forked from extern/easydiffusion
Merge pull request #346 from cmdr2/task_manager
Allow multiple tabs and computers to generate tasks without throwing errors (by @madrang)
This commit is contained in:
cmdr2
GitHub
commit
1c171d0f12
@ -16,7 +16,7 @@
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<div id="container">
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<div id="top-nav">
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<div id="logo">
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<h1>Stable Diffusion UI <small>v2.26 <span id="updateBranchLabel"></span></small></h1>
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<h1>Stable Diffusion UI <small>v2.27 <span id="updateBranchLabel"></span></small></h1>
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</div>
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<ul id="top-nav-items">
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<li class="dropdown">
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@ -326,13 +326,13 @@ img {
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height: 8pt;
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border-radius: 4pt; */
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font-size: 14pt;
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color: rgb(128, 87, 0);
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color: rgb(200, 139, 0);
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/* background-color: rgb(197, 1, 1); */
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/* transform: translateY(15%); */
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display: inline;
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}
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#server-status-msg {
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color: rgb(128, 87, 0);
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color: rgb(200, 139, 0);
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padding-left: 2pt;
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font-size: 10pt;
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}
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@ -479,7 +479,12 @@ img {
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.activeTaskLabel {
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background:rgb(0, 90, 30);
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border: 1px solid rgb(0, 75, 19);
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color:rgb(204, 255, 217)
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color:rgb(222, 253, 230)
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}
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.waitingTaskLabel {
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background:rgb(128, 89, 0);
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border: 1px solid rgb(0, 75, 19);
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color:rgb(255, 242, 211)
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}
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.secondaryButton {
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background: rgb(132, 8, 0);
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219
ui/media/main.js
219
ui/media/main.js
@ -20,7 +20,7 @@ const INPAINTING_EDITOR_SIZE = 450
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const IMAGE_REGEX = new RegExp('data:image/[A-Za-z]+;base64')
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let sessionId = new Date().getTime()
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let sessionId = Date.now()
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let promptField = document.querySelector('#prompt')
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let promptsFromFileSelector = document.querySelector('#prompt_from_file')
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@ -122,7 +122,7 @@ maskResetButton.innerHTML = 'Clear'
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maskResetButton.style.fontWeight = 'normal'
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maskResetButton.style.fontSize = '10pt'
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let serverStatus = 'offline'
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let serverState = {'status': 'Offline', 'time': Date.now()}
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let activeTags = []
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let modifiers = []
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let lastPromptUsed = ''
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@ -225,21 +225,38 @@ function getOutputFormat() {
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}
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function setStatus(statusType, msg, msgType) {
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if (statusType !== 'server') {
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return
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}
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}
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if (msgType == 'error') {
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// msg = '<span style="color: red">' + msg + '<span>'
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serverStatusColor.style.color = 'red'
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serverStatusMsg.style.color = 'red'
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serverStatusMsg.innerText = 'Stable Diffusion has stopped'
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} else if (msgType == 'success') {
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// msg = '<span style="color: green">' + msg + '<span>'
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serverStatusColor.style.color = 'green'
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serverStatusMsg.style.color = 'green'
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serverStatusMsg.innerText = 'Stable Diffusion is ready'
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serverStatus = 'online'
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function setServerStatus(msgType, msg) {
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switch(msgType) {
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case 'online':
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serverStatusColor.style.color = 'green'
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serverStatusMsg.style.color = 'green'
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serverStatusMsg.innerText = 'Stable Diffusion is ' + msg
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break
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case 'busy':
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serverStatusColor.style.color = 'rgb(200, 139, 0)'
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serverStatusMsg.style.color = 'rgb(200, 139, 0)'
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serverStatusMsg.innerText = 'Stable Diffusion is ' + msg
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break
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case 'error':
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serverStatusColor.style.color = 'red'
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serverStatusMsg.style.color = 'red'
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serverStatusMsg.innerText = 'Stable Diffusion has stopped'
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break
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}
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}
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function isServerAvailable() {
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if (typeof serverState !== 'object') {
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return false
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}
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switch (serverState.status) {
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case 'LoadingModel':
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case 'Rendering':
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case 'Online':
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return true
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default:
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return false
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}
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}
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@ -263,6 +280,11 @@ function logError(msg, res, outputMsg) {
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console.log('request error', res)
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setStatus('request', 'error', 'error')
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}
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function asyncDelay(timeout) {
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return new Promise(function(resolve, reject) {
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setTimeout(resolve, timeout, true)
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})
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}
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function playSound() {
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const audio = new Audio('/media/ding.mp3')
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@ -277,16 +299,40 @@ function playSound() {
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async function healthCheck() {
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try {
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let res = await fetch('/ping')
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res = await res.json()
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if (res[0] == 'OK') {
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setStatus('server', 'online', 'success')
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let res = undefined
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if (sessionId) {
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res = await fetch('/ping?session_id=' + sessionId)
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} else {
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setStatus('server', 'offline', 'error')
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res = await fetch('/ping')
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}
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serverState = await res.json()
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if (typeof serverState !== 'object' || typeof serverState.status !== 'string') {
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serverState = {'status': 'Offline', 'time': Date.now()}
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setServerStatus('error', 'offline')
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return
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}
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// Set status
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switch(serverState.status) {
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case 'Init':
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// Wait for init to complete before updating status.
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break
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case 'Online':
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setServerStatus('online', 'ready')
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break
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case 'LoadingModel':
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setServerStatus('busy', 'loading model')
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break
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case 'Rendering':
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setServerStatus('busy', 'rendering')
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break
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default: // Unavailable
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setServerStatus('error', serverState.status.toLowerCase())
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break
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}
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serverState.time = Date.now()
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} catch (e) {
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setStatus('server', 'offline', 'error')
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serverState = {'status': 'Offline', 'time': Date.now()}
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setServerStatus('error', 'offline')
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}
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}
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function resizeInpaintingEditor() {
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@ -329,7 +375,7 @@ function showImages(reqBody, res, outputContainer, livePreview) {
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if(typeof res != 'object') return
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res.output.reverse()
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res.output.forEach((result, index) => {
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const imageData = result?.data || result?.path + '?t=' + new Date().getTime(),
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const imageData = result?.data || result?.path + '?t=' + Date.now(),
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imageSeed = result?.seed,
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imagePrompt = reqBody.prompt,
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imageInferenceSteps = reqBody.num_inference_steps,
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@ -440,8 +486,8 @@ function getSaveImageHandler(imageItemElem, outputFormat) {
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}
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function getStartNewTaskHandler(reqBody, imageItemElem, mode) {
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return function() {
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if (serverStatus !== 'online') {
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alert('The server is still starting up..')
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if (!isServerAvailable()) {
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alert('The server is not available.')
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return
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}
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const imageElem = imageItemElem.querySelector('img')
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@ -507,37 +553,76 @@ async function doMakeImage(task) {
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const progressBar = task['progressBar']
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let res = undefined
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let stepUpdate = undefined
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try {
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res = await fetch('/image', {
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method: 'POST',
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const lastTask = serverState.task
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let renderRequest = undefined
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do {
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res = await fetch('/render', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json'
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},
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body: JSON.stringify(reqBody)
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})
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renderRequest = await res.json()
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// status_code 503, already a task running.
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} while (renderRequest.status_code === 503 && await asyncDelay(30 * 1000))
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if (typeof renderRequest?.stream !== 'string') {
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console.log('Endpoint response: ', renderRequest)
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throw new Error('Endpoint response does not contains a response stream url.')
