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**Updated** Released Karras Exponential

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KittensX's Avatar
KittensX
Mar 12, 2025
news
**Updated** Released Karras Exponential

Changelog 3/12/2025

I think this is going to be the final version release.

  • Bugfixes for randomization.

  • Bugfixes for sigmas.

New file has been added to github and is available for download.

This version is made for A1111.

Changelog 1/28/2025

I'm working on seperating this into a scheduler and a sampler, and after I verify that each part works seamlessly, I'll be working on registering them as extensions instead of modifying the code base as part of its install. So some reading up on extensions is required and no timeframe for the next update. I'll be posting the original non-class back to my github so the way to install the older version remains the same.

No new updates at this time.

Previews

Version 1 (original)woman::{blonde_hair, midriff}_;, {red, microskirt::plaid;}, bra_and_wrap, high_heels::black_and_white;, full body, {lipstick}

Negative prompt: sleeves, wearing jewelry, bracelet, plaid_shirt, watermark, lettering, purse, sweater,

Steps: 35, Sampler: DPM++ 2M, Schedule type: Karras Exponential, CFG scale: 7, Seed: 94979616261, Size: 640x1160, Model hash: 989b125599, Model: Qasar_anireal, Clip skip: 2, Hypertile U-Net: True, Version: v1.10.1, Hashes: {"model": "989b125599"}

Version 2 (updated version)

woman::{blonde_hair, midriff}_;, {red, microskirt::plaid;}, bra_and_wrap, high_heels::black_and_white;, full body, {lipstick}

Negative prompt: sleeves, wearing jewelry, bracelet, plaid_shirt, watermark, lettering, purse, sweater,

Steps: 35, Sampler: DPM++ 2M, Schedule type: Karras Exponential, CFG scale: 7, Seed: 94979616261, Size: 640x1160, Model hash: 989b125599, Model: Qasar_anireal, Clip skip: 2, Hypertile U-Net: True, Version: v1.10.1, Hashes: {"model": "989b125599"}

Introduction

The Karras Exponential Scheduler is a method used in machine learning models, particularly diffusion models like Stable Diffusion, to control the noise levels and smooth transitions between different noise stages during the image generation process. It combines two mathematical approaches—Karras and Exponential—to dynamically adjust the noise over time, blending between them in a smooth and controlled way.

Unique Approach to Schedulers

  • Blending of Karras and Exponential methods for a smoother and more creative image generation process.

  • Dynamic adaptation of noise control, based on the stage of the generation, allowing for better detail retention and flexibility.

  • Customization and randomization options, enabling users to experiment and explore new creative outputs.

  • Smooth transitions between noise reduction techniques, avoiding abrupt or harsh changes in the image quality.

  • Focus on preventing oversmoothing, ensuring high-detail results even when blending two different methods.

  • User-friendly configurability, empowering users to tweak parameters for both stability and variety in the generated images.

These unique aspects make the Karras Exponential Scheduler highly versatile and powerful for diffusion-based image generation, appealing to both casual users and experts who want more control and creativity in their image synthesis.

Blending Two Methods (Karras and Exponential)

By blending these two methods, the Karras Exponential Scheduler creates a balance that gives both the smooth, high-quality results of Karras and the flexibility of the Exponential method. This dynamic blend is adaptively controlled based on the stage of the image generation process, making it unique in its ability to fine-tune how noise is removed at each step.

Dynamic and Adaptive Noise Control

The Karras Exponential Scheduler doesn't follow a fixed approach for the entire image generation process. It dynamically adjusts the noise reduction and blending parameters (like step size, blend factor, and noise scale) based on progress.

This adaptive nature allows the scheduler to handle different scenarios more flexibly than most other schedulers, which typically follow a fixed pattern.

Customization and Randomization Options

Unlike traditional schedulers that operate with fixed or predefined parameters, this scheduler includes customization and even randomization options. Users can tweak parameters like sigma_min, sigma_max, step size, and noise scale factor, or allow the system to randomize these within a set range.

This randomness adds an extra layer of uniqueness, giving users the ability to introduce variety in the generated images. It’s particularly useful for creating more diverse outputs with the same prompts or model, allowing for more exploratory creativity.

