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Merge branch 'make_gif_share'

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Qing 2023-02-07 21:47:11 +08:00
commit f837e4be8a
49 changed files with 15954 additions and 12250 deletions
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1
.gitignore vendored
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@ -7,3 +7,4 @@ build
!lama_cleaner/app/build
dist/
lama_cleaner.egg-info/
venv/

6
README.md
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@ -59,6 +59,6 @@ Only needed if you plan to modify the frontend and recompile yourself.
Frontend code are modified from [cleanup.pictures](https://github.com/initml/cleanup.pictures), You can experience their
great online services [here](https://cleanup.pictures/).
- Install dependencies:`cd lama_cleaner/app/ && yarn`
- Start development server: `yarn start`
- Build: `yarn build`
- Install dependencies:`cd lama_cleaner/app/ && pnpm install`
- Start development server: `pnpm start`
- Build: `pnpm build`

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lama_cleaner/app/build/asset-manifest.json
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@ -1,7 +1,8 @@
{
"files": {
"main.css": "/static/css/main.c28d98ca.css",
"main.js": "/static/js/main.1bd455bc.js",
"main.css": "/static/css/main.e24c9a9b.css",
"main.js": "/static/js/main.23732b19.js",
"static/media/coffee-machine-lineal.gif": "/static/media/coffee-machine-lineal.ee32631219cc3986f861.gif",
"static/media/WorkSans-SemiBold.ttf": "/static/media/WorkSans-SemiBold.1e98db4eb705b586728e.ttf",
"static/media/WorkSans-Bold.ttf": "/static/media/WorkSans-Bold.2bea7a7f7d052c74da25.ttf",
"static/media/WorkSans-Regular.ttf": "/static/media/WorkSans-Regular.bb287b894b27372d8ea7.ttf",
@ -9,7 +10,7 @@
"index.html": "/index.html"
},
"entrypoints": [
"static/css/main.c28d98ca.css",
"static/js/main.1bd455bc.js"
"static/css/main.e24c9a9b.css",
"static/js/main.23732b19.js"
]
}

2
lama_cleaner/app/build/index.html
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@ -1 +1 @@
<!doctype html><html lang="en"><head><meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate"/><meta http-equiv="Pragma" content="no-cache"/><meta http-equiv="Expires" content="0"/><meta charset="utf-8"/><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1,user-scalable=0"/><meta name="theme-color" content="#ffffff"/><title>lama-cleaner - Image inpainting powered by SOTA AI model</title><script defer="defer" src="/static/js/main.1bd455bc.js"></script><link href="/static/css/main.c28d98ca.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>
<!doctype html><html lang="en"><head><meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate"/><meta http-equiv="Pragma" content="no-cache"/><meta http-equiv="Expires" content="0"/><meta charset="utf-8"/><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1,user-scalable=0"/><meta name="theme-color" content="#ffffff"/><title>lama-cleaner - Image inpainting powered by SOTA AI model</title><script defer="defer" src="/static/js/main.23732b19.js"></script><link href="/static/css/main.e24c9a9b.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div></body></html>

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lama_cleaner/app/build/static/js/main.1bd455bc.js.LICENSE.txt → lama_cleaner/app/build/static/js/main.23732b19.js.LICENSE.txt
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2
lama_cleaner/app/package.json
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@ -2,7 +2,7 @@
"name": "lama-cleaner",
"version": "0.1.0",
"private": true,
"proxy": "http://localhost:8080",
"proxy": "http://127.0.0.1:8080",
"dependencies": {
"@babel/core": "^7.16.0",
"@heroicons/react": "^2.0.0",

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38
lama_cleaner/app/src/adapters/inpainting.ts
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@ -1,5 +1,5 @@
import { Rect, Settings } from '../store/Atoms'
import { dataURItoBlob, srcToFile } from '../utils'
import { dataURItoBlob, loadImage, srcToFile } from '../utils'
export const API_ENDPOINT = `${process.env.REACT_APP_INPAINTING_URL}`
@ -82,6 +82,11 @@ export default async function inpaint(
fd.append('paintByExampleImage', paintByExampleImage)
}
// InstructPix2Pix
fd.append('p2pSteps', settings.p2pSteps.toString())
fd.append('p2pImageGuidanceScale', settings.p2pImageGuidanceScale.toString())
fd.append('p2pGuidanceScale', settings.p2pGuidanceScale.toString())
if (sizeLimit === undefined) {
fd.append('sizeLimit', '1080')
} else {
@ -223,3 +228,34 @@ export async function downloadToOutput(
throw new Error(`Something went wrong: ${error}`)
}
}
export async function makeGif(
originFile: File,
cleanImage: HTMLImageElement,
filename: string,
mimeType: string
) {
const cleanFile = await srcToFile(cleanImage.src, filename, mimeType)
const fd = new FormData()
fd.append('origin_img', originFile)
fd.append('clean_img', cleanFile)
fd.append('filename', filename)
try {
const res = await fetch(`${API_ENDPOINT}/make_gif`, {
method: 'POST',
body: fd,
})
if (!res.ok) {
const errMsg = await res.text()
throw new Error(errMsg)
}
const blob = await res.blob()
const newImage = new Image()
await loadImage(newImage, URL.createObjectURL(blob))
return newImage
} catch (error) {
throw new Error(`Something went wrong: ${error}`)
}
}

26
lama_cleaner/app/src/components/CoffeeIcon/CoffeeIcon.tsx
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@ -2,6 +2,7 @@ import React, { useState } from 'react'
import { Coffee } from 'react-feather'
import Button from '../shared/Button'
import Modal from '../shared/Modal'
import CoffeeMachineGif from '../../media/coffee-machine-lineal.gif'
const CoffeeIcon = () => {
const [show, setShow] = useState(false)
@ -24,10 +25,26 @@ const CoffeeIcon = () => {
show={show}
showCloseIcon={false}
>
<h4 style={{ lineHeight: '24px' }}>
Hi there, If you found my project is useful, and want to help keep it
alive please consider donating! Thank you for your support!
</h4>
<div
style={{
display: 'flex',
flexDirection: 'column',
}}
>
<h4 style={{ lineHeight: '24px' }}>
Hi, if you found my project is useful, please conside buy me a
coffee to support my work. Thanks!
</h4>
<img
src={CoffeeMachineGif}
alt="coffee machine"
style={{
height: 150,
objectFit: 'contain',
}}
/>
</div>
<div
style={{
display: 'flex',
@ -53,7 +70,6 @@ const CoffeeIcon = () => {
}}
>
Sure
<Coffee />
</div>
</Button>
</a>

13
lama_cleaner/app/src/components/Editor/Editor.scss
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@ -55,10 +55,9 @@
position: fixed;
bottom: 0.5rem;
border-radius: 3rem;
padding: 0.6rem 3rem;
display: grid;
grid-template-areas: 'toolkit-size-selector toolkit-brush-slider toolkit-btns';
column-gap: 2rem;
padding: 0.6rem 32px;
display: flex;
gap: 16px;
align-items: center;
justify-content: center;
backdrop-filter: blur(12px);
@ -96,10 +95,8 @@
}
.editor-toolkit-btns {
grid-area: toolkit-btns;
display: grid;
grid-auto-flow: column;
column-gap: 1rem;
display: flex;
gap: 12px;
}
.brush-shape {

58
lama_cleaner/app/src/components/Editor/Editor.tsx
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@ -16,10 +16,11 @@ import {
TransformComponent,
TransformWrapper,
} from 'react-zoom-pan-pinch'
import { useRecoilState, useRecoilValue } from 'recoil'
import { useRecoilState, useRecoilValue, useSetRecoilState } from 'recoil'
import { useWindowSize, useKey, useKeyPressEvent } from 'react-use'
import inpaint, {
downloadToOutput,
makeGif,
postInteractiveSeg,
} from '../../adapters/inpainting'
import Button from '../shared/Button'
@ -43,10 +44,12 @@ import {
imageHeightState,
imageWidthState,
interactiveSegClicksState,
isDiffusionModelsState,
isInpaintingState,
isInteractiveSegRunningState,
isInteractiveSegState,
isPaintByExampleState,
isPix2PixState,
isSDState,
negativePropmtState,
propmtState,
@ -66,6 +69,7 @@ import FileSelect from '../FileSelect/FileSelect'
import InteractiveSeg from '../InteractiveSeg/InteractiveSeg'
import InteractiveSegConfirmActions from '../InteractiveSeg/ConfirmActions'
import InteractiveSegReplaceModal from '../InteractiveSeg/ReplaceModal'
import MakeGIF from './MakeGIF'
const TOOLBAR_SIZE = 200
const MIN_BRUSH_SIZE = 10
@ -112,11 +116,11 @@ export default function Editor() {
const settings = useRecoilValue(settingState)
const [seedVal, setSeed] = useRecoilState(seedState)
const croperRect = useRecoilValue(croperState)
const [toastVal, setToastState] = useRecoilState(toastState)
const setToastState = useSetRecoilState(toastState)
const [isInpainting, setIsInpainting] = useRecoilState(isInpaintingState)
const runMannually = useRecoilValue(runManuallyState)
const isSD = useRecoilValue(isSDState)
const isPaintByExample = useRecoilValue(isPaintByExampleState)
const isDiffusionModels = useRecoilValue(isDiffusionModelsState)
const isPix2Pix = useRecoilValue(isPix2PixState)
const [isInteractiveSeg, setIsInteractiveSeg] = useRecoilState(
isInteractiveSegState
)
@ -181,8 +185,8 @@ export default function Editor() {
const [redoLineGroups, setRedoLineGroups] = useState<LineGroup[]>([])
const enableFileManager = useRecoilValue(enableFileManagerState)
const [imageWidth, setImageWidth] = useRecoilState(imageWidthState)
const [imageHeight, setImageHeight] = useRecoilState(imageHeightState)
const setImageWidth = useSetRecoilState(imageWidthState)
const setImageHeight = useSetRecoilState(imageHeightState)
const app = useRecoilValue(appState)
const draw = useCallback(
@ -253,13 +257,40 @@ export default function Editor() {
_lineGroups.forEach(lineGroup => {
drawLines(ctx, lineGroup, 'white')
})
if (
(maskImage === undefined || maskImage === null) &&
_lineGroups.length === 1 &&
_lineGroups[0].length === 0 &&
isPix2Pix
) {
// For InstructPix2Pix without mask
drawLines(
ctx,
[
{
size: 9999999999,
pts: [
{ x: 0, y: 0 },
{ x: original.naturalWidth, y: 0 },
{ x: original.naturalWidth, y: original.naturalHeight },
{ x: 0, y: original.naturalHeight },
],
},
],
'white'
)
}
},
[context, maskCanvas]
[context, maskCanvas, isPix2Pix]
)
const hadDrawSomething = useCallback(() => {
if (isPix2Pix) {
return true
}
return curLineGroup.length !== 0
}, [curLineGroup])
}, [curLineGroup, isPix2Pix])
const drawOnCurrentRender = useCallback(
(lineGroup: LineGroup) => {
@ -424,6 +455,8 @@ export default function Editor() {
} else if (prevInteractiveSegMask) {
// 使用上一次 IS 的 mask 生成
runInpainting(false, undefined, prevInteractiveSegMask)
} else if (isPix2Pix) {
runInpainting(false, undefined, null)
} else {
setToastState({
open: true,
@ -839,7 +872,7 @@ export default function Editor() {
}
if (
(isSD || isPaintByExample) &&
isDiffusionModels &&
settings.showCroper &&
isOutsideCroper(mouseXY(ev))
) {
@ -1385,7 +1418,7 @@ export default function Editor() {
minHeight={Math.min(256, original.naturalHeight)}
minWidth={Math.min(256, original.naturalWidth)}
scale={scale}
show={(isSD || isPaintByExample) && settings.showCroper}
show={isDiffusionModels && settings.showCroper}
/>
{isInteractiveSeg ? <InteractiveSeg /> : <></>}
@ -1439,7 +1472,7 @@ export default function Editor() {
)}
<div className="editor-toolkit-panel">
{isSD || isPaintByExample || file === undefined ? (
{isDiffusionModels || file === undefined ? (
<></>
) : (
<SizeSelector
@ -1534,6 +1567,7 @@ export default function Editor() {
}}
disabled={renders.length === 0}
/>
<MakeGIF renders={renders} />
<Button
toolTip="Save Image"
icon={<ArrowDownTrayIcon />}
@ -1541,7 +1575,7 @@ export default function Editor() {
onClick={download}
/>
{settings.runInpaintingManually && !isSD && !isPaintByExample && (
{settings.runInpaintingManually && !isDiffusionModels && (
<Button
toolTip="Run Inpainting"
icon={