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}
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task['taskStatusLabel'].innerText = "Waiting"
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task['taskStatusLabel'].classList.add('waitingTaskLabel')
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task['taskStatusLabel'].classList.remove('activeTaskLabel')
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do { // Wait for server status to update.
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await asyncDelay(250)
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if (!isServerAvailable()) {
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throw new Error('Connexion with server lost.')
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}
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} while (serverState.time > (Date.now() - (10 * 1000)) && serverState.task !== renderRequest.task)
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if (serverState.session !== 'pending' && serverState.session !== 'running' && serverState.session !== 'buffer') {
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if (serverState.session === 'stopped') {
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return false
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}
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throw new Error('Unexpected server task state: ' + serverState.session || 'Undefined')
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}
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while (serverState.task === renderRequest.task && serverState.session === 'pending') {
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// Wait for task to start on server.
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await asyncDelay(1500)
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}
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// Task started!
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res = await fetch(renderRequest.stream, {
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headers: {
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'Content-Type': 'application/json'
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},
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body: JSON.stringify(reqBody)
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})
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task['taskStatusLabel'].innerText = "Processing"
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task['taskStatusLabel'].classList.add('activeTaskLabel')
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task['taskStatusLabel'].classList.remove('waitingTaskLabel')
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let stepUpdate = undefined
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let reader = res.body.getReader()
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let textDecoder = new TextDecoder()
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let finalJSON = ''
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let prevTime = -1
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let readComplete = false
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while (true) {
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let t = new Date().getTime()
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while (!readComplete || finalJSON.length > 0) {
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let t = Date.now()
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let jsonStr = ''
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if (!readComplete) {
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const {value, done} = await reader.read()
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if (done) {
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readComplete = true
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}
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if (done && finalJSON.length <= 0 && !value) {
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break
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}
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if (value) {
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jsonStr = textDecoder.decode(value)
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}
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}
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stepUpdate = undefined
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try {
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// hack for a middleman buffering all the streaming updates, and unleashing them on the poor browser in one shot.
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// this results in having to parse JSON like {"step": 1}{"step": 2}{"step": 3}{"ste...
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@ -571,9 +656,6 @@ async function doMakeImage(task) {
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throw e
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}
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}
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if (readComplete && finalJSON.length <= 0) {
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break
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}
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if (typeof stepUpdate === 'object' && 'step' in stepUpdate) {
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let batchSize = stepUpdate.total_steps
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let overallStepCount = stepUpdate.step + task.batchesDone * batchSize
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@ -598,6 +680,23 @@ async function doMakeImage(task) {
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showImages(reqBody, stepUpdate, outputContainer, true)
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}
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}
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if (stepUpdate?.status) {
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break
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}
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if (readComplete && finalJSON.length <= 0) {
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if (res.status === 200) {
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await asyncDelay(5000)
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res = await fetch(renderRequest.stream, {
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headers: {
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'Content-Type': 'application/json'
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},
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})
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reader = res.body.getReader()
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readComplete = false
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} else {
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console.log('Stream stopped: ', res)
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}
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}
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prevTime = t
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}
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@ -614,27 +713,28 @@ async function doMakeImage(task) {
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3. Try generating a smaller image.<br/>`
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}
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} else {
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msg = `Unexpected Read Error:<br/><pre>StepUpdate:${JSON.stringify(stepUpdate, undefined, 4)}</pre>`
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msg = `Unexpected Read Error:<br/><pre>StepUpdate: ${JSON.stringify(stepUpdate, undefined, 4)}</pre>`
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}
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logError(msg, res, outputMsg)
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return false
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}
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if (typeof stepUpdate !== 'object' || !res || res.status != 200) {
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if (serverStatus !== 'online') {
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if (!isServerAvailable()) {
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logError("Stable Diffusion is still starting up, please wait. If this goes on beyond a few minutes, Stable Diffusion has probably crashed. Please check the error message in the command-line window.", res, outputMsg)
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} else if (typeof res === 'object') {
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let msg = 'Stable Diffusion had an error reading the response: '
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try { // 'Response': body stream already read
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msg += 'Read: ' + await res.text()
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} catch(e) {
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msg += 'No error response. '
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msg += 'Unexpected end of stream. '
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}
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if (finalJSON) {
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msg += 'Buffered data: ' + finalJSON
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}
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logError(msg, res, outputMsg)
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} else {
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msg = `Unexpected Read Error:<br/><pre>Response:${res}<br/>StepUpdate:${typeof stepUpdate === 'object' ? JSON.stringify(stepUpdate, undefined, 4) : stepUpdate}</pre>`
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let msg = `Unexpected Read Error:<br/><pre>Response: ${res}<br/>StepUpdate: ${typeof stepUpdate === 'object' ? JSON.stringify(stepUpdate, undefined, 4) : stepUpdate}</pre>`
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logError(msg, res, outputMsg)
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}
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progressBar.style.display = 'none'
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return false
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@ -682,14 +782,14 @@ async function checkTasks() {
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let task = taskQueue.pop()
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currentTask = task
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let time = new Date().getTime()
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let time = Date.now()
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let successCount = 0
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task.isProcessing = true
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task['stopTask'].innerHTML = '<i class="fa-solid fa-circle-stop"></i> Stop'
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task['taskStatusLabel'].innerText = "Processing"
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task['taskStatusLabel'].className += " activeTaskLabel"
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task['taskStatusLabel'].innerText = "Starting"
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task['taskStatusLabel'].classList.add('waitingTaskLabel')
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const genSeeds = Boolean(typeof task.reqBody.seed !== 'number' || (task.reqBody.seed === task.seed && task.numOutputsTotal > 1))
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const startSeed = task.reqBody.seed || task.seed
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@ -724,7 +824,7 @@ async function checkTasks() {
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task['stopTask'].innerHTML = '<i class="fa-solid fa-trash-can"></i> Remove'
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task['taskStatusLabel'].style.display = 'none'
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time = new Date().getTime() - time
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time = Date.now() - time
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time /= 1000
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if (successCount === task.batchCount) {
|
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@ -814,8 +914,8 @@ function getCurrentUserRequest() {
|
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}
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|
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function makeImage() {
|
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if (serverStatus !== 'online') {
|
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alert('The server is still starting up..')
|
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if (!isServerAvailable()) {
|
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alert('The server is not available.')
|
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return
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}
|
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const taskTemplate = getCurrentUserRequest()
|
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@ -868,7 +968,7 @@ function createTask(task) {
|
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if (task['isProcessing']) {
|
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task.isProcessing = false
|
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try {
|
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let res = await fetch('/image/stop')
|
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let res = await fetch('/image/stop?session_id=' + sessionId)
|
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} catch (e) {
|
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console.log(e)
|
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}
|
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@ -1008,7 +1108,7 @@ async function stopAllTasks() {
|
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}
|
||||
|
||||
try {
|
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let res = await fetch('/image/stop')
|
||||
let res = await fetch('/image/stop?session_id=' + sessionId)
|
||||
} catch (e) {
|
||||
console.log(e)
|
||||
}
|
||||
@ -1135,9 +1235,9 @@ promptStrengthField.addEventListener('input', updatePromptStrengthSlider)
|
||||
updatePromptStrength()
|
||||
|
||||
useBetaChannelField.addEventListener('click', async function(e) {
|
||||
if (serverStatus !== 'online') {
|
||||
if (!isServerAvailable()) {
|
||||
// logError('The server is still starting up..')
|
||||
alert('The server is still starting up..')
|
||||
alert('The server is not available.')
