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I think the [WAS node suite](https://github.com/WASasquatch/was-node-suite-comfyui) also has quite a few noise-related nodes. 4 | 5 | While messing around with the stable diffusion VAE, I noticed the latent space behaves mostly linearly. I figured it should be possible to generate latent noise directly if I map out the per channel limits and give the whole thing an offset. 6 | 7 | This repo also has some decoders, but those are a proof of concept tier at best. [TAESD](https://github.com/madebyollin/taesd) is better in every way. 8 | 9 | ## LatentGaussianNoise 10 | 11 | I'm not sure if the way I coded this even makes sense, or if it's even gaussian noise. It's just `torch.random` with a bunch of stuff like scaling/per channel random/etc. 12 | 13 | ![LATENT_SPACE_NOISE](https://github.com/city96/SD-Advanced-Noise/assets/125218114/a3b1d790-4632-4290-b450-ec0919a8265c) 14 | 15 | ## Linearity / linear_encoder 16 | 17 | This is the simplest encoder. It uses the fact that a change in the RGB channels creates a (mostly?) linear change in the 4 latents channels (which I just called A/B/C/D since I couldn't find any info about them). 18 | 19 | The next step would be to plot at a higher precision and fit them onto a polynomial. 20 | 21 | ![LINEAR_ENCODER](https://github.com/city96/SD-Advanced-Noise/assets/125218114/f68b7e48-8def-480b-93a4-3f1843ba492c) 22 | 23 | ![NL](https://github.com/city96/SD-Advanced-Noise/assets/125218114/0fbffd6f-b062-441d-aa14-764249216926) 24 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | # only import if running as a custom node 2 | try: 3 | import comfy.utils 4 | except ImportError: 5 | pass 6 | else: 7 | from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 8 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] 9 | -------------------------------------------------------------------------------- /latent_math_encoder.py: -------------------------------------------------------------------------------- 1 | # 2 | # These are currently near-useless, but at least they're instant. 3 | # 4 | import json 5 | import torch 6 | 7 | def linear_encoder(img, ver="v1", weights="./linear_weights.json"): 8 | """Encodes tensor RGB[3,W,H](0.0-1.0) into tensor LATENT[4,W,H]""" 9 | with open(weights) as f: 10 | w = json.load(f) 11 | w = w[ver] 12 | lat = torch.stack([ 13 | ( # A channel 14 | (img[0]*w["A"]["R"]) + # R 15 | (img[1]*w["A"]["G"]) + # G 16 | (img[2]*w["A"]["B"]) + # B 17 | w["A"]["C"] # Constant 18 | ),( # B channel 19 | (img[0]*w["B"]["R"]) + # R 20 | (img[1]*w["B"]["G"]) + # G 21 | (img[2]*w["B"]["B"]) + # B 22 | w["B"]["C"] # Constant 23 | ),( # C channel 24 | (img[0]*w["C"]["R"]) + # R 25 | (img[1]*w["C"]["G"]) + # G 26 | (img[2]*w["C"]["B"]) + # B 27 | w["C"]["C"] # Constant 28 | ),( # D channel 29 | (img[0]*w["D"]["R"]) + # R 30 | (img[1]*w["D"]["G"]) + # G 31 | (img[2]*w["D"]["B"]) + # B 32 | w["D"]["C"] # Constant 33 | ), 34 | ]) 35 | return lat 36 | -------------------------------------------------------------------------------- /latent_noise_generator.py: -------------------------------------------------------------------------------- 1 | import json 2 | import torch 3 | import numpy as np 4 | 5 | def gaussian_latent_noise(width=64, height=64, ver="v1", seed=-1, fac=0.6, nul=0.0, srnd=True): 6 | limit = { 7 | "v1": { 8 | "min": {"A": -5.5618, "B":-17.1368, "C":-10.3445, "D": -8.6218}, 9 | "max": {"A": 13.5369, "B": 11.1997, "C": 16.3043, "D": 10.6343}, 10 | "nul": {"A": -5.3870, "B":-14.2931, "C": 6.2738, "D": 7.1220}, 11 | }, 12 | "xl": { 13 | "min": {"A":-22.2127, "B":-20.0131, "C":-17.7673, "D":-14.9434}, 14 | "max": {"A": 17.9334, "B": 26.3043, "C": 33.1648, "D": 8.9380}, 15 | "nul": {"A":-21.9287, "B": 3.8783, "C": 2.5879, "D": 2.5435}, 16 | } 17 | } 18 | # seed 19 | if seed >= 0: torch.manual_seed(seed) 20 | 21 | limit = limit[ver] 22 | if srnd: # shared random 23 | rand = torch.rand([height,width]) 24 | lat = torch.stack([ 25 | (limit["min"]["A"] + torch.clone(rand)*(limit["max"]["A"]-limit["min"]["A"])), 26 | (limit["min"]["B"] + torch.clone(rand)*(limit["max"]["B"]-limit["min"]["B"])), 27 | (limit["min"]["C"] + torch.clone(rand)*(limit["max"]["C"]-limit["min"]["C"])), 28 | (limit["min"]["D"] + torch.clone(rand)*(limit["max"]["D"]-limit["min"]["D"])), 29 | ]) 30 | else: # separate random 31 | lat = torch.stack([ 32 | (limit["min"]["A"] + torch.rand([height,width])*(limit["max"]["A"]-limit["min"]["A"])), 33 | (limit["min"]["B"] + torch.rand([height,width])*(limit["max"]["B"]-limit["min"]["B"])), 34 | (limit["min"]["C"] + torch.rand([height,width])*(limit["max"]["C"]-limit["min"]["C"])), 35 | (limit["min"]["D"] + torch.rand([height,width])*(limit["max"]["D"]-limit["min"]["D"])), 