feat: ✨ Batch node utilities
Usefull for animateDiff Updates the node list
This commit is contained in:
melMass
+54
-46
@@ -1,46 +1,54 @@
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{
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"Animation Builder (mtb)": "Convenient way to manage basic animation maths at the core of many of my workflows",
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"Any To String (mtb)": "Tries to take any input and convert it to a string",
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"Bbox (mtb)": "The bounding box (BBOX) custom type used by other nodes",
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"Bbox From Mask (mtb)": "From a mask extract the bounding box",
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"Blur (mtb)": "Blur an image using a Gaussian filter.",
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"Color Correct (mtb)": "Various color correction methods",
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"Colored Image (mtb)": "Constant color image of given size",
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"Concat Images (mtb)": "Add images to batch",
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"Crop (mtb)": "Crops an image and an optional mask to a given bounding box\n\n The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n The BBOX input takes precedence over the tuple input\n ",
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"Debug (mtb)": "Experimental node to debug any Comfy values, support for more types and widgets is planned",
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"Deep Bump (mtb)": "Normal & height maps generation from single pictures",
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"Export With Ffmpeg (mtb)": "Export with FFmpeg (Experimental)",
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"Face Swap (mtb)": "Face swap using deepinsight/insightface models",
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"Film Interpolation (mtb)": "Google Research FILM frame interpolation for large motion",
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"Fit Number (mtb)": "Fit the input float using a source and target range",
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"Float To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" FLOAT to a NUMBER.",
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"Get Batch From History (mtb)": "Very experimental node to load images from the history of the server.\n\n Queue items without output are ignored in the count.",
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"Image Compare (mtb)": "Compare two images and return a difference image",
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"Image Premultiply (mtb)": "Premultiply image with mask",
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"Image Remove Background Rembg (mtb)": "Removes the background from the input using Rembg.",
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"Image Resize Factor (mtb)": "Extracted mostly from WAS Node Suite, with a few edits (most notably multiple image support) and less features.",
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"Int To Bool (mtb)": "Basic int to bool conversion",
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"Int To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" INT to a NUMBER.",
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"Latent Lerp (mtb)": "Linear interpolation (blend) between two latent vectors",
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"Load Face Analysis Model (mtb)": "Loads a face analysis model",
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"Load Face Enhance Model (mtb)": "Loads a GFPGan or RestoreFormer model for face enhancement.",
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"Load Face Swap Model (mtb)": "Loads a faceswap model",
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"Load Film Model (mtb)": "Loads a FILM model",
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"Load Image From Url (mtb)": "Load an image from the given URL",
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"Load Image Sequence (mtb)": "Load an image sequence from a folder. The current frame is used to determine which image to load.\n\n Usually used in conjunction with the `Primitive` node set to increment to load a sequence of images from a folder.\n Use -1 to load all matching frames as a batch.\n ",
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"Mask To Image (mtb)": "Converts a mask (alpha) to an RGB image with a color and background",
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"Qr Code (mtb)": "Basic QR Code generator",
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"Restore Face (mtb)": "Uses GFPGan to restore faces",
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"Save Gif (mtb)": "Save the images from the batch as a GIF",
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"Save Image Grid (mtb)": "Save all the images in the input batch as a grid of images.",
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"Save Image Sequence (mtb)": "Save an image sequence to a folder. The current frame is used to determine which image to save.\n\n This is merely a wrapper around the `save_images` function with formatting for the output folder and filename.\n ",
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"Save Tensors (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy",
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"Smart Step (mtb)": "Utils to control the steps start/stop of the KAdvancedSampler in percentage",
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"String Replace (mtb)": "Basic string replacement",
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"Styles Loader (mtb)": "Load csv files and populate a dropdown from the rows (\u00e0 la A111)",
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"Text To Image (mtb)": "Utils to convert text to image using a font\n\n\n The tool looks for any .ttf file in the Comfy folder hierarchy.\n ",
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"Transform Image (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy\n\n\n it return a tensor representing the transformed images with the same shape as the input tensor\n ",
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"Uncrop (mtb)": "Uncrops an image to a given bounding box\n\n The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n The BBOX input takes precedence over the tuple input",
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"Unsplash Image (mtb)": "Unsplash Image given a keyword and a size"
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}
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{
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"Animation Builder (mtb)": "Convenient way to manage basic animation maths at the core of many of my workflows",
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"Any To String (mtb)": "Tries to take any input and convert it to a string",
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"Batch Float (mtb)": "Generates a batch of float values with interpolation",
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"Batch Shape (mtb)": "Generates a batch of 2D shapes with optional shading (experimental)",
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"Batch Transform (mtb)": "Transform a batch of images using a batch of keyframes",
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"Bbox (mtb)": "The bounding box (BBOX) custom type used by other nodes",
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"Bbox From Mask (mtb)": "From a mask extract the bounding box",
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"Blur (mtb)": "Blur an image using a Gaussian filter.",