|
||||
e.preventDefault()
|
||||
return false
|
||||
}
|
||||
@ -1164,7 +1264,7 @@ useBetaChannelField.addEventListener('click', async function(e) {
|
||||
|
||||
async function getAppConfig() {
|
||||
try {
|
||||
let res = await fetch('/app_config')
|
||||
let res = await fetch('/get/app_config')
|
||||
const config = await res.json()
|
||||
|
||||
if (config.update_branch === 'beta') {
|
||||
@ -1180,7 +1280,7 @@ async function getAppConfig() {
|
||||
|
||||
async function getModels() {
|
||||
try {
|
||||
let res = await fetch('/models')
|
||||
let res = await fetch('/get/models')
|
||||
const models = await res.json()
|
||||
|
||||
let activeModel = models['active']
|
||||
@ -1451,10 +1551,10 @@ async function getDiskPath() {
|
||||
return
|
||||
}
|
||||
|
||||
let res = await fetch('/output_dir')
|
||||
let res = await fetch('/get/output_dir')
|
||||
if (res.status === 200) {
|
||||
res = await res.json()
|
||||
res = res[0]
|
||||
res = res.output_dir
|
||||
|
||||
document.querySelector('#diskPath').value = res
|
||||
}
|
||||
@ -1562,14 +1662,15 @@ function resizeModifierCards(val) {
|
||||
const classes = card.className.split(' ').filter(c => !c.startsWith(cardSizePrefix))
|
||||
card.className = classes.join(' ').trim()
|
||||
|
||||
if(val != 0)
|
||||
if(val != 0) {
|
||||
card.classList.add(cardSize(val))
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
async function loadModifiers() {
|
||||
try {
|
||||
let res = await fetch('/modifiers.json?v=2')
|
||||
let res = await fetch('/get/modifiers')
|
||||
if (res.status === 200) {
|
||||
res = await res.json()
|
||||
|
||||
|
||||
@ -197,6 +197,35 @@ def load_model_real_esrgan(real_esrgan_to_use):
|
||||
|
||||
print('loaded ', real_esrgan_to_use, 'to', device, 'precision', precision)
|
||||
|
||||
def get_base_path(disk_path, session_id, prompt, ext, suffix=None):
|
||||
if disk_path is None: return None
|
||||
if session_id is None: return None
|
||||
if ext is None: raise Exception('Missing ext')
|
||||
|
||||
session_out_path = os.path.join(disk_path, session_id)
|
||||
os.makedirs(session_out_path, exist_ok=True)
|
||||
|
||||
prompt_flattened = filename_regex.sub('_', prompt)[:50]
|
||||
img_id = str(uuid.uuid4())[-8:]
|
||||
|
||||
if suffix is not None:
|
||||
return os.path.join(session_out_path, f"{prompt_flattened}_{img_id}_{suffix}.{ext}")
|
||||
return os.path.join(session_out_path, f"{prompt_flattened}_{img_id}.{ext}")
|
||||
|
||||
def apply_filters(filter_name, image_data):
|
||||
print(f'Applying filter {filter_name}...')
|
||||
gc()
|
||||
|
||||
if filter_name == 'gfpgan':
|
||||
_, _, output = model_gfpgan.enhance(image_data[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
||||
image_data = output[:,:,::-1]
|
||||
|
||||
if filter_name == 'real_esrgan':
|
||||
output, _ = model_real_esrgan.enhance(image_data[:,:,::-1])
|
||||
image_data = output[:,:,::-1]
|
||||
|
||||
return image_data
|
||||
|
||||
def mk_img(req: Request):
|
||||
try:
|
||||
yield from do_mk_img(req)
|
||||
@ -283,23 +312,11 @@ def do_mk_img(req: Request):
|
||||
|
||||
opt_prompt = req.prompt
|
||||
opt_seed = req.seed
|
||||
opt_n_samples = req.num_outputs
|
||||
opt_n_iter = 1
|
||||
opt_scale = req.guidance_scale
|
||||
opt_C = 4
|
||||
opt_H = req.height
|
||||
opt_W = req.width
|
||||
opt_f = 8
|
||||
opt_ddim_steps = req.num_inference_steps
|
||||
opt_ddim_eta = 0.0
|
||||
opt_strength = req.prompt_strength
|
||||
opt_save_to_disk_path = req.save_to_disk_path
|
||||
opt_init_img = req.init_image
|
||||
opt_use_face_correction = req.use_face_correction
|
||||
opt_use_upscale = req.use_upscale
|
||||
opt_show_only_filtered = req.show_only_filtered_image
|
||||
opt_format = req.output_format
|
||||
opt_sampler_name = req.sampler
|
||||
|
||||
print(req.to_string(), '\n device', device)
|
||||
|
||||
@ -307,7 +324,7 @@ def do_mk_img(req: Request):
|
||||
|
||||
seed_everything(opt_seed)
|
||||
|
||||
batch_size = opt_n_samples
|
||||
batch_size = req.num_outputs
|
||||
prompt = opt_prompt
|
||||
assert prompt is not None
|
||||
data = [batch_size * [prompt]]
|
||||
@ -327,7 +344,7 @@ def do_mk_img(req: Request):
|
||||
else:
|
||||
handler = _img2img
|
||||
|
||||
init_image = load_img(req.init_image, opt_W, opt_H)
|
||||
init_image = load_img(req.init_image, req.width, req.height)
|
||||
init_image = init_image.to(device)
|
||||
|
||||
if device != "cpu" and precision == "autocast":
|
||||
@ -339,7 +356,7 @@ def do_mk_img(req: Request):
|
||||
init_latent = modelFS.get_first_stage_encoding(modelFS.encode_first_stage(init_image)) # move to latent space
|
||||
|
||||
if req.mask is not None:
|
||||
mask = load_mask(req.mask, opt_W, opt_H, init_latent.shape[2], init_latent.shape[3], True).to(device)
|
||||
mask = load_mask(req.mask, req.width, req.height, init_latent.shape[2], init_latent.shape[3], True).to(device)
|
||||
mask = mask[0][0].unsqueeze(0).repeat(4, 1, 1).unsqueeze(0)
|
||||
mask = repeat(mask, '1 ... -> b ...', b=batch_size)
|
||||
|
||||
@ -348,12 +365,12 @@ def do_mk_img(req: Request):
|
||||
|
||||
move_fs_to_cpu()
|
||||
|
||||
assert 0. <= opt_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
t_enc = int(opt_strength * opt_ddim_steps)
|
||||
assert 0. <= req.prompt_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
t_enc = int(req.prompt_strength * req.num_inference_steps)
|
||||
print(f"target t_enc is {t_enc} steps")
|
||||
|
||||
if opt_save_to_disk_path is not None:
|
||||
session_out_path = os.path.join(opt_save_to_disk_path, req.session_id)
|
||||
if req.save_to_disk_path is not None:
|
||||
session_out_path = os.path.join(req.save_to_disk_path, req.session_id)
|
||||
os.makedirs(session_out_path, exist_ok=True)
|
||||
else:
|
||||
session_out_path = None
|
||||
@ -366,7 +383,7 @@ def do_mk_img(req: Request):
|
||||
with precision_scope("cuda"):
|
||||
modelCS.to(device)
|
||||
uc = None
|
||||
if opt_scale != 1.0:
|
||||
if req.guidance_scale != 1.0:
|
||||
uc = modelCS.get_learned_conditioning(batch_size * [req.negative_prompt])
|
||||
if isinstance(prompts, tuple):
|
||||
prompts = list(prompts)
|
||||
@ -393,7 +410,7 @@ def do_mk_img(req: Request):
|
||||
partial_x_samples = x_samples
|
||||
|
||||
if req.stream_progress_updates:
|
||||
n_steps = opt_ddim_steps if req.init_image is None else t_enc
|
||||
n_steps = req.num_inference_steps if req.init_image is None else t_enc
|
||||
progress = {"step": i, "total_steps": n_steps}
|
||||
|
||||
if req.stream_image_progress and i % 5 == 0:
|
||||
@ -425,9 +442,9 @@ def do_mk_img(req: Request):
|
||||
# run the handler
|
||||
try:
|
||||
if handler == _txt2img:
|
||||
x_samples = _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, None, opt_C, opt_f, opt_ddim_eta, c, uc, opt_seed, img_callback, mask, opt_sampler_name)
