Qing
GitHub
commit
279f6d2138
@ -28,6 +28,7 @@
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1. [ZITS](https://github.com/DQiaole/ZITS_inpainting)
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1. [MAT](https://github.com/fenglinglwb/MAT)
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1. [FcF](https://github.com/SHI-Labs/FcF-Inpainting)
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1. [SD1.4](https://github.com/CompVis/stable-diffusion)
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- Support CPU & GPU
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- Various inpainting [strategy](#inpainting-strategy)
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- Run as a desktop APP
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@ -41,6 +42,7 @@
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| Remove Text |  |  |
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| Remove watermark |  |  |
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| Fix old photo |  |  |
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| Text Driven Inpainting |  |  |
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## Quick Start
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@ -55,8 +57,9 @@ lama-cleaner --model=lama --device=cpu --port=8080
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Available arguments:
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| Name | Description | Default |
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| ---------- | ---------------------------------------------------------------- | -------- |
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| --model | lama/ldm/zits. See details in [Inpaint Model](#inpainting-model) | lama |
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| ----------------- | -------------------------------------------------------------------------------------------------------- | -------- |
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| --model | lama/ldm/zits/mat/fcf/sd. See details in [Inpaint Model](#inpainting-model) | lama |
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| --hf_access_token | stable-diffusion(sd) model need huggingface access token https://huggingface.co/docs/hub/security-tokens | |
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| --device | cuda or cpu | cuda |
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| --port | Port for backend flask web server | 8080 |
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| --gui | Launch lama-cleaner as a desktop application | |
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@ -73,6 +76,7 @@ Available arguments:
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| ZITS | :+1: Better holistic structures compared with previous methods <br/> :neutral_face: Wireframe module is **very** slow on CPU | `Wireframe`: Enable edge and line detect |
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| MAT | TODO | |
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| FcF | :+1: Better structure and texture generation <br/> :neutral_face: Only support fixed size (512x512) input | |
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| SD1.4 | :+1: SOTA text-to-image diffusion model | |
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### LaMa vs LDM
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BIN
assets/dog.jpg
Normal file
BIN
assets/dog.jpg
Normal file
Binary file not shown.
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After Width: | Height: | Size: 44 KiB |
BIN
assets/fox.jpg
Normal file
BIN
assets/fox.jpg
Normal file
Binary file not shown.
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After Width: | Height: | Size: 43 KiB |
@ -6,6 +6,7 @@
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"dependencies": {
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"@heroicons/react": "^1.0.4",
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"@radix-ui/react-dialog": "0.1.8-rc.25",
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"@radix-ui/react-popover": "^1.0.0",
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"@radix-ui/react-select": "0.1.2-rc.27",
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"@radix-ui/react-switch": "^0.1.5",
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"@radix-ui/react-toast": "^0.1.1",
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@ -20,14 +21,17 @@
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"@types/react-dom": "^17.0.9",
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"cross-env": "7.x",
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"lodash": "^4.17.21",
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"mitt": "^3.0.0",
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"nanoid": "^4.0.0",
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"npm-run-all": "4.x",
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"react": "^17.0.2",
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"react-dom": "^17.0.2",
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"react-hotkeys-hook": "^3.4.7",
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"react-scripts": "4.0.3",
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"react-use": "^17.3.1",
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"react-zoom-pan-pinch": "^2.1.3",
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"recoil": "^0.6.1",
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"socket.io-client": "^4.5.2",
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"typescript": "4.x"
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},
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"scripts": {
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@ -1,5 +1,4 @@
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import React, { useEffect, useMemo } from 'react'
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import { useKeyPressEvent } from 'react-use'
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import { useRecoilState } from 'recoil'
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import { nanoid } from 'nanoid'
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import useInputImage from './hooks/useInputImage'
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@ -9,6 +8,7 @@ import Workspace from './components/Workspace'
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import { fileState } from './store/Atoms'
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import { keepGUIAlive } from './utils'
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import Header from './components/Header/Header'
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import useHotKey from './hooks/useHotkey'
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// Keeping GUI Window Open
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keepGUIAlive()
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@ -24,11 +24,15 @@ function App() {
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}, [userInputImage, setFile])
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// Dark Mode Hotkey
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useKeyPressEvent('D', ev => {
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ev?.preventDefault()
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useHotKey(
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'shift+d',
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() => {
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const newTheme = theme === 'light' ? 'dark' : 'light'
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setTheme(newTheme)
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})
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},
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{},
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[theme]
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)
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useEffect(() => {
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document.body.setAttribute('data-theme', theme)
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@ -1,4 +1,4 @@
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import { Settings } from '../store/Atoms'
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import { Rect, Settings } from '../store/Atoms'
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import { dataURItoBlob } from '../utils'
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export const API_ENDPOINT = `${process.env.REACT_APP_INPAINTING_URL}`
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@ -7,7 +7,10 @@ export default async function inpaint(
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imageFile: File,
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maskBase64: string,
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settings: Settings,
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sizeLimit?: string
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croperRect: Rect,
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prompt?: string,
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sizeLimit?: string,
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seed?: number
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) {
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// 1080, 2000, Original
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const fd = new FormData()
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@ -30,23 +33,38 @@ export default async function inpaint(
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hdSettings.hdStrategyResizeLimit.toString()
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)
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fd.append('prompt', prompt === undefined ? '' : prompt)
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fd.append('croperX', croperRect.x.toString())
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fd.append('croperY', croperRect.y.toString())
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fd.append('croperHeight', croperRect.height.toString())
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fd.append('croperWidth', croperRect.width.toString())
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fd.append('useCroper', settings.showCroper ? 'true' : 'false')
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fd.append('sdMaskBlur', settings.sdMaskBlur.toString())
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fd.append('sdStrength', settings.sdStrength.toString())
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fd.append('sdSteps', settings.sdSteps.toString())
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fd.append('sdGuidanceScale', settings.sdGuidanceScale.toString())
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fd.append('sdSampler', settings.sdSampler.toString())
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fd.append('sdSeed', seed ? seed.toString() : '-1')
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if (sizeLimit === undefined) {
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fd.append('sizeLimit', '1080')
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} else {
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fd.append('sizeLimit', sizeLimit)
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}
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try {
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const res = await fetch(`${API_ENDPOINT}/inpaint`, {
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method: 'POST',
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body: fd,
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}).then(async r => {
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if (r.ok) {
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return r.blob()
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}
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throw new Error('Something went wrong on server side.')
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})
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return URL.createObjectURL(res)
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if (res.ok) {
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const blob = await res.blob()
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const newSeed = res.headers.get('x-seed')
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return { blob: URL.createObjectURL(blob), seed: newSeed }
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}
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} catch {
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throw new Error('Something went wrong on server side.')
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}
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}
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export function switchModel(name: string) {
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167
lama_cleaner/app/src/components/Croper/Croper.scss
Normal file
167
lama_cleaner/app/src/components/Croper/Croper.scss
Normal file
@ -0,0 +1,167 @@
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@use 'sass:math';
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$drag-handle-shortside: 12px;
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$drag-handle-longside: 40px;
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$drag-bar-size: 12px;
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$half-handle-shortside: math.div($drag-handle-shortside, 2);
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$half-handle-longside: math.div($drag-handle-longside, 2);
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$half-drag-bar-size: math.div($drag-bar-size, 2);
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.crop-border {
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outline-color: var(--yellow-accent);
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outline-style: dashed;
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}
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.info-bar {
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position: absolute;
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pointer-events: auto;
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font-size: 1rem;
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padding: 0.2rem 0.8rem;
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display: flex;
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align-items: center;
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justify-content: center;
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gap: 12px;
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color: var(--text-color);
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background-color: var(--page-bg);
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border-radius: 9999px;
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border: var(--editor-toolkit-panel-border);
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box-shadow: 0 0 0 1px #0000001a, 0 3px 16px #00000014, 0 2px 6px 1px #00000017;
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&:hover {
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cursor: move;
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}
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}
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.croper-wrapper {
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position: absolute;
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height: 100%;
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width: 100%;
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z-index: 2;
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overflow: hidden;
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pointer-events: none;
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}
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.croper {
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position: relative;
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top: 0;
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bottom: 0;
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left: 0;
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right: 0;
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z-index: 2;
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pointer-events: none;
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// display: flex;
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// flex-direction: column;
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// align-items: center;
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box-shadow: 0 0 0 9999px rgba(0, 0, 0, 0.5);
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}
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.drag-bar {
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position: absolute;
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pointer-events: auto;
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// display: none;
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&.ord-top {
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top: 0;
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left: 0;
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width: 100%;
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height: $drag-bar-size;
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margin-top: -$half-drag-bar-size;
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cursor: ns-resize;
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}
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&.ord-right {
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right: 0;
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top: 0;
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width: $drag-bar-size;
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height: 100%;
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margin-right: -$half-drag-bar-size;
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cursor: ew-resize;
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}
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&.ord-bottom {
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bottom: 0;
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left: 0;
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width: 100%;
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height: $drag-bar-size;
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margin-bottom: -$half-drag-bar-size;
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cursor: ns-resize;
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}
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&.ord-left {
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top: 0;
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left: 0;
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width: $drag-bar-size;
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height: 100%;
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margin-left: -$half-drag-bar-size;
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cursor: ew-resize;
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}
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}
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.drag-handle {
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width: $drag-handle-shortside;
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height: $drag-handle-shortside;
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z-index: 4;
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position: absolute;
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display: block;
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content: '';
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border: 2px solid var(--yellow-accent);
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background-color: var(--yellow-accent-light);
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pointer-events: auto;
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&:hover {
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background-color: var(--yellow-accent);
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}
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&.ord-topleft {
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cursor: nw-resize;
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top: (-$half-handle-shortside)-1px;
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left: (-$half-handle-shortside)-1px;
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}
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&.ord-topright {
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cursor: ne-resize;
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top: -($half-handle-shortside)-1px;
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right: -($half-handle-shortside)-1px;
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}
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&.ord-bottomright {
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cursor: se-resize;
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bottom: -($half-handle-shortside)-1px;
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right: -($half-handle-shortside)-1px;
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}
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&.ord-bottomleft {
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cursor: sw-resize;
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bottom: -($half-handle-shortside)-1px;
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left: -($half-handle-shortside)-1px;
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}
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&.ord-top,
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&.ord-bottom {
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left: calc(50% - $half-handle-shortside);
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cursor: ns-resize;
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}
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&.ord-top {
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top: (-$half-handle-shortside)-1px;
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}
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&.ord-bottom {
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bottom: -($half-handle-shortside)-1px;
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}
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&.ord-left,
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&.ord-right {
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top: calc(50% - $half-handle-shortside);
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cursor: ew-resize;
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}
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&.ord-left {
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left: (-$half-handle-shortside)-1px;
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}
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&.ord-right {
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right: -($half-handle-shortside)-1px;
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}
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}
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381
lama_cleaner/app/src/components/Croper/Croper.tsx
Normal file
381
lama_cleaner/app/src/components/Croper/Croper.tsx
Normal file
@ -0,0 +1,381 @@
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import { ChevronLeftIcon, ChevronRightIcon } from '@heroicons/react/outline'
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import React, { useEffect, useState } from 'react'
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import { useRecoilState, useRecoilValue } from 'recoil'
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import {
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croperHeight,
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croperWidth,
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croperX,
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croperY,
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isInpaintingState,