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"Color Correct (mtb)": "Various color correction methods",
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"Colored Image (mtb)": "Constant color image of given size",
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"Concat Images (mtb)": "Add images to batch",
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"Crop (mtb)": "Crops an image and an optional mask to a given bounding box\n\n The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n The BBOX input takes precedence over the tuple input\n ",
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"Debug (mtb)": "Experimental node to debug any Comfy values, support for more types and widgets is planned",
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"Deep Bump (mtb)": "Normal & height maps generation from single pictures",
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"Export With Ffmpeg (mtb)": "Export with FFmpeg (Experimental)",
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"Face Swap (mtb)": "Face swap using deepinsight/insightface models",
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"Film Interpolation (mtb)": "Google Research FILM frame interpolation for large motion",
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"Fit Number (mtb)": "Fit the input float using a source and target range",
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"Float To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" FLOAT to a NUMBER.",
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"Get Batch From History (mtb)": "Very experimental node to load images from the history of the server.\n\n Queue items without output are ignored in the count.",
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"Image Compare (mtb)": "Compare two images and return a difference image",
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"Image Premultiply (mtb)": "Premultiply image with mask",
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"Image Remove Background Rembg (mtb)": "Removes the background from the input using Rembg.",
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"Image Resize Factor (mtb)": "Extracted mostly from WAS Node Suite, with a few edits (most notably multiple image support) and less features.",
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"Image Tile Offset (mtb)": "Mimics an old photoshop technique to check for seamless textures",
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"Int To Bool (mtb)": "Basic int to bool conversion",
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"Int To Number (mtb)": "Node addon for the WAS Suite. Converts a \"comfy\" INT to a NUMBER.",
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"Interpolate Clip Sequential (mtb)": null,
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"Latent Lerp (mtb)": "Linear interpolation (blend) between two latent vectors",
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"Load Face Analysis Model (mtb)": "Loads a face analysis model",
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"Load Face Enhance Model (mtb)": "Loads a GFPGan or RestoreFormer model for face enhancement.",
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"Load Face Swap Model (mtb)": "Loads a faceswap model",
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"Load Film Model (mtb)": "Loads a FILM model",
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"Load Image From Url (mtb)": "Load an image from the given URL",
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"Load Image Sequence (mtb)": "Load an image sequence from a folder. The current frame is used to determine which image to load.\n\n Usually used in conjunction with the `Primitive` node set to increment to load a sequence of images from a folder.\n Use -1 to load all matching frames as a batch.\n ",
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"Mask To Image (mtb)": "Converts a mask (alpha) to an RGB image with a color and background",
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"Model Patch Seamless (mtb)": "Uses the stable diffusion 'hack' to infer seamless images by setting the model layers padding mode to circular (experimental)",
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"Qr Code (mtb)": "Basic QR Code generator",
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"Restore Face (mtb)": "Uses GFPGan to restore faces",
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"Save Gif (mtb)": "Save the images from the batch as a GIF",
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"Save Image Grid (mtb)": "Save all the images in the input batch as a grid of images.",
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"Save Image Sequence (mtb)": "Save an image sequence to a folder. The current frame is used to determine which image to save.\n\n This is merely a wrapper around the `save_images` function with formatting for the output folder and filename.\n ",
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"Save Tensors (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy",
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"Smart Step (mtb)": "Utils to control the steps start/stop of the KAdvancedSampler in percentage",
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"Stack Images (mtb)": "Stack the input images horizontally or vertically",
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"String Replace (mtb)": "Basic string replacement",
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"Styles Loader (mtb)": "Load csv files and populate a dropdown from the rows (\u00e0 la A111)",
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"Text To Image (mtb)": "Utils to convert text to image using a font\n\n\n The tool looks for any .ttf file in the Comfy folder hierarchy.\n ",
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"Transform Image (mtb)": "Save torch tensors (image, mask or latent) to disk, useful to debug things outside comfy\n\n\n it return a tensor representing the transformed images with the same shape as the input tensor\n ",
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"Uncrop (mtb)": "Uncrops an image to a given bounding box\n\n The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type\n The BBOX input takes precedence over the tuple input",
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"Unsplash Image (mtb)": "Unsplash Image given a keyword and a size",
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"Vae Decode (mtb)": "Wrapper for the 2 core decoders but also adding the sd seamless hack, taken from: FlyingFireCo/tiled_ksampler"
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}
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+260
@@ -0,0 +1,260 @@
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import os
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from pathlib import Path
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from typing import List
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import cv2
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import folder_paths
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import numpy as np
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import torch
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from ..utils import apply_easing, pil2tensor
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from .transform import TransformImage
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def hex_to_rgb(hex_color, bgr=False):
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hex_color = hex_color.lstrip("#")
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if bgr:
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return tuple(int(hex_color[i : i + 2], 16) for i in (4, 2, 0))
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return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))