|
||||
x_samples = _txt2img(req.width, req.height, req.num_outputs, req.num_inference_steps, req.guidance_scale, None, opt_C, opt_f, opt_ddim_eta, c, uc, opt_seed, img_callback, mask, req.sampler)
|
||||
else:
|
||||
x_samples = _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed, img_callback, mask)
|
||||
x_samples = _img2img(init_latent, t_enc, batch_size, req.guidance_scale, c, uc, req.num_inference_steps, opt_ddim_eta, opt_seed, img_callback, mask)
|
||||
|
||||
yield from x_samples
|
||||
|
||||
@ -447,69 +464,49 @@ def do_mk_img(req: Request):
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
img = Image.fromarray(x_sample)
|
||||
|
||||
has_filters = (opt_use_face_correction is not None and opt_use_face_correction.startswith('GFPGAN')) or \
|
||||
(opt_use_upscale is not None and opt_use_upscale.startswith('RealESRGAN'))
|
||||
has_filters = (req.use_face_correction is not None and req.use_face_correction.startswith('GFPGAN')) or \
|
||||
(req.use_upscale is not None and req.use_upscale.startswith('RealESRGAN'))
|
||||
|
||||
return_orig_img = not has_filters or not opt_show_only_filtered
|
||||
return_orig_img = not has_filters or not req.show_only_filtered_image
|
||||
|
||||
if stop_processing:
|
||||
return_orig_img = True
|
||||
|
||||
if opt_save_to_disk_path is not None:
|
||||
prompt_flattened = filename_regex.sub('_', prompts[0])
|
||||
prompt_flattened = prompt_flattened[:50]
|
||||
|
||||
img_id = str(uuid.uuid4())[-8:]
|
||||
|
||||
file_path = f"{prompt_flattened}_{img_id}"
|
||||
img_out_path = os.path.join(session_out_path, f"{file_path}.{opt_format}")
|
||||
meta_out_path = os.path.join(session_out_path, f"{file_path}.txt")
|
||||
|
||||
if req.save_to_disk_path is not None:
|
||||
if return_orig_img:
|
||||
img_out_path = get_base_path(req.save_to_disk_path, req.session_id, prompts[0], req.output_format)
|
||||
save_image(img, img_out_path)
|
||||
|
||||
save_metadata(meta_out_path, prompts, opt_seed, opt_W, opt_H, opt_ddim_steps, opt_scale, opt_strength, opt_use_face_correction, opt_use_upscale, opt_sampler_name, req.negative_prompt, ckpt_file)
|
||||
meta_out_path = get_base_path(req.save_to_disk_path, req.session_id, prompts[0], 'txt')
|
||||
save_metadata(meta_out_path, req, prompts[0], opt_seed)
|
||||
|
||||
if return_orig_img:
|
||||
img_data = img_to_base64_str(img, opt_format)
|
||||
img_data = img_to_base64_str(img, req.output_format)
|
||||
res_image_orig = ResponseImage(data=img_data, seed=opt_seed)
|
||||
res.images.append(res_image_orig)
|
||||
|
||||
if opt_save_to_disk_path is not None:
|
||||
if req.save_to_disk_path is not None:
|
||||
res_image_orig.path_abs = img_out_path
|
||||
|
||||
del img
|
||||
|
||||
if has_filters and not stop_processing:
|
||||
print('Applying filters..')
|
||||
|
||||
gc()
|
||||
filters_applied = []
|
||||
|
||||
if opt_use_face_correction:
|
||||
_, _, output = model_gfpgan.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
||||
x_sample = output[:,:,::-1]
|
||||
filters_applied.append(opt_use_face_correction)
|
||||
|
||||
if opt_use_upscale:
|
||||
output, _ = model_real_esrgan.enhance(x_sample[:,:,::-1])
|
||||
x_sample = output[:,:,::-1]
|
||||
filters_applied.append(opt_use_upscale)
|
||||
|
||||
filtered_image = Image.fromarray(x_sample)
|
||||
|
||||
filtered_img_data = img_to_base64_str(filtered_image, opt_format)
|
||||
res_image_filtered = ResponseImage(data=filtered_img_data, seed=opt_seed)
|
||||
res.images.append(res_image_filtered)
|
||||
|
||||
filters_applied = "_".join(filters_applied)
|
||||
|
||||
if opt_save_to_disk_path is not None:
|
||||
filtered_img_out_path = os.path.join(session_out_path, f"{file_path}_{filters_applied}.{opt_format}")
|
||||
save_image(filtered_image, filtered_img_out_path)
|
||||
res_image_filtered.path_abs = filtered_img_out_path
|
||||
|
||||
del filtered_image
|
||||
if req.use_face_correction:
|
||||
x_sample = apply_filters('gfpgan', x_sample)
|
||||
filters_applied.append(req.use_face_correction)
|
||||
if req.use_upscale:
|
||||
x_sample = apply_filters('real_esrgan', x_sample)
|
||||
filters_applied.append(req.use_upscale)
|
||||
if (len(filters_applied) > 0):
|
||||
filtered_image = Image.fromarray(x_sample)
|
||||
filtered_img_data = img_to_base64_str(filtered_image, req.output_format)
|
||||
response_image = ResponseImage(data=filtered_img_data, seed=req.seed)
|
||||
res.images.append(response_image)
|
||||
if req.save_to_disk_path is not None:
|
||||
filtered_img_out_path = get_base_path(req.save_to_disk_path, req.session_id, prompts[0], req.output_format, "_".join(filters_applied))
|
||||
save_image(filtered_image, filtered_img_out_path)
|
||||
response_image.path_abs = filtered_img_out_path
|
||||
del filtered_image
|
||||
|
||||
seeds += str(opt_seed) + ","
|
||||
opt_seed += 1
|
||||
@ -529,9 +526,20 @@ def save_image(img, img_out_path):
|
||||
except:
|
||||
print('could not save the file', traceback.format_exc())
|
||||
|
||||
def save_metadata(meta_out_path, prompts, opt_seed, opt_W, opt_H, opt_ddim_steps, opt_scale, opt_prompt_strength, opt_correct_face, opt_upscale, sampler_name, negative_prompt, ckpt_file):
|
||||
metadata = f"{prompts[0]}\nWidth: {opt_W}\nHeight: {opt_H}\nSeed: {opt_seed}\nSteps: {opt_ddim_steps}\nGuidance Scale: {opt_scale}\nPrompt Strength: {opt_prompt_strength}\nUse Face Correction: {opt_correct_face}\nUse Upscaling: {opt_upscale}\nSampler: {sampler_name}\nNegative Prompt: {negative_prompt}\nStable Diffusion Model: {ckpt_file + '.ckpt'}"
|
||||
|
||||
def save_metadata(meta_out_path, req, prompt, opt_seed):
|
||||
metadata = f"""{prompt}
|
||||
Width: {req.width}
|
||||
Height: {req.height}
|
||||
Seed: {opt_seed}
|
||||
Steps: {req.num_inference_steps}
|
||||
Guidance Scale: {req.guidance_scale}
|
||||
Prompt Strength: {req.prompt_strength}
|
||||
Use Face Correction: {req.use_face_correction}
|
||||
Use Upscaling: {req.use_upscale}
|
||||
Sampler: {req.sampler}
|
||||
Negative Prompt: {req.negative_prompt}
|
||||
Stable Diffusion Model: {req.use_stable_diffusion_model + '.ckpt'}
|
||||
"""
|
||||
try:
|
||||
with open(meta_out_path, 'w') as f:
|
||||
f.write(metadata)
|
||||
|
||||
298
ui/sd_internal/task_manager.py
Normal file
298
ui/sd_internal/task_manager.py
Normal file
@ -0,0 +1,298 @@
|
||||
import json
|
||||
import traceback
|
||||
|
||||
TASK_TTL = 15 * 60 # Discard last session's task timeout
|
||||
|
||||
import queue, threading, time
|
||||
from typing import Any, Generator, Hashable, Optional, Union
|
||||
|
||||
from pydantic import BaseModel
|
||||
from sd_internal import Request, Response
|
||||
|
||||
class SymbolClass(type): # Print nicely formatted Symbol names.