114
lama_cleaner/app/src/components/Editor/MakeGIF.tsx Normal file
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@ -0,0 +1,114 @@
import React, { useState } from 'react'
import { GifIcon } from '@heroicons/react/24/outline'
import { useRecoilState, useRecoilValue, useSetRecoilState } from 'recoil'
import Button from '../shared/Button'
import { fileState, gifImageState, toastState } from '../../store/Atoms'
import { makeGif } from '../../adapters/inpainting'
import Modal from '../shared/Modal'
import { LoadingIcon } from '../shared/Toast'
import { downloadImage } from '../../utils'
interface Props {
renders: HTMLImageElement[]
}
const MakeGIF = (props: Props) => {
const { renders } = props
const [gifImg, setGifImg] = useRecoilState(gifImageState)
const file = useRecoilValue(fileState)
const setToastState = useSetRecoilState(toastState)
const [show, setShow] = useState(false)
const handleOnClose = () => {
setShow(false)
}
const handleDownload = () => {
if (gifImg) {
const name = file.name.replace(/\.[^/.]+$/, '.gif')
downloadImage(gifImg.src, name)
}
}
return (
<div>
<Button
toolTip="Make Gif"
icon={<GifIcon />}
disabled={!renders.length}
onClick={async () => {
setShow(true)
setGifImg(null)
try {
const gif = await makeGif(
file,
renders[renders.length - 1],
file.name,
file.type
)
if (gif) {
setGifImg(gif)
}
} catch (e: any) {
setToastState({
open: true,
desc: e.message ? e.message : e.toString(),
state: 'error',
duration: 2000,
})
}
}}
/>
<Modal
onClose={handleOnClose}
title="GIF"
className="modal-setting"
show={show}
>
<div
style={{
display: 'flex',
alignItems: 'center',
justifyContent: 'center',
flexDirection: 'column',
gap: 16,
}}
>
{gifImg ? (
<img src={gifImg.src} style={{ borderRadius: 8 }} alt="gif" />
) : (
<div
style={{
display: 'flex',
alignItems: 'center',
justifyContent: 'center',
gap: 8,
}}
>
<LoadingIcon />
Generating GIF...
</div>
)}
{gifImg && (
<div
style={{
display: 'flex',
width: '100%',
justifyContent: 'flex-end',
alignItems: 'center',
gap: '12px',
}}
>
<Button onClick={handleDownload} border>
Download
</Button>
</div>
)}
</div>
</Modal>
</div>
)
}
export default MakeGIF

4
lama_cleaner/app/src/components/Header/Header.tsx
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@ -7,6 +7,7 @@ import {
enableFileManagerState,
fileState,
isInpaintingState,
isPix2PixState,
isSDState,
maskState,
runManuallyState,
@ -30,6 +31,7 @@ const Header = () => {
const [uploadElemId] = useState(`file-upload-${Math.random().toString()}`)
const [maskUploadElemId] = useState(`mask-upload-${Math.random().toString()}`)
const isSD = useRecoilValue(isSDState)
const isPix2Pix = useRecoilValue(isPix2PixState)
const runManually = useRecoilValue(runManuallyState)
const [openMaskPopover, setOpenMaskPopover] = useState(false)
const [showFileManager, setShowFileManager] =
@ -172,7 +174,7 @@ const Header = () => {
</div>
</div>
{isSD && file ? <PromptInput /> : <></>}
{(isSD || isPix2Pix) && file ? <PromptInput /> : <></>}
<div className="header-icons-wrapper">
<CoffeeIcon />

20
lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx
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@ -179,28 +179,16 @@ function ModelSettingBlock() {
const renderOptionDesc = (): ReactNode => {
switch (setting.model) {
case AIModel.LAMA:
return undefined
case AIModel.LDM:
return renderLDMModelDesc()
case AIModel.ZITS:
return renderZITSModelDesc()
case AIModel.MAT:
return undefined
case AIModel.FCF:
return renderFCFModelDesc()
case AIModel.SD15:
return undefined
case AIModel.SD2:
return undefined
case AIModel.PAINT_BY_EXAMPLE:
return undefined
case AIModel.Mange:
return undefined
case AIModel.CV2:
return renderOpenCV2Desc()
default:
return <></>
return undefined
}
}
@ -266,6 +254,12 @@ function ModelSettingBlock() {
'https://arxiv.org/abs/2211.13227',
'https://github.com/Fantasy-Studio/Paint-by-Example'
)
case AIModel.PIX2PIX:
return renderModelDesc(
'InstructPix2Pix',
'https://arxiv.org/abs/2211.09800',
'https://github.com/timothybrooks/instruct-pix2pix'
)
default:
return <></>
}

25
lama_cleaner/app/src/components/Settings/SettingIcon.tsx
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@ -1,5 +1,6 @@
import React from 'react'
import { useRecoilState } from 'recoil'
import { Cog6ToothIcon } from '@heroicons/react/24/outline'
import { settingState } from '../../store/Atoms'
import Button from '../shared/Button'
@ -16,29 +17,7 @@ const SettingIcon = () => {
onClick={onClick}
toolTip="Settings"
style={{ border: 0 }}
icon={
<svg
xmlns="http://www.w3.org/2000/svg"
fill="none"
role="img"
width="28"
height="28"
viewBox="0 0 24 24"
stroke="currentColor"
strokeWidth="2"
>
<path
strokeLinecap="round"
strokeLinejoin="round"
d="M10.325 4.317c.426-1.756 2.924-1.756 3.35 0a1.724 1.724 0 002.573 1.066c1.543-.94 3.31.826 2.37 2.37a1.724 1.724 0 001.065 2.572c1.756.426 1.756 2.924 0 3.35a1.724 1.724 0 00-1.066 2.573c.94 1.543-.826 3.31-2.37 2.37a1.724 1.724 0 00-2.572 1.065c-.426 1.756-2.924 1.756-3.35 0a1.724 1.724 0 00-2.573-1.066c-1.543.94-3.31-.826-2.37-2.37a1.724 1.724 0 00-1.065-2.572c-1.756-.426-1.756-2.924 0-3.35a1.724 1.724 0 001.066-2.573c-.94-1.543.826-3.31 2.37-2.37.996.608 2.296.07 2.572-1.065z"
/>
<path
strokeLinecap="round"
strokeLinejoin="round"
d="M15 12a3 3 0 11-6 0 3 3 0 016 0z"
/>
</svg>
}
icon={<Cog6ToothIcon />}
/>
</div>
)

1
lama_cleaner/app/src/components/Settings/Settings.scss
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@ -8,7 +8,6 @@
color: var(--modal-text-color);
box-shadow: 0px 0px 20px rgb(0, 0, 40, 0.2);
width: 680px;
min-height: 420px;
@include mobile {
display: grid;

7
lama_cleaner/app/src/components/Settings/SettingsModal.tsx
View File

@ -1,6 +1,7 @@
import React from 'react'
import { useRecoilState, useRecoilValue } from 'recoil'
import {
isDiffusionModelsState,
isPaintByExampleState,
isSDState,
settingState,
@ -28,7 +29,7 @@ export default function SettingModal(props: SettingModalProps) {
const { onClose } = props
const [setting, setSettingState] = useRecoilState(settingState)
const isSD = useRecoilValue(isSDState)
const isPaintByExample = useRecoilValue(isPaintByExampleState)
const isDiffusionModels = useRecoilValue(isDiffusionModelsState)
const handleOnClose = () => {
setSettingState(old => {
@ -56,9 +57,9 @@ export default function SettingModal(props: SettingModalProps) {
show={setting.show}
>
<DownloadMaskSettingBlock />
{isSD || isPaintByExample ? <></> : <ManualRunInpaintingSettingBlock />}
{isDiffusionModels ? <></> : <ManualRunInpaintingSettingBlock />}
<ModelSettingBlock />
{isSD ? <></> : <HDSettingBlock />}
{isDiffusionModels ? <></> : <HDSettingBlock />}
</Modal>
)
}

