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https://github.com/kahsolt/stable-diffusion-webui-sonar

Wrapped k-diffuison samplers with tricks to improve the generated image quality (maybe?), extension script for AUTOMATIC1111/stable-diffusion-webui
https://github.com/kahsolt/stable-diffusion-webui-sonar

extensions stable-diffusion stable-diffusion-webui-plugin

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

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# stable-diffusion-webui-sonar



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



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ℹ This is the sister repo of [https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel](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



![momentum.png](img/momentum.png)



![grid.jpg](img/grid.jpg)



How momentum works:



- Basically at each sample step, the denoiser will modify the latent image by a `dx`

  - using [sd-extension-steps-animation](https://github.com/vladmandic/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](https://en.wikipedia.org/wiki/Damping#Damped_sine_wave)

- 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)



 Parameter tuning reference: 



- 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 `::`:

  - `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



![ref.png](img/ref.png)



⚠ 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 🤣



![ui](img/ui.png)



### 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_()`, 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



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by Armit

2022/11/16