Added a new node RS Image to Latent
This commit is contained in:
Raykosan
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import torch
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import math
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import comfy.utils
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class RS_ImageToLatent:
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@classmethod
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def INPUT_TYPES(cls):
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presets = [
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"Square - 512x512 (1:1)",
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"Square - 640x640 (1:1)",
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"Square - 768x768 (1:1)",
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"Square - 1024x1024 (1:1)",
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"Square - 1280x1280 (1:1)",
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"Square - 1536x1536 (1:1)",
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"Square - 1920x1920 (1:1)",
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"Portrait - 480x640 (3:4)",
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"Portrait - 512x768 (3:2)",
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"Portrait - 768x1024 (3:4)",
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"Portrait - 864x1152 (3:4)",
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"Portrait - 960x1280 (3:4)",
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"Portrait - 768x1152 (2:3)",
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"Portrait - 896x1344 (2:3)",
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"Portrait - 768x1344 (9:16)",
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"Portrait - 832x1152 (3:4)",
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"Portrait - 832x1216 (13:19)",
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"Portrait - 896x1088 (14:17)",
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"Portrait - 896x1152 (7:9)",
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"Portrait - 960x1024 (15:16)",
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"Portrait - 960x1088 (15:17)",
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"Portrait - 1024x1280 (4:5)",
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"Portrait - 1280x1536 (5:6)",
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"Portrait - 1344x1728 (7:9)",
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"Portrait - 1440x1920 (3:4)",
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"Portrait - 1024x1536 (2:3)",
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"Portrait - 1088x1856 (~6:10)",
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"Portrait - 1280x1920 (2:3)",
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"Portrait - 1080x1920 (9:16)",
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"Portrait - 816x1920 (21:9)",
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"Landscape - 640x480 (4:3)",
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"Landscape - 768x512 (3:2)",
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"Landscape - 1024x768 (4:3)",
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"Landscape - 1280x864 (4:3)",
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"Landscape - 1280x960 (4:3)",
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"Landscape - 1152x768 (3:2)",
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"Landscape - 1344x896 (3:2)",
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"Landscape - 1344x768 (7:4)",
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"Landscape - 1152x832 (9:7)",
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"Landscape - 1216x832 (19:13)",
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"Landscape - 1088x896 (17:14)",
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"Landscape - 1152x896 (9:7)",
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"Landscape - 1024x960 (16:15)",
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"Landscape - 1088x960 (17:15)",
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"Landscape - 1280x1024 (5:4)",
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"Landscape - 1536x1280 (6:5)",
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"Landscape - 1728x1344 (9:7)",
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"Landscape - 1920x1440 (4:3)",
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"Landscape - 1536x1024 (3:2)",
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"Landscape - 1856x1088 (~16:9)",
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"Landscape - 1920x1280 (3:2)",
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"Landscape - 1920x1080 (16:9)",
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"Landscape - 1920x1024 (15:8)",
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"Landscape - 1920x816 (20:9)",
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]
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return {
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"required": {
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"image": ("IMAGE", {"tooltip": "Input image to convert to latent"}),
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"vae": ("VAE", {"tooltip": "VAE model for encoding (auto-detects divisibility)"}),
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"mode": (
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["auto", "preset", "custom", "megapixels"],
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{
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"default": "auto",
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"tooltip": "Size selection mode:\n"
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"- auto: Preserve image size with minimal rounding\n"
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"- preset: Use predefined resolution\n"
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"- custom: Manually enter width/height\n"
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"- megapixels: Set target megapixels"
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}
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),
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"preset": (presets, {"default": "Square - 1024x1024 (1:1)"}),
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"width": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 8}),
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"height": ("INT", {"default": 1024, "min": 64, "max": 4096, "step": 8}),
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"megapixels": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 16.0, "step": 0.1, "display": "slider"}),
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"aspect_ratio": (
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["1:1", "3:2", "2:3", "4:3", "3:4", "16:9", "9:16", "21:9", "9:21"],
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{"default": "1:1"}
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),
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"divisibility_mode": (
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["auto", "8", "16", "32", "64", "112", "128"],
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{"default": "auto", "tooltip": "Divisibility requirement (auto = detect from VAE)"}
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),
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"upscale_method": (
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["lanczos", "bilinear", "nearest-exact", "bicubic"],
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{"default": "lanczos"}
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),
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"rounding_mode": (
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["auto", "shrink", "expand"],
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{
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"default": "auto",