14
lama_cleaner/app/src/components/Shortcuts/Shortcuts.scss
View File

@ -15,13 +15,14 @@
}
.shortcut-options {
display: grid;
row-gap: 1rem;
display: flex;
gap: 48px;
flex-direction: row;
.shortcut-option {
display: grid;
grid-template-columns: repeat(2, auto);
column-gap: 6rem;
column-gap: 2rem;
align-items: center;
@include mobile {
@ -67,3 +68,10 @@
}
}
}
.shortcut-options-column {
display: flex;
flex-direction: column;
gap: 12px;
width: 320px;
}

49
lama_cleaner/app/src/components/Shortcuts/ShortcutsModal.tsx
View File

@ -50,24 +50,37 @@ export default function ShortcutsModal() {
show={shortcutsShow}
>
<div className="shortcut-options">
<ShortCut
content="Multi-Stroke Mask Drawing"
keys={[`Hold ${CmdOrCtrl}`]}
/>
<ShortCut content="Cancel Mask Drawing" keys={['Esc']} />
<ShortCut content="Run Inpainting Manually" keys={['Shift', 'R']} />
<ShortCut content="Interactive Segmentation" keys={['I']} />
<ShortCut content="Undo Inpainting" keys={[CmdOrCtrl, 'Z']} />
<ShortCut content="Redo Inpainting" keys={[CmdOrCtrl, 'Shift', 'Z']} />
<ShortCut content="View Original Image" keys={['Hold Tab']} />
<ShortCut content="Pan" keys={['Space + Drag']} />
<ShortCut content="Reset Zoom/Pan" keys={['Esc']} />
<ShortCut content="Decrease Brush Size" keys={['[']} />
<ShortCut content="Increase Brush Size" keys={[']']} />
<ShortCut content="Toggle Dark Mode" keys={['Shift', 'D']} />
<ShortCut content="Toggle Hotkeys Dialog" keys={['H']} />
<ShortCut content="Toggle Settings Dialog" keys={['S']} />
<ShortCut content="Toggle File Manager" keys={['F']} />
<div className="shortcut-options-column">
<ShortCut content="Pan" keys={['Space + Drag']} />
<ShortCut content="Reset Zoom/Pan" keys={['Esc']} />
<ShortCut content="Decrease Brush Size" keys={['[']} />
<ShortCut content="Increase Brush Size" keys={[']']} />
<ShortCut content="View Original Image" keys={['Hold Tab']} />
<ShortCut
content="Multi-Stroke Drawing"
keys={[`Hold ${CmdOrCtrl}`]}
/>
<ShortCut content="Cancel Drawing" keys={['Esc']} />
</div>
<div className="shortcut-options-column">
<ShortCut content="Undo" keys={[CmdOrCtrl, 'Z']} />
<ShortCut content="Redo" keys={[CmdOrCtrl, 'Shift', 'Z']} />
<ShortCut content="Copy Result" keys={[CmdOrCtrl, 'C']} />
<ShortCut content="Paste Image" keys={[CmdOrCtrl, 'V']} />
<ShortCut
content="Trigger Manually Inpainting"
keys={['Shift', 'R']}
/>
<ShortCut content="Trigger Interactive Segmentation" keys={['I']} />
</div>
<div className="shortcut-options-column">
<ShortCut content="Switch Theme" keys={['Shift', 'D']} />
<ShortCut content="Toggle Hotkeys Dialog" keys={['H']} />
<ShortCut content="Toggle Settings Dialog" keys={['S']} />
<ShortCut content="Toggle File Manager" keys={['F']} />
</div>
</div>
</Modal>
)

224
lama_cleaner/app/src/components/SidePanel/P2PSidePanel.tsx Normal file
View File

@ -0,0 +1,224 @@
import React, { FormEvent } from 'react'
import { useRecoilState, useRecoilValue } from 'recoil'
import * as PopoverPrimitive from '@radix-ui/react-popover'
import { useToggle } from 'react-use'
import {
isInpaintingState,
negativePropmtState,
propmtState,
settingState,
} from '../../store/Atoms'
import NumberInputSetting from '../Settings/NumberInputSetting'
import SettingBlock from '../Settings/SettingBlock'
import { Switch, SwitchThumb } from '../shared/Switch'
import TextAreaInput from '../shared/Textarea'
import emitter, { EVENT_PROMPT } from '../../event'
import ImageResizeScale from './ImageResizeScale'
const INPUT_WIDTH = 30
const P2PSidePanel = () => {
const [open, toggleOpen] = useToggle(true)
const [setting, setSettingState] = useRecoilState(settingState)
const [negativePrompt, setNegativePrompt] =
useRecoilState(negativePropmtState)
const isInpainting = useRecoilValue(isInpaintingState)
const prompt = useRecoilValue(propmtState)
const handleOnInput = (evt: FormEvent<HTMLTextAreaElement>) => {
evt.preventDefault()
evt.stopPropagation()
const target = evt.target as HTMLTextAreaElement
setNegativePrompt(target.value)
}
const onKeyUp = (e: React.KeyboardEvent) => {
if (
e.key === 'Enter' &&
(e.ctrlKey || e.metaKey) &&
prompt.length !== 0 &&
!isInpainting
) {
emitter.emit(EVENT_PROMPT)
}
}
return (
<div className="side-panel">
<PopoverPrimitive.Root open={open}>
<PopoverPrimitive.Trigger
className="btn-primary side-panel-trigger"
onClick={() => toggleOpen()}
>
Config
</PopoverPrimitive.Trigger>
<PopoverPrimitive.Portal>
<PopoverPrimitive.Content className="side-panel-content">
<SettingBlock
title="Croper"
input={
<Switch
checked={setting.showCroper}
onCheckedChange={value => {
setSettingState(old => {
return { ...old, showCroper: value }
})
}}
>
<SwitchThumb />
</Switch>
}
/>
<ImageResizeScale />
<NumberInputSetting
title="Steps"
width={INPUT_WIDTH}
value={`${setting.p2pSteps}`}
desc="The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference."
onValue={value => {
const val = value.length === 0 ? 0 : parseInt(value, 10)
setSettingState(old => {
return { ...old, p2pSteps: val }
})
}}
/>
<NumberInputSetting
title="Guidance Scale"
width={INPUT_WIDTH}
allowFloat
value={`${setting.p2pGuidanceScale}`}
desc="Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality."
onValue={value => {
const val = value.length === 0 ? 0 : parseFloat(value)
setSettingState(old => {
return { ...old, p2pGuidanceScale: val }
})
}}
/>
<NumberInputSetting
title="Image Guidance Scale"
width={INPUT_WIDTH}
allowFloat
value={`${setting.p2pImageGuidanceScale}`}
desc=""
onValue={value => {
const val = value.length === 0 ? 0 : parseFloat(value)
setSettingState(old => {
return { ...old, p2pImageGuidanceScale: val }
})
}}
/>
{/* <NumberInputSetting
title="Mask Blur"
width={INPUT_WIDTH}
value={`${setting.sdMaskBlur}`}
desc="Blur the edge of mask area. The higher the number the smoother blend with the original image"
onValue={value => {
const val = value.length === 0 ? 0 : parseInt(value, 10)
setSettingState(old => {
return { ...old, sdMaskBlur: val }
})
}}
/> */}
{/* <SettingBlock
title="Match Histograms"
desc="Match the inpainting result histogram to the source image histogram, will improves the inpainting quality for some images."
input={
<Switch
checked={setting.sdMatchHistograms}
onCheckedChange={value => {
setSettingState(old => {
return { ...old, sdMatchHistograms: value }
})
}}
>
<SwitchThumb />
</Switch>
}
/> */}
{/* <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',
}}
>
<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>
}
/>
<SettingBlock
className="sub-setting-block"
title="Negative prompt"
layout="v"
input={
<TextAreaInput
className="negative-prompt"
value={negativePrompt}
onInput={handleOnInput}
onKeyUp={onKeyUp}
placeholder=""
/>
}
/>
</PopoverPrimitive.Content>
</PopoverPrimitive.Portal>
</PopoverPrimitive.Root>
</div>
)
}
export default P2PSidePanel

4
lama_cleaner/app/src/components/Workspace.tsx
View File

@ -8,6 +8,7 @@ import {
AIModel,
fileState,
isPaintByExampleState,
isPix2PixState,
isSDState,
settingState,
showFileManagerState,
@ -22,6 +23,7 @@ import {
import SidePanel from './SidePanel/SidePanel'
import PESidePanel from './SidePanel/PESidePanel'
import FileManager from './FileManager/FileManager'
import P2PSidePanel from './SidePanel/P2PSidePanel'
const Workspace = () => {
const setFile = useSetRecoilState(fileState)
@ -29,6 +31,7 @@ const Workspace = () => {
const [toastVal, setToastState] = useRecoilState(toastState)
const isSD = useRecoilValue(isSDState)
const isPaintByExample = useRecoilValue(isPaintByExampleState)
const isPix2Pix = useRecoilValue(isPix2PixState)
const [showFileManager, setShowFileManager] =
useRecoilState(showFileManagerState)
@ -98,6 +101,7 @@ const Workspace = () => {
<>
{isSD ? <SidePanel /> : <></>}
{isPaintByExample ? <PESidePanel /> : <></>}
{isPix2Pix ? <P2PSidePanel /> : <></>}
<FileManager
photoWidth={256}
show={showFileManager}