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} from '../../store/Atoms'
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const DOC_MOVE_OPTS = { capture: true, passive: false }
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const DRAG_HANDLE_BORDER = 2
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const DRAG_HANDLE_SHORT = 12
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const DRAG_HANDLE_LONG = 40
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interface EVData {
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initX: number
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initY: number
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initHeight: number
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initWidth: number
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startResizeX: number
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startResizeY: number
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ord: string // top/right/bottom/left
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}
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interface Props {
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maxHeight: number
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maxWidth: number
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scale: number
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minHeight: number
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minWidth: number
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}
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const Croper = (props: Props) => {
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const { minHeight, minWidth, maxHeight, maxWidth, scale } = props
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const [x, setX] = useRecoilState(croperX)
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const [y, setY] = useRecoilState(croperY)
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const [height, setHeight] = useRecoilState(croperHeight)
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const [width, setWidth] = useRecoilState(croperWidth)
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const isInpainting = useRecoilValue(isInpaintingState)
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const [isResizing, setIsResizing] = useState(false)
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const [isMoving, setIsMoving] = useState(false)
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useEffect(() => {
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setX(Math.round((maxWidth - 512) / 2))
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setY(Math.round((maxHeight - 512) / 2))
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}, [maxHeight, maxWidth, minHeight, minWidth])
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const [evData, setEVData] = useState<EVData>({
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initX: 0,
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initY: 0,
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initHeight: 0,
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initWidth: 0,
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startResizeX: 0,
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startResizeY: 0,
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ord: 'top',
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})
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|
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const onDragFocus = () => {
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console.log('focus')
|
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}
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|
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const checkTopBottomLimit = (newY: number, newHeight: number) => {
|
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if (newY > 0 && newHeight > minHeight && newY + newHeight <= maxHeight) {
|
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return true
|
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}
|
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return false
|
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}
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|
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const checkLeftRightLimit = (newX: number, newWidth: number) => {
|
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if (newX > 0 && newWidth > minWidth && newX + newWidth <= maxWidth) {
|
||||
return true
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
const onPointerMove = (e: PointerEvent) => {
|
||||
if (isInpainting) {
|
||||
return
|
||||
}
|
||||
const curX = e.clientX
|
||||
const curY = e.clientY
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||||
|
||||
const offsetY = Math.round((curY - evData.startResizeY) / scale)
|
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const offsetX = Math.round((curX - evData.startResizeX) / scale)
|
||||
|
||||
const moveTop = () => {
|
||||
const newHeight = evData.initHeight - offsetY
|
||||
const newY = evData.initY + offsetY
|
||||
if (checkTopBottomLimit(newY, newHeight)) {
|
||||
setHeight(newHeight)
|
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setY(newY)
|
||||
}
|
||||
}
|
||||
|
||||
const moveBottom = () => {
|
||||
const newHeight = evData.initHeight + offsetY
|
||||
if (checkTopBottomLimit(evData.initY, newHeight)) {
|
||||
setHeight(newHeight)
|
||||
}
|
||||
}
|
||||
|
||||
const moveLeft = () => {
|
||||
const newWidth = evData.initWidth - offsetX
|
||||
const newX = evData.initX + offsetX
|
||||
if (checkLeftRightLimit(newX, newWidth)) {
|
||||
setWidth(newWidth)
|
||||
setX(newX)
|
||||
}
|
||||
}
|
||||
|
||||
const moveRight = () => {
|
||||
const newWidth = evData.initWidth + offsetX
|
||||
if (checkLeftRightLimit(evData.initX, newWidth)) {
|
||||
setWidth(newWidth)
|
||||
}
|
||||
}
|
||||
|
||||
if (isResizing) {
|
||||
switch (evData.ord) {
|
||||
case 'topleft': {
|
||||
moveTop()
|
||||
moveLeft()
|
||||
break
|
||||
}
|
||||
case 'topright': {
|
||||
moveTop()
|
||||
moveRight()
|
||||
break
|
||||
}
|
||||
case 'bottomleft': {
|
||||
moveBottom()
|
||||
moveLeft()
|
||||
break
|
||||
}
|
||||
case 'bottomright': {
|
||||
moveBottom()
|
||||
moveRight()
|
||||
break
|
||||
}
|
||||
case 'top': {
|
||||
moveTop()
|
||||
break
|
||||
}
|
||||
case 'right': {
|
||||
moveRight()
|
||||
break
|
||||
}
|
||||
case 'bottom': {
|
||||
moveBottom()
|
||||
break
|
||||
}
|
||||
case 'left': {
|
||||
moveLeft()
|
||||
break
|
||||
}
|
||||
|
||||
default:
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
if (isMoving) {
|
||||
const newX = evData.initX + offsetX
|
||||
const newY = evData.initY + offsetY
|
||||
if (
|
||||
checkLeftRightLimit(newX, evData.initWidth) &&
|
||||
checkTopBottomLimit(newY, evData.initHeight)
|
||||
) {
|
||||
setX(newX)
|
||||
setY(newY)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const onPointerDone = (e: PointerEvent) => {
|
||||
if (isResizing) {
|
||||
setIsResizing(false)
|
||||
}
|
||||
|
||||
if (isMoving) {
|
||||
setIsMoving(false)
|
||||
}
|
||||
}
|
||||
|
||||
useEffect(() => {
|
||||
if (isResizing || isMoving) {
|
||||
document.addEventListener('pointermove', onPointerMove, DOC_MOVE_OPTS)
|
||||
document.addEventListener('pointerup', onPointerDone, DOC_MOVE_OPTS)
|
||||
document.addEventListener('pointercancel', onPointerDone, DOC_MOVE_OPTS)
|
||||
return () => {
|
||||
document.removeEventListener(
|
||||
'pointermove',
|
||||
onPointerMove,
|
||||
DOC_MOVE_OPTS
|
||||
)
|
||||
document.removeEventListener('pointerup', onPointerDone, DOC_MOVE_OPTS)
|
||||
document.removeEventListener(
|
||||
'pointercancel',
|
||||
onPointerDone,
|
||||
DOC_MOVE_OPTS
|
||||
)
|
||||
}
|
||||
}
|
||||
}, [isResizing, isMoving, width, height, evData])
|
||||
|
||||
const onCropPointerDown = (e: React.PointerEvent<HTMLDivElement>) => {
|
||||
const { ord } = (e.target as HTMLElement).dataset
|
||||
if (ord) {
|
||||
setIsResizing(true)
|
||||
setEVData({
|
||||
initX: x,
|
||||
initY: y,
|
||||
initHeight: height,
|
||||
initWidth: width,
|
||||
startResizeX: e.clientX,
|
||||
startResizeY: e.clientY,
|
||||
ord,
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
const createCropSelection = () => {
|
||||
return (
|
||||
<div
|
||||
className="drag-elements"
|
||||
onFocus={onDragFocus}
|
||||
onPointerDown={onCropPointerDown}
|
||||
>
|
||||
<div
|
||||
className="drag-bar ord-top"
|
||||
data-ord="top"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
<div
|
||||
className="drag-bar ord-right"
|
||||
data-ord="right"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
<div
|
||||
className="drag-bar ord-bottom"
|
||||
data-ord="bottom"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
<div
|
||||
className="drag-bar ord-left"
|
||||
data-ord="left"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
|
||||
<div
|
||||
className="drag-handle ord-topleft"
|
||||
data-ord="topleft"
|
||||
aria-label="topleft"
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
|
||||
<div
|
||||
className="drag-handle ord-topright"
|
||||
data-ord="topright"
|
||||
aria-label="topright"
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
|
||||
<div
|
||||
className="drag-handle ord-bottomleft"
|
||||
data-ord="bottomleft"
|
||||
aria-label="bottomleft"
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
|
||||
<div
|
||||
className="drag-handle ord-bottomright"
|
||||
data-ord="bottomright"
|
||||
aria-label="bottomright"
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
|
||||
<div
|
||||
className="drag-handle ord-top"
|
||||
data-ord="top"
|
||||
aria-label="top"
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
<div
|
||||
className="drag-handle ord-right"
|
||||
data-ord="right"
|
||||
aria-label="right"
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
<div
|
||||
className="drag-handle ord-bottom"
|
||||
data-ord="bottom"
|
||||
aria-label="bottom"
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
<div
|
||||
className="drag-handle ord-left"
|
||||
data-ord="left"
|
||||
aria-label="left"
|
||||
tabIndex={0}
|
||||
role="button"
|
||||
style={{ transform: `scale(${1 / scale})` }}
|
||||
/>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
const onInfoBarPointerDown = (e: React.PointerEvent<HTMLDivElement>) => {
|
||||
setIsMoving(true)
|
||||
setEVData({
|
||||
initX: x,
|
||||
initY: y,
|
||||
initHeight: height,
|
||||
initWidth: width,
|
||||
startResizeX: e.clientX,
|
||||
startResizeY: e.clientY,
|
||||
ord: '',
|
||||
})
|
||||
}
|
||||
|
||||
const createInfoBar = () => {
|
||||
return (
|
||||
<div
|
||||
className="info-bar"
|
||||
onPointerDown={onInfoBarPointerDown}
|
||||
style={{
|
||||
transform: `scale(${1 / scale})`,
|
||||
top: `${10 / scale}px`,
|
||||
left: `${10 / scale}px`,
|
||||
}}
|
||||
>
|
||||
<div className="crop-size">
|
||||
{width} x {height}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
const createBorder = () => {
|
||||
return (
|
||||
<div
|
||||
className="crop-border"
|
||||
style={{
|
||||
height,
|
||||
width,
|
||||
outlineWidth: `${DRAG_HANDLE_BORDER / scale}px`,
|
||||
}}
|
||||
/>
|
||||
)
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="croper-wrapper">
|
||||
<div className="croper" style={{ height, width, left: x, top: y }}>
|
||||
{createBorder()}
|
||||
{createInfoBar()}
|
||||
{createCropSelection()}
|
||||
</div>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default Croper
|
||||
@ -55,7 +55,7 @@
|
||||
position: fixed;
|
||||
bottom: 0.5rem;
|
||||
border-radius: 3rem;
|
||||
padding: 1rem 3rem;
|
||||
padding: 0.6rem 3rem;
|
||||
display: grid;
|
||||
grid-template-areas: 'toolkit-size-selector toolkit-brush-slider toolkit-btns';
|
||||
column-gap: 2rem;
|
||||
|
||||
@ -22,7 +22,6 @@ import Button from '../shared/Button'
|
||||
import Slider from './Slider'
|
||||
import SizeSelector from './SizeSelector'
|
||||
import {
|
||||
dataURItoBlob,
|
||||
downloadImage,
|
||||
isMidClick,
|
||||
isRightClick,
|
||||
@ -30,7 +29,19 @@ import {
|
||||
srcToFile,
|
||||
useImage,
|
||||
} from '../../utils'
|
||||
import { settingState, toastState } from '../../store/Atoms'
|
||||
import {
|
||||
croperState,
|
||||
isInpaintingState,
|
||||
isSDState,
|
||||
propmtState,
|
||||
runManuallyState,
|
||||
seedState,
|
||||
settingState,
|
||||
toastState,
|
||||
} from '../../store/Atoms'
|
||||
import useHotKey from '../../hooks/useHotkey'
|
||||
import Croper from '../Croper/Croper'
|
||||
import emitter, { EVENT_PROMPT } from '../../event'
|
||||
|
||||
const TOOLBAR_SIZE = 200
|
||||
const BRUSH_COLOR = '#ffcc00bb'
|
||||
@ -74,8 +85,15 @@ function mouseXY(ev: SyntheticEvent) {
|
||||
|
||||
export default function Editor(props: EditorProps) {
|
||||
const { file } = props
|
||||
const promptVal = useRecoilValue(propmtState)
|
||||
const settings = useRecoilValue(settingState)
|
||||
const [seedVal, setSeed] = useRecoilState(seedState)
|
||||
const croperRect = useRecoilValue(croperState)
|
||||
const [toastVal, setToastState] = useRecoilState(toastState)
|
||||
const [isInpainting, setIsInpainting] = useRecoilState(isInpaintingState)
|
||||
const runMannually = useRecoilValue(runManuallyState)
|
||||
const isSD = useRecoilValue(isSDState)
|
||||
|
||||
const [brushSize, setBrushSize] = useState(40)
|
||||
const [original, isOriginalLoaded] = useImage(file)
|
||||
const [renders, setRenders] = useState<HTMLImageElement[]>([])
|
||||
@ -84,13 +102,13 @@ export default function Editor(props: EditorProps) {
|
||||
return document.createElement('canvas')
|
||||
})
|
||||
const [lineGroups, setLineGroups] = useState<LineGroup[]>([])
|
||||
const [lastLineGroup, setLastLineGroup] = useState<LineGroup>([])
|
||||
const [curLineGroup, setCurLineGroup] = useState<LineGroup>([])
|
||||
const [{ x, y }, setCoords] = useState({ x: -1, y: -1 })
|
||||
const [showBrush, setShowBrush] = useState(false)
|
||||
const [showRefBrush, setShowRefBrush] = useState(false)
|
||||
const [isPanning, setIsPanning] = useState<boolean>(false)
|
||||
const [showOriginal, setShowOriginal] = useState(false)
|
||||
const [isInpaintingLoading, setIsInpaintingLoading] = useState(false)
|
||||
const [scale, setScale] = useState<number>(1)
|
||||
const [panned, setPanned] = useState<boolean>(false)
|
||||
const [minScale, setMinScale] = useState<number>(1.0)
|
||||
@ -130,7 +148,8 @@ export default function Editor(props: EditorProps) {
|
||||
[context, original]
|
||||
)
|
||||
|
||||
const drawLinesOnMask = (_lineGroups: LineGroup[]) => {
|
||||
const drawLinesOnMask = useCallback(
|
||||
(_lineGroups: LineGroup[]) => {
|
||||
if (!context?.canvas.width || !context?.canvas.height) {
|
||||
throw new Error('canvas has invalid size')
|
||||
}
|
||||
@ -144,69 +163,13 @@ export default function Editor(props: EditorProps) {
|
||||
_lineGroups.forEach(lineGroup => {
|
||||
drawLines(ctx, lineGroup, 'white')
|
||||
})
|
||||
}
|
||||
|
||||
const runInpainting = async () => {
|
||||
if (!hadDrawSomething()) {
|
||||
return
|
||||
}
|
||||
|
||||
const newLineGroups = [...lineGroups, curLineGroup]
|
||||
setCurLineGroup([])
|
||||
setIsDraging(false)
|
||||
setIsInpaintingLoading(true)
|
||||
if (settings.graduallyInpainting) {
|
||||
drawLinesOnMask([curLineGroup])
|
||||
} else {
|
||||
drawLinesOnMask(newLineGroups)
|
||||
}
|
||||
|
||||
let targetFile = file
|
||||
if (settings.graduallyInpainting === true && renders.length > 0) {
|
||||
console.info('gradually inpainting on last result')
|
||||
const lastRender = renders[renders.length - 1]
|
||||
targetFile = await srcToFile(lastRender.currentSrc, file.name, file.type)
|
||||
}
|
||||
|
||||
try {
|
||||
const res = await inpaint(
|
||||
targetFile,
|
||||
maskCanvas.toDataURL(),
|
||||
settings,
|
||||
sizeLimit.toString()
|
||||
},
|
||||
[context, maskCanvas]
|
||||
)
|
||||
if (!res) {
|
||||
throw new Error('empty response')
|
||||
}
|
||||
const newRender = new Image()
|
||||
await loadImage(newRender, res)
|
||||
const newRenders = [...renders, newRender]
|
||||
setRenders(newRenders)
|
||||
draw(newRender, [])
|
||||
// Only append new LineGroup after inpainting success
|
||||
setLineGroups(newLineGroups)
|
||||
|
||||
// clear redo stack
|
||||
resetRedoState()
|
||||
} catch (e: any) {
|
||||
setToastState({
|
||||
open: true,
|
||||
desc: e.message ? e.message : e.toString(),
|
||||
state: 'error',
|
||||
duration: 2000,
|
||||
})
|
||||
drawOnCurrentRender([])
|
||||
}
|
||||
setIsInpaintingLoading(false)
|
||||
}
|
||||
|
||||
const hadDrawSomething = () => {
|
||||
const hadDrawSomething = useCallback(() => {
|
||||
return curLineGroup.length !== 0
|
||||
}
|
||||
|
||||
const hadRunInpainting = () => {
|
||||
return renders.length !== 0
|
||||
}
|
||||
}, [curLineGroup])
|
||||
|
||||
const drawOnCurrentRender = useCallback(
|
||||
(lineGroup: LineGroup) => {
|
||||
@ -219,8 +182,154 @@ export default function Editor(props: EditorProps) {
|
||||
[original, renders, draw]
|
||||
)
|
||||
|
||||
const runInpainting = useCallback(
|
||||
async (prompt?: string, useLastLineGroup?: boolean) => {
|
||||
// useLastLineGroup 的影响
|
||||
// 1. 使用上一次的 mask
|
||||
// 2. 结果替换当前 render
|
||||
console.log('runInpainting')
|
||||
|
||||
let maskLineGroup = []
|
||||
if (useLastLineGroup === true) {
|
||||
if (lastLineGroup.length === 0) {
|
||||
return
|
||||
}
|
||||
maskLineGroup = lastLineGroup
|
||||
} else {
|
||||
if (!hadDrawSomething()) {
|
||||
return
|
||||
}
|
||||
|
||||
setLastLineGroup(curLineGroup)
|
||||
maskLineGroup = curLineGroup
|
||||
}
|
||||
|
||||
const newLineGroups = [...lineGroups, maskLineGroup]
|
||||
|
||||
setCurLineGroup([])
|
||||
setIsDraging(false)
|
||||
setIsInpainting(true)
|
||||
if (settings.graduallyInpainting) {
|
||||
drawLinesOnMask([maskLineGroup])
|
||||
} else {
|
||||
drawLinesOnMask(newLineGroups)
|
||||
}
|
||||
|
||||
let targetFile = file
|
||||
if (settings.graduallyInpainting === true) {
|
||||
if (useLastLineGroup === true) {
|
||||
// renders.length == 1 还是用原来的
|
||||
if (renders.length > 1) {
|
||||
const lastRender = renders[renders.length - 2]
|
||||
targetFile = await srcToFile(
|
||||
lastRender.currentSrc,
|
||||
file.name,
|
||||
file.type
|
||||
)
|
||||
}
|
||||
} else if (renders.length > 0) {
|
||||
console.info('gradually inpainting on last result')
|
||||
|
||||
const lastRender = renders[renders.length - 1]
|
||||
targetFile = await srcToFile(
|
||||
lastRender.currentSrc,
|
||||
file.name,
|
||||
file.type
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
const sdSeed = settings.sdSeedFixed ? settings.sdSeed : -1
|
||||
|
||||
try {
|
||||
const res = await inpaint(
|
||||
targetFile,
|
||||
maskCanvas.toDataURL(),
|
||||
settings,
|
||||
croperRect,
|
||||
prompt,
|
||||
sizeLimit.toString(),
|
||||
sdSeed
|
||||
)
|
||||
if (!res) {
|
||||
throw new Error('empty response')
|
||||
}
|
||||
const { blob, seed } = res
|
||||
console.log(seed)
|
||||
console.log(settings.sdSeedFixed)
|
||||
if (seed && !settings.sdSeedFixed) {
|
||||
setSeed(parseInt(seed, 10))
|
||||
}
|
||||
const newRender = new Image()
|
||||
await loadImage(newRender, blob)
|
||||
|
||||
if (useLastLineGroup === true) {
|
||||
const prevRenders = renders.slice(0, -1)
|
||||
const newRenders = [...prevRenders, newRender]
|
||||
setRenders(newRenders)
|
||||
} else {
|
||||
const newRenders = [...renders, newRender]
|
||||
setRenders(newRenders)
|
||||
}
|
||||
|
||||
draw(newRender, [])
|
||||
// Only append new LineGroup after inpainting success
|
||||
setLineGroups(newLineGroups)
|
||||
|
||||
// clear redo stack
|
||||
resetRedoState()
|
||||
} catch (e: any) {
|
||||
setToastState({
|
||||
open: true,
|
||||
desc: e.message ? e.message : e.toString(),
|
||||
state: 'error',
|
||||
duration: 4000,
|
||||
})
|
||||
drawOnCurrentRender([])
|
||||
}
|
||||
setIsInpainting(false)
|
||||
},
|
||||
[
|
||||
lineGroups,
|
||||
curLineGroup,
|
||||
maskCanvas,
|
||||
settings.graduallyInpainting,
|
||||
settings,
|
||||
croperRect,
|
||||
sizeLimit,
|
||||
promptVal,
|
||||
drawOnCurrentRender,
|
||||
hadDrawSomething,
|
||||
drawLinesOnMask,
|
||||
]
|
||||
)
|
||||
|
||||
useEffect(() => {
|
||||
emitter.on(EVENT_PROMPT, () => {
|
||||
if (hadDrawSomething()) {
|
||||
runInpainting(promptVal)
|
||||
} else if (lastLineGroup.length !== 0) {
|
||||
runInpainting(promptVal, true)
|
||||
} else {
|
||||
setToastState({
|
||||
open: true,
|
||||
desc: 'Please draw mask on picture',
|
||||
state: 'error',
|
||||
duration: 1500,
|
||||
})
|
||||
}
|
||||
})
|
||||
return () => {
|
||||
emitter.off(EVENT_PROMPT)
|
||||
}
|
||||
}, [hadDrawSomething, runInpainting, prompt])
|
||||
|
||||
const hadRunInpainting = () => {
|
||||
return renders.length !== 0
|
||||
}
|
||||
|
||||
const handleMultiStrokeKeyDown = () => {
|
||||
if (isInpaintingLoading) {
|
||||
if (isInpainting) {
|
||||
return
|
||||
}
|
||||
setIsMultiStrokeKeyPressed(true)
|
||||
@ -230,13 +339,13 @@ export default function Editor(props: EditorProps) {
|
||||
if (!isMultiStrokeKeyPressed) {
|
||||
return
|
||||
}
|
||||
if (isInpaintingLoading) {
|
||||
if (isInpainting) {
|
||||
return
|
||||
}
|
||||
|
||||
setIsMultiStrokeKeyPressed(false)
|
||||
|
||||
if (!settings.runInpaintingManually) {
|
||||
if (!runMannually) {
|
||||
runInpainting()
|
||||
}
|
||||
}
|
||||
@ -246,7 +355,7 @@ export default function Editor(props: EditorProps) {
|
||||
}
|
||||
|
||||
useKey(predicate, handleMultiStrokeKeyup, { event: 'keyup' }, [
|
||||
isInpaintingLoading,
|
||||
isInpainting,
|
||||
isMultiStrokeKeyPressed,
|
||||
hadDrawSomething,
|
||||
])
|
||||
@ -257,7 +366,7 @@ export default function Editor(props: EditorProps) {
|
||||
{
|
||||
event: 'keydown',
|
||||
},
|
||||
[isInpaintingLoading]
|
||||
[isInpainting]
|
||||
)
|
||||
|
||||
// Draw once the original image is loaded
|
||||
@ -341,7 +450,7 @@ export default function Editor(props: EditorProps) {
|
||||
}, [windowSize, resetZoom])
|
||||
|
||||
const handleEscPressed = () => {
|
||||
if (isInpaintingLoading) {
|
||||
if (isInpainting) {
|
||||
return
|
||||
}
|
||||
if (isDraging || isMultiStrokeKeyPressed) {
|
||||
@ -361,7 +470,7 @@ export default function Editor(props: EditorProps) {
|
||||
},
|
||||
[
|
||||
isDraging,
|
||||
isInpaintingLoading,
|
||||
isInpainting,
|
||||
isMultiStrokeKeyPressed,
|
||||
resetZoom,
|
||||
drawOnCurrentRender,
|
||||
@ -404,7 +513,7 @@ export default function Editor(props: EditorProps) {
|
||||
if (!canvas) {
|
||||
return
|
||||
}
|
||||
if (isInpaintingLoading) {
|
||||
if (isInpainting) {
|
||||
return
|
||||
}
|
||||
if (!isDraging) {
|
||||
@ -416,13 +525,29 @@ export default function Editor(props: EditorProps) {
|
||||
return
|
||||
}
|
||||
|
||||
if (settings.runInpaintingManually) {
|
||||
if (runMannually) {
|
||||
setIsDraging(false)
|
||||
} else {
|
||||
runInpainting()
|
||||
}
|
||||
}
|
||||
|
||||
const isOutsideCroper = (clickPnt: { x: number; y: number }) => {
|
||||
if (clickPnt.x < croperRect.x) {
|
||||
return true
|
||||
}
|
||||
if (clickPnt.y < croperRect.y) {
|
||||
return true
|
||||
}
|
||||
if (clickPnt.x > croperRect.x + croperRect.width) {
|
||||
return true
|
||||
}
|
||||
if (clickPnt.y > croperRect.y + croperRect.height) {
|
||||
return true
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
const onMouseDown = (ev: SyntheticEvent) => {
|
||||
if (isPanning) {
|
||||
return
|
||||
@ -434,7 +559,7 @@ export default function Editor(props: EditorProps) {
|
||||
if (!canvas) {
|
||||
return
|
||||
}
|
||||
if (isInpaintingLoading) {
|
||||
if (isInpainting) {
|
||||
return
|
||||
}
|
||||
|
||||
@ -447,10 +572,14 @@ export default function Editor(props: EditorProps) {
|
||||
return
|
||||
}
|
||||
|
||||
if (isSD && settings.showCroper && isOutsideCroper(mouseXY(ev))) {
|
||||
return
|
||||
}
|
||||
|
||||
setIsDraging(true)
|
||||
|
||||
let lineGroup: LineGroup = []
|
||||
if (isMultiStrokeKeyPressed || settings.runInpaintingManually) {
|
||||
if (isMultiStrokeKeyPressed || runMannually) {
|
||||
lineGroup = [...curLineGroup]
|
||||
}
|
||||
lineGroup.push({ size: brushSize, pts: [mouseXY(ev)] })
|
||||
@ -462,6 +591,7 @@ export default function Editor(props: EditorProps) {
|
||||
if (curLineGroup.length === 0) {
|
||||
return
|
||||
}
|
||||
setLastLineGroup([])
|
||||
|
||||
const lastLine = curLineGroup.pop()!
