add paint by example
This commit is contained in:
Qing
parent
6e9d3d8442
commit
203f2bc9c7
@ -12,7 +12,8 @@ export default async function inpaint(
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sizeLimit?: string,
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seed?: number,
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maskBase64?: string,
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customMask?: File
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customMask?: File,
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paintByExampleImage?: File
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) {
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// 1080, 2000, Original
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const fd = new FormData()
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@ -48,6 +49,7 @@ export default async function inpaint(
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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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@ -59,6 +61,26 @@ export default async function inpaint(
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fd.append('cv2Radius', settings.cv2Radius.toString())
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fd.append('cv2Flag', settings.cv2Flag.toString())
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fd.append('paintByExampleSteps', settings.paintByExampleSteps.toString())
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fd.append(
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'paintByExampleGuidanceScale',
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settings.paintByExampleGuidanceScale.toString()
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)
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fd.append('paintByExampleSeed', seed ? seed.toString() : '-1')
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fd.append(
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'paintByExampleMaskBlur',
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settings.paintByExampleMaskBlur.toString()
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)
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fd.append(
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'paintByExampleMatchHistograms',
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settings.paintByExampleMatchHistograms ? 'true' : 'false'
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)
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// TODO: resize image's shortest_edge to 224 before pass to backend, save network time?
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// https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPImageProcessor
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if (paintByExampleImage) {
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fd.append('paintByExampleImage', paintByExampleImage)
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}
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if (sizeLimit === undefined) {
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fd.append('sizeLimit', '1080')
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} else {
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@ -39,6 +39,7 @@ import {
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isInpaintingState,
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isInteractiveSegRunningState,
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isInteractiveSegState,
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isPaintByExampleState,
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isSDState,
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negativePropmtState,
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propmtState,
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@ -53,6 +54,7 @@ import emitter, {
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EVENT_PROMPT,
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EVENT_CUSTOM_MASK,
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CustomMaskEventData,
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EVENT_PAINT_BY_EXAMPLE,
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} from '../../event'
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import FileSelect from '../FileSelect/FileSelect'
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import InteractiveSeg from '../InteractiveSeg/InteractiveSeg'
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@ -108,6 +110,7 @@ export default function Editor() {
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const [isInpainting, setIsInpainting] = useRecoilState(isInpaintingState)
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const runMannually = useRecoilValue(runManuallyState)
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const isSD = useRecoilValue(isSDState)
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const isPaintByExample = useRecoilValue(isPaintByExampleState)
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const [isInteractiveSeg, setIsInteractiveSeg] = useRecoilState(
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isInteractiveSegState
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)
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@ -262,8 +265,11 @@ export default function Editor() {
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async (
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useLastLineGroup?: boolean,
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customMask?: File,
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maskImage?: HTMLImageElement | null
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maskImage?: HTMLImageElement | null,
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paintByExampleImage?: File
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) => {
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// customMask: mask uploaded by user
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// maskImage: mask from interactive segmentation
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if (file === undefined) {
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return
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}
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@ -328,9 +334,6 @@ export default function Editor() {
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}
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}
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const sdSeed = settings.sdSeedFixed ? settings.sdSeed : -1
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console.log({ useCustomMask })
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try {
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const res = await inpaint(
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targetFile,
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@ -339,15 +342,16 @@ export default function Editor() {
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promptVal,
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negativePromptVal,
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sizeLimit.toString(),
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sdSeed,
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seedVal,
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useCustomMask ? undefined : maskCanvas.toDataURL(),
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useCustomMask ? customMask : undefined
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useCustomMask ? customMask : undefined,
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paintByExampleImage
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)
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if (!res) {
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throw new Error('Something went wrong on server side.')
