Maxed-Out-99
e7d96b947f
update help popup to register under MXD and change settings key rename jsnodes extension to MXD.jsnodes switch python node categories to MXD namespace
430 lines
16 KiB
Python
430 lines
16 KiB
Python
from __future__ import annotations
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import torch, math, comfy
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import comfy.utils
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import comfy.model_management
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from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
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import node_helpers
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########################################################################################################################
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# Flux Empty Latent Image (SD3-compatible)
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class FluxEmptyLatentImage:
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DESCRIPTION = """
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- Provides a wide selection of resolutions for easy selection.
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- Meant to save time from manually entering
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the resolution in the "Empty Latent Image" node.
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"""
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TITLE = "Flux Empty Latent Image"
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CATEGORY = "MXD/Latent"
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RESOLUTIONS = {
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"— High Resolutions —": None,
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"Square (1:1) 1408x1408": (1408, 1408),
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"Standard (4:3) 1664x1216": (1664, 1216),
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"Landscape (3:2) 1728x1152": (1728, 1152),
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"Widescreen (16:9) 1920x1088": (1920, 1088),
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"Ultrawide (21:9) 2176x960": (2176, 960),
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"— Standard Resolutions —": None,
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"Square (1:1) 1024x1024": (1024, 1024),
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"Standard (4:3) 1152x896": (1152, 896),
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"Landscape (3:2) 1216x832": (1216, 832),
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"Widescreen (16:9) 1344x768": (1344, 768),
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"Ultrawide (21:9) 1536x640": (1536, 640),
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"— Low Resolutions —": None,
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"Square (1:1) 320x320": (320, 320),
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"Standard (4:3) 448x320": (448, 320),
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"Landscape (3:2) 384x256": (384, 256),
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"Widescreen (16:9) 448x256": (448, 256),
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"Ultrawide (21:9) 576x256": (576, 256),
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}
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def __init__(self):
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self.device = comfy.model_management.intermediate_device()
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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"resolution": (
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list(cls.RESOLUTIONS.keys()),
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{"default": "Square (1:1) 1024x1024"}
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),
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"vertical": ("BOOLEAN", {"default": False}),
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"batch_size": (
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"INT",
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{
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"default": 1,
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"min": 1,
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"max": 4096,
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"tooltip": "The number of latent images in the batch."
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}
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)
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}
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}
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RETURN_TYPES = ("LATENT",)
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OUTPUT_TOOLTIPS = ("The empty latent image batch.",)
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FUNCTION = "generate"
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def generate(self, resolution, vertical, batch_size=1) -> tuple:
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size = self.RESOLUTIONS.get(resolution)
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if size is None:
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raise ValueError(f"'{resolution}' is a header or invalid option.")
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width, height = size
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if vertical:
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width, height = height, width
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latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
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return ({"samples": latent},)
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########################################################################################################################
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# Sdxl Empty Latent Image
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class SdxlEmptyLatentImage:
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DESCRIPTION = """
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- Generates empty latent images.
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- All supported SDXL resolutions
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are predefined for ease of use.
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- Meant to save time from manually entering
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the resolution in the "Empty Latent Image" node.
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"""
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TITLE = "Sdxl Empty Latent Image (With Resolutions)"
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CATEGORY = "MXD/Latent"
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# SDXL predefined resolutions (width, height)
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RESOLUTIONS = {
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"Square (1:1) 1024x1024": (1024, 1024),
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"Standard (4:3) 1152x896": (1152, 896),
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"Landscape (3:2) 1216x832": (1216, 832),
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"Widescreen (16:9) 1344x768": (1344, 768),
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"Ultra-Wide (21:9) 1536x640": (1536, 640),
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}
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def __init__(self):
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# Retrieve the intermediate device (usually the GPU) from ComfyUI's model management.
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self.device = comfy.model_management.intermediate_device()
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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# Dropdown selection for one of the predefined SDXL resolutions.
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"resolution": (list(cls.RESOLUTIONS.keys()),),
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# Toggle for vertical mode (swaps width and height).
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"vertical": ("BOOLEAN", {"default": False}),
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# Number of latent images to create in the batch.
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"batch_size": (
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"INT",
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{
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"default": 1,
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"min": 1,
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"max": 4096,
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"tooltip": "The number of latent images in the batch."
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}
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)
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}
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}
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RETURN_TYPES = ("LATENT",)
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OUTPUT_TOOLTIPS = ("The empty latent image batch.",)
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FUNCTION = "generate"
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def generate(self, resolution, vertical, batch_size=1) -> tuple:
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# Get the selected resolution tuple (width, height)
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width, height = self.RESOLUTIONS[resolution]
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# If vertical mode is enabled, swap width and height.
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if vertical:
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width, height = height, width
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# Create an empty latent tensor.
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# Typically, the latent space has 4 channels and each spatial dimension is 1/8th of the image.
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latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
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return ({"samples": latent},)
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########################################################################################################################
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# Image Scale To Total Pixels (SDXL Safe)
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class ImageScaleToTotalPixelsSafe:
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DESCRIPTION = """
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- Scales to target megapixel count, preserving aspect ratio.