|
||||
const newRedoCurLines = [...redoCurLines, lastLine]
|
||||
@ -478,8 +608,8 @@ export default function Editor(props: EditorProps) {
|
||||
}
|
||||
|
||||
// save line Group
|
||||
const lastLineGroup = lineGroups.pop()!
|
||||
setRedoLineGroups([...redoLineGroups, lastLineGroup])
|
||||
const latestLineGroup = lineGroups.pop()!
|
||||
setRedoLineGroups([...redoLineGroups, latestLineGroup])
|
||||
// If render is undo, clear strokes
|
||||
setRedoCurLines([])
|
||||
|
||||
@ -501,7 +631,7 @@ export default function Editor(props: EditorProps) {
|
||||
}, [draw, renders, redoRenders, redoLineGroups, lineGroups, original])
|
||||
|
||||
const undo = () => {
|
||||
if (settings.runInpaintingManually && curLineGroup.length !== 0) {
|
||||
if (runMannually && curLineGroup.length !== 0) {
|
||||
undoStroke()
|
||||
} else {
|
||||
undoRender()
|
||||
@ -510,6 +640,7 @@ export default function Editor(props: EditorProps) {
|
||||
|
||||
// Handle Cmd+Z
|
||||
const undoPredicate = (event: KeyboardEvent) => {
|
||||
// TODO: fix prompt input ctrl+z
|
||||
const isCmdZ =
|
||||
(event.metaKey || event.ctrlKey) && !event.shiftKey && event.key === 'z'
|
||||
// Handle tab switch
|
||||
@ -524,17 +655,17 @@ export default function Editor(props: EditorProps) {
|
||||
return false
|
||||
}
|
||||
|
||||
useKey(undoPredicate, undo, undefined, [undoStroke, undoRender])
|
||||
useKey(undoPredicate, undo, undefined, [undoStroke, undoRender, isSD])
|
||||
|
||||
const disableUndo = () => {
|
||||
if (isInpaintingLoading) {
|
||||
if (isInpainting) {
|
||||
return true
|
||||
}
|
||||
if (renders.length > 0) {
|
||||
return false
|
||||
}
|
||||
|
||||
if (settings.runInpaintingManually) {
|
||||
if (runMannually) {
|
||||
if (curLineGroup.length === 0) {
|
||||
return true
|
||||
}
|
||||
@ -575,7 +706,7 @@ export default function Editor(props: EditorProps) {
|
||||
}, [draw, renders, redoRenders, redoLineGroups, lineGroups, original])
|
||||
|
||||
const redo = () => {
|
||||
if (settings.runInpaintingManually && redoCurLines.length !== 0) {
|
||||
if (runMannually && redoCurLines.length !== 0) {
|
||||
redoStroke()
|
||||
} else {
|
||||
redoRender()
|
||||
@ -600,17 +731,17 @@ export default function Editor(props: EditorProps) {
|
||||
return false
|
||||
}
|
||||
|
||||
useKey(redoPredicate, redo, undefined, [redoStroke, redoRender])
|
||||
useKey(redoPredicate, redo, undefined, [redoStroke, redoRender, isSD])
|
||||
|
||||
const disableRedo = () => {
|
||||
if (isInpaintingLoading) {
|
||||
if (isInpainting) {
|
||||
return true
|
||||
}
|
||||
if (redoRenders.length > 0) {
|
||||
return false
|
||||
}
|
||||
|
||||
if (settings.runInpaintingManually) {
|
||||
if (runMannually) {
|
||||
if (redoCurLines.length === 0) {
|
||||
return true
|
||||
}
|
||||
@ -688,7 +819,7 @@ export default function Editor(props: EditorProps) {
|
||||
}, [showBrush, isPanning])
|
||||
|
||||
// Standard Hotkeys for Brush Size
|
||||
useKeyPressEvent('[', () => {
|
||||
useHotKey('[', () => {
|
||||
setBrushSize(currentBrushSize => {
|
||||
if (currentBrushSize > 10) {
|
||||
return currentBrushSize - 10
|
||||
@ -700,18 +831,23 @@ export default function Editor(props: EditorProps) {
|
||||
})
|
||||
})
|
||||
|
||||
useKeyPressEvent(']', () => {
|
||||
useHotKey(']', () => {
|
||||
setBrushSize(currentBrushSize => {
|
||||
return currentBrushSize + 10
|
||||
})
|
||||
})
|
||||
|
||||
// Manual Inpainting Hotkey
|
||||
useKeyPressEvent('R', () => {
|
||||
if (settings.runInpaintingManually && hadDrawSomething()) {
|
||||
useHotKey(
|
||||
'shift+r',
|
||||
() => {
|
||||
if (runMannually && hadDrawSomething()) {
|
||||
runInpainting()
|
||||
}
|
||||
})
|
||||
},
|
||||
{},
|
||||
[runMannually]
|
||||
)
|
||||
|
||||
// Toggle clean/zoom tool on spacebar.
|
||||
useKeyPressEvent(
|
||||
@ -792,7 +928,7 @@ export default function Editor(props: EditorProps) {
|
||||
}}
|
||||
>
|
||||
<TransformComponent
|
||||
contentClass={isInpaintingLoading ? 'editor-canvas-loading' : ''}
|
||||
contentClass={isInpainting ? 'editor-canvas-loading' : ''}
|
||||
contentStyle={{
|
||||
visibility: initialCentered ? 'visible' : 'hidden',
|
||||
}}
|
||||
@ -852,10 +988,22 @@ export default function Editor(props: EditorProps) {
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{settings.showCroper ? (
|
||||
<Croper
|
||||
maxHeight={original.naturalHeight}
|
||||
maxWidth={original.naturalWidth}
|
||||
minHeight={Math.min(256, original.naturalHeight)}
|
||||
minWidth={Math.min(256, original.naturalWidth)}
|
||||
scale={scale}
|
||||
/>
|
||||
) : (
|
||||
<></>
|
||||
)}
|
||||
</TransformComponent>
|
||||
</TransformWrapper>
|
||||
|
||||
{showBrush && !isInpaintingLoading && !isPanning && (
|
||||
{showBrush && !isInpainting && !isPanning && (
|
||||
<div className="brush-shape" style={getBrushStyle(x, y)} />
|
||||
)}
|
||||
|
||||
@ -867,11 +1015,15 @@ export default function Editor(props: EditorProps) {
|
||||
)}
|
||||
|
||||
<div className="editor-toolkit-panel">
|
||||
{isSD ? (
|
||||
<></>
|
||||
) : (
|
||||
<SizeSelector
|
||||
onChange={onSizeLimitChange}
|
||||
originalWidth={original.naturalWidth}
|
||||
originalHeight={original.naturalHeight}
|
||||
/>
|
||||
)}
|
||||
<Slider
|
||||
label="Brush"
|
||||
min={10}
|
||||
@ -977,9 +1129,9 @@ export default function Editor(props: EditorProps) {
|
||||
/>
|
||||
</svg>
|
||||
}
|
||||
disabled={!hadDrawSomething() || isInpaintingLoading}
|
||||
disabled={!hadDrawSomething() || isInpainting}
|
||||
onClick={() => {
|
||||
if (!isInpaintingLoading && hadDrawSomething()) {
|
||||
if (!isInpainting && hadDrawSomething()) {
|
||||
runInpainting()
|
||||
}
|
||||
}}
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
header {
|
||||
height: 60px;
|
||||
padding: 1rem 2rem;
|
||||
padding: 1rem 1.5rem;
|
||||
position: absolute;
|
||||
top: 0;
|
||||
display: flex;
|
||||
|
||||
@ -1,17 +1,19 @@
|
||||
import { ArrowLeftIcon, UploadIcon } from '@heroicons/react/outline'
|
||||
import React, { useState } from 'react'
|
||||
import { useRecoilState } from 'recoil'
|
||||
import { fileState } from '../../store/Atoms'
|
||||
import { useRecoilState, useRecoilValue } from 'recoil'
|
||||
import { fileState, isSDState } from '../../store/Atoms'
|
||||
import Button from '../shared/Button'
|
||||
import Shortcuts from '../Shortcuts/Shortcuts'
|
||||
import useResolution from '../../hooks/useResolution'
|
||||
import { ThemeChanger } from './ThemeChanger'
|
||||
import SettingIcon from '../Settings/SettingIcon'
|
||||
import PromptInput from './PromptInput'
|
||||
|
||||
const Header = () => {
|
||||
const [file, setFile] = useRecoilState(fileState)
|
||||
const resolution = useResolution()
|
||||
const [uploadElemId] = useState(`file-upload-${Math.random().toString()}`)
|
||||
const isSD = useRecoilValue(isSDState)
|
||||
|
||||
const renderHeader = () => {
|
||||
return (
|
||||
@ -37,6 +39,8 @@ const Header = () => {
|
||||
</label>
|
||||
</div>
|
||||
|
||||
{isSD && file ? <PromptInput /> : <></>}
|
||||
|
||||
<div className="header-icons-wrapper">
|
||||
<ThemeChanger />
|
||||
{file && (
|
||||
|
||||
18
lama_cleaner/app/src/components/Header/PromptInput.scss
Normal file
18
lama_cleaner/app/src/components/Header/PromptInput.scss
Normal file
@ -0,0 +1,18 @@
|
||||
.prompt-wrapper {
|
||||
display: flex;
|
||||
gap: 12px;
|
||||
}
|
||||
|
||||
.prompt-wrapper input {
|
||||
all: unset;
|
||||
border-width: 0;
|
||||
border-radius: 0.5rem;
|
||||
min-width: 600px;
|
||||
padding: 0 0.8rem;
|
||||
outline: 1px solid var(--border-color);
|
||||
|
||||
&:focus-visible {
|
||||
border-width: 0;
|
||||
outline: 1px solid var(--yellow-accent);
|
||||
}
|
||||
}
|
||||
61
lama_cleaner/app/src/components/Header/PromptInput.tsx
Normal file
61
lama_cleaner/app/src/components/Header/PromptInput.tsx
Normal file
@ -0,0 +1,61 @@
|
||||
import React, { FormEvent, useRef, useState } from 'react'
|
||||
import { useClickAway } from 'react-use'
|
||||
import { useRecoilState } from 'recoil'
|
||||
import emitter, { EVENT_PROMPT } from '../../event'
|
||||
import { appState, propmtState } from '../../store/Atoms'
|
||||
import Button from '../shared/Button'
|
||||
import TextInput from '../shared/Input'
|
||||
|
||||
// TODO: show progress in input
|
||||
const PromptInput = () => {
|
||||
const [app, setAppState] = useRecoilState(appState)
|
||||
const [prompt, setPrompt] = useRecoilState(propmtState)
|
||||
const ref = useRef(null)
|
||||
|
||||
const handleOnInput = (evt: FormEvent<HTMLInputElement>) => {
|
||||
evt.preventDefault()
|
||||
evt.stopPropagation()
|
||||
const target = evt.target as HTMLInputElement
|
||||
setPrompt(target.value)
|
||||
}
|
||||
|
||||
const handleRepaintClick = () => {
|
||||
if (prompt.length !== 0 && !app.isInpainting) {
|
||||
emitter.emit(EVENT_PROMPT)
|
||||
}
|
||||
}
|
||||
|
||||
useClickAway<MouseEvent>(ref, () => {
|
||||
if (ref?.current) {
|
||||
const input = ref.current as HTMLInputElement
|
||||
input.blur()
|
||||
}
|
||||
})
|
||||
|
||||
const onKeyUp = (e: React.KeyboardEvent) => {
|
||||
if (e.key === 'Enter') {
|
||||
handleRepaintClick()
|
||||
}
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="prompt-wrapper">
|
||||
<TextInput
|
||||
ref={ref}
|
||||
value={prompt}
|
||||
onInput={handleOnInput}
|
||||
onKeyUp={onKeyUp}
|
||||
placeholder="I want to repaint of..."