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}
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const { blob, seed } = res
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if (seed && !settings.sdSeedFixed) {
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if (seed) {
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setSeed(parseInt(seed, 10))
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}
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const newRender = new Image()
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@ -395,6 +399,7 @@ export default function Editor() {
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drawOnCurrentRender,
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hadDrawSomething,
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drawLinesOnMask,
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seedVal,
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]
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)
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@ -439,6 +444,31 @@ export default function Editor() {
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}
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}, [runInpainting])
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useEffect(() => {
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emitter.on(EVENT_PAINT_BY_EXAMPLE, (data: any) => {
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if (hadDrawSomething() || interactiveSegMask) {
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runInpainting(false, undefined, interactiveSegMask, data.image)
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} else if (lastLineGroup.length !== 0) {
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// 使用上一次手绘的 mask 生成
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runInpainting(true, undefined, prevInteractiveSegMask, data.image)
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} else if (prevInteractiveSegMask) {
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// 使用上一次 IS 的 mask 生成
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runInpainting(false, undefined, prevInteractiveSegMask, data.image)
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} else {
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setToastState({
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open: true,
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desc: 'Please draw mask on picture',
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state: 'error',
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duration: 1500,
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})
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}
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})
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return () => {
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emitter.off(EVENT_PAINT_BY_EXAMPLE)
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}
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}, [runInpainting])
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const hadRunInpainting = () => {
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return renders.length !== 0
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}
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@ -793,7 +823,11 @@ export default function Editor() {
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return
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}
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if (isSD && settings.showCroper && isOutsideCroper(mouseXY(ev))) {
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if (
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(isSD || isPaintByExample) &&
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settings.showCroper &&
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isOutsideCroper(mouseXY(ev))
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) {
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return
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}
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@ -876,7 +910,12 @@ export default function Editor() {
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return false
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}
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useKey(undoPredicate, undo, undefined, [undoStroke, undoRender, isSD])
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useKey(undoPredicate, undo, undefined, [
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undoStroke,
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undoRender,
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runMannually,
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curLineGroup,
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])
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const disableUndo = () => {
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if (isInteractiveSeg) {
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@ -955,7 +994,12 @@ export default function Editor() {
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return false
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}
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useKey(redoPredicate, redo, undefined, [redoStroke, redoRender, isSD])
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useKey(redoPredicate, redo, undefined, [
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redoStroke,
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redoRender,
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runMannually,
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redoCurLines,
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])
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const disableRedo = () => {
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if (isInteractiveSeg) {
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@ -1295,7 +1339,7 @@ export default function Editor() {
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</div>
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</div>
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{isSD && settings.showCroper ? (
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{(isSD || isPaintByExample) && settings.showCroper ? (
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<Croper
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maxHeight={original.naturalHeight}
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maxWidth={original.naturalWidth}
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@ -1358,7 +1402,7 @@ export default function Editor() {
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)}
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<div className="editor-toolkit-panel">
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{isSD || file === undefined ? (
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{isSD || isPaintByExample || file === undefined ? (
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<></>
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) : (
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<SizeSelector
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@ -1466,7 +1510,7 @@ export default function Editor() {
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onClick={download}
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/>
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{settings.runInpaintingManually && !isSD && (
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{settings.runInpaintingManually && !isSD && !isPaintByExample && (
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<Button
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toolTip="Run Inpainting"
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tooltipPosition="top"
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@ -193,6 +193,8 @@ function ModelSettingBlock() {
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return undefined
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case AIModel.SD2:
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return undefined
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case AIModel.PAINT_BY_EXAMPLE:
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return undefined
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case AIModel.Mange:
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return undefined
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case AIModel.CV2:
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@ -258,6 +260,12 @@ function ModelSettingBlock() {
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'https://docs.opencv.org/4.6.0/df/d3d/tutorial_py_inpainting.html',
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'https://docs.opencv.org/4.6.0/df/d3d/tutorial_py_inpainting.html'
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)
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case AIModel.PAINT_BY_EXAMPLE:
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return renderModelDesc(
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'Paint by Example',
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'https://arxiv.org/abs/2211.13227',
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'https://github.com/Fantasy-Studio/Paint-by-Example'
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)
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default:
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return <></>
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}
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@ -270,7 +278,6 @@ function ModelSettingBlock() {
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titleSuffix={renderPaperCodeBadge()}
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input={
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<Selector
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width={80}
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value={setting.model as string}