Smooth Transition Between Noise Reduction Techniques

One of the key features that sets this scheduler apart is how it handles smooth transitions between the Karras and Exponential methods. Most schedulers stick with one noise schedule (e.g., linear, exponential), but this scheduler can shift gradually between the two techniques during the image generation.

This makes the Karras Exponential Scheduler particularly well-suited for balancing stability (by following Karras for predictable results) and creativity (by adding variety through Exponential blending).

Avoiding Oversmoothing and Retaining Details

A common issue with many schedulers is that they can cause oversmoothing of the generated images, which can lead to a loss of detail and sharpness. The step size factor and noise scale factor used in this scheduler are designed to dynamically prevent oversmoothing, allowing the generated images to retain more detail and texture.

This is particularly useful for creating images that are both high-quality and rich in detail, without falling into the trap of being too "soft" or overly polished.

User-Friendly and Configurable

This scheduler is designed with user customization in mind, allowing for fine-tuning through a YAML configuration file. Users can adjust the ranges for each parameter (e.g., noise levels, blending factors) or even turn on full randomization to let the system generate a wide variety of outputs. The configuration file is a yaml file, and each configuration option has detailed tips on suggested settings.

I've focused on creating a scheduler which is very user friendly and customizable. You can tweak the settings manually or enjoy using the randomizer, and you never have to modify code, just tweak the configuration file.

The ability to configure the scheduler to such an extent is not as common with other schedulers, making the Karras Exponential Scheduler particularly appealing to advanced users who want more control over their image generation process.

Advanced Features for Exploratory Creativity

With its ability to blend noise reduction techniques, customize parameters, and randomize behavior, this scheduler is ideal for users who want to experiment with their models. The randomness and adaptability allow for creative exploration, giving users the opportunity to see unexpected and novel results that they might not get with more rigid, deterministic schedulers.

Overview

Schedulers

A scheduler is like a "controller" that decides how much noise should be added or removed in each step during image creation. Think of it as the rhythm or pace of the noise removal process.

Karras Method:

The Karras method follows a specific pattern to remove noise step by step, ensuring a smooth reduction of noise over time. It is well-suited for generating detailed images as it is designed to avoid sudden jumps in noise levels.

Exponential Method:

The Exponential method reduces noise more aggressively, removing larger chunks of noise at the start and then gradually slowing down. It’s more flexible but can lead to sharper or more varied image transitions compared to Karras.

Blending the Two Methods:

The Karras Exponential Scheduler combines these two methods. It starts off by following the Karras method (smooth and gradual) but incorporates elements of the Exponential method to add more variety or sharp transitions when needed.

The key idea is to blend between these two approaches, adapting the noise removal based on the current stage of image generation.

Key Functions:

  1. Sigma Values (Noise Levels):

    • Sigma represents the amount of noise applied to the image at each step. The scheduler controls these sigma values, blending between smooth (Karras) and aggressive (Exponential) noise removal as the image is generated.

  2. Dynamic Blending:

    • The scheduler adjusts the blend between Karras and Exponential based on a factor called the blend factor. At the start, it uses more of the Karras method (for smoothness), and as the generation progresses, it introduces more of the Exponential method (for variation and detail).

  3. Adaptive Parameters:

    • The scheduler also includes adaptive parameters like "step size" (which is different from steps used to create an image) and "noise scale" to fine-tune the generation. These adjust how large the steps are when removing noise and how much variation to introduce, making the image generation more flexible and tailored to the user's settings.

Imagine you're baking a cake, and you have two ways to bake it. One method (Karras) is slow and steady, baking the cake evenly over time. The other method (Exponential) cranks up the heat at the start but slows down as the cake gets closer to being done.

The Karras Exponential Scheduler is like a smart oven that mixes both methods. It starts baking slowly (like Karras) to ensure things go smoothly, but then uses bursts of heat (like Exponential) to speed things up and add some interesting textures.

In the world of image generation, this method controls how noise is added or removed from the image, helping to produce more creative and varied results while still keeping things smooth and controlled.

Why Is This Important?

  • Flexibility: It gives more control over how images are generated, allowing users to experiment with different effects.

