.INPUT_TYPES = _byaxis_input_types
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empty_latent_by_axis.RETURN_TYPES = ("LATENT",)
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empty_latent_by_axis.CATEGORY = "ComfyUI Aspect Ratio Preset"
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# ─── Expose for ComfyUI ───────────────────────────────────────────────
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NODE_CLASS_MAPPINGS = {
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"Empty Latent (Aspect Ratio Preset)": empty_latent_preset,
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"Empty Latent (Aspect Ratio by Axis)": empty_latent_by_axis,
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}
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NODE_DISPLAY_NAME = "ComfyUI Aspect Ratio Preset"
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latent = torch.zeros([batch_size, 4, h // 8, w // 8], dtype=torch.float32)
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return ({"samples": latent},)
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