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options={Object.values(AIModel)}
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onChange={val => onModelChange(val as AIModel)}
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231
lama_cleaner/app/src/components/SidePanel/PESidePanel.tsx
Normal file
231
lama_cleaner/app/src/components/SidePanel/PESidePanel.tsx
Normal file
@ -0,0 +1,231 @@
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import React, { useState } from 'react'
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import { useRecoilState, useRecoilValue } from 'recoil'
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import * as PopoverPrimitive from '@radix-ui/react-popover'
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import { useToggle } from 'react-use'
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import { UploadIcon } from '@radix-ui/react-icons'
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import {
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isInpaintingState,
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paintByExampleImageState,
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settingState,
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} from '../../store/Atoms'
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import NumberInputSetting from '../Settings/NumberInputSetting'
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import SettingBlock from '../Settings/SettingBlock'
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import { Switch, SwitchThumb } from '../shared/Switch'
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import Button from '../shared/Button'
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import emitter, { EVENT_PAINT_BY_EXAMPLE } from '../../event'
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import { useImage } from '../../utils'
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const INPUT_WIDTH = 30
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const PESidePanel = () => {
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const [open, toggleOpen] = useToggle(true)
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const [setting, setSettingState] = useRecoilState(settingState)
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const [paintByExampleImage, setPaintByExampleImage] = useRecoilState(
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paintByExampleImageState
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)
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const [uploadElemId] = useState(
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`example-file-upload-${Math.random().toString()}`
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)
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const [exampleImage, isExampleImageLoaded] = useImage(paintByExampleImage)
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const isInpainting = useRecoilValue(isInpaintingState)
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const renderUploadIcon = () => {
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return (
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<label htmlFor={uploadElemId}>
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<Button
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border
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toolTip="Upload example image"
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tooltipPosition="top"
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icon={<UploadIcon />}
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style={{ padding: '0.3rem', gap: 0 }}
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>
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<input
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style={{ display: 'none' }}
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id={uploadElemId}
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name={uploadElemId}
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type="file"
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onChange={ev => {
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const newFile = ev.currentTarget.files?.[0]
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if (newFile) {
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setPaintByExampleImage(newFile)
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}
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}}
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accept="image/png, image/jpeg"
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/>
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</Button>
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</label>
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)
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}
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return (
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<div className="side-panel">
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<PopoverPrimitive.Root open={open}>
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<PopoverPrimitive.Trigger
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className="btn-primary side-panel-trigger"
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onClick={() => toggleOpen()}
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>
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Configurations
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</PopoverPrimitive.Trigger>
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<PopoverPrimitive.Portal>
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<PopoverPrimitive.Content className="side-panel-content">
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<SettingBlock
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title="Croper"
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input={
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<Switch
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checked={setting.showCroper}
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onCheckedChange={value => {
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setSettingState(old => {
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return { ...old, showCroper: value }
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})
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}}
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>
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<SwitchThumb />
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</Switch>
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}
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/>
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<NumberInputSetting
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title="Steps"
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width={INPUT_WIDTH}
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value={`${setting.paintByExampleSteps}`}
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desc="The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference."
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onValue={value => {
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const val = value.length === 0 ? 0 : parseInt(value, 10)
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setSettingState(old => {
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return { ...old, paintByExampleSteps: val }
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})
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}}
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/>
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<NumberInputSetting
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title="Guidance Scale"
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width={INPUT_WIDTH}
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allowFloat
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value={`${setting.paintByExampleGuidanceScale}`}
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desc="Higher guidance scale encourages to generate images that are close to the example image"
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onValue={value => {
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const val = value.length === 0 ? 0 : parseFloat(value)
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setSettingState(old => {
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return { ...old, paintByExampleGuidanceScale: val }
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})
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}}
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/>
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<NumberInputSetting
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title="Mask Blur"
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width={INPUT_WIDTH}
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value={`${setting.paintByExampleMaskBlur}`}
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desc="Blur the edge of mask area. The higher the number the smoother blend with the original image"
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onValue={value => {
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const val = value.length === 0 ? 0 : parseInt(value, 10)
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setSettingState(old => {
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return { ...old, paintByExampleMaskBlur: val }
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})
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}}
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/>
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<SettingBlock
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title="Match Histograms"
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desc="Match the inpainting result histogram to the source image histogram, will improves the inpainting quality for some images."