2
lama_cleaner/app/src/components/shared/Toast.tsx
View File

@ -3,7 +3,7 @@ import * as ToastPrimitive from '@radix-ui/react-toast'
import { ToastProps } from '@radix-ui/react-toast'
import { CheckIcon, ExclamationCircleIcon } from '@heroicons/react/24/outline'
const LoadingIcon = () => {
export const LoadingIcon = () => {
return (
<span className="loading-icon">
<svg

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lama_cleaner/app/src/media/coffee-machine-lineal.gif Normal file
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After

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55
lama_cleaner/app/src/store/Atoms.tsx
View File

@ -14,6 +14,7 @@ export enum AIModel {
CV2 = 'cv2',
Mange = 'manga',
PAINT_BY_EXAMPLE = 'paint_by_example',
PIX2PIX = 'instruct_pix2pix',
}
export const maskState = atom<File | undefined>({
@ -45,6 +46,7 @@ interface AppState {
interactiveSegClicks: number[][]
showFileManager: boolean
enableFileManager: boolean
gifImage: HTMLImageElement | undefined
}
export const appState = atom<AppState>({
@ -61,6 +63,7 @@ export const appState = atom<AppState>({
interactiveSegClicks: [],
showFileManager: false,
enableFileManager: false,
gifImage: undefined,
},
})
@ -134,6 +137,18 @@ export const enableFileManagerState = selector({
},
})
export const gifImageState = selector({
key: 'gifImageState',
get: ({ get }) => {
const app = get(appState)
return app.gifImage
},
set: ({ get, set }, newValue: any) => {
const app = get(appState)
set(appState, { ...app, gifImage: newValue })
},
})
export const fileState = selector({
key: 'fileState',
get: ({ get }) => {
@ -329,6 +344,11 @@ export interface Settings {
paintByExampleSeedFixed: boolean
paintByExampleMaskBlur: number
paintByExampleMatchHistograms: boolean
// InstructPix2Pix
p2pSteps: number
p2pImageGuidanceScale: number
p2pGuidanceScale: number
}
const defaultHDSettings: ModelsHDSettings = {
@ -388,6 +408,13 @@ const defaultHDSettings: ModelsHDSettings = {
hdStrategyCropMargin: 128,
enabled: false,
},
[AIModel.PIX2PIX]: {
hdStrategy: HDStrategy.ORIGINAL,
hdStrategyResizeLimit: 768,
hdStrategyCropTrigerSize: 512,
hdStrategyCropMargin: 128,
enabled: false,
},
[AIModel.Mange]: {
hdStrategy: HDStrategy.CROP,
hdStrategyResizeLimit: 1280,
@ -457,6 +484,11 @@ export const settingStateDefault: Settings = {
paintByExampleMaskBlur: 5,
paintByExampleSeedFixed: false,
paintByExampleMatchHistograms: false,
// InstructPix2Pix
p2pSteps: 50,
p2pImageGuidanceScale: 1.5,
p2pGuidanceScale: 7.5,
}
const localStorageEffect =
@ -553,12 +585,33 @@ export const isPaintByExampleState = selector({
},
})
export const isPix2PixState = selector({
key: 'isPix2PixState',
get: ({ get }) => {
const settings = get(settingState)
return settings.model === AIModel.PIX2PIX
},
})
export const runManuallyState = selector({
key: 'runManuallyState',
get: ({ get }) => {
const settings = get(settingState)
const isSD = get(isSDState)
const isPaintByExample = get(isPaintByExampleState)
return settings.runInpaintingManually || isSD || isPaintByExample
const isPix2Pix = get(isPix2PixState)
return (
settings.runInpaintingManually || isSD || isPaintByExample || isPix2Pix
)
},
})
export const isDiffusionModelsState = selector({
key: 'isDiffusionModelsState',
get: ({ get }) => {
const isSD = get(isSDState)
const isPaintByExample = get(isPaintByExampleState)
const isPix2Pix = get(isPix2PixState)
return isSD || isPaintByExample || isPix2Pix
},
})

11861
lama_cleaner/app/yarn.lock
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10
lama_cleaner/const.py
View File

@ -1,5 +1,12 @@
import os
MPS_SUPPORT_MODELS = [
"instruct_pix2pix",
"sd1.5",
"sd2",
"paint_by_example"
]
DEFAULT_MODEL = "lama"
AVAILABLE_MODELS = [
"lama",
@ -11,7 +18,8 @@ AVAILABLE_MODELS = [
"cv2",
"manga",
"sd2",
"paint_by_example"
"paint_by_example",
"instruct_pix2pix",
]
AVAILABLE_DEVICES = ["cuda", "cpu", "mps"]

27
lama_cleaner/helper.py
View File

@ -62,7 +62,7 @@ def load_model(model: torch.nn.Module, url_or_path, device):
model_path = download_model(url_or_path)
try:
state_dict = torch.load(model_path, map_location='cpu')
state_dict = torch.load(model_path, map_location="cpu")
model.load_state_dict(state_dict, strict=True)
model.to(device)
logger.info(f"Load model from: {model_path}")
@ -85,26 +85,43 @@ def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes:
return image_bytes
def load_img(img_bytes, gray: bool = False):
def pil_to_bytes(pil_img, ext: str, exif=None) -> bytes:
with io.BytesIO() as output:
pil_img.save(output, format=ext, exif=exif, quality=95)
image_bytes = output.getvalue()
return image_bytes
def load_img(img_bytes, gray: bool = False, return_exif: bool = False):
alpha_channel = None
image = Image.open(io.BytesIO(img_bytes))
try:
if return_exif:
exif = image.getexif()
except:
exif = None
logger.error("Failed to extract exif from image")
try:
image = ImageOps.exif_transpose(image)
except:
pass
if gray:
image = image.convert('L')
image = image.convert("L")
np_img = np.array(image)
else:
if image.mode == 'RGBA':
if image.mode == "RGBA":
np_img = np.array(image)
alpha_channel = np_img[:, :, -1]
np_img = cv2.cvtColor(np_img, cv2.COLOR_RGBA2RGB)
else:
image = image.convert('RGB')
image = image.convert("RGB")
np_img = np.array(image)
if return_exif:
return np_img, alpha_channel, exif
return np_img, alpha_channel

125
lama_cleaner/make_gif.py Normal file
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@ -0,0 +1,125 @@
import io
import math
from pathlib import Path
from PIL import Image, ImageDraw
def keep_ratio_resize(img, size, resample=Image.BILINEAR):
if img.width > img.height:
w = size
h = int(img.height * size / img.width)
else:
h = size
w = int(img.width * size / img.height)
return img.resize((w, h), resample)
def cubic_bezier(p1, p2, duration: int, frames: int):
"""
Args:
p1:
p2:
duration: Total duration of the curve
frames:
Returns:
"""
x0, y0 = (0, 0)
x1, y1 = p1
x2, y2 = p2
x3, y3 = (1, 1)
def cal_y(t):
return math.pow(1 - t, 3) * y0 + \
3 * math.pow(1 - t, 2) * t * y1 + \
3 * (1 - t) * math.pow(t, 2) * y2 + \
math.pow(t, 3) * y3
def cal_x(t):
return math.pow(1 - t, 3) * x0 + \
3 * math.pow(1 - t, 2) * t * x1 + \
3 * (1 - t) * math.pow(t, 2) * x2 + \
math.pow(t, 3) * x3
res = []
for t in range(0, 1 * frames, duration):
t = t / frames
res.append((cal_x(t), cal_y(t)))
res.append((1, 0))
return res
def make_compare_gif(
clean_img: Image.Image,
src_img: Image.Image,
max_side_length: int = 600,
splitter_width: int = 5,
splitter_color=(255, 203, 0, int(255 * 0.73))
):
if clean_img.size != src_img.size:
clean_img = clean_img.resize(src_img.size, Image.BILINEAR)
duration_per_frame = 20
num_frames = 50
# erase-in-out
cubic_bezier_points = cubic_bezier((0.33, 0), (0.66, 1), 1, num_frames)
cubic_bezier_points.reverse()
max_side_length = min(max_side_length, max(clean_img.size))
src_img = keep_ratio_resize(src_img, max_side_length)
clean_img = keep_ratio_resize(clean_img, max_side_length)
width, height = src_img.size
# Generate images to make Gif from right to left
images = []
for i in range(num_frames):
new_frame = Image.new('RGB', (width, height))
new_frame.paste(clean_img, (0, 0))
left = int(cubic_bezier_points[i][0] * width)
cropped_src_img = src_img.crop((left, 0, width, height))
new_frame.paste(cropped_src_img, (left, 0, width, height))
if i != num_frames - 1:
# draw a yellow splitter on the edge of the cropped image
draw = ImageDraw.Draw(new_frame)
draw.line([(left, 0), (left, height)], width=splitter_width, fill=splitter_color)
images.append(new_frame)
for i in range(10):
images.append(src_img)
cubic_bezier_points.reverse()
# Generate images to make Gif from left to right
for i in range(num_frames):
new_frame = Image.new('RGB', (width, height))
new_frame.paste(src_img, (0, 0))
right = int(cubic_bezier_points[i][0] * width)
cropped_src_img = clean_img.crop((0, 0, right, height))
new_frame.paste(cropped_src_img, (0, 0, right, height))
if i != num_frames - 1:
# draw a yellow splitter on the edge of the cropped image
draw = ImageDraw.Draw(new_frame)
draw.line([(right, 0), (right, height)], width=splitter_width, fill=splitter_color)
images.append(new_frame)
images.append(clean_img)
img_byte_arr = io.BytesIO()
clean_img.save(
img_byte_arr,
format='GIF',
save_all=True,
include_color_table=True,
append_images=images,
optimize=False,
duration=duration_per_frame,
loop=0
)
return img_byte_arr.getvalue()