|
||||
/>
|
||||
<Button
|
||||
border
|
||||
onClick={handleRepaintClick}
|
||||
disabled={prompt.length === 0 || app.isInpainting}
|
||||
>
|
||||
Dream
|
||||
</Button>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default PromptInput
|
||||
@ -1,6 +1,6 @@
|
||||
import React, { ReactNode } from 'react'
|
||||
import { useRecoilState } from 'recoil'
|
||||
import { AIModel, settingState } from '../../store/Atoms'
|
||||
import { AIModel, SDSampler, settingState } from '../../store/Atoms'
|
||||
import Selector from '../shared/Selector'
|
||||
import { Switch, SwitchThumb } from '../shared/Switch'
|
||||
import Tooltip from '../shared/Tooltip'
|
||||
@ -145,6 +145,8 @@ function ModelSettingBlock() {
|
||||
return undefined
|
||||
case AIModel.FCF:
|
||||
return renderFCFModelDesc()
|
||||
case AIModel.SD14:
|
||||
return undefined
|
||||
default:
|
||||
return <></>
|
||||
}
|
||||
@ -182,6 +184,12 @@ function ModelSettingBlock() {
|
||||
'https://arxiv.org/abs/2208.03382',
|
||||
'https://github.com/SHI-Labs/FcF-Inpainting'
|
||||
)
|
||||
case AIModel.SD14:
|
||||
return renderModelDesc(
|
||||
'Stable Diffusion',
|
||||
'https://ommer-lab.com/research/latent-diffusion-models/',
|
||||
'https://github.com/CompVis/stable-diffusion'
|
||||
)
|
||||
default:
|
||||
return <></>
|
||||
}
|
||||
|
||||
@ -1,17 +1,41 @@
|
||||
import React from 'react'
|
||||
import React, { useRef } from 'react'
|
||||
import { useClickAway } from 'react-use'
|
||||
import NumberInput from '../shared/NumberInput'
|
||||
import SettingBlock from './SettingBlock'
|
||||
|
||||
interface NumberInputSettingProps {
|
||||
title: string
|
||||
allowFloat?: boolean
|
||||
desc?: string
|
||||
value: string
|
||||
suffix?: string
|
||||
width?: number
|
||||
widthUnit?: string
|
||||
disable?: boolean
|
||||
onValue: (val: string) => void
|
||||
}
|
||||
|
||||
function NumberInputSetting(props: NumberInputSettingProps) {
|
||||
const { title, desc, value, suffix, onValue } = props
|
||||
const {
|
||||
title,
|
||||
allowFloat,
|
||||
desc,
|
||||
value,
|
||||
suffix,
|
||||
onValue,
|
||||
width,
|
||||
widthUnit,
|
||||
disable,
|
||||
} = props
|
||||
|
||||
const ref = useRef(null)
|
||||
|
||||
useClickAway<MouseEvent>(ref, () => {
|
||||
if (ref?.current) {
|
||||
const input = ref.current as HTMLInputElement
|
||||
input.blur()
|
||||
}
|
||||
})
|
||||
|
||||
return (
|
||||
<SettingBlock
|
||||
@ -28,9 +52,12 @@ function NumberInputSetting(props: NumberInputSettingProps) {
|
||||
}}
|
||||
>
|
||||
<NumberInput
|
||||
style={{ width: '80px' }}
|
||||
value={`${value}`}
|
||||
allowFloat={allowFloat}
|
||||
style={{ width: `${width}${widthUnit}` }}
|
||||
value={value}
|
||||
disabled={disable}
|
||||
onValue={onValue}
|
||||
ref={ref}
|
||||
/>
|
||||
{suffix && <span>{suffix}</span>}
|
||||
</div>
|
||||
@ -39,4 +66,11 @@ function NumberInputSetting(props: NumberInputSettingProps) {
|
||||
)
|
||||
}
|
||||
|
||||
NumberInputSetting.defaultProps = {
|
||||
allowFloat: false,
|
||||
width: 80,
|
||||
widthUnit: 'px',
|
||||
disable: false,
|
||||
}
|
||||
|
||||
export default NumberInputSetting
|
||||
|
||||
@ -1,13 +1,14 @@
|
||||
import React from 'react'
|
||||
|
||||
import { useRecoilState } from 'recoil'
|
||||
import { settingState } from '../../store/Atoms'
|
||||
import { useRecoilState, useRecoilValue } from 'recoil'
|
||||
import { isSDState, settingState } from '../../store/Atoms'
|
||||
import Modal from '../shared/Modal'
|
||||
import ManualRunInpaintingSettingBlock from './ManualRunInpaintingSettingBlock'
|
||||
import HDSettingBlock from './HDSettingBlock'
|
||||
import ModelSettingBlock from './ModelSettingBlock'
|
||||
import GraduallyInpaintingSettingBlock from './GraduallyInpaintingSettingBlock'
|
||||
import DownloadMaskSettingBlock from './DownloadMaskSettingBlock'
|
||||
import useHotKey from '../../hooks/useHotkey'
|
||||
|
||||
interface SettingModalProps {
|
||||
onClose: () => void
|
||||
@ -15,6 +16,7 @@ interface SettingModalProps {
|
||||
export default function SettingModal(props: SettingModalProps) {
|
||||
const { onClose } = props
|
||||
const [setting, setSettingState] = useRecoilState(settingState)
|
||||
const isSD = useRecoilValue(isSDState)
|
||||
|
||||
const handleOnClose = () => {
|
||||
setSettingState(old => {
|
||||
@ -23,6 +25,17 @@ export default function SettingModal(props: SettingModalProps) {
|
||||
onClose()
|
||||
}
|
||||
|
||||
useHotKey(
|
||||
's',
|
||||
() => {
|
||||
setSettingState(old => {
|
||||
return { ...old, show: !old.show }
|
||||
})
|
||||
},
|
||||
{},
|
||||
[]
|
||||
)
|
||||
|
||||
return (
|
||||
<Modal
|
||||
onClose={handleOnClose}
|
||||
@ -30,11 +43,12 @@ export default function SettingModal(props: SettingModalProps) {
|
||||
className="modal-setting"
|
||||
show={setting.show}
|
||||
>
|
||||
<ManualRunInpaintingSettingBlock />
|
||||
<GraduallyInpaintingSettingBlock />
|
||||
{isSD ? <></> : <ManualRunInpaintingSettingBlock />}
|
||||
|
||||
{/* <GraduallyInpaintingSettingBlock /> */}
|
||||
<DownloadMaskSettingBlock />
|
||||
<ModelSettingBlock />
|
||||
<HDSettingBlock />
|
||||
{isSD ? <></> : <HDSettingBlock />}
|
||||
</Modal>
|
||||
)
|
||||
}
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
import React from 'react'
|
||||
import { useKeyPressEvent } from 'react-use'
|
||||
import { useRecoilState } from 'recoil'
|
||||
import useHotKey from '../../hooks/useHotkey'
|
||||
import { shortcutsState } from '../../store/Atoms'
|
||||
import Button from '../shared/Button'
|
||||
|
||||
@ -13,8 +13,7 @@ const Shortcuts = () => {
|
||||
})
|
||||
}
|
||||
|
||||
useKeyPressEvent('h', ev => {
|
||||
ev?.preventDefault()
|
||||
useHotKey('h', () => {
|
||||
shortcutStateHandler()
|
||||
})
|
||||
|
||||
|
||||
@ -64,7 +64,8 @@ export default function ShortcutsModal() {
|
||||
<ShortCut content="Decrease Brush Size" keys={['[']} />
|
||||
<ShortCut content="Increase Brush Size" keys={[']']} />
|
||||
<ShortCut content="Toggle Dark Mode" keys={['Shift', 'D']} />
|
||||
<ShortCut content="Toggle Hotkeys Panel" keys={['H']} />
|
||||
<ShortCut content="Toggle Hotkeys Dialog" keys={['H']} />
|
||||
<ShortCut content="Toggle Settings Dialog" keys={['S']} />
|
||||
</div>
|
||||
</Modal>
|
||||
)
|
||||
|
||||
57
lama_cleaner/app/src/components/SidePanel/SidePanel.scss
Normal file
57
lama_cleaner/app/src/components/SidePanel/SidePanel.scss
Normal file
@ -0,0 +1,57 @@
|
||||
@use '../../styles/Mixins/' as *;
|
||||
|
||||
.side-panel {
|
||||
position: absolute;
|
||||
top: 68px;
|
||||
right: 1.5rem;
|
||||
padding: 0.3rem 0.3rem;
|
||||
z-index: 4;
|
||||
|
||||
border-radius: 0.8rem;
|
||||
border-style: solid;
|
||||
border-color: var(--border-color);
|
||||
border-width: 1px;
|
||||
}
|
||||
|
||||
.side-panel-trigger {
|
||||
font-family: 'WorkSans', sans-serif;
|
||||
font-size: 16px;
|
||||
border: 0px;
|
||||
}
|
||||
|
||||
.side-panel-content {
|
||||
position: relative;
|
||||
font-family: 'WorkSans', sans-serif;
|
||||
font-size: 14px;
|
||||
top: 1rem;
|
||||
right: 1.5rem;
|
||||
padding: 1rem 1rem;
|
||||
z-index: 9;
|
||||
|
||||
// backdrop-filter: blur(12px);
|
||||
color: var(--text-color);
|
||||
background-color: var(--page-bg);
|
||||
|
||||
border-radius: 0.8rem;
|
||||
border-style: solid;
|
||||
border-color: var(--border-color);
|
||||
border-width: 1px;
|
||||
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: 12px;
|
||||
|
||||
.setting-block-content {
|
||||
gap: 1rem;
|
||||
}
|
||||
|
||||
// input {
|
||||
// height: 24px;
|
||||
// // border-radius: 4px;
|
||||
// }
|
||||
|
||||
// button {
|
||||
// height: 28px;
|
||||
// // border-radius: 4px;
|
||||
// }
|
||||
}
|
||||
177
lama_cleaner/app/src/components/SidePanel/SidePanel.tsx
Normal file
177
lama_cleaner/app/src/components/SidePanel/SidePanel.tsx
Normal file
@ -0,0 +1,177 @@
|
||||
import React, { useState } from 'react'
|
||||
import { useRecoilState } from 'recoil'
|
||||
import * as PopoverPrimitive from '@radix-ui/react-popover'
|
||||
import { useToggle } from 'react-use'
|
||||
import { SDSampler, settingState } from '../../store/Atoms'
|
||||
import NumberInputSetting from '../Settings/NumberInputSetting'
|
||||
import SettingBlock from '../Settings/SettingBlock'
|
||||
import Selector from '../shared/Selector'
|
||||
import { Switch, SwitchThumb } from '../shared/Switch'
|
||||
|
||||
const INPUT_WIDTH = 30
|
||||
|
||||
// TODO: 添加收起来的按钮
|
||||
const SidePanel = () => {
|
||||
const [open, toggleOpen] = useToggle(true)
|
||||
const [setting, setSettingState] = useRecoilState(settingState)
|
||||
|
||||
return (
|
||||
<div className="side-panel">
|
||||
<PopoverPrimitive.Root open={open}>
|
||||
<PopoverPrimitive.Trigger
|
||||
className="btn-primary side-panel-trigger"
|
||||
onClick={() => toggleOpen()}
|
||||
>
|
||||
Stable Diffusion
|
||||
</PopoverPrimitive.Trigger>
|
||||
<PopoverPrimitive.Portal>
|
||||
<PopoverPrimitive.Content className="side-panel-content">
|
||||
<SettingBlock
|
||||
title="Show Croper"
|
||||
input={
|
||||
<Switch
|
||||
checked={setting.showCroper}
|
||||
onCheckedChange={value => {
|
||||
setSettingState(old => {
|
||||
return { ...old, showCroper: value }
|
||||
})
|
||||
}}
|
||||
>
|
||||
<SwitchThumb />
|
||||
</Switch>
|
||||
}
|
||||
/>
|
||||
{/*
|
||||
<NumberInputSetting
|
||||
title="Num Samples"
|
||||
width={INPUT_WIDTH}
|
||||
value={`${setting.sdNumSamples}`}
|
||||
desc=""
|
||||
onValue={value => {
|
||||
const val = value.length === 0 ? 0 : parseInt(value, 10)
|
||||
setSettingState(old => {
|
||||
return { ...old, sdNumSamples: val }
|
||||
})
|
||||
}}
|
||||
/> */}
|
||||
|
||||
<NumberInputSetting
|
||||
title="Steps"
|
||||
width={INPUT_WIDTH}
|
||||
value={`${setting.sdSteps}`}
|
||||
desc="Large steps result in better result, but more time-consuming"
|
||||
onValue={value => {
|
||||
const val = value.length === 0 ? 0 : parseInt(value, 10)
|
||||
setSettingState(old => {
|
||||
return { ...old, sdSteps: val }
|
||||
})
|
||||
}}
|
||||
/>
|
||||
|
||||
<NumberInputSetting
|
||||
title="Strength"
|
||||
width={INPUT_WIDTH}
|
||||
allowFloat
|
||||
value={`${setting.sdStrength}`}
|
||||
desc="TODO"
|
||||
onValue={value => {
|
||||
const val = value.length === 0 ? 0 : parseFloat(value)
|
||||
console.log(val)
|
||||
setSettingState(old => {
|
||||
return { ...old, sdStrength: val }
|
||||
})
|
||||
}}
|
||||
/>
|
||||
|
||||
<NumberInputSetting
|
||||
title="Guidance Scale"
|
||||
width={INPUT_WIDTH}
|
||||
allowFloat
|
||||
value={`${setting.sdGuidanceScale}`}
|
||||
desc="TODO"
|
||||
onValue={value => {
|
||||
const val = value.length === 0 ? 0 : parseFloat(value)
|
||||
setSettingState(old => {
|
||||
return { ...old, sdGuidanceScale: val }
|
||||
})
|
||||
}}
|
||||
/>
|
||||
|
||||
<NumberInputSetting
|
||||
title="Mask Blur"
|
||||
width={INPUT_WIDTH}
|
||||
value={`${setting.sdMaskBlur}`}
|
||||
desc="TODO"
|
||||
onValue={value => {
|
||||
const val = value.length === 0 ? 0 : parseInt(value, 10)
|
||||
setSettingState(old => {
|
||||
return { ...old, sdMaskBlur: val }
|
||||
})
|
||||
}}
|
||||
/>
|
||||
|
||||
<SettingBlock
|
||||
className="sub-setting-block"
|
||||
title="Sampler"
|
||||
input={
|
||||
<Selector
|
||||
width={80}
|
||||
value={setting.sdSampler as string}
|
||||
options={Object.values(SDSampler)}
|
||||
onChange={val => {
|
||||
const sampler = val as SDSampler
|
||||
setSettingState(old => {
|
||||
return { ...old, sdSampler: sampler }
|
||||
})
|
||||
}}
|
||||
/>
|
||||
}
|
||||
/>
|
||||
|
||||
<SettingBlock
|
||||
title="Seed"
|
||||
input={
|
||||
<div
|
||||
style={{
|
||||
display: 'flex',
|
||||
gap: 0,
|
||||
justifyContent: 'center',
|
||||
alignItems: 'center',
|
||||
}}
|
||||
>
|
||||
{/* 每次会从服务器返回更新该值 */}
|
||||
<NumberInputSetting
|
||||
title=""
|
||||
width={80}
|
||||
value={`${setting.sdSeed}`}
|
||||
desc=""
|
||||
disable={!setting.sdSeedFixed}
|
||||
onValue={value => {
|
||||
const val = value.length === 0 ? 0 : parseInt(value, 10)
|
||||
setSettingState(old => {
|
||||
return { ...old, sdSeed: val }
|
||||
})
|
||||
}}
|
||||
/>
|
||||
<Switch
|
||||
checked={setting.sdSeedFixed}
|
||||
onCheckedChange={value => {
|
||||
setSettingState(old => {
|
||||
return { ...old, sdSeedFixed: value }
|
||||
})
|
||||
}}
|
||||
style={{ marginLeft: '8px' }}
|
||||
>
|
||||
<SwitchThumb />
|
||||
</Switch>
|
||||
</div>
|
||||
}
|
||||
/>
|
||||
</PopoverPrimitive.Content>
|
||||
</PopoverPrimitive.Portal>
|
||||
</PopoverPrimitive.Root>
|
||||
</div>
|
||||
)
|
||||
}
|
||||
|
||||
export default SidePanel
|
||||
@ -1,15 +1,16 @@
|
||||
import React, { useEffect } from 'react'
|
||||
import { useRecoilState } from 'recoil'
|
||||
import { useRecoilState, useRecoilValue } from 'recoil'
|
||||
import Editor from './Editor/Editor'
|
||||
import ShortcutsModal from './Shortcuts/ShortcutsModal'
|
||||
import SettingModal from './Settings/SettingsModal'
|
||||
import Toast from './shared/Toast'
|
||||
import { AIModel, settingState, toastState } from '../store/Atoms'
|
||||
import { AIModel, isSDState, settingState, toastState } from '../store/Atoms'
|
||||
import {
|
||||
currentModel,
|
||||
modelDownloaded,
|
||||
switchModel,
|
||||
} from '../adapters/inpainting'
|
||||
import SidePanel from './SidePanel/SidePanel'
|
||||
|
||||
interface WorkspaceProps {
|
||||
file: File
|
||||
@ -18,6 +19,7 @@ interface WorkspaceProps {
|
||||
const Workspace = ({ file }: WorkspaceProps) => {
|
||||
const [settings, setSettingState] = useRecoilState(settingState)
|
||||
const [toastVal, setToastState] = useRecoilState(toastState)
|
||||
const isSD = useRecoilValue(isSDState)
|
||||
|
||||
const onSettingClose = async () => {
|
||||
const curModel = await currentModel().then(res => res.text())
|
||||
@ -82,6 +84,7 @@ const Workspace = ({ file }: WorkspaceProps) => {
|
||||
|
||||
return (
|
||||
<>
|
||||
{isSD ? <SidePanel /> : <></>}
|
||||
<Editor file={file} />
|
||||
<SettingModal onClose={onSettingClose} />
|
||||
<ShortcutsModal />
|
||||
|
||||
@ -4,6 +4,7 @@
|
||||
display: grid;
|
||||
grid-auto-flow: column;
|
||||
column-gap: 1rem;
|
||||
background-color: var(--page-bg);
|
||||
color: var(--btn-text-color);
|
||||
font-family: 'WorkSans', sans-serif;
|
||||