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input={
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<Switch
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checked={setting.paintByExampleMatchHistograms}
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onCheckedChange={value => {
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setSettingState(old => {
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return { ...old, paintByExampleMatchHistograms: value }
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})
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}}
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>
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<SwitchThumb />
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</Switch>
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}
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/>
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<SettingBlock
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title="Seed"
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input={
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<div
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style={{
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display: 'flex',
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gap: 0,
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justifyContent: 'center',
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alignItems: 'center',
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}}
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>
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{/* 每次会从服务器返回更新该值 */}
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<NumberInputSetting
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title=""
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width={80}
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value={`${setting.paintByExampleSeed}`}
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desc=""
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disable={!setting.paintByExampleSeedFixed}
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onValue={value => {
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const val = value.length === 0 ? 0 : parseInt(value, 10)
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setSettingState(old => {
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return { ...old, paintByExampleSeed: val }
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})
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}}
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/>
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<Switch
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checked={setting.paintByExampleSeedFixed}
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onCheckedChange={value => {
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setSettingState(old => {
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return { ...old, paintByExampleSeedFixed: value }
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})
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}}
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style={{ marginLeft: '8px' }}
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>
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<SwitchThumb />
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</Switch>
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</div>
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}
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/>
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<div style={{ display: 'flex', flexDirection: 'column' }}>
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<SettingBlock title="Example Image" input={renderUploadIcon()} />
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{paintByExampleImage ? (
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<div
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style={{
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display: 'flex',
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justifyContent: 'center',
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alignItems: 'center',
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}}
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>
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<img
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src={exampleImage.src}
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alt="example"
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style={{
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maxWidth: 200,
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maxHeight: 200,
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margin: 12,
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}}
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/>
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</div>
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) : (
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<></>
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)}
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</div>
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<Button
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border
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disabled={!isExampleImageLoaded || isInpainting}
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style={{ width: '100%' }}
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onClick={() => {