|
||||
def __repr__(self): return self.__qualname__
|
||||
def __str__(self): return self.__name__
|
||||
class Symbol(metaclass=SymbolClass): pass
|
||||
|
||||
class ServerStates:
|
||||
class Init(Symbol): pass
|
||||
class LoadingModel(Symbol): pass
|
||||
class Online(Symbol): pass
|
||||
class Rendering(Symbol): pass
|
||||
class Unavailable(Symbol): pass
|
||||
|
||||
class RenderTask(): # Task with output queue and completion lock.
|
||||
def __init__(self, req: Request):
|
||||
self.request: Request = req # Initial Request
|
||||
self.response: Any = None # Copy of the last reponse
|
||||
self.temp_images:[] = [None] * req.num_outputs * (1 if req.show_only_filtered_image else 2)
|
||||
self.error: Exception = None
|
||||
self.lock: threading.Lock = threading.Lock() # Locks at task start and unlocks when task is completed
|
||||
self.buffer_queue: queue.Queue = queue.Queue() # Queue of JSON string segments
|
||||
async def read_buffer_generator(self):
|
||||
try:
|
||||
while not self.buffer_queue.empty():
|
||||
res = self.buffer_queue.get(block=False)
|
||||
self.buffer_queue.task_done()
|
||||
yield res
|
||||
except queue.Empty as e: yield
|
||||
|
||||
# defaults from https://huggingface.co/blog/stable_diffusion
|
||||
class ImageRequest(BaseModel):
|
||||
session_id: str = "session"
|
||||
prompt: str = ""
|
||||
negative_prompt: str = ""
|
||||
init_image: str = None # base64
|
||||
mask: str = None # base64
|
||||
num_outputs: int = 1
|
||||
num_inference_steps: int = 50
|
||||
guidance_scale: float = 7.5
|
||||
width: int = 512
|
||||
height: int = 512
|
||||
seed: int = 42
|
||||
prompt_strength: float = 0.8
|
||||
sampler: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
|
||||
# allow_nsfw: bool = False
|
||||
save_to_disk_path: str = None
|
||||
turbo: bool = True
|
||||
use_cpu: bool = False
|
||||
use_full_precision: bool = False
|
||||
use_face_correction: str = None # or "GFPGANv1.3"
|
||||
use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
|
||||
use_stable_diffusion_model: str = "sd-v1-4"
|
||||
show_only_filtered_image: bool = False
|
||||
output_format: str = "jpeg" # or "png"
|
||||
|
||||
stream_progress_updates: bool = False
|
||||
stream_image_progress: bool = False
|
||||
|
||||
# Temporary cache to allow to query tasks results for a short time after they are completed.
|
||||
class TaskCache():
|
||||
def __init__(self):
|
||||
self._base = dict()
|
||||
self._lock: threading.Lock = threading.RLock()
|
||||
def _get_ttl_time(self, ttl: int) -> int:
|
||||
return int(time.time()) + ttl
|
||||
def _is_expired(self, timestamp: int) -> bool:
|
||||
return int(time.time()) >= timestamp
|
||||
def clean(self) -> None:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.clean failed to acquire lock within timeout.')
|
||||
try:
|
||||
# Create a list of expired keys to delete
|
||||
to_delete = []
|
||||
for key in self._base:
|
||||
ttl, _ = self._base[key]
|
||||
if self._is_expired(ttl):
|
||||
to_delete.append(key)
|
||||
# Remove Items
|
||||
for key in to_delete:
|
||||
del self._base[key]
|
||||
print(f'Session {key} expired. Data removed.')
|
||||
finally:
|
||||
self._lock.release()
|
||||
def clear(self) -> None:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.clear failed to acquire lock within timeout.')
|
||||
try: self._base.clear()
|
||||
finally: self._lock.release()
|
||||
def delete(self, key: Hashable) -> bool:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.delete failed to acquire lock within timeout.')
|
||||
try:
|
||||
if key not in self._base:
|
||||
return False
|
||||
del self._base[key]
|
||||
return True
|
||||
finally:
|
||||
self._lock.release()
|
||||
def keep(self, key: Hashable, ttl: int) -> bool:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.keep failed to acquire lock within timeout.')
|
||||
try:
|
||||
if key in self._base:
|
||||
_, value = self._base.get(key)
|
||||
self._base[key] = (self._get_ttl_time(ttl), value)
|
||||
return True
|
||||
return False
|
||||
finally:
|
||||
self._lock.release()
|
||||
def put(self, key: Hashable, value: Any, ttl: int) -> bool:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.put failed to acquire lock within timeout.')
|
||||
try:
|
||||
self._base[key] = (
|
||||
self._get_ttl_time(ttl), value
|
||||
)
|
||||
except Exception as e:
|
||||
print(str(e))
|
||||
print(traceback.format_exc())
|
||||
return False
|
||||
else:
|
||||
return True
|
||||
finally:
|
||||
self._lock.release()
|
||||
def tryGet(self, key: Hashable) -> Any:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.tryGet failed to acquire lock within timeout.')
|
||||
try:
|
||||
ttl, value = self._base.get(key, (None, None))
|
||||
if ttl is not None and self._is_expired(ttl):
|
||||
print(f'Session {key} expired. Discarding data.')