width: max-content;
|
||||
@ -25,6 +26,13 @@
|
||||
}
|
||||
|
||||
.btn-primary-disabled {
|
||||
background-color: var(--page-bg);
|
||||
pointer-events: none;
|
||||
opacity: 0.5;
|
||||
}
|
||||
|
||||
.btn-border {
|
||||
border-color: var(--btn-border-color);
|
||||
border-width: 1px;
|
||||
border-style: solid;
|
||||
}
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
import React, { ReactNode } from 'react'
|
||||
|
||||
interface ButtonProps {
|
||||
border?: boolean
|
||||
disabled?: boolean
|
||||
children?: ReactNode
|
||||
className?: string
|
||||
@ -17,6 +18,7 @@ interface ButtonProps {
|
||||
const Button: React.FC<ButtonProps> = props => {
|
||||
const {
|
||||
children,
|
||||
border,
|
||||
className,
|
||||
disabled,
|
||||
icon,
|
||||
@ -55,6 +57,7 @@ const Button: React.FC<ButtonProps> = props => {
|
||||
toolTip ? 'info-tooltip' : '',
|
||||
tooltipPosition ? `info-tooltip-${tooltipPosition}` : '',
|
||||
className,
|
||||
border ? `btn-border` : '',
|
||||
].join(' ')}
|
||||
>
|
||||
{icon}
|
||||
@ -65,6 +68,7 @@ const Button: React.FC<ButtonProps> = props => {
|
||||
|
||||
Button.defaultProps = {
|
||||
disabled: false,
|
||||
border: false,
|
||||
}
|
||||
|
||||
export default Button
|
||||
|
||||
43
lama_cleaner/app/src/components/shared/Input.tsx
Normal file
43
lama_cleaner/app/src/components/shared/Input.tsx
Normal file
@ -0,0 +1,43 @@
|
||||
import React, { FocusEvent, InputHTMLAttributes, RefObject } from 'react'
|
||||
import { useClickAway } from 'react-use'
|
||||
import { useRecoilState } from 'recoil'
|
||||
import { appState } from '../../store/Atoms'
|
||||
|
||||
const TextInput = React.forwardRef<
|
||||
HTMLInputElement,
|
||||
InputHTMLAttributes<HTMLInputElement>
|
||||
>((props, ref) => {
|
||||
const { onFocus, onBlur, ...itemProps } = props
|
||||
const [_, setAppState] = useRecoilState(appState)
|
||||
|
||||
const handleOnFocus = (evt: FocusEvent<any>) => {
|
||||
setAppState(old => {
|
||||
return { ...old, disableShortCuts: true }
|
||||
})
|
||||
onFocus?.(evt)
|
||||
}
|
||||
|
||||
const handleOnBlur = (evt: FocusEvent<any>) => {
|
||||
setAppState(old => {
|
||||
return { ...old, disableShortCuts: false }
|
||||
})
|
||||
onBlur?.(evt)
|
||||
}
|
||||
|
||||
return (
|
||||
<input
|
||||
{...itemProps}
|
||||
ref={ref}
|
||||
type="text"
|
||||
onFocus={handleOnFocus}
|
||||
onBlur={handleOnBlur}
|
||||
onKeyDown={e => {
|
||||
if (e.key === 'Escape') {
|
||||
e.currentTarget.blur()
|
||||
}
|
||||
}}
|
||||
/>
|
||||
)
|
||||
})
|
||||
|
||||
export default TextInput
|
||||
@ -1,7 +1,9 @@
|
||||
import { XIcon } from '@heroicons/react/outline'
|
||||
import React, { ReactNode } from 'react'
|
||||
import { useRecoilState } from 'recoil'
|
||||
import * as DialogPrimitive from '@radix-ui/react-dialog'
|
||||
import Button from './Button'
|
||||
import { appState } from '../../store/Atoms'
|
||||
|
||||
export interface ModalProps {
|
||||
show: boolean
|
||||
@ -16,10 +18,14 @@ const Modal = React.forwardRef<
|
||||
ModalProps
|
||||
>((props, forwardedRef) => {
|
||||
const { show, children, onClose, className, title } = props
|
||||
const [_, setAppState] = useRecoilState(appState)
|
||||
|
||||
const onOpenChange = (open: boolean) => {
|
||||
if (!open) {
|
||||
onClose?.()
|
||||
setAppState(old => {
|
||||
return { ...old, disableShortCuts: false }
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -5,8 +5,13 @@
|
||||
padding: 0 0.8rem;
|
||||
outline: 1px solid var(--border-color);
|
||||
height: 32px;
|
||||
text-align: right;
|
||||
|
||||
&:focus-visible {
|
||||
outline: 1px solid var(--yellow-accent);
|
||||
}
|
||||
|
||||
&:disabled {
|
||||
color: var(--border-color);
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,31 +1,53 @@
|
||||
import React, { FormEvent, InputHTMLAttributes } from 'react'
|
||||
import React, {
|
||||
FormEvent,
|
||||
InputHTMLAttributes,
|
||||
useEffect,
|
||||
useState,
|
||||
} from 'react'
|
||||
import TextInput from './Input'
|
||||
|
||||
interface NumberInputProps extends InputHTMLAttributes<HTMLInputElement> {
|
||||
value: string
|
||||
allowFloat?: boolean
|
||||
onValue?: (val: string) => void
|
||||
}
|
||||
|
||||
const NumberInput = React.forwardRef<HTMLInputElement, NumberInputProps>(
|
||||
(props: NumberInputProps, forwardedRef) => {
|
||||
const { value, onValue, ...itemProps } = props
|
||||
const { value, allowFloat, onValue, ...itemProps } = props
|
||||
const [innerValue, setInnerValue] = useState(value)
|
||||
|
||||
useEffect(() => {
|
||||
setInnerValue(value)
|
||||
}, [value])
|
||||
|
||||
const handleOnInput = (evt: FormEvent<HTMLInputElement>) => {
|
||||
const target = evt.target as HTMLInputElement
|
||||
const val = target.value.replace(/\D/g, '')
|
||||
let val = target.value
|
||||
if (allowFloat) {
|
||||
val = val.replace(/[^0-9.]/g, '').replace(/(\..*?)\..*/g, '$1')
|
||||
onValue?.(val)
|
||||
} else {
|
||||
val = val.replace(/\D/g, '')
|
||||
onValue?.(val)
|
||||
}
|
||||
setInnerValue(val)
|
||||
}
|
||||
|
||||
return (
|
||||
<input
|
||||
value={value}
|
||||
<TextInput
|
||||
value={innerValue}
|
||||
onInput={handleOnInput}
|
||||
className="number-input"
|
||||
{...itemProps}
|
||||
ref={forwardedRef}
|
||||
type="text"
|
||||
/>
|
||||
)
|
||||
}
|
||||
)
|
||||
|
||||
NumberInput.defaultProps = {
|
||||
allowFloat: false,
|
||||
}
|
||||
|
||||
export default NumberInput
|
||||
|
||||
@ -51,6 +51,7 @@ const Selector = (props: Props) => {
|
||||
className="select-trigger"
|
||||
style={{ width }}
|
||||
ref={contentRef}
|
||||
onKeyDown={e => e.preventDefault()}
|
||||
>
|
||||
<Select.Value />
|
||||
<Select.Icon>
|
||||
|
||||
@ -12,6 +12,7 @@ const Switch = React.forwardRef<
|
||||
{...itemProps}
|
||||
ref={forwardedRef}
|
||||
className={`switch-root ${className}`}
|
||||
onKeyDown={e => e.preventDefault()}
|
||||
/>
|
||||
)
|
||||
})
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
.toast-viewpoint {
|
||||
position: fixed;
|
||||
top: 48px;
|
||||
right: 0;
|
||||
bottom: 48px;
|
||||
right: 1.5rem;
|
||||
display: flex;
|
||||
flex-direction: row;
|
||||
padding: 25px;
|
||||
|
||||
7
lama_cleaner/app/src/event.ts
Normal file
7
lama_cleaner/app/src/event.ts
Normal file
@ -0,0 +1,7 @@
|
||||
import mitt from 'mitt'
|
||||
|
||||
export const EVENT_PROMPT = 'prompt'
|
||||
|
||||
const emitter = mitt()
|
||||
|
||||
export default emitter
|
||||
22
lama_cleaner/app/src/hooks/useHotkey.tsx
Normal file
22
lama_cleaner/app/src/hooks/useHotkey.tsx
Normal file
@ -0,0 +1,22 @@
|
||||
import { Options, useHotkeys } from 'react-hotkeys-hook'
|
||||
import { useRecoilValue } from 'recoil'
|
||||
import { appState } from '../store/Atoms'
|
||||
|
||||
const useHotKey = (
|
||||
keys: string,
|
||||
callback: any,
|
||||
options?: Options,
|
||||
deps?: any[]
|
||||
) => {
|
||||
const app = useRecoilValue(appState)
|
||||
|
||||
const ref = useHotkeys(
|
||||
keys,
|
||||
callback,
|
||||
{ ...options, enabled: !app.disableShortCuts },
|
||||
deps
|
||||
)
|
||||
return ref
|
||||
}
|
||||
|
||||
export default useHotKey
|
||||
@ -9,6 +9,7 @@ export enum AIModel {
|
||||
ZITS = 'zits',
|
||||
MAT = 'mat',
|
||||
FCF = 'fcf',
|
||||
SD14 = 'sd1.4',
|
||||
}
|
||||
|
||||
export const fileState = atom<File | undefined>({
|
||||
@ -16,6 +17,89 @@ export const fileState = atom<File | undefined>({
|
||||
default: undefined,
|
||||
})
|
||||
|
||||
export interface Rect {
|
||||
x: number
|
||||
y: number
|
||||
width: number
|
||||
height: number
|
||||
}
|
||||
|
||||
interface AppState {
|
||||
disableShortCuts: boolean
|
||||
isInpainting: boolean
|
||||
}
|
||||
|
||||
export const appState = atom<AppState>({
|
||||
key: 'appState',
|
||||
default: {
|
||||
disableShortCuts: false,
|
||||
isInpainting: false,
|
||||
},
|
||||
})
|
||||
|
||||
export const propmtState = atom<string>({
|
||||
key: 'promptState',
|
||||
default: '',
|
||||
})
|
||||
|
||||
export const isInpaintingState = selector({
|
||||
key: 'isInpainting',
|
||||
get: ({ get }) => {
|
||||
const app = get(appState)
|
||||
return app.isInpainting
|
||||
},
|
||||
set: ({ get, set }, newValue: any) => {
|
||||
const app = get(appState)
|
||||
set(appState, { ...app, isInpainting: newValue })
|
||||
},
|
||||
})
|
||||
|
||||
export const croperState = atom<Rect>({
|
||||
key: 'croperState',
|
||||
default: {
|
||||
x: 0,
|
||||
y: 0,
|
||||
width: 512,
|
||||
height: 512,
|
||||
},
|
||||
})
|
||||
|
||||
export const croperX = selector({
|
||||
key: 'croperX',
|
||||
get: ({ get }) => get(croperState).x,
|
||||
set: ({ get, set }, newValue: any) => {
|
||||
const rect = get(croperState)
|
||||
set(croperState, { ...rect, x: newValue })
|
||||
},
|
||||
})
|
||||
|
||||
export const croperY = selector({
|
||||
key: 'croperY',
|
||||
get: ({ get }) => get(croperState).y,
|
||||
set: ({ get, set }, newValue: any) => {
|
||||
const rect = get(croperState)
|
||||
set(croperState, { ...rect, y: newValue })
|
||||
},
|
||||
})
|
||||
|
||||
export const croperHeight = selector({
|
||||
key: 'croperHeight',
|
||||
get: ({ get }) => get(croperState).height,
|
||||
set: ({ get, set }, newValue: any) => {
|
||||
const rect = get(croperState)
|
||||
set(croperState, { ...rect, height: newValue })
|
||||
},
|
||||
})
|
||||
|
||||
export const croperWidth = selector({
|
||||
key: 'croperWidth',
|
||||
get: ({ get }) => get(croperState).width,
|
||||
set: ({ get, set }, newValue: any) => {
|
||||
const rect = get(croperState)
|
||||
set(croperState, { ...rect, width: newValue })
|
||||
},
|
||||
})
|
||||
|
||||
interface ToastAtomState {
|
||||
open: boolean
|
||||
desc: string
|
||||
@ -50,6 +134,7 @@ type ModelsHDSettings = { [key in AIModel]: HDSettings }
|
||||
|
||||
export interface Settings {
|
||||
show: boolean
|
||||
showCroper: boolean
|
||||
downloadMask: boolean
|
||||
graduallyInpainting: boolean
|
||||
runInpaintingManually: boolean
|
||||
@ -62,6 +147,17 @@ export interface Settings {
|
||||
|
||||
// For ZITS
|
||||
zitsWireframe: boolean
|
||||
|
||||
// For SD
|
||||
sdMaskBlur: number
|
||||
sdMode: SDMode
|
||||
sdStrength: number
|
||||
sdSteps: number
|
||||
sdGuidanceScale: number
|
||||
sdSampler: SDSampler
|
||||
sdSeed: number
|
||||
sdSeedFixed: boolean // true: use sdSeed, false: random generate seed on backend
|
||||
sdNumSamples: number
|
||||
}
|
||||
|
||||
const defaultHDSettings: ModelsHDSettings = {
|
||||
@ -100,10 +196,29 @@ const defaultHDSettings: ModelsHDSettings = {
|
||||
hdStrategyCropMargin: 128,
|
||||
enabled: false,
|
||||
},
|
||||
[AIModel.SD14]: {
|
||||
hdStrategy: HDStrategy.ORIGINAL,
|
||||
hdStrategyResizeLimit: 768,
|
||||
hdStrategyCropTrigerSize: 512,
|
||||
hdStrategyCropMargin: 128,
|
||||
enabled: true,
|
||||
},
|
||||
}
|
||||
|
||||
export enum SDSampler {
|
||||
ddim = 'ddim',
|
||||
pndm = 'pndm',
|
||||
}
|
||||
|
||||
export enum SDMode {
|
||||
text2img = 'text2img',
|
||||
img2img = 'img2img',
|
||||
inpainting = 'inpainting',
|
||||
}
|
||||
|
||||
export const settingStateDefault: Settings = {
|
||||
show: false,
|
||||
showCroper: false,
|
||||
downloadMask: false,
|
||||
graduallyInpainting: true,
|
||||
runInpaintingManually: false,
|
||||
@ -114,6 +229,17 @@ export const settingStateDefault: Settings = {
|
||||
ldmSampler: LDMSampler.plms,
|
||||
|
||||
zitsWireframe: true,
|
||||
|
||||
// SD
|
||||
sdMaskBlur: 5,
|
||||
sdMode: SDMode.inpainting,
|
||||
sdStrength: 0.75,
|
||||
sdSteps: 50,
|
||||
sdGuidanceScale: 7.5,
|
||||
sdSampler: SDSampler.ddim,
|
||||
sdSeed: 42,
|
||||
sdSeedFixed: true,
|
||||
sdNumSamples: 1,
|
||||
}
|
||||
|
||||
const localStorageEffect =
|
||||
@ -138,7 +264,7 @@ const localStorageEffect =
|
||||
)
|
||||
}
|
||||
|
||||
const ROOT_STATE_KEY = 'settingsState2'
|
||||
const ROOT_STATE_KEY = 'settingsState3'
|
||||
// Each atom can reference an array of these atom effect functions which are called in priority order when the atom is initialized
|
||||
// https://recoiljs.org/docs/guides/atom-effects/#local-storage-persistence
|
||||
export const settingState = atom<Settings>({
|
||||
@ -147,6 +273,18 @@ export const settingState = atom<Settings>({
|
||||
effects: [localStorageEffect(ROOT_STATE_KEY)],
|
||||
})
|
||||
|
||||
export const seedState = selector({
|
||||
key: 'seed',
|
||||
get: ({ get }) => {
|
||||
const settings = get(settingState)
|
||||
return settings.sdSeed
|
||||
},
|
||||
set: ({ get, set }, newValue: any) => {
|
||||
const settings = get(settingState)
|
||||
set(settingState, { ...settings, sdSeed: newValue })
|
||||
},
|
||||
})
|
||||
|
||||
export const hdSettingsState = selector({
|
||||
key: 'hdSettings',
|
||||
get: ({ get }) => {
|
||||
@ -164,3 +302,20 @@ export const hdSettingsState = selector({
|
||||
})
|
||||
},
|
||||
})
|
||||
|
||||
export const isSDState = selector({
|
||||
key: 'isSD',
|
||||
get: ({ get }) => {
|
||||
const settings = get(settingState)
|
||||
return settings.model === AIModel.SD14
|
||||
},
|
||||
})
|
||||
|
||||
export const runManuallyState = selector({
|
||||
key: 'runManuallyState',
|
||||
get: ({ get }) => {
|
||||
const settings = get(settingState)
|
||||
const isSD = get(isSDState)
|
||||
return settings.runInpaintingManually || isSD
|
||||
},
|
||||
})
|
||||
|
||||
@ -6,6 +6,7 @@
|
||||
--page-bg-light: rgb(255, 255, 255, 0.5);
|
||||
--page-text-color: #040404;
|
||||
--yellow-accent: #ffcc00;
|
||||
--yellow-accent-light: #ffcc0055;
|
||||
--link-color: rgb(0, 0, 0);
|
||||
--border-color: rgb(100, 100, 120);
|
||||
--border-color-light: rgba(100, 100, 120, 0.5);
|
||||
@ -57,4 +58,6 @@
|
||||
--box-shadow: inset 0 0.5px rgba(255, 255, 255, 0.1),
|
||||
inset 0 1px 5px hsl(210 16.7% 97.6%), 0px 0px 0px 0.5px hsl(205 10.7% 78%),
|
||||
0px 2px 1px -1px hsl(205 10.7% 78%), 0 1px hsl(205 10.7% 78%);
|
||||
|
||||
--croper-bg: rgba(0, 0, 0, 0.5);
|
||||
}
|
||||
|
||||
@ -6,6 +6,7 @@
|
||||
--page-bg-light: #04040488;
|
||||
--page-text-color: #f9f9f9;
|
||||
--yellow-accent: #ffcc00;
|
||||
--yellow-accent-light: #ffcc0055;
|
||||
--link-color: var(--yellow-accent);
|
||||
--border-color: rgb(100, 100, 120);
|
||||
--border-color-light: rgba(102, 102, 102);
|
||||
@ -55,4 +56,6 @@
|
||||
--box-shadow: inset 0 0.5px rgba(255, 255, 255, 0.1),