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if (isExampleImageLoaded) {
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emitter.emit(EVENT_PAINT_BY_EXAMPLE, {
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image: paintByExampleImage,
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})
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}
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}}
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>
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Paint
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</Button>
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</PopoverPrimitive.Content>
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</PopoverPrimitive.Portal>
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</PopoverPrimitive.Root>
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</div>
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)
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}
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export default PESidePanel
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@ -7,6 +7,7 @@ import Toast from './shared/Toast'
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import {
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AIModel,
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fileState,
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isPaintByExampleState,
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isSDState,
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settingState,
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toastState,
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@ -17,12 +18,14 @@ import {
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switchModel,
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} from '../adapters/inpainting'
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import SidePanel from './SidePanel/SidePanel'
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import PESidePanel from './SidePanel/PESidePanel'
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const Workspace = () => {
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const [file, setFile] = useRecoilState(fileState)
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const [settings, setSettingState] = useRecoilState(settingState)
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const [toastVal, setToastState] = useRecoilState(toastState)
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const isSD = useRecoilValue(isSDState)
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const isPaintByExample = useRecoilValue(isPaintByExampleState)
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||||
|
||||
const onSettingClose = async () => {
|
||||
const curModel = await currentModel().then(res => res.text())
|
||||
@ -88,6 +91,7 @@ const Workspace = () => {
|
||||
return (
|
||||
<>
|
||||
{isSD ? <SidePanel /> : <></>}
|
||||
{isPaintByExample ? <PESidePanel /> : <></>}
|
||||
<Editor />
|
||||
<SettingModal onClose={onSettingClose} />
|
||||
<ShortcutsModal />
|
||||
|
||||
@ -1,11 +1,17 @@
|
||||
import mitt from 'mitt'
|
||||
|
||||
export const EVENT_PROMPT = 'prompt'
|
||||
|
||||
export const EVENT_CUSTOM_MASK = 'custom_mask'
|
||||
export interface CustomMaskEventData {
|
||||
mask: File
|
||||
}
|
||||
|
||||
export const EVENT_PAINT_BY_EXAMPLE = 'paint_by_example'
|
||||
export interface PaintByExampleEventData {
|
||||
image: File
|
||||
}
|
||||
|
||||
const emitter = mitt()
|
||||
|
||||
export default emitter
|
||||
|
||||
@ -13,6 +13,7 @@ export enum AIModel {
|
||||
SD2 = 'sd2',
|
||||
CV2 = 'cv2',
|
||||
Mange = 'manga',
|
||||
PAINT_BY_EXAMPLE = 'paint_by_example',
|
||||
}
|
||||
|
||||
export const maskState = atom<File | undefined>({
|
||||
@ -20,6 +21,11 @@ export const maskState = atom<File | undefined>({
|
||||
default: undefined,
|
||||
})
|
||||
|
||||
export const paintByExampleImageState = atom<File | undefined>({
|
||||
key: 'paintByExampleImageState',
|
||||
default: undefined,
|
||||
})
|
||||
|
||||
export interface Rect {
|
||||
x: number
|
||||
y: number
|
||||
@ -252,6 +258,14 @@ export interface Settings {
|
||||
// For OpenCV2
|
||||
cv2Radius: number
|
||||
cv2Flag: CV2Flag
|
||||
|
||||
// Paint by Example
|
||||
paintByExampleSteps: number
|
||||
paintByExampleGuidanceScale: number
|
||||
paintByExampleSeed: number
|
||||
paintByExampleSeedFixed: boolean
|
||||
paintByExampleMaskBlur: number
|
||||
paintByExampleMatchHistograms: boolean
|
||||
}
|
||||
|
||||
const defaultHDSettings: ModelsHDSettings = {
|
||||
@ -304,6 +318,13 @@ const defaultHDSettings: ModelsHDSettings = {
|
||||
hdStrategyCropMargin: 128,
|
||||
enabled: false,
|
||||
},
|
||||
[AIModel.PAINT_BY_EXAMPLE]: {
|
||||
hdStrategy: HDStrategy.ORIGINAL,
|
||||
hdStrategyResizeLimit: 768,
|
||||
hdStrategyCropTrigerSize: 512,
|
||||
hdStrategyCropMargin: 128,
|
||||
enabled: false,
|
||||
},
|
||||
[AIModel.Mange]: {
|
||||
hdStrategy: HDStrategy.CROP,
|
||||
hdStrategyResizeLimit: 1280,
|
||||
@ -364,6 +385,14 @@ export const settingStateDefault: Settings = {
|
||||
// CV2
|
||||
cv2Radius: 5,
|
||||
cv2Flag: CV2Flag.INPAINT_NS,
|
||||
|
||||
// Paint by Example
|
||||
paintByExampleSteps: 50,
|
||||
paintByExampleGuidanceScale: 7.5,
|
||||
paintByExampleSeed: 42,
|
||||
paintByExampleMaskBlur: 5,
|
||||
paintByExampleSeedFixed: false,
|
||||
paintByExampleMatchHistograms: false,
|
||||
}
|
||||
|
||||
const localStorageEffect =
|
||||
@ -401,11 +430,28 @@ export const seedState = selector({
|
||||
key: 'seed',
|
||||
get: ({ get }) => {