39
lama_cleaner/model/base.py
View File

@ -245,3 +245,42 @@ class InpaintModel:
crop_img, crop_mask, [l, t, r, b] = self._crop_box(image, mask, box, config)
return self._pad_forward(crop_img, crop_mask, config), [l, t, r, b]
class DiffusionInpaintModel(InpaintModel):
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
images: [H, W, C] RGB, not normalized
masks: [H, W]
return: BGR IMAGE
"""
# boxes = boxes_from_mask(mask)
if config.use_croper:
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
else:
inpaint_result = self._scaled_pad_forward(image, mask, config)
return inpaint_result
def _scaled_pad_forward(self, image, mask, config: Config):
longer_side_length = int(config.sd_scale * max(image.shape[:2]))
origin_size = image.shape[:2]
downsize_image = resize_max_size(image, size_limit=longer_side_length)
downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
logger.info(
f"Resize image to do sd inpainting: {image.shape} -> {downsize_image.shape}"
)
inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
# only paste masked area result
inpaint_result = cv2.resize(
inpaint_result,
(origin_size[1], origin_size[0]),
interpolation=cv2.INTER_CUBIC,
)
original_pixel_indices = mask < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
return inpaint_result

83
lama_cleaner/model/instruct_pix2pix.py Normal file
View File

@ -0,0 +1,83 @@
import PIL.Image
import cv2
import torch
from loguru import logger
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import set_seed
from lama_cleaner.schema import Config
class InstructPix2Pix(DiffusionInpaintModel):
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
from diffusers import StableDiffusionInstructPix2PixPipeline
fp16 = not kwargs.get('no_half', False)
model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False):
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False
))
use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix",
revision="fp16" if use_gpu and fp16 else "main",
torch_dtype=torch_dtype,
**model_kwargs
)
self.model.enable_attention_slicing()
if kwargs.get('enable_xformers', False):
self.model.enable_xformers_memory_efficient_attention()
if kwargs.get('cpu_offload', False) and use_gpu:
logger.info("Enable sequential cpu offload")
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
self.model = self.model.to(device)
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
edit = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images[0]
"""
set_seed(config.sd_seed)
output = self.model(
image=PIL.Image.fromarray(image),
prompt=config.prompt,
negative_prompt=config.negative_prompt,
num_inference_steps=config.p2p_steps,
image_guidance_scale=config.p2p_image_guidance_scale,
guidance_scale=config.p2p_guidance_scale,
output_type="np.array",
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
#
# def forward_post_process(self, result, image, mask, config):
# if config.sd_match_histograms:
# result = self._match_histograms(result, image[:, :, ::-1], mask)
#
# if config.sd_mask_blur != 0:
# k = 2 * config.sd_mask_blur + 1
# mask = cv2.GaussianBlur(mask, (k, k), 0)
# return result, image, mask
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings
return True

51
lama_cleaner/model/paint_by_example.py
View File

@ -1,19 +1,16 @@
import random
import PIL
import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import DiffusionPipeline
from loguru import logger
from lama_cleaner.helper import resize_max_size
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import set_seed
from lama_cleaner.schema import Config
class PaintByExample(InpaintModel):
class PaintByExample(DiffusionInpaintModel):
pad_mod = 8
min_size = 512
@ -53,11 +50,7 @@ class PaintByExample(InpaintModel):
mask: [H, W, 1] 255 means area to repaint
return: BGR IMAGE
"""
seed = config.paint_by_example_seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
set_seed(config.paint_by_example_seed)
output = self.model(
image=PIL.Image.fromarray(image),
@ -71,42 +64,6 @@ class PaintByExample(InpaintModel):
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
def _scaled_pad_forward(self, image, mask, config: Config):
longer_side_length = int(config.sd_scale * max(image.shape[:2]))
origin_size = image.shape[:2]
downsize_image = resize_max_size(image, size_limit=longer_side_length)
downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
logger.info(
f"Resize image to do paint_by_example: {image.shape} -> {downsize_image.shape}"
)
inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
# only paste masked area result
inpaint_result = cv2.resize(
inpaint_result,
(origin_size[1], origin_size[0]),
interpolation=cv2.INTER_CUBIC,
)
original_pixel_indices = mask < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
return inpaint_result
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
images: [H, W, C] RGB, not normalized
masks: [H, W]
return: BGR IMAGE
"""
if config.use_croper:
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
else:
inpaint_result = self._scaled_pad_forward(image, mask, config)
return inpaint_result
def forward_post_process(self, result, image, mask, config):
if config.paint_by_example_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)

105
lama_cleaner/model/sd.py
View File

@ -4,20 +4,25 @@ import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \
EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
from diffusers import (
PNDMScheduler,
DDIMScheduler,
LMSDiscreteScheduler,
EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
)
from loguru import logger
from lama_cleaner.helper import resize_max_size
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.model.utils import torch_gc
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import torch_gc, set_seed
from lama_cleaner.schema import Config, SDSampler
class CPUTextEncoderWrapper:
def __init__(self, text_encoder, torch_dtype):
self.config = text_encoder.config
self.text_encoder = text_encoder.to(torch.device('cpu'), non_blocking=True)
self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True)
self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
self.torch_dtype = torch_dtype
del text_encoder
@ -25,27 +30,40 @@ class CPUTextEncoderWrapper:
def __call__(self, x, **kwargs):
input_device = x.device
return [self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0].to(input_device).to(self.torch_dtype)]
return [
self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0]
.to(input_device)
.to(self.torch_dtype)
]
@property
def dtype(self):
return self.torch_dtype
class SD(InpaintModel):
class SD(DiffusionInpaintModel):
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
fp16 = not kwargs.get('no_half', False)
model_kwargs = {"local_files_only": kwargs.get('local_files_only', kwargs['sd_run_local'])}
if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False):
fp16 = not kwargs.get("no_half", False)
model_kwargs = {
"local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"])
}
if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False
))
model_kwargs.update(
dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
)
use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
self.model = StableDiffusionInpaintPipeline.from_pretrained(
self.model_id_or_path,
@ -58,40 +76,23 @@ class SD(InpaintModel):
# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
self.model.enable_attention_slicing()
# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
if kwargs.get('enable_xformers', False):
if kwargs.get("enable_xformers", False):
self.model.enable_xformers_memory_efficient_attention()
if kwargs.get('cpu_offload', False) and use_gpu:
if kwargs.get("cpu_offload", False) and use_gpu:
# TODO: gpu_id
logger.info("Enable sequential cpu offload")
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
self.model = self.model.to(device)
if kwargs['sd_cpu_textencoder']:
if kwargs["sd_cpu_textencoder"]:
logger.info("Run Stable Diffusion TextEncoder on CPU")
self.model.text_encoder = CPUTextEncoderWrapper(self.model.text_encoder, torch_dtype)
self.model.text_encoder = CPUTextEncoderWrapper(
self.model.text_encoder, torch_dtype
)
self.callback = kwargs.pop("callback", None)
def _scaled_pad_forward(self, image, mask, config: Config):
longer_side_length = int(config.sd_scale * max(image.shape[:2]))
origin_size = image.shape[:2]
downsize_image = resize_max_size(image, size_limit=longer_side_length)
downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
logger.info(
f"Resize image to do sd inpainting: {image.shape} -> {downsize_image.shape}"
)
inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
# only paste masked area result
inpaint_result = cv2.resize(
inpaint_result,
(origin_size[1], origin_size[0]),
interpolation=cv2.INTER_CUBIC,
)
original_pixel_indices = mask < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
return inpaint_result
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
image: [H, W, C] RGB
@ -118,11 +119,7 @@ class SD(InpaintModel):
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)
set_seed(config.sd_seed)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
@ -147,24 +144,6 @@ class SD(InpaintModel):
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
"""
# boxes = boxes_from_mask(mask)
if config.use_croper:
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
else:
inpaint_result = self._scaled_pad_forward(image, mask, config)
return inpaint_result
def forward_post_process(self, result, image, mask, config):
if config.sd_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)

8
lama_cleaner/model/utils.py
View File

@ -1,4 +1,5 @@
import math
import random
from typing import Any
import torch
@ -713,3 +714,10 @@ def torch_gc():
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def set_seed(seed: int):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)

3
lama_cleaner/model_manager.py
View File

@ -7,13 +7,14 @@ from lama_cleaner.model.ldm import LDM
from lama_cleaner.model.manga import Manga
from lama_cleaner.model.mat import MAT
from lama_cleaner.model.paint_by_example import PaintByExample
from lama_cleaner.model.instruct_pix2pix import InstructPix2Pix
from lama_cleaner.model.sd import SD15, SD2
from lama_cleaner.model.zits import ZITS
from lama_cleaner.model.opencv2 import OpenCV2
from lama_cleaner.schema import Config
models = {"lama": LaMa, "ldm": LDM, "zits": ZITS, "mat": MAT, "fcf": FcF, "sd1.5": SD15, "cv2": OpenCV2, "manga": Manga,
"sd2": SD2, "paint_by_example": PaintByExample}
"sd2": SD2, "paint_by_example": PaintByExample, "instruct_pix2pix": InstructPix2Pix}
class ModelManager:

98
lama_cleaner/parse_args.py
View File

@ -5,9 +5,25 @@ from pathlib import Path
from loguru import logger
from lama_cleaner.const import AVAILABLE_MODELS, NO_HALF_HELP, CPU_OFFLOAD_HELP, DISABLE_NSFW_HELP, \
SD_CPU_TEXTENCODER_HELP, LOCAL_FILES_ONLY_HELP, AVAILABLE_DEVICES, ENABLE_XFORMERS_HELP, MODEL_DIR_HELP, \
OUTPUT_DIR_HELP, INPUT_HELP, GUI_HELP, DEFAULT_DEVICE, NO_GUI_AUTO_CLOSE_HELP, DEFAULT_MODEL_DIR
from lama_cleaner.const import (
AVAILABLE_MODELS,
NO_HALF_HELP,
CPU_OFFLOAD_HELP,
DISABLE_NSFW_HELP,
SD_CPU_TEXTENCODER_HELP,
LOCAL_FILES_ONLY_HELP,
AVAILABLE_DEVICES,
ENABLE_XFORMERS_HELP,
MODEL_DIR_HELP,
OUTPUT_DIR_HELP,
INPUT_HELP,
GUI_HELP,
DEFAULT_DEVICE,
NO_GUI_AUTO_CLOSE_HELP,
DEFAULT_MODEL_DIR,
DEFAULT_MODEL,
MPS_SUPPORT_MODELS,
)
from lama_cleaner.runtime import dump_environment_info
@ -16,22 +32,40 @@ def parse_args():
parser.add_argument("--host", default="127.0.0.1")
parser.add_argument("--port", default=8080, type=int)
parser.add_argument("--config-installer", action="store_true",
help="Open config web page, mainly for windows installer")
parser.add_argument("--load-installer-config", action="store_true",
help="Load all cmd args from installer config file")
parser.add_argument("--installer-config", default=None, help="Config file for windows installer")
parser.add_argument(
"--config-installer",
action="store_true",
help="Open config web page, mainly for windows installer",
)
parser.add_argument(
"--load-installer-config",
action="store_true",
help="Load all cmd args from installer config file",
)
parser.add_argument(
"--installer-config", default=None, help="Config file for windows installer"
)
parser.add_argument("--model", default="lama", choices=AVAILABLE_MODELS)
parser.add_argument("--model", default=DEFAULT_MODEL, choices=AVAILABLE_MODELS)
parser.add_argument("--no-half", action="store_true", help=NO_HALF_HELP)
parser.add_argument("--cpu-offload", action="store_true", help=CPU_OFFLOAD_HELP)
parser.add_argument("--disable-nsfw", action="store_true", help=DISABLE_NSFW_HELP)
parser.add_argument("--sd-cpu-textencoder", action="store_true", help=SD_CPU_TEXTENCODER_HELP)
parser.add_argument("--local-files-only", action="store_true", help=LOCAL_FILES_ONLY_HELP)
parser.add_argument("--enable-xformers", action="store_true", help=ENABLE_XFORMERS_HELP)
parser.add_argument("--device", default=DEFAULT_DEVICE, type=str, choices=AVAILABLE_DEVICES)
parser.add_argument(
"--sd-cpu-textencoder", action="store_true", help=SD_CPU_TEXTENCODER_HELP
)
parser.add_argument(
"--local-files-only", action="store_true", help=LOCAL_FILES_ONLY_HELP
)
parser.add_argument(
"--enable-xformers", action="store_true", help=ENABLE_XFORMERS_HELP
)
parser.add_argument(
"--device", default=DEFAULT_DEVICE, type=str, choices=AVAILABLE_DEVICES
)
parser.add_argument("--gui", action="store_true", help=GUI_HELP)
parser.add_argument("--no-gui-auto-close", action="store_true", help=NO_GUI_AUTO_CLOSE_HELP)
parser.add_argument(
"--no-gui-auto-close", action="store_true", help=NO_GUI_AUTO_CLOSE_HELP
)
parser.add_argument(
"--gui-size",
default=[1600, 1000],
@ -41,8 +75,14 @@ def parse_args():
)
parser.add_argument("--input", type=str, default=None, help=INPUT_HELP)
parser.add_argument("--output-dir", type=str, default=None, help=OUTPUT_DIR_HELP)
parser.add_argument("--model-dir", type=str, default=DEFAULT_MODEL_DIR, help=MODEL_DIR_HELP)
parser.add_argument("--disable-model-switch", action="store_true", help="Disable model switch in frontend")
parser.add_argument(
"--model-dir", type=str, default=DEFAULT_MODEL_DIR, help=MODEL_DIR_HELP
)
parser.add_argument(
"--disable-model-switch",
action="store_true",
help="Disable model switch in frontend",
)
parser.add_argument("--debug", action="store_true")
# useless args
@ -64,7 +104,7 @@ def parse_args():
parser.add_argument(
"--sd-enable-xformers",
action="store_true",
help="Enable xFormers optimizations. Requires that xformers package has been installed. See: https://github.com/facebookresearch/xformers"
help="Enable xFormers optimizations. Requires that xformers package has been installed. See: https://github.com/facebookresearch/xformers",
)
args = parser.parse_args()
@ -74,14 +114,18 @@ def parse_args():
if args.config_installer:
if args.installer_config is None:
parser.error(f"args.config_installer==True, must set args.installer_config to store config file")
parser.error(
f"args.config_installer==True, must set args.installer_config to store config file"
)
from lama_cleaner.web_config import main
logger.info(f"Launching installer web config page")
main(args.installer_config)
exit()
if args.load_installer_config:
from lama_cleaner.web_config import load_config
if args.installer_config and not os.path.exists(args.installer_config):
parser.error(f"args.installer_config={args.installer_config} not exists")
@ -93,9 +137,17 @@ def parse_args():
if args.device == "cuda":
import torch
if torch.cuda.is_available() is False:
parser.error(
"torch.cuda.is_available() is False, please use --device cpu or check your pytorch installation")
"torch.cuda.is_available() is False, please use --device cpu or check your pytorch installation"
)
if args.device == "mps":
if args.model not in MPS_SUPPORT_MODELS:
parser.error(
f"mps only support: {MPS_SUPPORT_MODELS}, but got {args.model}"
)
if args.model_dir and args.model_dir is not None:
if os.path.isfile(args.model_dir):
@ -115,7 +167,9 @@ def parse_args():
parser.error(f"invalid --input: {args.input} is not a valid image file")
else:
if args.output_dir is None:
parser.error(f"invalid --input: {args.input} is a directory, --output-dir is required")
parser.error(
f"invalid --input: {args.input} is a directory, --output-dir is required"
)
else:
output_dir = Path(args.output_dir)
if not output_dir.exists():
@ -123,6 +177,8 @@ def parse_args():
output_dir.mkdir(parents=True)
else:
if not output_dir.is_dir():
parser.error(f"invalid --output-dir: {output_dir} is not a directory")
parser.error(
f"invalid --output-dir: {output_dir} is not a directory"
)
return args

5
lama_cleaner/schema.py
View File

@ -88,3 +88,8 @@ class Config(BaseModel):
paint_by_example_seed: int = 42
paint_by_example_match_histograms: bool = False
paint_by_example_example_image: Image = None
# InstructPix2Pix
p2p_steps: int = 50
p2p_image_guidance_scale: float = 1.5
p2p_guidance_scale: float = 7.5