|
||||
inset 0 1px 5px hsl(195 7.1% 11%), 0px 0px 0px 0.5px hsl(207 5.6% 31.6%),
|
||||
0px 2px 1px -1px hsl(207 5.6% 31.6%), 0 1px hsl(207 5.6% 31.6%);
|
||||
|
||||
--croper-bg: rgba(0, 0, 0, 0.5);
|
||||
}
|
||||
|
||||
@ -9,9 +9,12 @@
|
||||
@use '../components/Editor/Editor';
|
||||
@use '../components/LandingPage/LandingPage';
|
||||
@use '../components/Header/Header';
|
||||
@use '../components/Header/PromptInput';
|
||||
@use '../components/Header/ThemeChanger';
|
||||
@use '../components/Shortcuts/Shortcuts';
|
||||
@use '../components/Settings/Settings.scss';
|
||||
@use '../components/SidePanel/SidePanel.scss';
|
||||
@use '../components/Croper/Croper.scss';
|
||||
|
||||
// Shared
|
||||
@use '../components/FileSelect/FileSelect';
|
||||
|
||||
@ -1241,6 +1241,26 @@
|
||||
minimatch "^3.0.4"
|
||||
strip-json-comments "^3.1.1"
|
||||
|
||||
"@floating-ui/core@^0.7.3":
|
||||
version "0.7.3"
|
||||
resolved "https://registry.npmmirror.com/@floating-ui/core/-/core-0.7.3.tgz#d274116678ffae87f6b60e90f88cc4083eefab86"
|
||||
integrity sha512-buc8BXHmG9l82+OQXOFU3Kr2XQx9ys01U/Q9HMIrZ300iLc8HLMgh7dcCqgYzAzf4BkoQvDcXf5Y+CuEZ5JBYg==
|
||||
|
||||
"@floating-ui/dom@^0.5.3":
|
||||
version "0.5.4"
|
||||
resolved "https://registry.npmmirror.com/@floating-ui/dom/-/dom-0.5.4.tgz#4eae73f78bcd4bd553ae2ade30e6f1f9c73fe3f1"
|
||||
integrity sha512-419BMceRLq0RrmTSDxn8hf9R3VCJv2K9PUfugh5JyEFmdjzDo+e8U5EdR8nzKq8Yj1htzLm3b6eQEEam3/rrtg==
|
||||
dependencies:
|
||||
"@floating-ui/core" "^0.7.3"
|
||||
|
||||
"@floating-ui/react-dom@0.7.2":
|
||||
version "0.7.2"
|
||||
resolved "https://registry.npmmirror.com/@floating-ui/react-dom/-/react-dom-0.7.2.tgz#0bf4ceccb777a140fc535c87eb5d6241c8e89864"
|
||||
integrity sha512-1T0sJcpHgX/u4I1OzIEhlcrvkUN8ln39nz7fMoE/2HDHrPiMFoOGR7++GYyfUmIQHkkrTinaeQsO3XWubjSvGg==
|
||||
dependencies:
|
||||
"@floating-ui/dom" "^0.5.3"
|
||||
use-isomorphic-layout-effect "^1.1.1"
|
||||
|
||||
"@gar/promisify@^1.0.1":
|
||||
version "1.1.2"
|
||||
resolved "https://registry.npmjs.org/@gar/promisify/-/promisify-1.1.2.tgz"
|
||||
@ -1566,6 +1586,13 @@
|
||||
dependencies:
|
||||
"@babel/runtime" "^7.13.10"
|
||||
|
||||
"@radix-ui/primitive@1.0.0":
|
||||
version "1.0.0"
|
||||
resolved "https://registry.npmmirror.com/@radix-ui/primitive/-/primitive-1.0.0.tgz#e1d8ef30b10ea10e69c76e896f608d9276352253"
|
||||
integrity sha512-3e7rn8FDMin4CgeL7Z/49smCA3rFYY3Ha2rUQ7HRWFadS5iCRw08ZgVT1LaNTCNqgvrUiyczLflrVrF0SRQtNA==
|
||||
dependencies:
|
||||
"@babel/runtime" "^7.13.10"
|
||||
|
||||
"@radix-ui/react-arrow@0.1.4":
|
||||
version "0.1.4"
|
||||
resolved "https://registry.npmmirror.com/@radix-ui/react-arrow/-/react-arrow-0.1.4.tgz#a871448a418cd3507d83840fdd47558cb961672b"
|
||||
@ -1574,6 +1601,14 @@
|
||||
"@babel/runtime" "^7.13.10"
|
||||
"@radix-ui/react-primitive" "0.1.4"
|
||||
|
||||
"@radix-ui/react-arrow@1.0.0":
|
||||
version "1.0.0"
|
||||
resolved "https://registry.npmmirror.com/@radix-ui/react-arrow/-/react-arrow-1.0.0.tgz#c461f4c2cab3317e3d42a1ae62910a4cbb0192a1"
|
||||
integrity sha512-1MUuv24HCdepi41+qfv125EwMuxgQ+U+h0A9K3BjCO/J8nVRREKHHpkD9clwfnjEDk9hgGzCnff4aUKCPiRepw==
|
||||
dependencies:
|
||||
"@babel/runtime" "^7.13.10"
|
||||
"@radix-ui/react-primitive" "1.0.0"
|
||||
|
||||
"@radix-ui/react-collection@0.1.5-rc.18":
|
||||
version "0.1.5-rc.18"
|
||||
resolved "https://registry.npmmirror.com/@radix-ui/react-collection/-/react-collection-0.1.5-rc.18.tgz#4dc03a8f464643748c0dad781b472f149d671d5c"
|
||||
@ -1599,6 +1634,13 @@
|
||||
dependencies:
|
||||
"@babel/runtime" "^7.13.10"
|
||||
|
||||
"@radix-ui/react-compose-refs@1.0.0":
|
||||
version "1.0.0"
|
||||
resolved "https://registry.npmmirror.com/@radix-ui/react-compose-refs/-/react-compose-refs-1.0.0.tgz#37595b1f16ec7f228d698590e78eeed18ff218ae"
|
||||
integrity sha512-0KaSv6sx787/hK3eF53iOkiSLwAGlFMx5lotrqD2pTjB18KbybKoEIgkNZTKC60YECDQTKGTRcDBILwZVqVKvA==
|
||||
dependencies:
|
||||
"@babel/runtime" "^7.13.10"
|
||||
|
||||
"@radix-ui/react-context@0.1.1":
|
||||
version "0.1.1"
|
||||
resolved "https://registry.npmmirror.com/@radix-ui/react-context/-/react-context-0.1.1.tgz#06996829ea124d9a1bc1dbe3e51f33588fab0875"
|
||||
@ -1613,6 +1655,13 @@
|
||||
dependencies:
|
||||
"@babel/runtime" "^7.13.10"
|
||||
|
||||
"@radix-ui/react-context@1.0.0":
|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
@ -2055,6 +2265,11 @@
|
||||
dependencies:
|
||||
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|
||||
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||||
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||||
@ -4621,6 +4836,13 @@ debug@^3.1.1, debug@^3.2.6, debug@^3.2.7:
|
||||
dependencies:
|
||||
ms "^2.1.1"
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
version "1.2.0"
|
||||
resolved "https://registry.npmjs.org/decamelize/-/decamelize-1.2.0.tgz"
|
||||
@ -5004,6 +5226,22 @@ end-of-stream@^1.0.0, end-of-stream@^1.1.0:
|
||||
dependencies:
|
||||
once "^1.4.0"
|
||||
|
||||
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|
||||
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||||
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|
||||
debug "~4.3.1"
|
||||
engine.io-parser "~5.0.3"
|
||||
ws "~8.2.3"
|
||||
xmlhttprequest-ssl "~2.0.0"
|
||||
|
||||
engine.io-parser@~5.0.3:
|
||||
version "5.0.4"
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@ -6215,6 +6453,11 @@ hosted-git-info@^2.1.4:
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hpack.js@^2.1.6:
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||||
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@ -7785,16 +8028,11 @@ lodash.uniq@^4.5.0:
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integrity sha1-0CJTc662Uq3BvILklFM5qEJ1R3M=
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|
||||
"lodash@>=3.5 <5", lodash@^4.17.11, lodash@^4.17.14, lodash@^4.17.15, lodash@^4.17.19, lodash@^4.17.20, lodash@^4.17.21, lodash@^4.17.5, lodash@^4.7.0:
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||||
version "4.17.21"
|
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resolved "https://registry.npmjs.org/lodash/-/lodash-4.17.21.tgz"
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lodash@^4.17.21:
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version "4.17.21"
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resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.21.tgz#679591c564c3bffaae8454cf0b3df370c3d6911c"
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||||
loglevel@^1.6.8:
|
||||
version "1.7.1"
|
||||
resolved "https://registry.npmjs.org/loglevel/-/loglevel-1.7.1.tgz"
|
||||
@ -8095,6 +8333,11 @@ mississippi@^3.0.0:
|
||||
stream-each "^1.1.0"
|
||||
through2 "^2.0.0"
|
||||
|
||||
mitt@^3.0.0:
|
||||
version "3.0.0"
|
||||
resolved "https://registry.npmmirror.com/mitt/-/mitt-3.0.0.tgz#69ef9bd5c80ff6f57473e8d89326d01c414be0bd"
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integrity sha512-7dX2/10ITVyqh4aOSVI9gdape+t9l2/8QxHrFmUXu4EEUpdlxl6RudZUPZoc+zuY2hk1j7XxVroIVIan/pD/SQ==
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mixin-deep@^1.2.0:
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version "1.3.2"
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||||
resolved "https://registry.npmjs.org/mixin-deep/-/mixin-deep-1.3.2.tgz"
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||||
@ -9924,6 +10167,13 @@ react-error-overlay@^6.0.9:
|
||||
resolved "https://registry.npmjs.org/react-error-overlay/-/react-error-overlay-6.0.9.tgz"
|
||||
integrity sha512-nQTTcUu+ATDbrSD1BZHr5kgSD4oF8OFjxun8uAaL8RwPBacGBNPf/yAuVVdx17N8XNzRDMrZ9XcKZHCjPW+9ew==
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react-hotkeys-hook@^3.4.7:
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version "3.4.7"
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resolved "https://registry.npmmirror.com/react-hotkeys-hook/-/react-hotkeys-hook-3.4.7.tgz#e16a0a85f59feed9f48d12cfaf166d7df4c96b7a"
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integrity sha512-+bbPmhPAl6ns9VkXkNNyxlmCAIyDAcWbB76O4I0ntr3uWCRuIQf/aRLartUahe9chVMPj+OEzzfk3CQSjclUEQ==
|
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dependencies:
|
||||
hotkeys-js "3.9.4"
|
||||
|
||||
react-is@^16.8.1:
|
||||
version "16.13.1"
|
||||
resolved "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz"
|
||||
@ -9947,6 +10197,25 @@ react-remove-scroll-bar@^2.3.0:
|
||||
react-style-singleton "^2.2.0"
|
||||
tslib "^2.0.0"
|
||||
|
||||
react-remove-scroll-bar@^2.3.3:
|
||||
version "2.3.3"
|
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resolved "https://registry.npmmirror.com/react-remove-scroll-bar/-/react-remove-scroll-bar-2.3.3.tgz#e291f71b1bb30f5f67f023765b7435f4b2b2cd94"
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dependencies:
|
||||
react-style-singleton "^2.2.1"
|
||||
tslib "^2.0.0"
|
||||
|
||||
react-remove-scroll@2.5.4:
|
||||
version "2.5.4"
|
||||
resolved "https://registry.npmmirror.com/react-remove-scroll/-/react-remove-scroll-2.5.4.tgz#afe6491acabde26f628f844b67647645488d2ea0"
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integrity sha512-xGVKJJr0SJGQVirVFAUZ2k1QLyO6m+2fy0l8Qawbp5Jgrv3DeLalrfMNBFSlmz5kriGGzsVBtGVnf4pTKIhhWA==
|
||||
dependencies:
|
||||
react-remove-scroll-bar "^2.3.3"
|
||||
react-style-singleton "^2.2.1"
|
||||
tslib "^2.1.0"
|
||||
use-callback-ref "^1.3.0"
|
||||
use-sidecar "^1.1.2"
|
||||
|
||||
react-remove-scroll@^2.4.0:
|
||||
version "2.5.1"
|
||||
resolved "https://registry.npmmirror.com/react-remove-scroll/-/react-remove-scroll-2.5.1.tgz#28c318c2e076040e5d6172bf28aab2916ad89b46"
|
||||
@ -10033,6 +10302,15 @@ react-style-singleton@^2.2.0:
|
||||
invariant "^2.2.4"
|
||||
tslib "^2.0.0"
|
||||
|
||||
react-style-singleton@^2.2.1:
|
||||
version "2.2.1"
|
||||
resolved "https://registry.npmmirror.com/react-style-singleton/-/react-style-singleton-2.2.1.tgz#f99e420492b2d8f34d38308ff660b60d0b1205b4"
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||||
integrity sha512-ZWj0fHEMyWkHzKYUr2Bs/4zU6XLmq9HsgBURm7g5pAVfyn49DgUiNgY2d4lXRlYSiCif9YBGpQleewkcqddc7g==
|
||||
dependencies:
|
||||
get-nonce "^1.0.0"
|
||||
invariant "^2.2.4"
|
||||
tslib "^2.0.0"
|
||||
|
||||
react-universal-interface@^0.6.2:
|
||||
version "0.6.2"
|
||||
resolved "https://registry.npmjs.org/react-universal-interface/-/react-universal-interface-0.6.2.tgz"
|
||||
@ -10845,6 +11123,24 @@ snapdragon@^0.8.1:
|
||||
source-map-resolve "^0.5.0"
|
||||
use "^3.1.0"
|
||||
|
||||
socket.io-client@^4.5.2:
|
||||
version "4.5.2"
|
||||
resolved "https://registry.npmmirror.com/socket.io-client/-/socket.io-client-4.5.2.tgz#9481518c560388c980c88b01e3cf62f367f04c96"
|
||||
integrity sha512-naqYfFu7CLDiQ1B7AlLhRXKX3gdeaIMfgigwavDzgJoIUYulc1qHH5+2XflTsXTPY7BlPH5rppJyUjhjrKQKLg==
|
||||
dependencies:
|
||||
"@socket.io/component-emitter" "~3.1.0"
|
||||
debug "~4.3.2"
|
||||
engine.io-client "~6.2.1"
|
||||
socket.io-parser "~4.2.0"
|
||||
|
||||
socket.io-parser@~4.2.0:
|
||||
version "4.2.1"
|
||||
resolved "https://registry.npmmirror.com/socket.io-parser/-/socket.io-parser-4.2.1.tgz#01c96efa11ded938dcb21cbe590c26af5eff65e5"
|
||||
integrity sha512-V4GrkLy+HeF1F/en3SpUaM+7XxYXpuMUWLGde1kSSh5nQMN4hLrbPIkD+otwh6q9R6NOQBN4AMaOZ2zVjui82g==
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||||
dependencies:
|
||||
"@socket.io/component-emitter" "~3.1.0"
|
||||
debug "~4.3.1"
|
||||
|
||||
sockjs-client@^1.5.0:
|
||||
version "1.5.2"
|
||||
resolved "https://registry.npmjs.org/sockjs-client/-/sockjs-client-1.5.2.tgz"
|
||||
@ -11855,6 +12151,11 @@ use-callback-ref@^1.3.0:
|
||||
dependencies:
|
||||
tslib "^2.0.0"
|
||||
|
||||
use-isomorphic-layout-effect@^1.1.1:
|
||||
version "1.1.2"
|
||||
resolved "https://registry.npmmirror.com/use-isomorphic-layout-effect/-/use-isomorphic-layout-effect-1.1.2.tgz#497cefb13d863d687b08477d9e5a164ad8c1a6fb"
|
||||
integrity sha512-49L8yCO3iGT/ZF9QttjwLF/ZD9Iwto5LnH5LmEdk/6cFmXddqi2ulF0edxTwjj+7mqvpVVGQWvbXZdn32wRSHA==
|
||||
|
||||
use-sidecar@^1.1.2:
|
||||
version "1.1.2"
|
||||
resolved "https://registry.npmmirror.com/use-sidecar/-/use-sidecar-1.1.2.tgz#2f43126ba2d7d7e117aa5855e5d8f0276dfe73c2"
|
||||
@ -12410,6 +12711,11 @@ ws@^7.4.6:
|
||||
resolved "https://registry.npmjs.org/ws/-/ws-7.5.5.tgz"
|
||||
integrity sha512-BAkMFcAzl8as1G/hArkxOxq3G7pjUqQ3gzYbLL0/5zNkph70e+lCoxBGnm6AW1+/aiNeV4fnKqZ8m4GZewmH2w==
|
||||
|
||||
ws@~8.2.3:
|
||||
version "8.2.3"
|
||||
resolved "https://registry.npmmirror.com/ws/-/ws-8.2.3.tgz#63a56456db1b04367d0b721a0b80cae6d8becbba"
|
||||
integrity sha512-wBuoj1BDpC6ZQ1B7DWQBYVLphPWkm8i9Y0/3YdHjHKHiohOJ1ws+3OccDWtH+PoC9DZD5WOTrJvNbWvjS6JWaA==
|
||||
|
||||
xml-name-validator@^3.0.0:
|
||||
version "3.0.0"
|
||||
resolved "https://registry.npmjs.org/xml-name-validator/-/xml-name-validator-3.0.0.tgz"
|
||||
@ -12420,6 +12726,11 @@ xmlchars@^2.2.0:
|
||||
resolved "https://registry.npmjs.org/xmlchars/-/xmlchars-2.2.0.tgz"
|
||||
integrity sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw==
|
||||
|
||||
xmlhttprequest-ssl@~2.0.0:
|
||||
version "2.0.0"
|
||||
resolved "https://registry.npmmirror.com/xmlhttprequest-ssl/-/xmlhttprequest-ssl-2.0.0.tgz#91360c86b914e67f44dce769180027c0da618c67"
|
||||
integrity sha512-QKxVRxiRACQcVuQEYFsI1hhkrMlrXHPegbbd1yn9UHOmRxY+si12nQYzri3vbzt8VdTTRviqcKxcyllFas5z2A==
|
||||
|
||||
xtend@^4.0.0, xtend@~4.0.1:
|
||||
version "4.0.2"
|
||||
resolved "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz"
|
||||
|
||||
@ -14,17 +14,17 @@ class InpaintModel:
|
||||
pad_mod = 8
|
||||
pad_to_square = False
|
||||
|
||||
def __init__(self, device):
|
||||
def __init__(self, device, **kwargs):
|
||||
"""
|
||||
|
||||
Args:
|
||||
device:
|
||||
"""
|
||||
self.device = device
|
||||
self.init_model(device)
|
||||
self.init_model(device, **kwargs)
|
||||
|
||||
@abc.abstractmethod
|
||||
def init_model(self, device):
|
||||
def init_model(self, device, **kwargs):
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
@ -36,15 +36,19 @@ class InpaintModel:
|
||||
def forward(self, image, mask, config: Config):
|
||||
"""Input images and output images have same size
|
||||
images: [H, W, C] RGB
|
||||
masks: [H, W] 255 为 masks 区域
|
||||
masks: [H, W, 1] 255 为 masks 区域
|
||||
return: BGR IMAGE
|
||||
"""
|
||||
...
