Awesome

stable-diffusion-webui-sonar

Wrapped k-diffuison samplers with tricks to improve the generated image quality (maybe?), extension script for AUTOMATIC1111/stable-diffusion-webui

ℹ This is the sister repo of https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel, it focuses on single prompt optimization rather than traveling between multiple prompts.

The core idea of Sonar is to search for similar (yet even better!) images in the neighborhood of some known image generated by a normal denoising process. Technically to do this, we hack into the samplers and sampling processing, do some tricks like:

• momentum on latent difference
• txt2img under hard guidance of another given image (yet another img2img-like in a shallow fusion manner...)

to get image latents with higher quality (~perhaps!), and just pray again for good results 🤣

⚠ 我们成立了插件反馈 QQ 群: 616795645 (赤狐屿)，欢迎出建议、意见、报告bug等 (w
⚠ We have a QQ chat group now: 616795645, any suggeustions, discussions and bug reports are highly wellllcome !!

Change Log

⚪ Features

• 2023/10/17: work with SDXL models
• 2023/03/09: switch between two morphs (as AlwaysVisible for working with other scripts, as Script for supporting auto grid search)
• 2023/03/08: add grid search (free your hands!!)
• 2023/01/28: add upcale (issue #3)
• 2023/01/12: remove gradient-related functionality due to webui code change
• 2022/11/27: add momentum on Euler, add hard ref-image guidance on Naive
• 2022/11/20: add an Euler-like Naive, the simplest difference-estimation-based sampler with momentum & gradient
• 2022/11/18: add momentum on Euler a

⚪ Fixups

• 2023/05/09: updates to sd-webui v1.1.0 (issue #14: TypeError: CFGDenoiser.forward() missing 's_min_uncond')
• 2023/01/28: keep up with webui's updates (issue #4: NameError: name 'CFGDenoiser' is not defined)
• 2023/01/28: keep up with webui's updates of extra-networks
• 2023/01/12: keep up with webui's updates (issue #2: AttributeError: 'Options' object has no attribute 'filter_nsfw')
• 2023/01/03: keep up with webui's updates (issue #1: AttributeError: 'StableDiffusionProcessingTxt2Img' object has no attribute 'firstphase_height')

Examples

⚪ momentum

How momentum works:

• Basically at each sample step, the denoiser will modify the latent image by a dx
  • using sd-extension-steps-animation you can inspect into the sampling process
  • this dx is also to some extent understood as gradient or differential
• And the sigma schedule (like karras) controls denoiser's step-size (i.e. magnitude of dx), to assure the process annealing so that the final output converges at some place
  • see damping signal
• Momentum mechanism memorizes kind of history of dx, and is used to increase (when pos) or reduce (when neg) the damping magitude
  • a bit like the LMS/PLMS sampler, but works on gradient level
  • by this way, you can anneal your noisy latent image to some other places nearby (works like subseed...)
  • by this way, you could retain some details which is normally taken as noise and removed by a denoiser (subseed might not be capable for this)

<del> Parameter tuning reference: </del>

• set Momentum (current) to 1, this will give you an image without momentum mechanism
• tune Momentum (current) and Momentum (history) to see what will vary...
  • Momentum (current) is weighted-accumulated 1st-order gradient, usually should >= 0.85
  • Momentum (history) is weighted-accumulated of all higher-than-1st-order gradients
  • probably keep Momentum sign = pos and Momentum history init = zero
  • because other parameters are pure experimental and I could not explain in brief... 🤣

=> Just run a grid search first:

• set Momentum (current) search list and Momentum (history) search list
• the full syntax is <start>:<stop>:<step>:
  • 0.75:0.95:5 => [0.75, 0.8, 0.85, 0.9, 0.95], when step > 1 is an int, parse as step count
  • 0.95:0.75:-0.1 => [0.95, 0.85, 0.75], when -1.0 < step < 1.0 is a float, parse as step size
  • 0.75:0.95 => [0.75, 0.85, 0.95], by default step=3
  • 0.75 => [0.75], just one constant
  • when left empty, will read from the Slider value accordingly
• run Generate!

⚪ ref_img guide

⚠ Above images are not intented to show the best results, but showing the trendency. You shall carefully tune these hparams all by yourself to get good results. 🤣

How hard ref_img guidance works:

• After each denoise step, make an extra step towards the given image in latent space
• It is a kind of shallow fusion, thus ...
  • in fact needs a carefully step-size scheduling, but not implemented yet :(
  • when the given condition (the digested prompts) mismatches the ref_img very much, they will fight, and the canvas would be again and again overwriten, giving bad final results 🤣

Options

• base_sampler: (categorical)
  • Eular a: Eular with ancestral noise sampling
  • Eular: Eular the original
  • Naive: a so simple, sometimes naive sampler but to start anything from
• momentum_*
  • momentum: (float), momentum of current latent difference
    • the larger, approving the current difference, (set 1.0 to disable momentum mechanism)
    • the smaller, approving the history difference momentum
  • momentum_hist: (float), momentum of memorized difference history
    • the larger, approving current history
    • the smaller, approving former histories
  • momentum_hist_init: (categorical), init value of history, aka. the genesis (experimental)
    • zero: use the first denoised latent
    • rand_init: just use the init latent noise
    • rand_new: use a new guassian noise
  • momentum_sign: (categorical), momentum direction to apply correction (experimental)
    • pos: correct by direction of history momentum, affirming the history
    • neg: correct by opposite direction of history momentum, denying the history
    • rand: random choose from above at each sampling step
    • NOTE: option neg works well only if momentum_hist is enough large (~0.9)
• ref_*
  • ref_img: (file), reference image file
  • ref_meth: (categorical)
    • linear: linear interpolate between current and ref latent
    • euler: make an eular step from current to ref latent
  • ref_start_step: (float, int)
  • ref_stop_step: (float, int)
    • sampling step range which enables the ref guidance mechanism (kind of scheduling)
    • if > 1, parse as step number; if <= 1, parse as percentage of total steps
• upscale_*
  • upscaler: (categorical)
  • ratio: (float)
  • width: (int)
  • height: (int)
    • if width==height==0, upscale by the specified ratio
    • if width==0 or height==0, the zero one will be auto calculated to match the non-zero one, keeping the aspect-raio
    • if width!=0 and height!=0, upscale while keeping the aspect-raio to cover the target size, then crop the excess if necessary

Developers

This repo allows your to quickly implement your own k-diffusion samplers, follow to do this:

• creart a sampling procedure sample_<name>(), you can refer to the skeleton sampler sample_naive()
• add a SamplerData entry in all_samplers_sonar
• design ui components for your sampler hparams in ui(), then modify swith_sampler() to show/hide related tabs
• restart webui and play with your own sampler~

Installation

Easiest way to install it is to:

1. Go to the "Extensions" tab in the webui, switch to the "Install from URL" tab
2. Paste https://github.com/Kahsolt/stable-diffusion-webui-sonar.git into "URL for extension's git repository" and click install

Manual install:

1. Copy this repo folder to the 'extensions' folder of https://github.com/AUTOMATIC1111/stable-diffusion-webui

by Armit 2022/11/16