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- If image matches SDXL resolutions (e.g. those used in
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"SDXL Empty Latent Image" node), scaling is skipped.
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- Meant for SDXL workflows (e.g. image-to-image, inpainting)
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to auto-scale random images but not images already made with SDXL.
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"""
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upscale_methods = ["bilinear", "bicubic", "lanczos"]
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# SDXL-safe resolutions (width, height) – store one orientation only,
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# the code will check both (w, h) and (h, w)
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SDXL_SAFE_RESOLUTIONS = [
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(1024, 1024),
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(1152, 896),
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(1216, 832),
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(1344, 768),
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(1536, 640),
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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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"image": ("IMAGE",),
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"upscale_method": (cls.upscale_methods, {"default": "bilinear"}),
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"total_megapixels": (
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"FLOAT",
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{
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"default": 1.0,
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"min": 0.01,
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"max": 128.0,
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"step": 0.01,
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"tooltip": "Set the total megapixels (e.g., 1.0 = 1 MP)",
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},
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),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "upscale"
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CATEGORY = "MXD/Upscaling"
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def upscale(self, image, upscale_method, total_megapixels):
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b, h, w, c = image.shape
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# Skip scaling if the image already matches an SDXL-safe resolution
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if (w, h) in self.SDXL_SAFE_RESOLUTIONS or (h, w) in self.SDXL_SAFE_RESOLUTIONS:
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return (image,)
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# ComfyUI-native megapixel math
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samples = image.movedim(-1, 1)
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orig_h, orig_w = samples.shape[2], samples.shape[3]
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target_pixels = int(round(total_megapixels * 1024 * 1024))
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scale_by = math.sqrt(target_pixels / (orig_w * orig_h))
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new_w = max(1, round(orig_w * scale_by))
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new_h = max(1, round(orig_h * scale_by))
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scaled = comfy.utils.common_upscale(samples, new_w, new_h, upscale_method, "disabled")
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scaled = scaled.movedim(1, -1)
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return (scaled,)
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########################################################################################################################
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# Flux Image Scale To Total Pixels (Flux Safe)
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class FluxImageScaleToTotalPixelsSafe:
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DESCRIPTION = """
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- Scales to target megapixel count, preserving aspect ratio.
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- If image matches Flux-safe resolutions (e.g. those used in
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the Flux Empty Latent Image node), scaling is skipped.
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- Meant for image-to-image or inpainting workflows to auto-scale
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arbitrary images, but skip images already matching Flux resolutions.
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"""
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upscale_methods = ["bilinear", "bicubic", "lanczos"]
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# Flux-safe resolutions (width, height) – stored in one orientation only
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FLUX_SAFE_RESOLUTIONS = [
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(1408, 1408),
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(1728, 1152),
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(1664, 1216),
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(1920, 1088),
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(2176, 960),
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(1024, 1024),
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(1216, 832),
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(1152, 896),
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(1344, 768),
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(1536, 640),
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(320, 320),
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(384, 256),
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(448, 320),
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(448, 256),
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(576, 256),
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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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"image": ("IMAGE",),
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"upscale_method": (cls.upscale_methods, {"default": "bilinear"}),
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"total_megapixels": (
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"FLOAT",
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{
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"default": 1.0,
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"min": 0.01,
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"max": 128.0,
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"step": 0.01,
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"tooltip": "Set the total megapixels (e.g., 1.0 = 1 MP)",
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},
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),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "upscale"
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CATEGORY = "MXD/Upscaling"
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def upscale(self, image, upscale_method, total_megapixels):
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b, h, w, c = image.shape
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# Skip scaling if image matches any Flux-safe resolution
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if (w, h) in self.FLUX_SAFE_RESOLUTIONS or (h, w) in self.FLUX_SAFE_RESOLUTIONS:
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return (image,)
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samples = image.movedim(-1, 1)
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orig_h, orig_w = samples.shape[2], samples.shape[3]
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target_pixels = int(round(total_megapixels * 1024 * 1024))
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scale_by = math.sqrt(target_pixels / (orig_w * orig_h))
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new_w = max(1, round(orig_w * scale_by))
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new_h = max(1, round(orig_h * scale_by))
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scaled = comfy.utils.common_upscale(samples, new_w, new_h, upscale_method, "disabled")
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scaled = scaled.movedim(1, -1)
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return (scaled,)
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########################################################################################################################
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# Prompt with Guidance (Flux)
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class PromptWithGuidance(ComfyNodeABC):
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DESCRIPTION = """
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- Combines clip text encode with flux guidance to lower node count.
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- Also removes the need to convert them into node group within ComfyUI.
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"""
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@classmethod
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def INPUT_TYPES(cls) -> InputTypeDict:
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return {
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"required": {
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"text": (IO.STRING, {"multiline": True, "dynamicPrompts": True}),
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"clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}),
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"guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1})
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}
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}
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RETURN_TYPES = (IO.CONDITIONING,)
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FUNCTION = "encode_and_guide"
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CATEGORY = "MXD/conditioning"
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def encode_and_guide(self, text, clip, guidance):
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if clip is None:
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raise RuntimeError("CLIP model is None. Your checkpoint may not contain a text encoder.")