104
lama_cleaner/server.py
View File

@ -19,6 +19,7 @@ from loguru import logger
from watchdog.events import FileSystemEventHandler
from lama_cleaner.interactive_seg import InteractiveSeg, Click
from lama_cleaner.make_gif import make_compare_gif
from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config
from lama_cleaner.file_manager import FileManager
@ -31,7 +32,15 @@ try:
except:
pass
from flask import Flask, request, send_file, cli, make_response, send_from_directory, jsonify
from flask import (
Flask,
request,
send_file,
cli,
make_response,
send_from_directory,
jsonify,
)
# Disable ability for Flask to display warning about using a development server in a production environment.
# https://gist.github.com/jerblack/735b9953ba1ab6234abb43174210d356
@ -42,6 +51,7 @@ from lama_cleaner.helper import (
load_img,
numpy_to_bytes,
resize_max_size,
pil_to_bytes,
)
NUM_THREADS = str(multiprocessing.cpu_count())
@ -93,6 +103,25 @@ def diffuser_callback(i, t, latents):
# socketio.emit('diffusion_step', {'diffusion_step': step})
@app.route("/make_gif", methods=["POST"])
def make_gif():
input = request.files
filename = request.form["filename"]
origin_image_bytes = input["origin_img"].read()
clean_image_bytes = input["clean_img"].read()
origin_image, _ = load_img(origin_image_bytes)
clean_image, _ = load_img(clean_image_bytes)
gif_bytes = make_compare_gif(
Image.fromarray(origin_image), Image.fromarray(clean_image)
)
return send_file(
io.BytesIO(gif_bytes),
mimetype="image/gif",
as_attachment=True,
attachment_filename=filename,
)
@app.route("/save_image", methods=["POST"])
def save_image():
# all image in output directory
@ -100,12 +129,12 @@ def save_image():
origin_image_bytes = input["image"].read() # RGB
image, _ = load_img(origin_image_bytes)
thumb.save_to_output_directory(image, request.form["filename"])
return 'ok', 200
return "ok", 200
@app.route("/medias/<tab>")
def medias(tab):
if tab == 'image':
if tab == "image":
response = make_response(jsonify(thumb.media_names), 200)
else:
response = make_response(jsonify(thumb.output_media_names), 200)
@ -116,18 +145,18 @@ def medias(tab):
return response
@app.route('/media/<tab>/<filename>')
@app.route("/media/<tab>/<filename>")
def media_file(tab, filename):
if tab == 'image':
if tab == "image":
return send_from_directory(thumb.root_directory, filename)
return send_from_directory(thumb.output_dir, filename)
@app.route('/media_thumbnail/<tab>/<filename>')
@app.route("/media_thumbnail/<tab>/<filename>")
def media_thumbnail_file(tab, filename):
args = request.args
width = args.get('width')
height = args.get('height')
width = args.get("width")
height = args.get("height")
if width is None and height is None:
width = 256
if width:
@ -136,9 +165,11 @@ def media_thumbnail_file(tab, filename):
height = int(float(height))
directory = thumb.root_directory
if tab == 'output':
if tab == "output":
directory = thumb.output_dir
thumb_filename, (width, height) = thumb.get_thumbnail(directory, filename, width, height)
thumb_filename, (width, height) = thumb.get_thumbnail(
directory, filename, width, height
)
thumb_filepath = f"{app.config['THUMBNAIL_MEDIA_THUMBNAIL_ROOT']}{thumb_filename}"
response = make_response(send_file(thumb_filepath))
@ -152,13 +183,16 @@ def process():
input = request.files
# RGB
origin_image_bytes = input["image"].read()
image, alpha_channel = load_img(origin_image_bytes)
image, alpha_channel, exif = load_img(origin_image_bytes, return_exif=True)
mask, _ = load_img(input["mask"].read(), gray=True)
mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
if image.shape[:2] != mask.shape[:2]:
return f"Mask shape{mask.shape[:2]} not queal to Image shape{image.shape[:2]}", 400
return (
f"Mask shape{mask.shape[:2]} not queal to Image shape{image.shape[:2]}",
400,
)
original_shape = image.shape
interpolation = cv2.INTER_CUBIC
@ -171,7 +205,9 @@ def process():
size_limit = int(size_limit)
if "paintByExampleImage" in input:
paint_by_example_example_image, _ = load_img(input["paintByExampleImage"].read())
paint_by_example_example_image, _ = load_img(
input["paintByExampleImage"].read()
)
paint_by_example_example_image = Image.fromarray(paint_by_example_example_image)
else:
paint_by_example_example_image = None
@ -200,13 +236,16 @@ def process():
sd_seed=form["sdSeed"],
sd_match_histograms=form["sdMatchHistograms"],
cv2_flag=form["cv2Flag"],
cv2_radius=form['cv2Radius'],
cv2_radius=form["cv2Radius"],
paint_by_example_steps=form["paintByExampleSteps"],
paint_by_example_guidance_scale=form["paintByExampleGuidanceScale"],
paint_by_example_mask_blur=form["paintByExampleMaskBlur"],
paint_by_example_seed=form["paintByExampleSeed"],
paint_by_example_match_histograms=form["paintByExampleMatchHistograms"],
paint_by_example_example_image=paint_by_example_example_image,
p2p_steps=form["p2pSteps"],
p2p_image_guidance_scale=form["p2pImageGuidanceScale"],
p2p_guidance_scale=form["p2pGuidanceScale"],
)
if config.sd_seed == -1:
@ -235,6 +274,7 @@ def process():
logger.info(f"process time: {(time.time() - start) * 1000}ms")
torch.cuda.empty_cache()
res_np_img = cv2.cvtColor(res_np_img.astype(np.uint8), cv2.COLOR_BGR2RGB)
if alpha_channel is not None:
if alpha_channel.shape[:2] != res_np_img.shape[:2]:
alpha_channel = cv2.resize(
@ -246,9 +286,15 @@ def process():
ext = get_image_ext(origin_image_bytes)
if exif is not None:
bytes_io = io.BytesIO(pil_to_bytes(Image.fromarray(res_np_img), ext, exif=exif))
else:
bytes_io = io.BytesIO(pil_to_bytes(Image.fromarray(res_np_img), ext))
response = make_response(
send_file(
io.BytesIO(numpy_to_bytes(res_np_img, ext)),
# io.BytesIO(numpy_to_bytes(res_np_img, ext)),
bytes_io,
mimetype=f"image/{ext}",
)
)
@ -261,7 +307,7 @@ def interactive_seg():
input = request.files
origin_image_bytes = input["image"].read() # RGB
image, _ = load_img(origin_image_bytes)
if 'mask' in input:
if "mask" in input:
mask, _ = load_img(input["mask"].read(), gray=True)
else:
mask = None
@ -269,14 +315,16 @@ def interactive_seg():
_clicks = json.loads(request.form["clicks"])
clicks = []
for i, click in enumerate(_clicks):
clicks.append(Click(coords=(click[1], click[0]), indx=i, is_positive=click[2] == 1))
clicks.append(
Click(coords=(click[1], click[0]), indx=i, is_positive=click[2] == 1)
)
start = time.time()
new_mask = interactive_seg_model(image, clicks=clicks, prev_mask=mask)
logger.info(f"interactive seg process time: {(time.time() - start) * 1000}ms")
response = make_response(
send_file(
io.BytesIO(numpy_to_bytes(new_mask, 'png')),
io.BytesIO(numpy_to_bytes(new_mask, "png")),
mimetype=f"image/png",
)
)
@ -290,13 +338,13 @@ def current_model():
@app.route("/is_disable_model_switch")
def get_is_disable_model_switch():
res = 'true' if is_disable_model_switch else 'false'
res = "true" if is_disable_model_switch else "false"
return res, 200
@app.route("/is_enable_file_manager")
def get_is_enable_file_manager():
res = 'true' if is_enable_file_manager else 'false'
res = "true" if is_enable_file_manager else "false"
return res, 200
@ -365,14 +413,18 @@ def main(args):
is_disable_model_switch = args.disable_model_switch
is_desktop = args.gui
if is_disable_model_switch:
logger.info(f"Start with --disable-model-switch, model switch on frontend is disable")
logger.info(
f"Start with --disable-model-switch, model switch on frontend is disable"
)
if args.input and os.path.isdir(args.input):
logger.info(f"Initialize file manager")
thumb = FileManager(app)
is_enable_file_manager = True
app.config["THUMBNAIL_MEDIA_ROOT"] = args.input
app.config["THUMBNAIL_MEDIA_THUMBNAIL_ROOT"] = os.path.join(args.output_dir, 'lama_cleaner_thumbnails')
app.config["THUMBNAIL_MEDIA_THUMBNAIL_ROOT"] = os.path.join(
args.output_dir, "lama_cleaner_thumbnails"
)
thumb.output_dir = Path(args.output_dir)
# thumb.start()
# try:
@ -408,8 +460,12 @@ def main(args):
from flaskwebgui import FlaskUI
ui = FlaskUI(
app, width=app_width, height=app_height, host=args.host, port=args.port,
close_server_on_exit=not args.no_gui_auto_close
app,
width=app_width,
height=app_height,
host=args.host,
port=args.port,
close_server_on_exit=not args.no_gui_auto_close,
)
ui.run()
else:

0
lama_cleaner/tests/overture-creations-5sI6fQgYIuo_all_mask.png Normal file
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62
lama_cleaner/tests/test_instruct_pix2pix.py Normal file
View File

@ -0,0 +1,62 @@
from pathlib import Path
import pytest
import torch
from lama_cleaner.model_manager import ModelManager
from lama_cleaner.tests.test_model import get_config, assert_equal
from lama_cleaner.schema import HDStrategy
current_dir = Path(__file__).parent.absolute().resolve()
save_dir = current_dir / 'result'
save_dir.mkdir(exist_ok=True, parents=True)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
@pytest.mark.parametrize("disable_nsfw", [True, False])
@pytest.mark.parametrize("cpu_offload", [False, True])
def test_instruct_pix2pix(disable_nsfw, cpu_offload):
sd_steps = 50 if device == 'cuda' else 1
model = ModelManager(name="instruct_pix2pix",
device=torch.device(device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=False,
cpu_offload=cpu_offload)
cfg = get_config(strategy=HDStrategy.ORIGINAL, prompt='What if it were snowing?', p2p_steps=sd_steps, sd_scale=1.1)
name = f"device_{device}_disnsfw_{disable_nsfw}_cpu_offload_{cpu_offload}"
assert_equal(
model,
cfg,
f"instruct_pix2pix_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.3
)
@pytest.mark.parametrize("disable_nsfw", [False])
@pytest.mark.parametrize("cpu_offload", [False])
def test_instruct_pix2pix_snow(disable_nsfw, cpu_offload):
sd_steps = 50 if device == 'cuda' else 1
model = ModelManager(name="instruct_pix2pix",
device=torch.device(device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=False,
cpu_offload=cpu_offload)
cfg = get_config(strategy=HDStrategy.ORIGINAL, prompt='What if it were snowing?', p2p_steps=sd_steps)
name = f"snow"
assert_equal(
model,
cfg,
f"instruct_pix2pix_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)

42
lama_cleaner/tests/test_save_exif.py Normal file
View File

@ -0,0 +1,42 @@
import io
from pathlib import Path
from PIL import Image
from lama_cleaner.helper import pil_to_bytes
current_dir = Path(__file__).parent.absolute().resolve()
png_img_p = current_dir / "image.png"
jpg_img_p = current_dir / "bunny.jpeg"
def print_exif(exif):
for k, v in exif.items():
print(f"{k}: {v}")
def test_png():
img = Image.open(png_img_p)
exif = img.getexif()
print_exif(exif)
pil_bytes = pil_to_bytes(img, ext="png", exif=exif)
res_img = Image.open(io.BytesIO(pil_bytes))
res_exif = res_img.getexif()
assert dict(exif) == dict(res_exif)
def test_jpeg():
img = Image.open(jpg_img_p)
exif = img.getexif()
print_exif(exif)
pil_bytes = pil_to_bytes(img, ext="jpeg", exif=exif)
res_img = Image.open(io.BytesIO(pil_bytes))
res_exif = res_img.getexif()
assert dict(exif) == dict(res_exif)