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class BatchShape:
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"""Generates a batch of 2D shapes with optional shading (experimental)"""
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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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"count": ("INT", {"default": 1}),
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"shape": (
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["Box", "Circle", "Diamond"],
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{"default": "Box"},
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),
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"image_width": ("INT", {"default": 512}),
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"image_height": ("INT", {"default": 512}),
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"shape_size": ("INT", {"default": 100}),
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"color": ("COLOR", {"default": "#ffffff"}),
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"bg_color": ("COLOR", {"default": "#000000"}),
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"shade_color": ("COLOR", {"default": "#000000"}),
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"shadex": ("FLOAT", {"default": 0.0}),
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"shadey": ("FLOAT", {"default": 0.0}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "generate_shapes"
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CATEGORY = "mtb/batch"
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def generate_shapes(
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self,
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count,
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shape,
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image_width,
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image_height,
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shape_size,
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color,
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bg_color,
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shade_color,
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shadex,
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shadey,
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):
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print(f"COLOR: {color}")
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print(f"BG_COLOR: {bg_color}")
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print(f"SHADE_COLOR: {shade_color}")
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# Parse color input to BGR tuple for OpenCV
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color = hex_to_rgb(color)
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bg_color = hex_to_rgb(bg_color)
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shade_color = hex_to_rgb(shade_color)
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res = []
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for x in range(count):
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# Initialize an image canvas
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canvas = np.full((image_height, image_width, 3), bg_color, dtype=np.uint8)
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mask = np.zeros((image_height, image_width), dtype=np.uint8)
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# Compute the center point of the shape
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center = (image_width // 2, image_height // 2)
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if shape == "Box":
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half_size = shape_size // 2
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top_left = (center[0] - half_size, center[1] - half_size)
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bottom_right = (center[0] + half_size, center[1] + half_size)
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cv2.rectangle(mask, top_left, bottom_right, 255, -1)
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elif shape == "Circle":
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cv2.circle(mask, center, shape_size // 2, 255, -1)
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elif shape == "Diamond":
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pts = np.array(
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[
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[center[0], center[1] - shape_size // 2],
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[center[0] + shape_size // 2, center[1]],
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[center[0], center[1] + shape_size // 2],
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[center[0] - shape_size // 2, center[1]],
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]
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)
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cv2.fillPoly(mask, [pts], 255)
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# Color the shape
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canvas[mask == 255] = color
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# Apply shading effects to a separate shading canvas
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shading = np.zeros_like(canvas, dtype=np.float32)
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shading[:, :, 0] = shadex * np.linspace(0, 1, image_width)
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shading[:, :, 1] = shadey * np.linspace(0, 1, image_height).reshape(-1, 1)
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shading_canvas = cv2.addWeighted(
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canvas.astype(np.float32), 1, shading, 1, 0
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).astype(np.uint8)
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# Apply shading only to the shape area using the mask
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canvas[mask == 255] = shading_canvas[mask == 255]
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res.append(canvas)
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return (pil2tensor(res),)
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class BatchFloat:
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"""Generates a batch of float values with interpolation"""
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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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"mode": (
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["Single", "Steps"],
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{"default": "Single"},
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),
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"count": ("INT", {"default": 1}),
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"min": ("FLOAT", {"default": 0.0}),
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"max": ("FLOAT", {"default": 1.0}),
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"easing": (
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[
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"Linear",
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"Sine In",
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"Sine Out",
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"Sine In/Out",
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"Quart In",
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"Quart Out",
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"Quart In/Out",
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"Cubic In",
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"Cubic Out",