|
||||
const settings = get(settingState)
|
||||
return settings.sdSeed
|
||||
switch (settings.model) {
|
||||
case AIModel.PAINT_BY_EXAMPLE:
|
||||
return settings.paintByExampleSeedFixed
|
||||
? settings.paintByExampleSeed
|
||||
: -1
|
||||
default:
|
||||
return settings.sdSeedFixed ? settings.sdSeed : -1
|
||||
}
|
||||
},
|
||||
set: ({ get, set }, newValue: any) => {
|
||||
const settings = get(settingState)
|
||||
set(settingState, { ...settings, sdSeed: newValue })
|
||||
switch (settings.model) {
|
||||
case AIModel.PAINT_BY_EXAMPLE:
|
||||
if (!settings.paintByExampleSeedFixed) {
|
||||
set(settingState, { ...settings, paintByExampleSeed: newValue })
|
||||
}
|
||||
break
|
||||
default:
|
||||
if (!settings.sdSeedFixed) {
|
||||
set(settingState, { ...settings, sdSeed: newValue })
|
||||
}
|
||||
}
|
||||
},
|
||||
})
|
||||
|
||||
@ -435,11 +481,20 @@ export const isSDState = selector({
|
||||
},
|
||||
})
|
||||
|
||||
export const isPaintByExampleState = selector({
|
||||
key: 'isPaintByExampleState',
|
||||
get: ({ get }) => {
|
||||
const settings = get(settingState)
|
||||
return settings.model === AIModel.PAINT_BY_EXAMPLE
|
||||
},
|
||||
})
|
||||
|
||||
export const runManuallyState = selector({
|
||||
key: 'runManuallyState',
|
||||
get: ({ get }) => {
|
||||
const settings = get(settingState)
|
||||
const isSD = get(isSDState)
|
||||
return settings.runInpaintingManually || isSD
|
||||
const isPaintByExample = get(isPaintByExampleState)
|
||||
return settings.runInpaintingManually || isSD || isPaintByExample
|
||||
},
|
||||
})
|
||||
|
||||
@ -211,6 +211,26 @@ class InpaintModel:
|
||||
|
||||
return result
|
||||
|
||||
def _apply_cropper(self, image, mask, config: Config):
|
||||
img_h, img_w = image.shape[:2]
|
||||
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]
|
||||
return crop_img, crop_mask, (l, t, r, b)
|
||||
|
||||
def _run_box(self, image, mask, box, config: Config):
|
||||
"""
|
||||
|
||||
|
||||
@ -0,0 +1,80 @@
|
||||
import random
|
||||
|
||||
import PIL
|
||||
import PIL.Image
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
from diffusers import DiffusionPipeline
|
||||
from lama_cleaner.model.base import InpaintModel
|
||||
from lama_cleaner.schema import Config
|
||||
|
||||
|
||||
class PaintByExample(InpaintModel):
|
||||
pad_mod = 8
|
||||
min_size = 512
|
||||
|
||||
def init_model(self, device: torch.device, **kwargs):
|
||||
use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
|
||||
torch_dtype = torch.float16 if use_gpu else torch.float32
|
||||
self.model = DiffusionPipeline.from_pretrained(
|
||||
"Fantasy-Studio/Paint-by-Example",
|
||||
torch_dtype=torch_dtype,
|
||||
)
|
||||
self.model.enable_attention_slicing()
|
||||
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
|
||||
"""
|
||||
seed = config.paint_by_example_seed
|
||||
random.seed(seed)
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
|
||||
output = self.model(
|
||||
image=PIL.Image.fromarray(image),
|
||||
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
|
||||
example_image=config.paint_by_example_example_image,
|
||||
num_inference_steps=config.paint_by_example_steps,
|
||||
output_type='np.array',
|
||||
).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
|
||||
"""
|
||||
if config.use_croper:
|
||||
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
|
||||
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
|
||||
|
||||
def forward_post_process(self, result, image, mask, config):
|
||||
if config.paint_by_example_match_histograms:
|
||||
result = self._match_histograms(result, image[:, :, ::-1], mask)
|
||||
|
||||
if config.paint_by_example_mask_blur != 0:
|
||||
k = 2 * config.paint_by_example_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
|
||||
@ -12,31 +12,6 @@ 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 CPUTextEncoderWrapper:
|
||||
def __init__(self, text_encoder, torch_dtype):
|
||||
self.config = text_encoder.config
|
||||
@ -92,17 +67,6 @@ class SD(InpaintModel):
|
||||
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)
|
||||
|
||||
scheduler_config = self.model.scheduler.config
|
||||
|
||||
if config.sd_sampler == SDSampler.ddim:
|
||||
@ -139,7 +103,6 @@ class SD(InpaintModel):
|
||||
prompt=config.prompt,
|
||||
negative_prompt=config.negative_prompt,
|
||||
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",
|
||||
@ -159,30 +122,10 @@ class SD(InpaintModel):
|
||||
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_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
|
||||
crop_image = self._pad_forward(crop_img, crop_mask, config)
|
||||
|
||||
inpaint_result = image[:, :, ::-1]
|
||||
inpaint_result[t:b, l:r, :] = crop_image
|
||||
else:
|
||||
|
||||
@ -5,13 +5,14 @@ from lama_cleaner.model.lama import LaMa
|
||||
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.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}
|
||||
"sd2": SD2, "paint_by_example": PaintByExample}
|
||||
|
||||
|
||||
class ModelManager:
|
||||
|
||||
@ -10,7 +10,7 @@ def parse_args():
|
||||
parser.add_argument(
|
||||
"--model",
|
||||
default="lama",
|
||||
choices=["lama", "ldm", "zits", "mat", "fcf", "sd1.5", "cv2", "manga", "sd2"],
|
||||
choices=["lama", "ldm", "zits", "mat", "fcf", "sd1.5", "cv2", "manga", "sd2", "paint_by_example"],
|
||||
)
|
||||
parser.add_argument(
|
||||
"--hf_access_token",
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
from enum import Enum
|
||||
|
||||
from PIL.Image import Image
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
@ -29,6 +30,9 @@ class SDSampler(str, Enum):
|
||||
|
||||
|
||||