|
||||
self.delete(key)
|
||||
return None
|
||||
return value
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
current_state = ServerStates.Init
|
||||
current_state_error:Exception = None
|
||||
current_model_path = None
|
||||
tasks_queue = queue.Queue()
|
||||
task_cache = TaskCache()
|
||||
default_model_to_load = None
|
||||
|
||||
def preload_model(file_path=None):
|
||||
global current_state, current_state_error, current_model_path
|
||||
if file_path == None:
|
||||
file_path = default_model_to_load
|
||||
if file_path == current_model_path:
|
||||
return
|
||||
current_state = ServerStates.LoadingModel
|
||||
try:
|
||||
from . import runtime
|
||||
runtime.load_model_ckpt(ckpt_to_use=file_path)
|
||||
current_model_path = file_path
|
||||
current_state_error = None
|
||||
current_state = ServerStates.Online
|
||||
except Exception as e:
|
||||
current_model_path = None
|
||||
current_state_error = e
|
||||
current_state = ServerStates.Unavailable
|
||||
print(traceback.format_exc())
|
||||
|
||||
def thread_render():
|
||||
global current_state, current_state_error, current_model_path
|
||||
from . import runtime
|
||||
current_state = ServerStates.Online
|
||||
preload_model()
|
||||
while True:
|
||||
task_cache.clean()
|
||||
if isinstance(current_state_error, SystemExit):
|
||||
current_state = ServerStates.Unavailable
|
||||
return
|
||||
task = None
|
||||
try:
|
||||
task = tasks_queue.get(timeout=1)
|
||||
except queue.Empty as e:
|
||||
if isinstance(current_state_error, SystemExit):
|
||||
current_state = ServerStates.Unavailable
|
||||
return
|
||||
else: continue
|
||||
#if current_model_path != task.request.use_stable_diffusion_model:
|
||||
# preload_model(task.request.use_stable_diffusion_model)
|
||||
if current_state_error:
|
||||
task.error = current_state_error
|
||||
continue
|
||||
print(f'Session {task.request.session_id} starting task {id(task)}')
|
||||
try:
|
||||
task.lock.acquire(blocking=False)
|
||||
res = runtime.mk_img(task.request)
|
||||
if current_model_path == task.request.use_stable_diffusion_model:
|
||||
current_state = ServerStates.Rendering
|
||||
else:
|
||||
current_state = ServerStates.LoadingModel
|
||||
except Exception as e:
|
||||
task.error = e
|
||||
task.lock.release()
|
||||
tasks_queue.task_done()
|
||||
print(traceback.format_exc())
|
||||
continue
|
||||
dataQueue = None
|
||||
if task.request.stream_progress_updates:
|
||||
dataQueue = task.buffer_queue
|
||||
for result in res:
|
||||
if current_state == ServerStates.LoadingModel:
|
||||
current_state = ServerStates.Rendering
|
||||
current_model_path = task.request.use_stable_diffusion_model
|
||||
if isinstance(current_state_error, SystemExit) or isinstance(current_state_error, StopAsyncIteration) or isinstance(task.error, StopAsyncIteration):
|
||||
runtime.stop_processing = True
|
||||
if isinstance(current_state_error, StopAsyncIteration):
|
||||
task.error = current_state_error
|
||||
current_state_error = None
|
||||
print(f'Session {task.request.session_id} sent cancel signal for task {id(task)}')
|
||||
if dataQueue:
|
||||
dataQueue.put(result)
|
||||
if isinstance(result, str):
|
||||
result = json.loads(result)
|
||||
task.response = result
|
||||
if 'output' in result:
|
||||
for out_obj in result['output']:
|
||||
if 'path' in out_obj:
|
||||
img_id = out_obj['path'][out_obj['path'].rindex('/') + 1:]
|
||||
task.temp_images[int(img_id)] = runtime.temp_images[out_obj['path'][11:]]
|
||||
elif 'data' in out_obj:
|
||||
task.temp_images[result['output'].index(out_obj)] = out_obj['data']
|
||||
task_cache.keep(task.request.session_id, TASK_TTL)
|
||||
# Task completed
|
||||
task.lock.release()
|
||||
tasks_queue.task_done()
|
||||
task_cache.keep(task.request.session_id, TASK_TTL)
|
||||
if isinstance(task.error, StopAsyncIteration):
|
||||
print(f'Session {task.request.session_id} task {id(task)} cancelled!')
|
||||
elif task.error is not None:
|
||||
print(f'Session {task.request.session_id} task {id(task)} failed!')
|
||||
else:
|
||||
print(f'Session {task.request.session_id} task {id(task)} completed.')
|
||||
current_state = ServerStates.Online
|
||||
|
||||
render_thread = threading.Thread(target=thread_render)
|
||||
|
||||
def start_render_thread():
|
||||
# Start Rendering Thread
|
||||
render_thread.daemon = True
|
||||
render_thread.start()
|
||||
|
||||
def shutdown_event(): # Signal render thread to close on shutdown
|
||||
global current_state_error
|
||||
current_state_error = SystemExit('Application shutting down.')
|
||||
|
||||
def render(req : ImageRequest):
|
||||
if not render_thread.is_alive(): # Render thread is dead
|
||||
raise ChildProcessError('Rendering thread has died.')
|
||||
# Alive, check if task in cache
|
||||
task = task_cache.tryGet(req.session_id)
|
||||
if task and not task.response and not task.error and not task.lock.locked():
|
||||
# Unstarted task pending, deny queueing more than one.
|
||||
raise ConnectionRefusedError(f'Session {req.session_id} has an already pending task.')
|
||||
#
|
||||
from . import runtime
|
||||
r = Request()
|
||||
r.session_id = req.session_id
|
||||
r.prompt = req.prompt
|
||||
r.negative_prompt = req.negative_prompt
|
||||
r.init_image = req.init_image
|
||||
r.mask = req.mask
|
||||
r.num_outputs = req.num_outputs
|
||||
r.num_inference_steps = req.num_inference_steps
|
||||
r.guidance_scale = req.guidance_scale
|
||||
r.width = req.width
|
||||
r.height = req.height
|
||||
r.seed = req.seed
|
||||
r.prompt_strength = req.prompt_strength
|
||||
r.sampler = req.sampler
|
||||
# r.allow_nsfw = req.allow_nsfw
|
||||
r.turbo = req.turbo
|
||||
r.use_cpu = req.use_cpu
|
||||
r.use_full_precision = req.use_full_precision
|
||||
r.save_to_disk_path = req.save_to_disk_path
|
||||
r.use_upscale: str = req.use_upscale
|
||||
r.use_face_correction = req.use_face_correction
|
||||
r.show_only_filtered_image = req.show_only_filtered_image
|
||||
r.output_format = req.output_format
|
||||
|
||||
r.stream_progress_updates = True # the underlying implementation only supports streaming
|
||||
r.stream_image_progress = req.stream_image_progress
|
||||
|
||||
if not req.stream_progress_updates:
|
||||
r.stream_image_progress = False
|
||||
|
||||
new_task = RenderTask(r)
|
||||
if task_cache.put(r.session_id, new_task, TASK_TTL):
|
||||
tasks_queue.put(new_task, block=True, timeout=30)
|
||||
return new_task
|
||||
raise RuntimeError('Failed to add task to cache.')