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"Cubic In/Out",
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"Circ In",
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"Circ Out",
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"Circ In/Out",
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"Back In",
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"Back Out",
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"Back In/Out",
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"Elastic In",
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"Elastic Out",
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"Elastic In/Out",
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"Bounce In",
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"Bounce Out",
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"Bounce In/Out",
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],
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{"default": "Linear"},
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),
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}
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}
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FUNCTION = "set_floats"
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RETURN_TYPES = ("FLOATS",)
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CATEGORY = "mtb/batch"
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def set_floats(self, mode, count, min, max, easing):
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keyframes = []
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if mode == "Single":
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keyframes = [min] * count
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return (keyframes,)
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for i in range(count):
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normalized_step = i / (count - 1)
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eased_step = apply_easing(normalized_step, easing)
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eased_value = min + (max - min) * eased_step
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keyframes.append(eased_value)
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return (keyframes,)
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class Batch2dTransform:
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"""Transform a batch of images using a batch of keyframes"""
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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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"border_handling": (
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["edge", "constant", "reflect", "symmetric"],
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{"default": "edge"},
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),
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"constant_color": ("COLOR", {"default": "#000000"}),
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},
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"optional": {
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"x": ("FLOATS",),
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"y": ("FLOATS",),
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"zoom": ("FLOATS",),
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"angle": ("FLOATS",),
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"shear": ("FLOATS",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "transform_batch"
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CATEGORY = "mtb/batch"
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def transform_batch(
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self,
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image: torch.Tensor,
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border_handling,
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constant_color,
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x=None,
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y=None,
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zoom=None,
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angle=None,
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shear=None,
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):
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if not any([x, y, zoom, angle]):
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raise ValueError("At least one transform parameter must be provided")
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keyframes = {"x": [], "y": [], "zoom": [], "angle": [], "shear": []}
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default_vals = {"x": 0, "y": 0, "zoom": 1.0, "angle": 0, "shear": 0}
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if x:
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keyframes["x"] = x
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if y:
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keyframes["y"] = y
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if zoom:
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keyframes["zoom"] = zoom
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if angle:
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keyframes["angle"] = angle
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if shear:
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keyframes["shear"] = shear
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for name, values in keyframes.items():
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count = len(values)
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if count > 0 and count != image.shape[0]:
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raise ValueError(
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f"Length of {name} values ({count}) must match number of images ({image.shape[0]})"
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)
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if count == 0:
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keyframes[name] = [default_vals[name]] * image.shape[0]
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transformer = TransformImage()
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res = [
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transformer.transform(
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image[i].unsqueeze(0),
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keyframes["x"][i],
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keyframes["y"][i],
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keyframes["zoom"][i],
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keyframes["angle"][i],
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keyframes["shear"][i],
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border_handling,
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constant_color,
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)[0]
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for i in range(image.shape[0])
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]
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return (torch.cat(res, dim=0),)
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__nodes__ = [BatchFloat, Batch2dTransform, BatchShape]
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@@ -49,6 +49,8 @@ class VaeDecode_:
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class ModelPatchSeamless:
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"""Uses the stable diffusion 'hack' to infer seamless images by setting the model layers padding mode to circular (experimental)"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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