nodes/budihartono-comfyui-aspect-ratio-presets
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Works for the first time

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Budi Hartono
2025-06-07 22:19:10 +07:00
parent eef1907b11
commit 8103bf98ad
2 changed files with 91 additions and 123 deletions
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__init__.py
+3 -5
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@@ -1,10 +1,8 @@
# __init__.py
from .nodes import empty_latent_preset, empty_latent_by_axis
from .nodes import EmptyLatentAspectPreset, EmptyLatentAspectByAxis
NODE_CLASS_MAPPINGS = {
"Empty Latent (Aspect Ratio Preset)": empty_latent_preset,
"Empty Latent (Aspect Ratio by Axis)": empty_latent_by_axis,
"Empty Latent (Aspect Ratio Preset)": EmptyLatentAspectPreset,
"Empty Latent (Aspect Ratio by Axis)": EmptyLatentAspectByAxis,
}
NODE_DISPLAY_NAME = "ComfyUI Aspect Ratio Preset"
nodes.py
+60 -90
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@@ -1,61 +1,52 @@
# nodes.py
import numpy as np
import torch
from .presets import PRESETS
# ─── Helpers ──────────────────────────────────────────────────────────
def _validate_dim(v: int):
if v <= 0:
raise ValueError("Dimension must be > 0")
if v % 8 != 0:
raise ValueError("Dimension must be divisible by 8")
# ─── Node 1: Preset ───────────────────────────────────────────────────
def empty_latent_preset(
preset: str,
batch_size: int
) -> np.ndarray:
"""
Empty Latent (Aspect Ratio Preset)
preset: e.g. "512x512 - 1:1 SD 1.5"
batch_size: how many latents to produce
"""
# find matching entry
for model, lbl, W, H in PRESETS:
key = f"{W}x{H} - {lbl.split()[0]} {model}"
if key == preset:
break
else:
raise ValueError(f"Unknown preset: {preset}")
_validate_dim(W); _validate_dim(H)
return np.zeros((batch_size, 4, H // 8, W // 8), dtype=np.float32)
if v <= 0 or v % 8 != 0:
raise ValueError("Dimension must be >0 and divisible by 8")
def _preset_input_types():
# Build the list of dropdown labels:
choices = [
f"{W}x{H} - {lbl.split()[0]} {model}"
for model, lbl, W, H in PRESETS
]
class EmptyLatentAspectPreset:
"""Creates a blank latent using one of the predefined presets."""
def __init__(self):
# Build a mapping from the dropdown key to (W, H)
self._map = {}
for model, lbl, w, h in PRESETS:
# "512x512 - 1:1 SD 1.5"
key = f"{w}x{h} - {lbl.split()[0]} {model}"
self._map[key] = (w, h)
@classmethod
def INPUT_TYPES(cls):
choices = [f"{w}x{h} - {lbl.split()[0]} {model}" for model, lbl, w, h in PRESETS]
return {
"required": {
# A true dropdown of strings:
"preset": (choices,),
"batch_size": ("INT", {"default": 1, "min": 1})
}
}
empty_latent_preset.INPUT_TYPES = _preset_input_types
empty_latent_preset.RETURN_TYPES = ("LATENT",)
empty_latent_preset.CATEGORY = "ComfyUI Aspect Ratio Preset"
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("empty_latent",)
FUNCTION = "generate"
CATEGORY = "ComfyUI Aspect Ratio Preset"
def generate(self, preset: str, batch_size: int):
if preset not in self._map:
raise ValueError(f"Unknown preset: {preset}")
w, h = self._map[preset]
_validate_dim(w)
_validate_dim(h)
latent = torch.zeros([batch_size, 4, h // 8, w // 8], dtype=torch.float32)
# return a single‐element tuple of a dict
return ({"samples": latent},)
# ─── Node 2: By Axis ──────────────────────────────────────────────────
class EmptyLatentAspectByAxis:
"""Creates a blank latent by fixing one axis and computing the other from an aspect ratio."""
# Precompute aspect choices
ASPECT_CHOICES = [
("1:1 Square", (1, 1)),
("3:2 Landscape", (3, 2)),
@@ -71,60 +62,39 @@ ASPECT_CHOICES = [
("7:5 Landscape", (7, 5)),
("5:7 Portrait", (5, 7)),
]
REFERENCE_CHOICES = ["Width", "Height"]
def empty_latent_by_axis(
primary_dim: int,
reference: str,
aspect_ratio: str,
batch_size: int
) -> np.ndarray:
"""
Empty Latent (Aspect Ratio by Axis)
primary_dim: numeric value for Width or Height
reference: "Width" or "Height"
aspect_ratio: e.g. "3:2 Landscape"
batch_size: how many latents to produce
"""
# Look up numeric ratio
for lbl, (wr, hr) in ASPECT_CHOICES:
if lbl == aspect_ratio:
break
else:
raise ValueError(f"Unknown aspect ratio: {aspect_ratio}")
_validate_dim(primary_dim)
if reference == "Width":
W = primary_dim
H = round(primary_dim * hr / wr)
else:
H = primary_dim
W = round(primary_dim * wr / hr)
_validate_dim(W); _validate_dim(H)
return np.zeros((batch_size, 4, H // 8, W // 8), dtype=np.float32)
def _byaxis_input_types():
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"primary_dim": ("INT", {"default": 512, "min": 8}),
# A true dropdown of strings:
"reference": (["Width", "Height"],),
"aspect_ratio": ([lbl for lbl, _ in ASPECT_CHOICES],),
"reference": (cls.REFERENCE_CHOICES,),
"aspect_ratio": ([lbl for lbl, _ in cls.ASPECT_CHOICES],),
"batch_size": ("INT", {"default": 1, "min": 1})
}
}
empty_latent_by_axis.INPUT_TYPES = _byaxis_input_types
empty_latent_by_axis.RETURN_TYPES = ("LATENT",)
empty_latent_by_axis.CATEGORY = "ComfyUI Aspect Ratio Preset"
RETURN_TYPES = ("LATENT",)
RETURN_NAMES = ("empty_latent",)
FUNCTION = "generate"
CATEGORY = "ComfyUI Aspect Ratio Preset"
def generate(self, primary_dim: int, reference: str, aspect_ratio: str, batch_size: int):
# Lookup numeric aspect tuple
ratio_map = dict(self.ASPECT_CHOICES)
if aspect_ratio not in ratio_map:
raise ValueError(f"Unknown aspect ratio: {aspect_ratio}")
wr, hr = ratio_map[aspect_ratio]
# ─── Expose for ComfyUI ───────────────────────────────────────────────
_validate_dim(primary_dim)
if reference == "Width":
w, h = primary_dim, round(primary_dim * hr / wr)
else:
h, w = primary_dim, round(primary_dim * wr / hr)
NODE_CLASS_MAPPINGS = {
"Empty Latent (Aspect Ratio Preset)": empty_latent_preset,
"Empty Latent (Aspect Ratio by Axis)": empty_latent_by_axis,
}
NODE_DISPLAY_NAME = "ComfyUI Aspect Ratio Preset"
_validate_dim(w)
_validate_dim(h)
latent = torch.zeros([batch_size, 4, h // 8, w // 8], dtype=torch.float32)
return ({"samples": latent},)