|
||||
280
ui/server.py
280
ui/server.py
@ -14,90 +14,32 @@ CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, '..', 'scripts'))
|
||||
MODELS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'models'))
|
||||
|
||||
OUTPUT_DIRNAME = "Stable Diffusion UI" # in the user's home folder
|
||||
TASK_TTL = 15 * 60 # Discard last session's task timeout
|
||||
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from starlette.responses import FileResponse, StreamingResponse
|
||||
from starlette.responses import FileResponse, JSONResponse, StreamingResponse
|
||||
from pydantic import BaseModel
|
||||
import logging
|
||||
import queue, threading, time
|
||||
from typing import Any, Generator, Hashable, Optional, Union
|
||||
|
||||
from sd_internal import Request, Response
|
||||
from sd_internal import Request, Response, task_manager
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
model_loaded = False
|
||||
model_is_loading = False
|
||||
|
||||
modifiers_cache = None
|
||||
outpath = os.path.join(os.path.expanduser("~"), OUTPUT_DIRNAME)
|
||||
|
||||
# don't show access log entries for URLs that start with the given prefix
|
||||
ACCESS_LOG_SUPPRESS_PATH_PREFIXES = ['/ping', '/modifier-thumbnails']
|
||||
ACCESS_LOG_SUPPRESS_PATH_PREFIXES = ['/ping', '/image', '/modifier-thumbnails']
|
||||
|
||||
NOCACHE_HEADERS={"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"}
|
||||
app.mount('/media', StaticFiles(directory=os.path.join(SD_UI_DIR, 'media/')), name="media")
|
||||
|
||||
# defaults from https://huggingface.co/blog/stable_diffusion
|
||||
class ImageRequest(BaseModel):
|
||||
session_id: str = "session"
|
||||
prompt: str = ""
|
||||
negative_prompt: str = ""
|
||||
init_image: str = None # base64
|
||||
mask: str = None # base64
|
||||
num_outputs: int = 1
|
||||
num_inference_steps: int = 50
|
||||
guidance_scale: float = 7.5
|
||||
width: int = 512
|
||||
height: int = 512
|
||||
seed: int = 42
|
||||
prompt_strength: float = 0.8
|
||||
sampler: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
|
||||
# allow_nsfw: bool = False
|
||||
save_to_disk_path: str = None
|
||||
turbo: bool = True
|
||||
use_cpu: bool = False
|
||||
use_full_precision: bool = False
|
||||
use_face_correction: str = None # or "GFPGANv1.3"
|
||||
use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
|
||||
use_stable_diffusion_model: str = "sd-v1-4"
|
||||
show_only_filtered_image: bool = False
|
||||
output_format: str = "jpeg" # or "png"
|
||||
|
||||
stream_progress_updates: bool = False
|
||||
stream_image_progress: bool = False
|
||||
|
||||
class SetAppConfigRequest(BaseModel):
|
||||
update_branch: str = "main"
|
||||
|
||||
@app.get('/')
|
||||
def read_root():
|
||||
headers = {"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"}
|
||||
return FileResponse(os.path.join(SD_UI_DIR, 'index.html'), headers=headers)
|
||||
|
||||
@app.get('/ping')
|
||||
async def ping():
|
||||
global model_loaded, model_is_loading
|
||||
|
||||
try:
|
||||
if model_loaded:
|
||||
return {'OK'}
|
||||
|
||||
if model_is_loading:
|
||||
return {'ERROR'}
|
||||
|
||||
model_is_loading = True
|
||||
|
||||
from sd_internal import runtime
|
||||
|
||||
runtime.load_model_ckpt(ckpt_to_use=get_initial_model_to_load())
|
||||
|
||||
model_loaded = True
|
||||
model_is_loading = False
|
||||
|
||||
return {'OK'}
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
return HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
# needs to support the legacy installations
|
||||
def get_initial_model_to_load():
|
||||
custom_weight_path = os.path.join(SD_DIR, 'custom-model.ckpt')
|
||||
@ -114,7 +56,6 @@ def get_initial_model_to_load():
|
||||
ckpt_to_use = model_path
|
||||
else:
|
||||
print('Could not find the configured custom model at:', model_path + '.ckpt', '. Using the default one:', ckpt_to_use + '.ckpt')
|
||||
|
||||
return ckpt_to_use
|
||||
|
||||
def resolve_model_to_use(model_name):
|
||||
@ -126,92 +67,110 @@ def resolve_model_to_use(model_name):
|
||||
model_path = legacy_model_path
|
||||
else:
|
||||
model_path = os.path.join(MODELS_DIR, 'stable-diffusion', model_name)
|
||||
|
||||
return model_path
|
||||
|
||||
@app.on_event("shutdown")
|
||||
def shutdown_event(): # Signal render thread to close on shutdown
|
||||
task_manager.current_state_error = SystemExit('Application shutting down.')
|
||||
|
||||
@app.get('/')
|
||||
def read_root():
|
||||
return FileResponse(os.path.join(SD_UI_DIR, 'index.html'), headers=NOCACHE_HEADERS)
|
||||
|
||||
@app.get('/ping') # Get server and optionally session status.
|
||||
def ping(session_id:str=None):
|
||||
if not task_manager.render_thread.is_alive(): # Render thread is dead.
|
||||
if task_manager.current_state_error: return HTTPException(status_code=500, detail=str(current_state_error))
|
||||
return HTTPException(status_code=500, detail='Render thread is dead.')
|
||||
if task_manager.current_state_error and not isinstance(task_manager.current_state_error, StopAsyncIteration): return HTTPException(status_code=500, detail=str(current_state_error))
|
||||
# Alive
|
||||
response = {'status': str(task_manager.current_state)}
|
||||
if session_id:
|
||||
task = task_manager.task_cache.tryGet(session_id)
|
||||
if task:
|
||||
response['task'] = id(task)
|
||||
if task.lock.locked():
|
||||
response['session'] = 'running'
|
||||
elif isinstance(task.error, StopAsyncIteration):
|
||||
response['session'] = 'stopped'
|
||||
elif task.error:
|
||||
response['session'] = 'error'
|
||||
elif not task.buffer_queue.empty():
|
||||
response['session'] = 'buffer'
|
||||
elif task.response:
|
||||
response['session'] = 'completed'
|
||||
else:
|
||||
response['session'] = 'pending'
|
||||
return JSONResponse(response, headers=NOCACHE_HEADERS)
|
||||
|
||||
def save_model_to_config(model_name):
|
||||
config = getConfig()
|
||||
if 'model' not in config:
|
||||
config['model'] = {}
|
||||
|
||||
config['model']['stable-diffusion'] = model_name
|
||||
|
||||
setConfig(config)
|
||||
|
||||
@app.post('/image')
|
||||
def image(req : ImageRequest):
|
||||
from sd_internal import runtime
|
||||
|
||||
r = Request()
|
||||
r.session_id = req.session_id
|
||||
r.prompt = req.prompt
|
||||
r.negative_prompt = req.negative_prompt
|
||||
r.init_image = req.init_image
|
||||
r.mask = req.mask
|
||||
r.num_outputs = req.num_outputs
|
||||
r.num_inference_steps = req.num_inference_steps
|
||||
r.guidance_scale = req.guidance_scale
|
||||
r.width = req.width
|
||||
r.height = req.height
|
||||
r.seed = req.seed
|
||||
r.prompt_strength = req.prompt_strength
|
||||
r.sampler = req.sampler
|
||||
# r.allow_nsfw = req.allow_nsfw
|
||||
r.turbo = req.turbo
|
||||
r.use_cpu = req.use_cpu
|
||||
r.use_full_precision = req.use_full_precision
|
||||
r.save_to_disk_path = req.save_to_disk_path
|
||||
r.use_upscale: str = req.use_upscale
|
||||
r.use_face_correction = req.use_face_correction
|
||||
r.show_only_filtered_image = req.show_only_filtered_image
|
||||
r.output_format = req.output_format
|
||||
|
||||
r.stream_progress_updates = True # the underlying implementation only supports streaming
|
||||
r.stream_image_progress = req.stream_image_progress
|
||||
|
||||
r.use_stable_diffusion_model = resolve_model_to_use(req.use_stable_diffusion_model)
|
||||
|
||||
save_model_to_config(req.use_stable_diffusion_model)
|
||||
|
||||
@app.post('/render')
|
||||
def render(req : task_manager.ImageRequest):
|
||||
try:
|
||||
if not req.stream_progress_updates:
|
||||
r.stream_image_progress = False
|
||||
|
||||
res = runtime.mk_img(r)
|
||||
|
||||
if req.stream_progress_updates:
|
||||
return StreamingResponse(res, media_type='application/json')
|
||||
else: # compatibility mode: buffer the streaming responses, and return the last one
|
||||
last_result = None
|
||||
|
||||
for result in res:
|
||||
last_result = result
|
||||
|
||||
return json.loads(last_result)
|
||||
save_model_to_config(req.use_stable_diffusion_model)
|
||||
req.use_stable_diffusion_model = resolve_model_to_use(req.use_stable_diffusion_model)
|
||||
new_task = task_manager.render(req)
|
||||
response = {
|
||||
'status': str(task_manager.current_state),
|
||||
'queue': task_manager.tasks_queue.qsize(),
|
||||
'stream': f'/image/stream/{req.session_id}/{id(new_task)}',
|
||||
'task': id(new_task)
|
||||
}
|
||||
return JSONResponse(response, headers=NOCACHE_HEADERS)
|
||||
except ChildProcessError as e: # Render thread is dead
|
||||
return HTTPException(status_code=500, detail=f'Rendering thread has died.') # HTTP500 Internal Server Error
|
||||
except ConnectionRefusedError as e: # Unstarted task pending, deny queueing more than one.