36 | ]) 37 | tnul = torch.stack([ # black image 38 | torch.ones([height,width])*limit["nul"]["A"], 39 | torch.ones([height,width])*limit["nul"]["B"], 40 | torch.ones([height,width])*limit["nul"]["C"], 41 | torch.ones([height,width])*limit["nul"]["D"], 42 | ]) 43 | out = ((lat*fac)*(1.0-nul) + tnul*nul)/2 44 | return out 45 | -------------------------------------------------------------------------------- /linear_weights.json: -------------------------------------------------------------------------------- 1 | { 2 | "v1": { 3 | "A": { 4 | "R": 12.3033447265625, 5 | "G": 9.709493637084961, 6 | "B": 1.9548652172088623, 7 | "C": -5.3870649337768555 8 | }, 9 | "B": { 10 | "R": 9.408391952514648, 11 | "G": 18.733842849731445, 12 | "B": -2.4815101623535156, 13 | "C": -14.293113708496094 14 | }, 15 | "C": { 16 | "R": -16.44525718688965, 17 | "G": 6.84106969833374, 18 | "B": -0.8256301879882812, 19 | "C": 6.273806095123291 20 | }, 21 | "D": { 22 | "R": -2.828339099884033, 23 | "G": 2.9502978324890137, 24 | "B": -14.843767166137695, 25 | "C": 7.122056484222412 26 | } 27 | }, 28 | "xl": { 29 | "A": { 30 | "R": 2.037425994873047, 31 | "G": 18.36268424987793, 32 | "B": 22.424095153808594, 33 | "C": -21.928709030151367 34 | }, 35 | "B": { 36 | "R": -23.891389846801758, 37 | "G": 10.339378356933594, 38 | "B": 12.638928413391113, 39 | "C": 3.878310441970825 40 | }, 41 | "C": { 42 | "R": 8.765694618225098, 43 | "G": 24.64052963256836, 44 | "B": -20.35533905029297, 45 | "C": 2.5879838466644287 46 | }, 47 | "D": { 48 | "R": -14.942508697509766, 49 | "G": 6.3944783210754395, 50 | "B": 1.6605620384216309, 51 | "C": 2.543539524078369 52 | } 53 | } 54 | } -------------------------------------------------------------------------------- /nodes.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import numpy as np 4 | from torchvision import transforms 5 | from .latent_math_encoder import linear_encoder 6 | from .latent_noise_generator import gaussian_latent_noise 7 | 8 | class MathEncode: 9 | """ 10 | Encode latents without using a NN. 11 | """ 12 | @classmethod 13 | def INPUT_TYPES(s): 14 | return { 15 | "required": { 16 | "pixels": ("IMAGE",), 17 | "latent_ver": (["v1", "xl"],), 18 | "mode": ([ 19 | "linear_encoder", 20 | ],), 21 | } 22 | } 23 | RETURN_TYPES = ("LATENT",) 24 | FUNCTION = "encode" 25 | CATEGORY = "latent" 26 | TITLE = "Math Encoder" 27 | 28 | def encode(self, pixels, latent_ver, mode): 29 | out = [] 30 | for batch, img in enumerate(pixels.numpy()): 31 | # target latent size 32 | lat_size = (round(img.shape[0]/8), round(img.shape[1]/8)) 33 | img = img.transpose((2, 0, 1)) # [W,H,3]=>[3,W,H] 34 | img = torch.from_numpy(img) 35 | img = transforms.Resize(lat_size, antialias=True)(img) 36 | # encode 37 | lat = linear_encoder(img, latent_ver) 38 | out.append(lat) 39 | return ({"samples":torch.stack(out)},) 40 | 41 | 42 | class LatentGaussianNoise: 43 | """ 44 | Create Gaussian noise directly in latent space. 45 | """ 46 | @classmethod 47 | def INPUT_TYPES(s): 48 | return { 49 | "required": { 50 | "latent_ver": (["v1", "xl"],), 51 | "width": ("INT", {"default": 768, "min": 64, "max": 8192, "step": 8}), 52 | "height": ("INT", {"default": 768, "min": 64, "max": 8192, "step": 8}), 53 | "factor": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05}), 54 | "null": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05}), 55 | "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}), 56 | "scale": ("INT", {"default": 1, "min": 1, "max": 8}), 57 | "random": (["shared", "per channel"],), 58 | "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), 59 | } 60 | } 61 | RETURN_TYPES = ("LATENT",) 62 | FUNCTION = "generate" 63 | CATEGORY = "noise" 64 | TITLE = "Gaussian Noise (Latent)" 65 | 66 | def generate(self, latent_ver, width, height, factor, null, batch_size, scale, random, seed): 67 | out = [] 68 | for b in range(batch_size): 69 | lat = gaussian_latent_noise( 70 | width = round(width/8/scale), 71 | height = round(height/8/scale), 72 | ver = latent_ver, 73 | seed = seed+b, 74 | fac = factor, 75 | nul = null, 76 | srnd = True if random == "shared" else False, 77 | ) 78 | if scale > 1: 79 | target = (round(height/8),round(width/8)) 80 | lat = transforms.Resize(target, antialias=True)(lat) 81 | out.append(lat) 82 | out = torch.stack(out) 83 | 84 | return ({"samples":out},) 85 | 86 | 87 | NODE_CLASS_MAPPINGS = { 88 | "MathEncode": MathEncode, 89 | "LatentGaussianNoise": LatentGaussianNoise, 90 | } 91 | 92 | NODE_DISPLAY_NAME_MAPPINGS = { 93 | "MathEncode": MathEncode.TITLE, 94 | "LatentGaussianNoise": LatentGaussianNoise.TITLE, 95 | } 96 | --------------------------------------------------------------------------------