  • Smooth Results: It avoids jarring transitions between noisy and clear stages, creating images that are visually appealing.

  • Creativity: By blending these two approaches, it allows for both predictable, smooth results and more surprising, varied images.

Sneak Peak

Here is a sneak peak at the process, viewed through a log file which can be generated when the config option for debug is true. I am looking into how to modify the A1111 code to generate the scheduler specific parameters along with the other generation data. Once that is complete, I plan on finishing up the documentation and then releasing it on github. Once the settings for randomized images can be created alongside each picture (the way infotext works in A1111), it will make generating the same image easier, as well as finding the best settings without manual testing.

With randomizer turned on, here are 4 images with the same settings, which show subtle variation between each picture. I did not have the log turned on for this image batch, so I will not be able to recreate any of these images exactly. Until the logs feature is added, I don't feel comfortable releasing this on github.

Prompt used was taken from an older version of Karras Exponential, with exact seed, size, and image parameters. The only difference being that the randomizer is turned on.

Reference Picture:

Image posted by KittensX

New Picture #1

beautiful young lady, upper body, wall-background, photo studio, bodycon dress, camoflauge,, (photographic realism, high-definition detail, lifelike textures, camera-like clarity, photo-like appearance),

Negative prompt: jewelry, crown, frame, painting, hairbow, tiara, child,, (((pixiv))),

Steps: 20, Sampler: DPM++ 2M Karras, Schedule type: Karras Exponential, CFG scale: 7, Seed: 3511977580, Size: 640x960, Model hash: 4a766596ae, Model: FusionX-Realistic_v2, Hypertile U-Net: True, Version: v1.10.1, Hashes: {"model": "4a766596ae"}

New Picture #2

beautiful young lady, upper body, wall-background, photo studio, bodycon dress, camoflauge,, (photographic realism, high-definition detail, lifelike textures, camera-like clarity, photo-like appearance),

Negative prompt: jewelry, crown, frame, painting, hairbow, tiara, child,, (((pixiv))),

Steps: 20, Sampler: DPM++ 2M Karras, Schedule type: Karras Exponential, CFG scale: 7, Seed: 3511977580, Size: 640x960, Model hash: 4a766596ae, Model: FusionX-Realistic_v2, Hypertile U-Net: True, Version: v1.10.1, Hashes: {"model": "4a766596ae"}

New Picture #3

beautiful young lady, upper body, wall-background, photo studio, bodycon dress, camoflauge,, (photographic realism, high-definition detail, lifelike textures, camera-like clarity, photo-like appearance),

Negative prompt: jewelry, crown, frame, painting, hairbow, tiara, child,, (((pixiv))),

Steps: 20, Sampler: DPM++ 2M Karras, Schedule type: Karras Exponential, CFG scale: 7, Seed: 3511977580, Size: 640x960, Model hash: 4a766596ae, Model: FusionX-Realistic_v2, Hypertile U-Net: True, Version: v1.10.1, Hashes: {"model": "4a766596ae"}

New Picture #4

beautiful young lady, upper body, wall-background, photo studio, bodycon dress, camoflauge,, (photographic realism, high-definition detail, lifelike textures, camera-like clarity, photo-like appearance),

Negative prompt: jewelry, crown, frame, painting, hairbow, tiara, child,, (((pixiv))),

Steps: 20, Sampler: DPM++ 2M Karras, Schedule type: Karras Exponential, CFG scale: 7, Seed: 3511977580, Size: 640x960, Model hash: 4a766596ae, Model: FusionX-Realistic_v2, Hypertile U-Net: True, Version: v1.10.1, Hashes: {"model": "4a766596ae"}