|
||||
return HTTPException(status_code=503, detail=f'Session {req.session_id} has an already pending task.') # HTTP503 Service Unavailable
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
return HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get('/image/stream/{session_id:str}/{task_id:int}')
|
||||
def stream(session_id:str, task_id:int):
|
||||
#TODO Move to WebSockets ??
|
||||
task = task_manager.task_cache.tryGet(session_id)
|
||||
if not task: return HTTPException(status_code=410, detail='No request received.') # HTTP410 Gone
|
||||
if (id(task) != task_id): return HTTPException(status_code=409, detail=f'Wrong task id received. Expected:{id(task)}, Received:{task_id}') # HTTP409 Conflict
|
||||
if task.buffer_queue.empty() and not task.lock.locked():
|
||||
if task.response:
|
||||
#print(f'Session {session_id} sending cached response')
|
||||
return JSONResponse(task.response, headers=NOCACHE_HEADERS)
|
||||
return HTTPException(status_code=425, detail='Too Early, task not started yet.') # HTTP425 Too Early
|
||||
#print(f'Session {session_id} opened live render stream {id(task.buffer_queue)}')
|
||||
return StreamingResponse(task.read_buffer_generator(), media_type='application/json')
|
||||
|
||||
@app.get('/image/stop')
|
||||
def stop():
|
||||
try:
|
||||
if model_is_loading:
|
||||
return {'ERROR'}
|
||||
|
||||
from sd_internal import runtime
|
||||
runtime.stop_processing = True
|
||||
|
||||
def stop(session_id:str=None):
|
||||
if not session_id:
|
||||
if task_manager.current_state == task_manager.ServerStates.Online or task_manager.current_state == task_manager.ServerStates.Unavailable:
|
||||
return HTTPException(status_code=409, detail='Not currently running any tasks.') # HTTP409 Conflict
|
||||
task_manager.current_state_error = StopAsyncIteration('')
|
||||
return {'OK'}
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
return HTTPException(status_code=500, detail=str(e))
|
||||
task = task_manager.task_cache.tryGet(session_id)
|
||||
if not task: return HTTPException(status_code=404, detail=f'Session {session_id} has no active task.') # HTTP404 Not Found
|
||||
if isinstance(task.error, StopAsyncIteration): return HTTPException(status_code=409, detail=f'Session {session_id} task is already stopped.') # HTTP409 Conflict
|
||||
task.error = StopAsyncIteration('')
|
||||
return {'OK'}
|
||||
|
||||
@app.get('/image/tmp/{session_id}/{img_id}')
|
||||
@app.get('/image/tmp/{session_id}/{img_id:int}')
|
||||
def get_image(session_id, img_id):
|
||||
from sd_internal import runtime
|
||||
buf = runtime.temp_images[session_id + '/' + img_id]
|
||||
buf.seek(0)
|
||||
return StreamingResponse(buf, media_type='image/jpeg')
|
||||
task = task_manager.task_cache.tryGet(session_id)
|
||||
if not task: return HTTPException(status_code=410, detail=f'Session {session_id} has not submitted a task.') # HTTP410 Gone
|
||||
if not task.temp_images[img_id]: return HTTPException(status_code=425, detail='Too Early, task data is not available yet.') # HTTP425 Too Early
|
||||
try:
|
||||
img_data = task.temp_images[img_id]
|
||||
if isinstance(img_data, str):
|
||||
return img_data
|
||||
img_data.seek(0)
|
||||
return StreamingResponse(img_data, media_type='image/jpeg')
|
||||
except KeyError as e:
|
||||
return HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post('/app_config')
|
||||
async def setAppConfig(req : SetAppConfigRequest):
|
||||
@ -242,42 +201,27 @@ async def setAppConfig(req : SetAppConfigRequest):
|
||||
print(traceback.format_exc())
|
||||
return HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get('/app_config')
|
||||
def getAppConfig():
|
||||
def getConfig(default_val={}):
|
||||
try:
|
||||
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
||||
|
||||
if not os.path.exists(config_json_path):
|
||||
return HTTPException(status_code=500, detail="No config file")
|
||||
|
||||
return default_val
|
||||
with open(config_json_path, 'r') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
print(str(e))
|
||||
print(traceback.format_exc())
|
||||
return HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
def getConfig():
|
||||
try:
|
||||
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
||||
|
||||
if not os.path.exists(config_json_path):
|
||||
return {}
|
||||
|
||||
with open(config_json_path, 'r') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
return {}
|
||||
return default_val
|
||||
|
||||
def setConfig(config):
|
||||
try:
|
||||
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
||||
|
||||
with open(config_json_path, 'w') as f:
|
||||
return json.dump(config, f)
|
||||
except:
|
||||
print(str(e))
|
||||
print(traceback.format_exc())
|
||||
|
||||
@app.get('/models')
|
||||
def getModels():
|
||||
models = {
|
||||
'active': {
|
||||
@ -307,14 +251,21 @@ def getModels():
|
||||
|
||||
return models
|
||||
|
||||
@app.get('/modifiers.json')
|
||||
def read_modifiers():
|
||||
headers = {"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"}
|
||||
return FileResponse(os.path.join(SD_UI_DIR, 'modifiers.json'), headers=headers)
|
||||
|
||||
@app.get('/output_dir')
|
||||
def read_home_dir():
|
||||
return {outpath}
|
||||
@app.get('/get/{key:path}')
|
||||
def read_web_data(key:str=None):
|
||||
if not key: # /get without parameters, stable-diffusion easter egg.
|
||||
return HTTPException(status_code=418, detail="StableDiffusion is drawing a teapot!") # HTTP418 I'm a teapot
|
||||
elif key == 'app_config':
|
||||
config = getConfig(default_val=None)
|
||||
if config is None:
|
||||
return HTTPException(status_code=500, detail="Config file is missing or unreadable")
|
||||
return JSONResponse(config, headers=NOCACHE_HEADERS)
|
||||
elif key == 'models':
|
||||
return JSONResponse(getModels(), headers=NOCACHE_HEADERS)
|
||||
elif key == 'modifiers': return FileResponse(os.path.join(SD_UI_DIR, 'modifiers.json'), headers=NOCACHE_HEADERS)
|
||||
elif key == 'output_dir': return JSONResponse({ 'output_dir': outpath }, headers=NOCACHE_HEADERS)
|
||||
else:
|
||||
return HTTPException(status_code=404, detail=f'Request for unknown {key}') # HTTP404 Not Found
|
||||
|
||||
# don't log certain requests
|
||||
class LogSuppressFilter(logging.Filter):
|
||||
@ -323,10 +274,11 @@ class LogSuppressFilter(logging.Filter):
|
||||
for prefix in ACCESS_LOG_SUPPRESS_PATH_PREFIXES:
|
||||
if path.find(prefix) != -1:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
logging.getLogger('uvicorn.access').addFilter(LogSuppressFilter())
|
||||
|
||||
task_manager.default_model_to_load = get_initial_model_to_load()
|
||||
task_manager.start_render_thread()
|
||||
|
||||
# start the browser ui
|
||||
import webbrowser; webbrowser.open('http://localhost:9000')
|
||||
Loading…
Reference in New Issue
Block a user