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"tooltip": "How to adjust size for divisibility:\n"
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"- auto: pick nearest valid size\n"
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"- shrink: only reduce (never increase)\n"
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"- expand: only increase (never reduce)"
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}
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),
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"batch_size": (
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"INT", {"default": 1, "min": 1, "max": 64, "step": 1,
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"tooltip": "Number of identical latents in batch (useful for batch processing)"}
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),
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}
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}
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RETURN_TYPES = ("LATENT", "INT", "INT", "INT", "INT")
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RETURN_NAMES = ("latent", "width_px", "height_px", "width_latent", "height_latent")
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FUNCTION = "convert"
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CATEGORY = "🦊 RaykoStudio"
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DESCRIPTION = "Converts any image to latent with automatic size optimization for models"
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def _get_divisibility_from_vae(self, vae):
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if not hasattr(self, '_cached_divisibility'):
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try:
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with torch.no_grad():
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test = torch.zeros(1, 64, 64, 3)
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latent = vae.encode(test)
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latent_h = latent["samples"].shape[2]
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self._cached_divisibility = 64 // latent_h
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except:
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self._cached_divisibility = 16
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return self._cached_divisibility
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def _apply_divisibility(self, value, divisor, rounding_mode):
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if rounding_mode == "shrink":
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return (value // divisor) * divisor
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elif rounding_mode == "expand":
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return math.ceil(value / divisor) * divisor
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else:
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return round(value / divisor) * divisor
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def _calculate_target_size_auto(self, img_w, img_h, divisibility, rounding_mode):
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target_w = self._apply_divisibility(img_w, divisibility, rounding_mode)
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target_h = self._apply_divisibility(img_h, divisibility, rounding_mode)
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return max(divisibility, target_w), max(divisibility, target_h)
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def _calculate_target_size_preset(self, preset, divisibility, rounding_mode):
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size_map = {
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"Square - 512x512 (1:1)": (512, 512),
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"Square - 640x640 (1:1)": (640, 640),
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"Square - 768x768 (1:1)": (768, 768),
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"Square - 1024x1024 (1:1)": (1024, 1024),
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"Square - 1280x1280 (1:1)": (1280, 1280),
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"Square - 1536x1536 (1:1)": (1536, 1536),
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"Square - 1920x1920 (1:1)": (1920, 1920),
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"Portrait - 480x640 (3:4)": (480, 640),
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"Portrait - 512x768 (3:2)": (512, 768),
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"Portrait - 768x1024 (3:4)": (768, 1024),
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"Portrait - 864x1152 (3:4)": (864, 1152),
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"Portrait - 960x1280 (3:4)": (960, 1280),
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"Portrait - 768x1152 (2:3)": (768, 1152),
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"Portrait - 896x1344 (2:3)": (896, 1344),
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"Portrait - 768x1344 (9:16)": (768, 1344),
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"Portrait - 832x1152 (3:4)": (832, 1152),
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"Portrait - 832x1216 (13:19)": (832, 1216),
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"Portrait - 896x1088 (14:17)": (896, 1088),
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"Portrait - 896x1152 (7:9)": (896, 1152),
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"Portrait - 960x1024 (15:16)": (960, 1024),
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"Portrait - 960x1088 (15:17)": (960, 1088),
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"Portrait - 1024x1280 (4:5)": (1024, 1280),
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"Portrait - 1280x1536 (5:6)": (1280, 1536),
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"Portrait - 1344x1728 (7:9)": (1344, 1728),
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"Portrait - 1440x1920 (3:4)": (1440, 1920),
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"Portrait - 1024x1536 (2:3)": (1024, 1536),
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"Portrait - 1088x1856 (~6:10)": (1088, 1856),
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"Portrait - 1280x1920 (2:3)": (1280, 1920),
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"Portrait - 1080x1920 (9:16)": (1080, 1920),
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"Portrait - 816x1920 (21:9)": (816, 1920),
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"Landscape - 640x480 (4:3)": (640, 480),
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"Landscape - 768x512 (3:2)": (768, 512),
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"Landscape - 1024x768 (4:3)": (1024, 768),
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"Landscape - 1280x864 (4:3)": (1280, 864),
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"Landscape - 1280x960 (4:3)": (1280, 960),
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"Landscape - 1152x768 (3:2)": (1152, 768),
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"Landscape - 1344x896 (3:2)": (1344, 896),
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"Landscape - 1344x768 (7:4)": (1344, 768),
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"Landscape - 1152x832 (9:7)": (1152, 832),
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"Landscape - 1216x832 (19:13)": (1216, 832),
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"Landscape - 1088x896 (17:14)": (1088, 896),
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"Landscape - 1152x896 (9:7)": (1152, 896),
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"Landscape - 1024x960 (16:15)": (1024, 960),
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"Landscape - 1088x960 (17:15)": (1088, 960),
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"Landscape - 1280x1024 (5:4)": (1280, 1024),
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"Landscape - 1536x1280 (6:5)": (1536, 1280),
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"Landscape - 1728x1344 (9:7)": (1728, 1344),