2024-10-22 01:37:07,498 - modules.simple_karras_exponential_scheduler - DEBUG - Using device: cpu
2024-10-22 01:37:07,506 - modules.simple_karras_exponential_scheduler - DEBUG - Generated sigma sequences. Karras: tensor([27.7349, 21.9697, 17.2637, 13.4497, 10.3820,  7.9347,  5.9995,  4.4838,
         3.3090,  2.4085,  1.7268,  1.2177,  0.8430,  0.5719,  0.3792,  0.2451,
         0.1539,  0.0934,  0.0546,  0.0305,  0.0000]), Exponential: tensor([27.7349, 19.3788, 13.5402,  9.4607,  6.6103,  4.6187,  3.2272,  2.2549,
         1.5755,  1.1008,  0.7692,  0.5374,  0.3755,  0.2624,  0.1833,  0.1281,
         0.0895,  0.0625,  0.0437,  0.0305,  0.0000])
2024-10-22 01:37:07,507 - modules.simple_karras_exponential_scheduler - DEBUG - Progress created tensor([0.0000, 0.0500, 0.1000, 0.1500, 0.2000, 0.2500, 0.3000, 0.3500, 0.4000,
        0.4500, 0.5000, 0.5500, 0.6000, 0.6500, 0.7000, 0.7500, 0.8000, 0.8500,
        0.9000, 0.9500, 1.0000])
2024-10-22 01:37:07,507 - modules.simple_karras_exponential_scheduler - DEBUG - Progress Using device: cpu
2024-10-22 01:37:07,507 - modules.simple_karras_exponential_scheduler - DEBUG - Sigs created tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
2024-10-22 01:37:07,507 - modules.simple_karras_exponential_scheduler - DEBUG - Sigs Using device: cpu
2024-10-22 01:37:07,508 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.7832680940628052
2024-10-22 01:37:07,508 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.19977597892284393
2024-10-22 01:37:07,508 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.3898767232894897
2024-10-22 01:37:07,508 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.060515791177749634
2024-10-22 01:37:07,508 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 27.73488426208496
2024-10-22 01:37:07,508 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.7516498565673828
2024-10-22 01:37:07,508 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.21512919664382935
2024-10-22 01:37:07,509 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.3508144617080688
2024-10-22 01:37:07,509 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.06899817287921906
2024-10-22 01:37:07,509 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 21.790895462036133
2024-10-22 01:37:07,509 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.7200316190719604
2024-10-22 01:37:07,509 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.23048241436481476
2024-10-22 01:37:07,509 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.3117523193359375
2024-10-22 01:37:07,509 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.07857006043195724
2024-10-22 01:37:07,509 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 16.971189498901367
2024-10-22 01:37:07,510 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.6884134411811829
2024-10-22 01:37:07,510 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.24583563208580017
2024-10-22 01:37:07,510 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.2726901769638062
2024-10-22 01:37:07,510 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.08934243768453598
2024-10-22 01:37:07,510 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 13.093320846557617
2024-10-22 01:37:07,511 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.6567952036857605
2024-10-22 01:37:07,511 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.2611888647079468
2024-10-22 01:37:07,511 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.2336279153823853
2024-10-22 01:37:07,511 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.10142917931079865
2024-10-22 01:37:07,511 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 9.99942398071289
2024-10-22 01:37:07,511 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.6251769065856934
2024-10-22 01:37:07,511 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.276542067527771
2024-10-22 01:37:07,511 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.1945656538009644
2024-10-22 01:37:07,512 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.114944688975811
2024-10-22 01:37:07,512 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 7.553524017333984
2024-10-22 01:37:07,512 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.5935586094856262
2024-10-22 01:37:07,512 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.2918952703475952
2024-10-22 01:37:07,512 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.1555033922195435
2024-10-22 01:37:07,512 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.13000056147575378
2024-10-22 01:37:07,512 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 5.639090061187744
2024-10-22 01:37:07,513 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.5619403719902039
2024-10-22 01:37:07,513 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.3072485029697418
2024-10-22 01:37:07,513 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.116441249847412
2024-10-22 01:37:07,513 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.14670167863368988
2024-10-22 01:37:07,513 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 4.156833648681641
2024-10-22 01:37:07,513 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.5303221940994263
2024-10-22 01:37:07,513 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.3226017355918884
2024-10-22 01:37:07,513 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.0773789882659912
2024-10-22 01:37:07,514 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.16514110565185547
2024-10-22 01:37:07,514 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 3.0227019786834717
2024-10-22 01:37:07,514 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.4987039268016815
2024-10-22 01:37:07,514 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.33795493841171265
2024-10-22 01:37:07,514 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 1.0383167266845703