class Config(BaseModel):
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
# Configs for ldm model
|
||||
ldm_steps: int
|
||||
ldm_sampler: str = LDMSampler.plms
|
||||
@ -73,3 +77,11 @@ class Config(BaseModel):
|
||||
# opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07
|
||||
cv2_flag: str = 'INPAINT_NS'
|
||||
cv2_radius: int = 4
|
||||
|
||||
# Paint by Example
|
||||
paint_by_example_steps: int = 50
|
||||
paint_by_example_guidance_scale: float = 7.5
|
||||
paint_by_example_mask_blur: int = 0
|
||||
paint_by_example_seed: int = 42
|
||||
paint_by_example_match_histograms: bool = False
|
||||
paint_by_example_example_image: Image = None
|
||||
|
||||
@ -10,6 +10,7 @@ import time
|
||||
import imghdr
|
||||
from pathlib import Path
|
||||
from typing import Union
|
||||
from PIL import Image
|
||||
|
||||
import cv2
|
||||
import torch
|
||||
@ -97,8 +98,8 @@ def process():
|
||||
input = request.files
|
||||
# RGB
|
||||
origin_image_bytes = input["image"].read()
|
||||
|
||||
image, alpha_channel = load_img(origin_image_bytes)
|
||||
|
||||
mask, _ = load_img(input["mask"].read(), gray=True)
|
||||
mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
|
||||
|
||||
@ -115,6 +116,12 @@ def process():
|
||||
else:
|
||||
size_limit = int(size_limit)
|
||||
|
||||
if "paintByExampleImage" in input:
|
||||
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
|
||||
|
||||
config = Config(
|
||||
ldm_steps=form["ldmSteps"],
|
||||
ldm_sampler=form["ldmSampler"],
|
||||
@ -138,11 +145,19 @@ 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,
|
||||
)
|
||||
|
||||
if config.sd_seed == -1:
|
||||
config.sd_seed = random.randint(1, 999999999)
|
||||
if config.paint_by_example_seed == -1:
|
||||
config.paint_by_example_seed = random.randint(1, 999999999)
|
||||
|
||||
logger.info(f"Origin image shape: {original_shape}")
|
||||
image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
|
||||
|
||||
BIN
lama_cleaner/tests/bunny.jpeg
Normal file
BIN
lama_cleaner/tests/bunny.jpeg
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lama_cleaner/tests/result/paint_by_example_Original.png
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lama_cleaner/tests/result/paint_by_example_Original.png
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@ -0,0 +1,50 @@
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from pathlib import Path
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import cv2
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import pytest
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import torch
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from PIL import Image
|
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|
||||
from lama_cleaner.model_manager import ModelManager
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from lama_cleaner.schema import HDStrategy
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from lama_cleaner.tests.test_model import get_config, get_data
|
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|
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current_dir = Path(__file__).parent.absolute().resolve()
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save_dir = current_dir / 'result'
|
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save_dir.mkdir(exist_ok=True, parents=True)
|
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
device = torch.device(device)
|
||||
|
||||
|
||||
def assert_equal(
|
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model, config, gt_name,
|
||||
fx: float = 1, fy: float = 1,
|
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img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
|
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mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
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example_p=current_dir / "rabbit.jpeg",
|
||||
):
|
||||
img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
|
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|
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example_image = cv2.imread(str(example_p))
|
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example_image = cv2.cvtColor(example_image, cv2.COLOR_BGRA2RGB)
|
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example_image = cv2.resize(example_image, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
|
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|
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print(f"Input image shape: {img.shape}, example_image: {example_image.shape}")
|
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config.paint_by_example_example_image = Image.fromarray(example_image)
|
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res = model(img, mask, config)
|
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cv2.imwrite(str(save_dir / gt_name), res)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
|
||||
def test_paint_by_example(strategy):
|
||||
model = ModelManager(name="paint_by_example", device=device)
|
||||
cfg = get_config(strategy, paint_by_example_steps=30)
|
||||
assert_equal(
|
||||
model,
|
||||
cfg,
|
||||
f"paint_by_example_{strategy.capitalize()}.png",
|
||||
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
|
||||
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
|
||||
fy=0.9,
|
||||
fx=1.3
|
||||
)
|
||||
@ -10,5 +10,5 @@ pytest
|
||||
yacs
|
||||
markupsafe==2.0.1
|
||||
scikit-image==0.19.3
|
||||
diffusers[torch]==0.9
|
||||
transformers==4.21.0
|
||||
diffusers[torch]==0.10.2
|
||||
transformers>=4.25.1
|
||||
|
||||
Loading…
Reference in New Issue
Block a user