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tokens = clip.tokenize(text)
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conditioning = clip.encode_from_tokens_scheduled(tokens)
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conditioning = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
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return (conditioning,)
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########################################################################################################################
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class FluxResolutionMatcher:
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"""
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A utility node for ComfyUI that takes an input image and finds the best matching
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resolution and orientation from a predefined list compatible with Flux/SD3.
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It outputs a resolution string and a boolean that can be piped directly into
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the 'Flux Empty Latent Image' node.
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"""
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# --- ComfyUI Setup ---
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TITLE = "Flux Resolution Matcher"
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CATEGORY = "MXD/Latent"
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DESCRIPTION = "Takes an image and finds the closest resolution for Flux Empty Latent."
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FUNCTION = "match_resolution"
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RETURN_NAMES = ("resolution", "vertical")
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# The list of resolutions this node will match against.
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RESOLUTIONS = {
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"— High Resolutions —": None,
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"Square (1:1) 1408x1408": (1408, 1408),
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"Standard (4:3) 1664x1216": (1664, 1216),
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"Landscape (3:2) 1728x1152": (1728, 1152),
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"Widescreen (16:9) 1920x1088": (1920, 1088),
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"Ultrawide (21:9) 2176x960": (2176, 960),
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"— Standard Resolutions —": None,
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"Square (1:1) 1024x1024": (1024, 1024),
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"Standard (4:3) 1152x896": (1152, 896),
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"Landscape (3:2) 1216x832": (1216, 832),
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"Widescreen (16:9) 1344x768": (1344, 768),
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"Ultrawide (21:9) 1536x640": (1536, 640),
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"— Low Resolutions —": None,
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"Square (1:1) 320x320": (320, 320),
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"Standard (4:3) 448x320": (448, 320),
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"Landscape (3:2) 384x256": (384, 256),
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"Widescreen (16:9) 448x256": (448, 256),
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"Ultrawide (21:9) 576x256": (576, 256),
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}
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# THIS IS THE CORRECTED LINE:
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# We define the output type as the list of resolution keys, which ComfyUI
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# recognizes as a COMBO type, allowing it to connect to the target node.
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RETURN_TYPES = (list(RESOLUTIONS.keys()), "BOOLEAN")
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# --- Pre-computation at Class Load Time ---
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ASPECT_RATIO_GROUPS = {}
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for res_str, dims in RESOLUTIONS.items():
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if dims is None:
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continue
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group_name = " ".join(res_str.split(' ')[:-1])
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if group_name not in ASPECT_RATIO_GROUPS:
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w, h = dims
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ratio = w / h
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ASPECT_RATIO_GROUPS[group_name] = {'ratio': ratio, 'resolutions': []}
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ASPECT_RATIO_GROUPS[group_name]['resolutions'].append(res_str)
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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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"image": ("IMAGE",),
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}
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}
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def match_resolution(self, image: torch.Tensor):
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if image.dim() < 4 or image.shape[1] < 1 or image.shape[2] < 1:
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print("Warning: Invalid image tensor received. Falling back to default resolution.")
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return ("Square (1:1) 1024x1024", False)
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_batch, height, width, _channels = image.shape
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is_vertical = height > width
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img_aspect_ratio = (height / width) if is_vertical else (width / height)
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img_area = height * width
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best_ar_group_name = min(
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self.ASPECT_RATIO_GROUPS.keys(),
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key=lambda name: abs(img_aspect_ratio - self.ASPECT_RATIO_GROUPS[name]['ratio'])
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)
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candidate_res_strings = self.ASPECT_RATIO_GROUPS[best_ar_group_name]['resolutions']
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best_res_string = min(
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candidate_res_strings,
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key=lambda res_str: abs(img_area - (self.RESOLUTIONS[res_str][0] * self.RESOLUTIONS[res_str][1]))
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)
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return (best_res_string, is_vertical)
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########################################################################################################################
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# NODE MAPPING
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NODE_CLASS_MAPPINGS = {
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"Flux Empty Latent Image": FluxEmptyLatentImage,
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"Sdxl Empty Latent Image": SdxlEmptyLatentImage,
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"Image Scale To Total Pixels (SDXL Safe)": ImageScaleToTotalPixelsSafe,
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"Flux Image Scale To Total Pixels (Flux Safe)": FluxImageScaleToTotalPixelsSafe,
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"Prompt With Guidance (Flux)": PromptWithGuidance,
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"FluxResolutionMatcher": FluxResolutionMatcher
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Flux Empty Latent Image": "Flux Empty Latent Image MXD",
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"Sdxl Empty Latent Image": "SDXL Empty Latent Image MXD",
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"Image Scale To Total Pixels (SDXL Safe)": "Scale Image (SDXL Safe) MXD",
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"Flux Image Scale To Total Pixels (Flux Safe)": "Scale Image (Flux Safe) MXD",
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"Prompt With Guidance (Flux)": "Prompt with Flux Guidance MXD",
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"FluxResolutionMatcher": "Flux Resolution Matcher MXD"
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}
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