|
||||
|
||||
def _pad_forward(self, image, mask, config: Config):
|
||||
origin_height, origin_width = image.shape[:2]
|
||||
pad_image = pad_img_to_modulo(image, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size)
|
||||
pad_mask = pad_img_to_modulo(mask, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size)
|
||||
pad_image = pad_img_to_modulo(
|
||||
image, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
|
||||
)
|
||||
pad_mask = pad_img_to_modulo(
|
||||
mask, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
|
||||
)
|
||||
|
||||
logger.info(f"final forward pad size: {pad_image.shape}")
|
||||
|
||||
@ -81,18 +85,30 @@ class InpaintModel:
|
||||
elif config.hd_strategy == HDStrategy.RESIZE:
|
||||
if max(image.shape) > config.hd_strategy_resize_limit:
|
||||
origin_size = image.shape[:2]
|
||||
downsize_image = resize_max_size(image, size_limit=config.hd_strategy_resize_limit)
|
||||
downsize_mask = resize_max_size(mask, size_limit=config.hd_strategy_resize_limit)
|
||||
downsize_image = resize_max_size(
|
||||
image, size_limit=config.hd_strategy_resize_limit
|
||||
)
|
||||
downsize_mask = resize_max_size(
|
||||
mask, size_limit=config.hd_strategy_resize_limit
|
||||
)
|
||||
|
||||
logger.info(f"Run resize strategy, origin size: {image.shape} forward size: {downsize_image.shape}")
|
||||
inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
|
||||
logger.info(
|
||||
f"Run resize strategy, origin size: {image.shape} forward size: {downsize_image.shape}"
|
||||
)
|
||||
inpaint_result = self._pad_forward(
|
||||
downsize_image, downsize_mask, config
|
||||
)
|
||||
|
||||
# only paste masked area result
|
||||
inpaint_result = cv2.resize(inpaint_result,
|
||||
inpaint_result = cv2.resize(
|
||||
inpaint_result,
|
||||
(origin_size[1], origin_size[0]),
|
||||
interpolation=cv2.INTER_CUBIC)
|
||||
interpolation=cv2.INTER_CUBIC,
|
||||
)
|
||||
original_pixel_indices = mask < 127
|
||||
inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
|
||||
inpaint_result[original_pixel_indices] = image[:, :, ::-1][
|
||||
original_pixel_indices
|
||||
]
|
||||
|
||||
if inpaint_result is None:
|
||||
inpaint_result = self._pad_forward(image, mask, config)
|
||||
@ -133,11 +149,11 @@ class InpaintModel:
|
||||
if _l < 0:
|
||||
r += abs(_l)
|
||||
if _r > img_w:
|
||||
l -= (_r - img_w)
|
||||
l -= _r - img_w
|
||||
if _t < 0:
|
||||
b += abs(_t)
|
||||
if _b > img_h:
|
||||
t -= (_b - img_h)
|
||||
t -= _b - img_h
|
||||
|
||||
l = max(l, 0)
|
||||
r = min(r, img_w)
|
||||
|
||||
@ -1135,7 +1135,7 @@ class FcF(InpaintModel):
|
||||
pad_mod = 512
|
||||
pad_to_square = True
|
||||
|
||||
def init_model(self, device):
|
||||
def init_model(self, device, **kwargs):
|
||||
seed = 0
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
|
||||
@ -18,16 +18,7 @@ LAMA_MODEL_URL = os.environ.get(
|
||||
class LaMa(InpaintModel):
|
||||
pad_mod = 8
|
||||
|
||||
def __init__(self, device):
|
||||
"""
|
||||
|
||||
Args:
|
||||
device:
|
||||
"""
|
||||
super().__init__(device)
|
||||
self.device = device
|
||||
|
||||
def init_model(self, device):
|
||||
def init_model(self, device, **kwargs):
|
||||
if os.environ.get("LAMA_MODEL"):
|
||||
model_path = os.environ.get("LAMA_MODEL")
|
||||
if not os.path.exists(model_path):
|
||||
|
||||
@ -11,7 +11,12 @@ from lama_cleaner.schema import Config, LDMSampler
|
||||
|
||||
torch.manual_seed(42)
|
||||
import torch.nn as nn
|
||||
from lama_cleaner.helper import download_model, norm_img, get_cache_path_by_url, load_jit_model
|
||||
from lama_cleaner.helper import (
|
||||
download_model,
|
||||
norm_img,
|
||||
get_cache_path_by_url,
|
||||
load_jit_model,
|
||||
)
|
||||
from lama_cleaner.model.utils import (
|
||||
make_beta_schedule,
|
||||
timestep_embedding,
|
||||
@ -139,7 +144,7 @@ class DDPM(nn.Module):
|
||||
)
|
||||
|
||||
if self.parameterization == "eps":
|
||||
lvlb_weights = self.betas ** 2 / (
|
||||
lvlb_weights = self.betas**2 / (
|
||||
2
|
||||
* self.posterior_variance
|
||||
* to_torch(alphas)
|
||||
@ -222,12 +227,12 @@ class LatentDiffusion(DDPM):
|
||||
class LDM(InpaintModel):
|
||||
pad_mod = 32
|
||||
|
||||
def __init__(self, device, fp16: bool = True):
|
||||
def __init__(self, device, fp16: bool = True, **kwargs):
|
||||
self.fp16 = fp16
|
||||
super().__init__(device)
|
||||
self.device = device
|
||||
|
||||
def init_model(self, device):
|
||||
def init_model(self, device, **kwargs):
|
||||
self.diffusion_model = load_jit_model(LDM_DIFFUSION_MODEL_URL, device)
|
||||
self.cond_stage_model_decode = load_jit_model(LDM_DECODE_MODEL_URL, device)
|
||||
self.cond_stage_model_encode = load_jit_model(LDM_ENCODE_MODEL_URL, device)
|
||||
|
||||
@ -1405,7 +1405,7 @@ class MAT(InpaintModel):
|
||||
pad_mod = 512
|
||||
pad_to_square = True
|
||||
|
||||
def init_model(self, device):
|
||||
def init_model(self, device, **kwargs):
|
||||
seed = 240 # pick up a random number
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
|
||||
176
lama_cleaner/model/sd.py
Normal file
176
lama_cleaner/model/sd.py
Normal file
@ -0,0 +1,176 @@
|
||||
import random
|
||||
|
||||
import PIL.Image
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
from diffusers import PNDMScheduler, DDIMScheduler
|
||||
from loguru import logger
|
||||
|
||||
from lama_cleaner.helper import norm_img
|
||||
|
||||
from lama_cleaner.model.base import InpaintModel
|
||||
from lama_cleaner.schema import Config, SDSampler
|
||||
|
||||
|
||||
#
|
||||
#
|
||||
# def preprocess_image(image):
|
||||
# w, h = image.size
|
||||
# w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
|
||||
# image = image.resize((w, h), resample=PIL.Image.LANCZOS)
|
||||
# image = np.array(image).astype(np.float32) / 255.0
|
||||
# image = image[None].transpose(0, 3, 1, 2)
|
||||
# image = torch.from_numpy(image)
|
||||
# # [-1, 1]
|
||||
# return 2.0 * image - 1.0
|
||||
#
|
||||
#
|
||||
# def preprocess_mask(mask):
|
||||
# mask = mask.convert("L")
|
||||
# w, h = mask.size
|
||||
# w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
|
||||
# mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
|
||||
# mask = np.array(mask).astype(np.float32) / 255.0
|
||||
# mask = np.tile(mask, (4, 1, 1))
|
||||
# mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
|
||||
# mask = 1 - mask # repaint white, keep black
|
||||
# mask = torch.from_numpy(mask)
|
||||
# return mask
|
||||
|
||||
|
||||
class SD(InpaintModel):
|
||||
pad_mod = 32
|
||||
min_size = 512
|
||||
|
||||
def init_model(self, device: torch.device, **kwargs):
|
||||
from .sd_pipeline import StableDiffusionInpaintPipeline
|
||||
|
||||
self.model = StableDiffusionInpaintPipeline.from_pretrained(
|
||||
self.model_id_or_path,
|
||||
revision="fp16" if torch.cuda.is_available() else "main",
|
||||
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
|
||||
use_auth_token=kwargs["hf_access_token"],
|
||||
)
|
||||
# https://huggingface.co/docs/diffusers/v0.3.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
|
||||
self.model.enable_attention_slicing()
|
||||
self.model = self.model.to(device)
|
||||
self.callbacks = kwargs.pop("callbacks", None)
|
||||
|
||||
@torch.cuda.amp.autocast()
|
||||
def forward(self, image, mask, config: Config):
|
||||
"""Input image and output image have same size
|
||||
image: [H, W, C] RGB
|
||||
mask: [H, W, 1] 255 means area to repaint
|
||||
return: BGR IMAGE
|
||||
"""
|
||||
|
||||
# image = norm_img(image) # [0, 1]
|
||||
# image = image * 2 - 1 # [0, 1] -> [-1, 1]
|
||||
|
||||
# resize to latent feature map size
|
||||
# h, w = mask.shape[:2]
|
||||
# mask = cv2.resize(mask, (h // 8, w // 8), interpolation=cv2.INTER_AREA)
|
||||
# mask = norm_img(mask)
|
||||
#
|
||||
# image = torch.from_numpy(image).unsqueeze(0).to(self.device)
|
||||
# mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
|
||||
|
||||
if config.sd_sampler == SDSampler.ddim:
|
||||
scheduler = DDIMScheduler(
|
||||
beta_start=0.00085,
|
||||
beta_end=0.012,
|
||||
beta_schedule="scaled_linear",
|
||||
clip_sample=False,
|
||||
set_alpha_to_one=False,
|
||||
)
|
||||
elif config.sd_sampler == SDSampler.pndm:
|
||||
PNDM_kwargs = {
|
||||
"tensor_format": "pt",
|
||||
"beta_schedule": "scaled_linear",
|
||||
"beta_start": 0.00085,
|
||||
"beta_end": 0.012,
|
||||
"num_train_timesteps": 1000,
|
||||
"skip_prk_steps": True,
|
||||
}
|
||||
scheduler = PNDMScheduler(**PNDM_kwargs)
|
||||
else:
|
||||
raise ValueError(config.sd_sampler)
|
||||
|
||||
self.model.scheduler = scheduler
|
||||
|
||||
seed = config.sd_seed
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
|
||||
if config.sd_mask_blur != 0:
|
||||
k = 2 * config.sd_mask_blur + 1
|
||||
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
|
||||
|
||||
output = self.model(
|
||||
prompt=config.prompt,
|
||||
init_image=PIL.Image.fromarray(image),
|
||||
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
|
||||
strength=config.sd_strength,
|
||||
num_inference_steps=config.sd_steps,
|
||||
guidance_scale=config.sd_guidance_scale,
|
||||
output_type="np.array",
|
||||
callbacks=self.callbacks,
|
||||
).images[0]
|
||||
|
||||
output = (output * 255).round().astype("uint8")
|
||||
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
||||
return output
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(self, image, mask, config: Config):
|
||||
"""
|
||||
images: [H, W, C] RGB, not normalized
|
||||
masks: [H, W]
|
||||
return: BGR IMAGE
|
||||
"""
|
||||
img_h, img_w = image.shape[:2]
|
||||
|
||||
# boxes = boxes_from_mask(mask)
|
||||
if config.use_croper:
|
||||
logger.info("use croper")
|
||||
l, t, w, h = (
|
||||
config.croper_x,
|
||||
config.croper_y,
|
||||
config.croper_width,
|
||||
config.croper_height,
|
||||
)
|
||||
r = l + w
|
||||
b = t + h
|
||||
|
||||
l = max(l, 0)
|
||||
r = min(r, img_w)
|
||||
t = max(t, 0)
|
||||
b = min(b, img_h)
|
||||
|
||||
crop_img = image[t:b, l:r, :]
|
||||
crop_mask = mask[t:b, l:r]
|
||||
|
||||
crop_image = self._pad_forward(crop_img, crop_mask, config)
|
||||
|
||||
inpaint_result = image[:, :, ::-1]
|
||||
inpaint_result[t:b, l:r, :] = crop_image
|
||||
else:
|
||||
inpaint_result = self._pad_forward(image, mask, config)
|
||||
|
||||
return inpaint_result
|
||||
|
||||
@staticmethod
|
||||
def is_downloaded() -> bool:
|
||||
# model will be downloaded when app start, and can't switch in frontend settings
|
||||
return True
|
||||
|
||||
|
||||
class SD14(SD):
|
||||
model_id_or_path = "CompVis/stable-diffusion-v1-4"
|
||||
|
||||
|
||||
class SD15(SD):
|
||||
model_id_or_path = "CompVis/stable-diffusion-v1-5"
|
||||
309
lama_cleaner/model/sd_pipeline.py
Normal file
309
lama_cleaner/model/sd_pipeline.py
Normal file
@ -0,0 +1,309 @@
|
||||
import inspect
|
||||
from typing import List, Optional, Union, Callable
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
import PIL
|
||||
from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, PNDMScheduler
|
||||
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker, StableDiffusionPipelineOutput
|
||||
from diffusers.utils import logging
|
||||
from tqdm.auto import tqdm
|
||||
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
def preprocess_image(image):
|
||||
w, h = image.size
|
||||
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
|
||||
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
|
||||
image = np.array(image).astype(np.float32) / 255.0
|
||||
image = image[None].transpose(0, 3, 1, 2)
|
||||
image = torch.from_numpy(image)
|
||||
return 2.0 * image - 1.0
|
||||
|
||||
|
||||
def preprocess_mask(mask):
|
||||
mask = mask.convert("L")
|
||||
w, h = mask.size
|
||||
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
|
||||
mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
|
||||
mask = np.array(mask).astype(np.float32) / 255.0
|
||||
mask = np.tile(mask, (4, 1, 1))
|
||||
mask = mask[None].transpose(0, 1, 2, 3) # what does this step do?
|
||||
mask = 1 - mask # repaint white, keep black
|
||||
mask = torch.from_numpy(mask)
|
||||
return mask
|
||||
|
||||
|
||||
class StableDiffusionInpaintPipeline(DiffusionPipeline):
|
||||
r"""
|
||||
Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*.
|
||||
|
||||
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
||||
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
||||
|
||||
Args:
|
||||
vae ([`AutoencoderKL`]):
|
||||
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
||||
text_encoder ([`CLIPTextModel`]):
|
||||
Frozen text-encoder. Stable Diffusion uses the text portion of
|
||||
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
||||
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
||||
tokenizer (`CLIPTokenizer`):
|
||||
Tokenizer of class
|
||||
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
||||
unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
||||
scheduler ([`SchedulerMixin`]):
|
||||
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
|
||||
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
||||
safety_checker ([`StableDiffusionSafetyChecker`]):
|
||||
Classification module that estimates whether generated images could be considered offsensive or harmful.
|
||||
Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
|
||||
feature_extractor ([`CLIPFeatureExtractor`]):
|
||||
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vae: AutoencoderKL,
|
||||
text_encoder: CLIPTextModel,
|
||||
tokenizer: CLIPTokenizer,
|
||||
unet: UNet2DConditionModel,
|
||||
scheduler: Union[DDIMScheduler, PNDMScheduler],
|
||||
safety_checker: StableDiffusionSafetyChecker,
|
||||
feature_extractor: CLIPFeatureExtractor,
|
||||
):
|
||||
super().__init__()
|
||||
scheduler = scheduler.set_format("pt")
|
||||
logger.info("`StableDiffusionInpaintPipeline` is experimental and will very likely change in the future.")
|
||||
self.register_modules(
|
||||
vae=vae,
|
||||
text_encoder=text_encoder,
|
||||
tokenizer=tokenizer,
|
||||
unet=unet,
|
||||
scheduler=scheduler,
|
||||
safety_checker=safety_checker,
|
||||
feature_extractor=feature_extractor,
|
||||
)
|
||||
|
||||
def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
|
||||
r"""
|
||||
Enable sliced attention computation.
|
||||
|
||||
When this option is enabled, the attention module will split the input tensor in slices, to compute attention
|
||||
in several steps. This is useful to save some memory in exchange for a small speed decrease.
|
||||
|
||||
Args:
|
||||
slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
|
||||
When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
|
||||
a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
|
||||
`attention_head_dim` must be a multiple of `slice_size`.
|
||||
"""
|
||||
if slice_size == "auto":
|
||||
# half the attention head size is usually a good trade-off between
|
||||
# speed and memory
|
||||
slice_size = self.unet.config.attention_head_dim // 2
|
||||
self.unet.set_attention_slice(slice_size)
|
||||
|
||||
def disable_attention_slicing(self):
|
||||
r"""
|
||||
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
|
||||
back to computing attention in one step.
|
||||
"""
|
||||
# set slice_size = `None` to disable `set_attention_slice`
|
||||
self.enable_attention_slice(None)
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(
|
||||
self,
|
||||
prompt: Union[str, List[str]],
|
||||
init_image: Union[torch.FloatTensor, PIL.Image.Image],
|
||||
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
|
||||
strength: float = 0.8,
|
||||
num_inference_steps: Optional[int] = 50,
|
||||
guidance_scale: Optional[float] = 7.5,
|
||||
eta: Optional[float] = 0.0,
|
||||
generator: Optional[torch.Generator] = None,
|
||||
output_type: Optional[str] = "pil",
|
||||
return_dict: bool = True,
|
||||
callbacks: List[Callable[[int], None]] = None
|
||||
):
|
||||
r"""
|
||||
Function invoked when calling the pipeline for generation.
|
||||
|
||||
Args:
|
||||
prompt (`str` or `List[str]`):
|
||||
The prompt or prompts to guide the image generation.
|
||||
init_image (`torch.FloatTensor` or `PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch, that will be used as the starting point for the
|
||||
process. This is the image whose masked region will be inpainted.
|
||||
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
|
||||
`Image`, or tensor representing an image batch, to mask `init_image`. White pixels in the mask will be
|
||||
replaced by noise and therefore repainted, while black pixels will be preserved. The mask image will be
|
||||
converted to a single channel (luminance) before use.
|
||||
strength (`float`, *optional*, defaults to 0.8):
|
||||
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
|
||||
is 1, the denoising process will be run on the masked area for the full number of iterations specified
|
||||
in `num_inference_steps`. `init_image` will be used as a reference for the masked area, adding more
|
||||
noise to that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
|
||||
num_inference_steps (`int`, *optional*, defaults to 50):
|
||||
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
|
||||
the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
|
||||
guidance_scale (`float`, *optional*, defaults to 7.5):
|
||||
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
||||
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
||||
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
||||
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
||||
usually at the expense of lower image quality.
|
||||
eta (`float`, *optional*, defaults to 0.0):
|
||||
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
||||
[`schedulers.DDIMScheduler`], will be ignored for others.
|
||||
generator (`torch.Generator`, *optional*):
|
||||
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
|
||||
deterministic.
|
||||
output_type (`str`, *optional*, defaults to `"pil"`):
|
||||
The output format of the generate image. Choose between
|
||||
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
|
||||
return_dict (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
||||
plain tuple.
|
||||
|
||||
Returns:
|
||||
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
||||
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
||||
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
||||
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
||||
(nsfw) content, according to the `safety_checker`.
|
||||
"""
|
||||
if isinstance(prompt, str):
|
||||
batch_size = 1
|
||||
elif isinstance(prompt, list):
|
||||
batch_size = len(prompt)
|
||||
else:
|
||||
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
||||
|
||||
if strength < 0 or strength > 1:
|
||||
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
|
||||
|
||||
# set timesteps
|
||||
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
|
||||
extra_set_kwargs = {}
|
||||
offset = 0
|
||||
if accepts_offset:
|
||||
offset = 1
|
||||
extra_set_kwargs["offset"] = 1
|
||||
|
||||
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
|
||||
|
||||
# preprocess image
|
||||
init_image = preprocess_image(init_image).to(self.device)
|
||||
|
||||
# encode the init image into latents and scale the latents
|
||||
init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
|
||||
init_latents = init_latent_dist.sample(generator=generator)
|
||||
|
||||
init_latents = 0.18215 * init_latents
|
||||
|
||||
# Expand init_latents for batch_size
|
||||
init_latents = torch.cat([init_latents] * batch_size)
|
||||
init_latents_orig = init_latents
|
||||
|
||||
# preprocess mask
|
||||
mask = preprocess_mask(mask_image).to(self.device)
|
||||
mask = torch.cat([mask] * batch_size)
|
||||
|
||||
# check sizes
|
||||
if not mask.shape == init_latents.shape:
|
||||
raise ValueError("The mask and init_image should be the same size!")