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"Landscape - 1920x1440 (4:3)": (1920, 1440),
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"Landscape - 1536x1024 (3:2)": (1536, 1024),
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"Landscape - 1856x1088 (~16:9)": (1856, 1088),
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"Landscape - 1920x1280 (3:2)": (1920, 1280),
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"Landscape - 1920x1080 (16:9)": (1920, 1080),
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"Landscape - 1920x1024 (15:8)": (1920, 1024),
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"Landscape - 1920x816 (20:9)": (1920, 816),
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}
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w, h = size_map[preset]
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w = self._apply_divisibility(w, divisibility, rounding_mode)
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h = self._apply_divisibility(h, divisibility, rounding_mode)
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return max(divisibility, w), max(divisibility, h)
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def _calculate_target_size_megapixels(self, megapixels, aspect_ratio, divisibility, rounding_mode):
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w_ratio, h_ratio = map(int, aspect_ratio.split(":"))
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target_pixels = megapixels * 1_000_000
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x = math.sqrt(target_pixels / (w_ratio * h_ratio))
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raw_w, raw_h = w_ratio * x, h_ratio * x
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w, h = int(raw_w), int(raw_h)
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w = self._apply_divisibility(w, divisibility, rounding_mode)
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h = self._apply_divisibility(h, divisibility, rounding_mode)
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return max(divisibility, w), max(divisibility, h)
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def convert(self, image, vae, mode, preset, width, height, megapixels,
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aspect_ratio, divisibility_mode, upscale_method, rounding_mode, batch_size):
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if divisibility_mode == "auto":
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divisibility = self._get_divisibility_from_vae(vae)
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else:
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divisibility = int(divisibility_mode)
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_, img_h, img_w, _ = image.shape
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if mode == "auto":
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target_w, target_h = self._calculate_target_size_auto(img_w, img_h, divisibility, rounding_mode)
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elif mode == "preset":
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target_w, target_h = self._calculate_target_size_preset(preset, divisibility, rounding_mode)
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elif mode == "custom":
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target_w = self._apply_divisibility(width, divisibility, rounding_mode)
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target_h = self._apply_divisibility(height, divisibility, rounding_mode)
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elif mode == "megapixels":
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target_w, target_h = self._calculate_target_size_megapixels(megapixels, aspect_ratio, divisibility, rounding_mode)
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else:
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target_w, target_h = img_w, img_h
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target_w = max(divisibility, target_w)
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target_h = max(divisibility, target_h)
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if (img_w, img_h) != (target_w, target_h):
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image = image.movedim(-1, 1)
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image = comfy.utils.common_upscale(image, target_w, target_h, upscale_method, "disabled")
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image = image.movedim(1, -1)
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if image.shape[-1] == 4:
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image = image[..., :3]
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if len(image.shape) == 3:
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image = image.unsqueeze(0)
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latent_output = vae.encode(image)
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if isinstance(latent_output, dict) and "samples" in latent_output:
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latent = latent_output["samples"]
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else:
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latent = latent_output
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if batch_size > 1 and latent.shape[0] == 1:
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latent = latent.repeat(batch_size, 1, 1, 1)
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latent_h = latent.shape[2]
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latent_w = latent.shape[3]
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return ({"samples": latent}, target_w, target_h, latent_w, latent_h)
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NODE_CLASS_MAPPINGS = {
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"RS_ImageToLatent": RS_ImageToLatent,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RS_ImageToLatent": "🦊 RS Image to Latent",
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}
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+2
-1
@@ -1,6 +1,6 @@
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{
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"name": "ComfyUI_RaykoStudio",
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"version": "0.17.8",
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"version": "0.18.0",
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"description": "Set of custom nodes for ComfyUI providing additional image processing capabilities",
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"author": "Raykosan",
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"license": "MIT",
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@@ -14,6 +14,7 @@
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"Rayko_Crop_Image.py"
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"Rayko_Image_Selector.py"
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"Rayko_Image_Text.py"
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"Rayko_Image_To_Latent.py"
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"Rayko_Insert_Crop.py"
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"Rayko_Intermediate_Spline_Mask.py"
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"Rayko_Last_Frame.py"
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+1
-1
@@ -1,7 +1,7 @@
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[project]
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name = "ComfyUI_RaykoStudio"
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description = "Set of custom nodes for ComfyUI providing additional image processing capabilities"
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version = "0.17.8"
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version = "0.18.0"
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license = { file = "LICENSE" }
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dependencies = ["torch>=1.7.0", "numpy>=1.19.0", "Pillow>=8.0.0", "freetype-py>=2.5.1"]
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