2024-10-22 01:37:07,514 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.18539462983608246
2024-10-22 01:37:07,514 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 2.166069984436035
2024-10-22 01:37:07,514 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.46708571910858154
2024-10-22 01:37:07,515 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.35330817103385925
2024-10-22 01:37:07,515 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.999254584312439
2024-10-22 01:37:07,515 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.20751474797725677
2024-10-22 01:37:07,515 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 1.528085708618164
2024-10-22 01:37:07,515 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.4354674816131592
2024-10-22 01:37:07,515 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.3686613440513611
2024-10-22 01:37:07,515 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.9601923823356628
2024-10-22 01:37:07,516 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.23152391612529755
2024-10-22 01:37:07,516 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 1.0601764917373657
2024-10-22 01:37:07,516 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.40384921431541443
2024-10-22 01:37:07,516 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.3840146064758301
2024-10-22 01:37:07,516 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.9211300611495972
2024-10-22 01:37:07,516 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.2574087977409363
2024-10-22 01:37:07,516 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.7226848006248474
2024-10-22 01:37:07,516 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.37223100662231445
2024-10-22 01:37:07,517 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.3993678092956543
2024-10-22 01:37:07,517 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.8820679187774658
2024-10-22 01:37:07,517 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.2851138114929199
2024-10-22 01:37:07,517 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.4836310148239136
2024-10-22 01:37:07,517 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.3406127691268921
2024-10-22 01:37:07,517 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.4147210419178009
2024-10-22 01:37:07,517 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.8430057764053345
2024-10-22 01:37:07,517 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.31453776359558105
2024-10-22 01:37:07,518 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.31758636236190796
2024-10-22 01:37:07,518 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.3089945316314697
2024-10-22 01:37:07,518 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.4300742447376251
2024-10-22 01:37:07,518 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.8039435148239136
2024-10-22 01:37:07,518 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.34553053975105286
2024-10-22 01:37:07,518 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.20465229451656342
2024-10-22 01:37:07,518 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.277376264333725
2024-10-22 01:37:07,519 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.44542747735977173
2024-10-22 01:37:07,519 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.7648812532424927
2024-10-22 01:37:07,521 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.3778936564922333
2024-10-22 01:37:07,521 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.12953639030456543
2024-10-22 01:37:07,521 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.2457580268383026
2024-10-22 01:37:07,521 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.46078068017959595
2024-10-22 01:37:07,521 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.7258190512657166
2024-10-22 01:37:07,521 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.4113825559616089
2024-10-22 01:37:07,522 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.08072526752948761
2024-10-22 01:37:07,522 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.21413981914520264
2024-10-22 01:37:07,522 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.47613391280174255
2024-10-22 01:37:07,522 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.6867568492889404
2024-10-22 01:37:07,522 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.44571301341056824
2024-10-22 01:37:07,522 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.04974747449159622
2024-10-22 01:37:07,523 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.18252156674861908
2024-10-22 01:37:07,523 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.49148714542388916
2024-10-22 01:37:07,523 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.6476945877075195
2024-10-22 01:37:07,523 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.48056939244270325
2024-10-22 01:37:07,523 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.03052930347621441
2024-10-22 01:37:07,523 - modules.simple_karras_exponential_scheduler - DEBUG - Step_size created 0.15090332925319672
2024-10-22 01:37:07,523 - modules.simple_karras_exponential_scheduler - DEBUG - Dynamic_blend_factor created 0.5068403482437134
2024-10-22 01:37:07,523 - modules.simple_karras_exponential_scheduler - DEBUG - noise_scale created 0.6086323857307434
2024-10-22 01:37:07,524 - modules.simple_karras_exponential_scheduler - DEBUG - smooth_blend created 0.5156158804893494
2024-10-22 01:37:07,524 - modules.simple_karras_exponential_scheduler - DEBUG - blended_sigma created 0.0
2024-10-22 01:37:07,524 - modules.simple_karras_exponential_scheduler - DEBUG - sharpen_mask created tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
        1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9445, 0.9445,
        0.9445, 0.9445, 0.9445]) with device cpu

9

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