|
||||
|
||||
# get the original timestep using init_timestep
|
||||
init_timestep = int(num_inference_steps * strength) + offset
|
||||
init_timestep = min(init_timestep, num_inference_steps)
|
||||
timesteps = self.scheduler.timesteps[-init_timestep]
|
||||
timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
|
||||
|
||||
# add noise to latents using the timesteps
|
||||
noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
|
||||
init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
|
||||
|
||||
# get prompt text embeddings
|
||||
text_input = self.tokenizer(
|
||||
prompt,
|
||||
padding="max_length",
|
||||
max_length=self.tokenizer.model_max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
|
||||
|
||||
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
||||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
||||
# corresponds to doing no classifier free guidance.
|
||||
do_classifier_free_guidance = guidance_scale > 1.0
|
||||
# get unconditional embeddings for classifier free guidance
|
||||
if do_classifier_free_guidance:
|
||||
max_length = text_input.input_ids.shape[-1]
|
||||
uncond_input = self.tokenizer(
|
||||
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
|
||||
)
|
||||
uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
|
||||
|
||||
# For classifier free guidance, we need to do two forward passes.
|
||||
# Here we concatenate the unconditional and text embeddings into a single batch
|
||||
# to avoid doing two forward passes
|
||||
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
|
||||
|
||||
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
||||
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
||||
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
||||
# and should be between [0, 1]
|
||||
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
extra_step_kwargs = {}
|
||||
if accepts_eta:
|
||||
extra_step_kwargs["eta"] = eta
|
||||
|
||||
latents = init_latents
|
||||
t_start = max(num_inference_steps - init_timestep + offset, 0)
|
||||
for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
|
||||
# expand the latents if we are doing classifier free guidance
|
||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
||||
|
||||
# predict the noise residual
|
||||
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
|
||||
|
||||
# perform guidance
|
||||
if do_classifier_free_guidance:
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
|
||||
|
||||
# masking
|
||||
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
|
||||
latents = (init_latents_proper * mask) + (latents * (1 - mask))
|
||||
|
||||
if callbacks is not None:
|
||||
for callback in callbacks:
|
||||
callback(i)
|
||||
|
||||
# scale and decode the image latents with vae
|
||||
latents = 1 / 0.18215 * latents
|
||||
image = self.vae.decode(latents).sample
|
||||
|
||||
image = (image / 2 + 0.5).clamp(0, 1)
|
||||
image = image.cpu().permute(0, 2, 3, 1).numpy()
|
||||
|
||||
# run safety checker
|
||||
safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
|
||||
image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
|
||||
|
||||
if output_type == "pil":
|
||||
image = self.numpy_to_pil(image)
|
||||
|
||||
if not return_dict:
|
||||
return (image, has_nsfw_concept)
|
||||
|
||||
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
||||
@ -206,7 +206,7 @@ class ZITS(InpaintModel):
|
||||
pad_mod = 32
|
||||
pad_to_square = True
|
||||
|
||||
def __init__(self, device):
|
||||
def __init__(self, device, **kwargs):
|
||||
"""
|
||||
|
||||
Args:
|
||||
@ -216,7 +216,7 @@ class ZITS(InpaintModel):
|
||||
self.device = device
|
||||
self.sample_edge_line_iterations = 1
|
||||
|
||||
def init_model(self, device):
|
||||
def init_model(self, device, **kwargs):
|
||||
self.wireframe = load_jit_model(ZITS_WIRE_FRAME_MODEL_URL, device)
|
||||
self.edge_line = load_jit_model(ZITS_EDGE_LINE_MODEL_URL, device)
|
||||
self.structure_upsample = load_jit_model(
|
||||
|
||||
@ -2,27 +2,23 @@ from lama_cleaner.model.fcf import FcF
|
||||
from lama_cleaner.model.lama import LaMa
|
||||
from lama_cleaner.model.ldm import LDM
|
||||
from lama_cleaner.model.mat import MAT
|
||||
from lama_cleaner.model.sd import SD14
|
||||
from lama_cleaner.model.zits import ZITS
|
||||
from lama_cleaner.schema import Config
|
||||
|
||||
models = {
|
||||
'lama': LaMa,
|
||||
'ldm': LDM,
|
||||
'zits': ZITS,
|
||||
'mat': MAT,
|
||||
'fcf': FcF
|
||||
}
|
||||
models = {"lama": LaMa, "ldm": LDM, "zits": ZITS, "mat": MAT, "fcf": FcF, "sd1.4": SD14}
|
||||
|
||||
|
||||
class ModelManager:
|
||||
def __init__(self, name: str, device):
|
||||
def __init__(self, name: str, device, **kwargs):
|
||||
self.name = name
|
||||
self.device = device
|
||||
self.model = self.init_model(name, device)
|
||||
self.kwargs = kwargs
|
||||
self.model = self.init_model(name, device, **kwargs)
|
||||
|
||||
def init_model(self, name: str, device):
|
||||
def init_model(self, name: str, device, **kwargs):
|
||||
if name in models:
|
||||
model = models[name](device)
|
||||
model = models[name](device, **kwargs)
|
||||
else:
|
||||
raise NotImplementedError(f"Not supported model: {name}")
|
||||
return model
|
||||
@ -40,7 +36,7 @@ class ModelManager:
|
||||
if new_name == self.name:
|
||||
return
|
||||
try:
|
||||
self.model = self.init_model(new_name, self.device)
|
||||
self.model = self.init_model(new_name, self.device, **self.kwargs)
|
||||
self.name = new_name
|
||||
except NotImplementedError as e:
|
||||
raise e
|
||||
|
||||
@ -7,7 +7,16 @@ def parse_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--host", default="127.0.0.1")
|
||||
parser.add_argument("--port", default=8080, type=int)
|
||||
parser.add_argument("--model", default="lama", choices=["lama", "ldm", "zits", "mat", 'fcf'])
|
||||
parser.add_argument(
|
||||
"--model",
|
||||
default="lama",
|
||||
choices=["lama", "ldm", "zits", "mat", "fcf", "sd1.4"],
|
||||
)
|
||||
parser.add_argument(
|
||||
"--hf_access_token",
|
||||
default="",
|
||||
help="huggingface access token. Check how to get token from: https://huggingface.co/docs/hub/security-tokens",
|
||||
)
|
||||
parser.add_argument("--device", default="cuda", type=str, choices=["cuda", "cpu"])
|
||||
parser.add_argument("--gui", action="store_true", help="Launch as desktop app")
|
||||
parser.add_argument(
|
||||
@ -29,4 +38,10 @@ def parse_args():
|
||||
if imghdr.what(args.input) is None:
|
||||
parser.error(f"invalid --input: {args.input} is not a valid image file")
|
||||
|
||||
if args.model.startswith("sd"):
|
||||
if not args.hf_access_token.startswith("hf_"):
|
||||
parser.error(
|
||||
f"sd(stable-diffusion) model requires huggingface access token. Check how to get token from: https://huggingface.co/docs/hub/security-tokens"
|
||||
)
|
||||
|
||||
return args
|
||||
|
||||
@ -4,14 +4,19 @@ from pydantic import BaseModel
|
||||
|
||||
|
||||
class HDStrategy(str, Enum):
|
||||
ORIGINAL = 'Original'
|
||||
RESIZE = 'Resize'
|
||||
CROP = 'Crop'
|
||||
ORIGINAL = "Original"
|
||||
RESIZE = "Resize"
|
||||
CROP = "Crop"
|
||||
|
||||
|
||||
class LDMSampler(str, Enum):
|
||||
ddim = 'ddim'
|
||||
plms = 'plms'
|
||||
ddim = "ddim"
|
||||
plms = "plms"
|
||||
|
||||
|
||||
class SDSampler(str, Enum):
|
||||
ddim = "ddim"
|
||||
pndm = "pndm"
|
||||
|
||||
|
||||
class Config(BaseModel):
|
||||
@ -22,3 +27,20 @@ class Config(BaseModel):
|
||||
hd_strategy_crop_margin: int
|
||||
hd_strategy_crop_trigger_size: int
|
||||
hd_strategy_resize_limit: int
|
||||
|
||||
prompt: str = ""
|
||||
# 始终是在原图尺度上的值
|
||||
use_croper: bool = False
|
||||
croper_x: int = None
|
||||
croper_y: int = None
|
||||
croper_height: int = None
|
||||
croper_width: int = None
|
||||
|
||||
# sd
|
||||
sd_mask_blur: int = 0
|
||||
sd_strength: float = 0.75
|
||||
sd_steps: int = 50
|
||||
sd_guidance_scale: float = 7.5
|
||||
sd_sampler: str = SDSampler.ddim
|
||||
# -1 mean random seed
|
||||
sd_seed: int = 42
|
||||
|
||||
@ -4,6 +4,7 @@ import io
|
||||
import logging
|
||||
import multiprocessing
|
||||
import os
|
||||
import random
|
||||
import time
|
||||
import imghdr
|
||||
from pathlib import Path
|
||||
@ -25,7 +26,7 @@ try:
|
||||
except:
|
||||
pass
|
||||
|
||||
from flask import Flask, request, send_file, cli
|
||||
from flask import Flask, request, send_file, cli, make_response
|
||||
|
||||
# Disable ability for Flask to display warning about using a development server in a production environment.
|
||||
# https://gist.github.com/jerblack/735b9953ba1ab6234abb43174210d356
|
||||
@ -41,7 +42,7 @@ from lama_cleaner.helper import (
|
||||
NUM_THREADS = str(multiprocessing.cpu_count())
|
||||
|
||||
# fix libomp problem on windows https://github.com/Sanster/lama-cleaner/issues/56
|
||||
os.environ["KMP_DUPLICATE_LIB_OK"]="True"
|
||||
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
|
||||
|
||||
os.environ["OMP_NUM_THREADS"] = NUM_THREADS
|
||||
os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
|
||||
@ -64,6 +65,10 @@ logging.getLogger("werkzeug").addFilter(NoFlaskwebgui())
|
||||
app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
|
||||
app.config["JSON_AS_ASCII"] = False
|
||||
CORS(app, expose_headers=["Content-Disposition"])
|
||||
# MAX_BUFFER_SIZE = 50 * 1000 * 1000 # 50 MB
|
||||
# async_mode 优先级: eventlet/gevent_uwsgi/gevent/threading
|
||||
# only threading works on macOS
|
||||
# socketio = SocketIO(app, max_http_buffer_size=MAX_BUFFER_SIZE, async_mode='threading')
|
||||
|
||||
model: ModelManager = None
|
||||
device = None
|
||||
@ -77,6 +82,11 @@ def get_image_ext(img_bytes):
|
||||
return w
|
||||
|
||||
|
||||
def diffuser_callback(step: int):
|
||||
pass
|
||||
# socketio.emit('diffusion_step', {'diffusion_step': step})
|
||||
|
||||
|
||||
@app.route("/inpaint", methods=["POST"])
|
||||
def process():
|
||||
input = request.files
|
||||
@ -102,8 +112,23 @@ def process():
|
||||
hd_strategy_crop_margin=form["hdStrategyCropMargin"],
|
||||
hd_strategy_crop_trigger_size=form["hdStrategyCropTrigerSize"],
|
||||
hd_strategy_resize_limit=form["hdStrategyResizeLimit"],
|
||||
prompt=form["prompt"],
|
||||
use_croper=form["useCroper"],
|
||||
croper_x=form["croperX"],
|
||||
croper_y=form["croperY"],
|
||||
croper_height=form["croperHeight"],
|
||||
croper_width=form["croperWidth"],
|
||||
sd_mask_blur=form["sdMaskBlur"],
|
||||
sd_strength=form["sdStrength"],
|
||||
sd_steps=form["sdSteps"],
|
||||
sd_guidance_scale=form["sdGuidanceScale"],
|
||||
sd_sampler=form["sdSampler"],
|
||||
sd_seed=form["sdSeed"],
|
||||
)
|
||||
|
||||
if config.sd_seed == -1:
|
||||
config.sd_seed = random.randint(1, 9999999)
|
||||
|
||||
logger.info(f"Origin image shape: {original_shape}")
|
||||
image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
|
||||
logger.info(f"Resized image shape: {image.shape}")
|
||||
@ -127,10 +152,15 @@ def process():
|
||||
)
|
||||
|
||||
ext = get_image_ext(origin_image_bytes)
|
||||
return send_file(
|
||||
|
||||
response = make_response(
|
||||
send_file(
|
||||
io.BytesIO(numpy_to_bytes(res_np_img, ext)),
|
||||
mimetype=f"image/{ext}",
|
||||
)
|
||||
)
|
||||
response.headers["X-Seed"] = str(config.sd_seed)
|
||||
return response
|
||||
|
||||
|
||||
@app.route("/model")
|
||||
@ -184,7 +214,12 @@ def main(args):
|
||||
device = torch.device(args.device)
|
||||
input_image_path = args.input
|
||||
|
||||
model = ModelManager(name=args.model, device=device)
|
||||
model = ModelManager(
|
||||
name=args.model,
|
||||
device=device,
|
||||
hf_access_token=args.hf_access_token,
|
||||
callbacks=[diffuser_callback],
|
||||
)
|
||||
|
||||
if args.gui:
|
||||
app_width, app_height = args.gui_size
|
||||
@ -195,4 +230,5 @@ def main(args):
|
||||
)
|
||||
ui.run()
|
||||
else:
|
||||
# TODO: socketio
|
||||
app.run(host=args.host, port=args.port, debug=args.debug)
|
||||
|
||||
BIN
lama_cleaner/tests/overture-creations-5sI6fQgYIuo.png
Normal file
BIN
lama_cleaner/tests/overture-creations-5sI6fQgYIuo.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 395 KiB |
BIN
lama_cleaner/tests/overture-creations-5sI6fQgYIuo_mask.png
Normal file
BIN
lama_cleaner/tests/overture-creations-5sI6fQgYIuo_mask.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 12 KiB |
BIN
lama_cleaner/tests/overture-creations-5sI6fQgYIuo_mask_blur.png
Normal file
BIN
lama_cleaner/tests/overture-creations-5sI6fQgYIuo_mask_blur.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 38 KiB |
@ -1,3 +1,4 @@
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
@ -5,16 +6,16 @@ import pytest
|
||||
import torch
|
||||
|
||||
from lama_cleaner.model_manager import ModelManager
|
||||
from lama_cleaner.schema import Config, HDStrategy, LDMSampler
|
||||
from lama_cleaner.schema import Config, HDStrategy, LDMSampler, SDSampler
|
||||
|
||||
current_dir = Path(__file__).parent.absolute().resolve()
|
||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
|
||||
|
||||
def get_data(fx=1, fy=1.0):
|
||||
img = cv2.imread(str(current_dir / "image.png"))
|
||||
def get_data(fx=1, fy=1.0, img_p=current_dir / "image.png", mask_p=current_dir / "mask.png"):
|
||||
img = cv2.imread(str(img_p))
|
||||
img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
|
||||
mask = cv2.imread(str(current_dir / "mask.png"), cv2.IMREAD_GRAYSCALE)
|
||||
mask = cv2.imread(str(mask_p), cv2.IMREAD_GRAYSCALE)
|
||||
|
||||
if fx != 1:
|
||||
img = cv2.resize(img, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
|
||||
@ -35,8 +36,8 @@ def get_config(strategy, **kwargs):
|
||||
return Config(**data)
|
||||
|
||||
|
||||
def assert_equal(model, config, gt_name, fx=1, fy=1):
|
||||
img, mask = get_data(fx=fx, fy=fy)
|
||||
def assert_equal(model, config, gt_name, fx=1, fy=1, img_p=current_dir / "image.png", mask_p=current_dir / "mask.png"):
|
||||
img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
|
||||
res = model(img, mask, config)
|
||||
cv2.imwrite(
|
||||
str(current_dir / gt_name),
|
||||
@ -153,3 +154,36 @@ def test_fcf(strategy):
|
||||
fx=3.8,
|
||||
fy=2
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
|
||||
@pytest.mark.parametrize("sampler", [SDSampler.ddim, SDSampler.pndm])
|
||||
def test_sd(strategy, sampler, capfd):
|
||||
def callback(step: int):
|
||||
print(f"sd_step_{step}")
|
||||
|
||||
sd_steps = 50
|
||||
model = ModelManager(name="sd1.4", device=device, hf_access_token=os.environ['HF_ACCESS_TOKEN'],
|
||||
callbacks=[callback])
|
||||
cfg = get_config(strategy, prompt='a cat sitting on a bench', sd_steps=sd_steps)
|
||||
cfg.sd_sampler = sampler
|
||||
|
||||
assert_equal(
|
||||
model,
|
||||
cfg,
|
||||
f"sd_{strategy.capitalize()}_{sampler}_result.png",
|
||||
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
|
||||
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
|
||||
)
|
||||
|
||||
assert_equal(
|
||||
model,
|
||||
cfg,
|
||||
f"sd_{strategy.capitalize()}_{sampler}_blur_mask_result.png",
|
||||
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
|
||||
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask_blur.png",
|
||||
)
|
||||
|
||||
# captured = capfd.readouterr()
|
||||
# for i in range(sd_steps):
|
||||
# assert f'sd_step_{i}' in captured.out
|
||||
|
||||
@ -10,3 +10,5 @@ pytest
|
||||
yacs
|
||||
markupsafe==2.0.1
|
||||
scikit-image==0.19.3
|
||||
diffusers==0.3.0
|
||||
transformers==4.20.0
|
||||
|
||||
Loading…
Reference in New Issue
Block a user