176
lama_cleaner/tests/test_sd_model.py
View File

@ -8,33 +8,37 @@ from lama_cleaner.schema import HDStrategy, SDSampler
from lama_cleaner.tests.test_model import get_config, assert_equal
current_dir = Path(__file__).parent.absolute().resolve()
save_dir = current_dir / 'result'
save_dir = current_dir / "result"
save_dir.mkdir(exist_ok=True, parents=True)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device)
@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.ddim])
@pytest.mark.parametrize("cpu_textencoder", [True, False])
@pytest.mark.parametrize("disable_nsfw", [True, False])
def test_runway_sd_1_5_ddim(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
def test_runway_sd_1_5_ddim(
sd_device, strategy, sampler, cpu_textencoder, disable_nsfw
):
def callback(i, t, latents):
print(f"sd_step_{i}")
pass
if sd_device == 'cuda' and not torch.cuda.is_available():
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == 'cuda' else 1
model = ModelManager(name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
callback=callback)
cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps)
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
callback=callback,
)
cfg = get_config(strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
@ -45,31 +49,35 @@ def test_runway_sd_1_5_ddim(sd_device, strategy, sampler, cpu_textencoder, disab
f"runway_sd_{strategy.capitalize()}_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.3
fx=1.3,
)
@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.pndm, SDSampler.k_lms, SDSampler.k_euler, SDSampler.k_euler_a])
@pytest.mark.parametrize(
"sampler", [SDSampler.pndm, SDSampler.k_lms, SDSampler.k_euler, SDSampler.k_euler_a]
)
@pytest.mark.parametrize("cpu_textencoder", [False])
@pytest.mark.parametrize("disable_nsfw", [True])
def test_runway_sd_1_5(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
def callback(i, t, latents):
print(f"sd_step_{i}")
if sd_device == 'cuda' and not torch.cuda.is_available():
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == 'cuda' else 1
model = ModelManager(name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
callback=callback)
cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps)
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
callback=callback,
)
cfg = get_config(strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
@ -80,35 +88,37 @@ def test_runway_sd_1_5(sd_device, strategy, sampler, cpu_textencoder, disable_ns
f"runway_sd_{strategy.capitalize()}_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.3
fx=1.3,
)
@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.ddim])
def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
def callback(i, t, latents):
pass
if sd_device == 'cuda' and not torch.cuda.is_available():
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == 'cuda' else 1
model = ModelManager(name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=False,
sd_cpu_textencoder=False,
callback=callback)
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=False,
sd_cpu_textencoder=False,
callback=callback,
)
cfg = get_config(
strategy,
sd_steps=sd_steps,
prompt='Face of a fox, high resolution, sitting on a park bench',
negative_prompt='orange, yellow, small',
prompt="Face of a fox, high resolution, sitting on a park bench",
negative_prompt="orange, yellow, small",
sd_sampler=sampler,
sd_match_histograms=True
sd_match_histograms=True,
)
name = f"{sampler}_negative_prompt"
@ -119,27 +129,33 @@ def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
f"runway_sd_{strategy.capitalize()}_{name}.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1
fx=1,
)
@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
@pytest.mark.parametrize("cpu_textencoder", [False])
@pytest.mark.parametrize("disable_nsfw", [False])
def test_runway_sd_1_5_sd_scale(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
if sd_device == 'cuda' and not torch.cuda.is_available():
def test_runway_sd_1_5_sd_scale(
sd_device, strategy, sampler, cpu_textencoder, disable_nsfw
):
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == 'cuda' else 1
model = ModelManager(name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder)
cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps, sd_scale=0.85)
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder,
)
cfg = get_config(
strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps, sd_scale=0.85
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
@ -150,26 +166,30 @@ def test_runway_sd_1_5_sd_scale(sd_device, strategy, sampler, cpu_textencoder, d
f"runway_sd_{strategy.capitalize()}_{name}_sdscale.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.3
fx=1.3,
)
@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("sd_device", ["cuda"])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler):
if sd_device == 'cuda' and not torch.cuda.is_available():
if sd_device == "cuda" and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == 'cuda' else 1
model = ModelManager(name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=True,
sd_cpu_textencoder=False,
cpu_offload=True)
cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps, sd_scale=0.85)
sd_steps = 50 if sd_device == "cuda" else 1
model = ModelManager(
name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=True,
sd_cpu_textencoder=False,
cpu_offload=True,
)
cfg = get_config(
strategy, prompt="a fox sitting on a bench", sd_steps=sd_steps, sd_scale=0.85
)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}"
@ -182,27 +202,3 @@ def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler):
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)
@pytest.mark.parametrize("sd_device", ['cpu'])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
def test_runway_sd_1_5_cpu_offload_cpu_device(sd_device, strategy, sampler):
model = ModelManager(name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
disable_nsfw=False,
sd_cpu_textencoder=False,
cpu_offload=True)
cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=1, sd_scale=0.85)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}"
assert_equal(
model,
cfg,
f"runway_sd_{strategy.capitalize()}_{name}_cpu_offload_cpu_device.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)

112
lama_cleaner/web_config.py
View File

@ -6,9 +6,24 @@ import gradio as gr
from loguru import logger
from pydantic import BaseModel
from lama_cleaner.const import AVAILABLE_MODELS, AVAILABLE_DEVICES, CPU_OFFLOAD_HELP, NO_HALF_HELP, DISABLE_NSFW_HELP, \
SD_CPU_TEXTENCODER_HELP, LOCAL_FILES_ONLY_HELP, ENABLE_XFORMERS_HELP, MODEL_DIR_HELP, OUTPUT_DIR_HELP, INPUT_HELP, \
GUI_HELP, DEFAULT_MODEL, DEFAULT_DEVICE, NO_GUI_AUTO_CLOSE_HELP, DEFAULT_MODEL_DIR
from lama_cleaner.const import (
AVAILABLE_MODELS,
AVAILABLE_DEVICES,
CPU_OFFLOAD_HELP,
NO_HALF_HELP,
DISABLE_NSFW_HELP,
SD_CPU_TEXTENCODER_HELP,
LOCAL_FILES_ONLY_HELP,
ENABLE_XFORMERS_HELP,
MODEL_DIR_HELP,
OUTPUT_DIR_HELP,
INPUT_HELP,
GUI_HELP,
DEFAULT_MODEL,
DEFAULT_DEVICE,
NO_GUI_AUTO_CLOSE_HELP,
DEFAULT_MODEL_DIR, MPS_SUPPORT_MODELS,
)
_config_file = None
@ -33,16 +48,28 @@ class Config(BaseModel):
def load_config(installer_config: str):
if os.path.exists(installer_config):
with open(installer_config, "r", encoding='utf-8') as f:
with open(installer_config, "r", encoding="utf-8") as f:
return Config(**json.load(f))
else:
return Config()
def save_config(
host, port, model, device, gui, no_gui_auto_close, no_half, cpu_offload,
disable_nsfw, sd_cpu_textencoder, enable_xformers, local_files_only,
model_dir, input, output_dir
host,
port,
model,
device,
gui,
no_gui_auto_close,
no_half,
cpu_offload,
disable_nsfw,
sd_cpu_textencoder,
enable_xformers,
local_files_only,
model_dir,
input,
output_dir,
):
config = Config(**locals())
print(config)
@ -63,6 +90,7 @@ def save_config(
def close_server(*args):
# TODO: make close both browser and server works
import os, signal
pid = os.getpid()
os.kill(pid, signal.SIGUSR1)
@ -86,33 +114,53 @@ def main(config_file: str):
port = gr.Number(init_config.port, label="Port", precision=0)
with gr.Row():
model = gr.Radio(AVAILABLE_MODELS, label="Model", value=init_config.model)
device = gr.Radio(AVAILABLE_DEVICES, label="Device", value=init_config.device)
device = gr.Radio(
AVAILABLE_DEVICES, label=f"Device(mps supports {MPS_SUPPORT_MODELS})", value=init_config.device
)
gui = gr.Checkbox(init_config.gui, label=f"{GUI_HELP}")
no_gui_auto_close = gr.Checkbox(init_config.no_gui_auto_close, label=f"{NO_GUI_AUTO_CLOSE_HELP}")
no_gui_auto_close = gr.Checkbox(
init_config.no_gui_auto_close, label=f"{NO_GUI_AUTO_CLOSE_HELP}"
)
no_half = gr.Checkbox(init_config.no_half, label=f"{NO_HALF_HELP}")
cpu_offload = gr.Checkbox(init_config.cpu_offload, label=f"{CPU_OFFLOAD_HELP}")
disable_nsfw = gr.Checkbox(init_config.disable_nsfw, label=f"{DISABLE_NSFW_HELP}")
sd_cpu_textencoder = gr.Checkbox(init_config.sd_cpu_textencoder, label=f"{SD_CPU_TEXTENCODER_HELP}")
enable_xformers = gr.Checkbox(init_config.enable_xformers, label=f"{ENABLE_XFORMERS_HELP}")
local_files_only = gr.Checkbox(init_config.local_files_only, label=f"{LOCAL_FILES_ONLY_HELP}")
disable_nsfw = gr.Checkbox(
init_config.disable_nsfw, label=f"{DISABLE_NSFW_HELP}"
)
sd_cpu_textencoder = gr.Checkbox(
init_config.sd_cpu_textencoder, label=f"{SD_CPU_TEXTENCODER_HELP}"
)
enable_xformers = gr.Checkbox(
init_config.enable_xformers, label=f"{ENABLE_XFORMERS_HELP}"
)
local_files_only = gr.Checkbox(
init_config.local_files_only, label=f"{LOCAL_FILES_ONLY_HELP}"
)
model_dir = gr.Textbox(init_config.model_dir, label=f"{MODEL_DIR_HELP}")
input = gr.Textbox(init_config.input, label=f"Input file or directory. {INPUT_HELP}")
output_dir = gr.Textbox(init_config.output_dir, label=f"Output directory. {OUTPUT_DIR_HELP}")
save_btn.click(save_config, [
host,
port,
model,
device,
gui,
no_gui_auto_close,
no_half,
cpu_offload,
disable_nsfw,
sd_cpu_textencoder,
enable_xformers,
local_files_only,
model_dir,
input,
output_dir,
], message)
input = gr.Textbox(
init_config.input, label=f"Input file or directory. {INPUT_HELP}"
)
output_dir = gr.Textbox(
init_config.output_dir, label=f"Output directory. {OUTPUT_DIR_HELP}"
)
save_btn.click(
save_config,
[
host,
port,
model,
device,
gui,
no_gui_auto_close,
no_half,
cpu_offload,
disable_nsfw,
sd_cpu_textencoder,
enable_xformers,
local_files_only,
model_dir,
input,
output_dir,
],
message,
)
demo.launch(inbrowser=True, show_api=False)

4
requirements.txt
View File

@ -1,6 +1,7 @@
torch>=1.9.0
opencv-python
flask_cors
Jinja2==2.11.3
flask==1.1.4
flaskwebgui==0.3.5
tqdm
@ -11,7 +12,8 @@ pytest
yacs
markupsafe==2.0.1
scikit-image==0.19.3
diffusers[torch]==0.10.2
diffusers[torch]==0.12.1
transformers>=4.25.1
watchdog==2.2.1
gradio
piexif==1.1.3

2
setup.py
View File

@ -21,7 +21,7 @@ def load_requirements():
# https://setuptools.readthedocs.io/en/latest/setuptools.html#including-data-files
setuptools.setup(
name="lama-cleaner",
version="0.34.0",
version="0.35.0",
author="PanicByte",
author_email="cwq1913@gmail.com",
description="Image inpainting tool powered by SOTA AI Model",