Merge pull request #346 from cmdr2/task_manager

Allow multiple tabs and computers to generate tasks without throwing errors (by @madrang)

ui/index.html

@@ -16,7 +16,7 @@
 <div id="container">
     <div id="top-nav">
         <div id="logo">
-            <h1>Stable Diffusion UI <small>v2.26 <span id="updateBranchLabel"></span></small></h1>
+            <h1>Stable Diffusion UI <small>v2.27 <span id="updateBranchLabel"></span></small></h1>
         </div>
         <ul id="top-nav-items">
             <li class="dropdown">

ui/media/main.css

@@ -326,13 +326,13 @@ img {
     height: 8pt;
     border-radius: 4pt; */
     font-size: 14pt;
-    color: rgb(128, 87, 0);
+    color: rgb(200, 139, 0);
     /* background-color: rgb(197, 1, 1); */
     /* transform: translateY(15%); */
     display: inline;
 }
 #server-status-msg {
-    color: rgb(128, 87, 0);
+    color: rgb(200, 139, 0);
     padding-left: 2pt;
     font-size: 10pt;
 }
@@ -479,7 +479,12 @@ img {
 .activeTaskLabel {
     background:rgb(0, 90, 30);
     border: 1px solid rgb(0, 75, 19);
-    color:rgb(204, 255, 217)
+    color:rgb(222, 253, 230)
+}
+.waitingTaskLabel {
+    background:rgb(128, 89, 0);
+    border: 1px solid rgb(0, 75, 19);
+    color:rgb(255, 242, 211)
 }
 .secondaryButton {
     background: rgb(132, 8, 0);

ui/media/main.js

@@ -20,7 +20,7 @@ const INPAINTING_EDITOR_SIZE = 450
 
 const IMAGE_REGEX = new RegExp('data:image/[A-Za-z]+;base64')
 
-let sessionId = new Date().getTime()
+let sessionId = Date.now()
 
 let promptField = document.querySelector('#prompt')
 let promptsFromFileSelector = document.querySelector('#prompt_from_file')
@@ -122,7 +122,7 @@ maskResetButton.innerHTML = 'Clear'
 maskResetButton.style.fontWeight = 'normal'
 maskResetButton.style.fontSize = '10pt'
 
-let serverStatus = 'offline'
+let serverState = {'status': 'Offline', 'time': Date.now()}
 let activeTags = []
 let modifiers = []
 let lastPromptUsed = ''
@@ -225,21 +225,38 @@ function getOutputFormat() {
 }
 
 function setStatus(statusType, msg, msgType) {
-    if (statusType !== 'server') {
+}
-        return
-    }
 
-    if (msgType == 'error') {
+function setServerStatus(msgType, msg) {
-        // msg = '<span style="color: red">' + msg + '<span>'
+    switch(msgType) {
+        case 'online':
+            serverStatusColor.style.color = 'green'
+            serverStatusMsg.style.color = 'green'
+            serverStatusMsg.innerText = 'Stable Diffusion is ' + msg
+            break
+        case 'busy':
+            serverStatusColor.style.color = 'rgb(200, 139, 0)'
+            serverStatusMsg.style.color = 'rgb(200, 139, 0)'
+            serverStatusMsg.innerText = 'Stable Diffusion is ' + msg
+            break
+        case 'error':
             serverStatusColor.style.color = 'red'
             serverStatusMsg.style.color = 'red'
             serverStatusMsg.innerText = 'Stable Diffusion has stopped'
-    } else if (msgType == 'success') {
+            break
-        // msg = '<span style="color: green">' + msg + '<span>'
+    }
-        serverStatusColor.style.color = 'green'
+}
-        serverStatusMsg.style.color = 'green'
+function isServerAvailable() {
-        serverStatusMsg.innerText = 'Stable Diffusion is ready'
+    if (typeof serverState !== 'object') {
-        serverStatus = 'online'
+        return false
+    }
+    switch (serverState.status) {
+        case 'LoadingModel':
+        case 'Rendering':
+        case 'Online':
+            return true
+        default:
+            return false
     }
 }
 
@@ -263,6 +280,11 @@ function logError(msg, res, outputMsg) {
     console.log('request error', res)
     setStatus('request', 'error', 'error')
 }
+function asyncDelay(timeout) {
+    return new Promise(function(resolve, reject) {
+        setTimeout(resolve, timeout, true)
+    })
+}
 
 function playSound() {
     const audio = new Audio('/media/ding.mp3')
@@ -277,16 +299,40 @@ function playSound() {
 
 async function healthCheck() {
     try {
-        let res = await fetch('/ping')
+        let res = undefined
-        res = await res.json()
+        if (sessionId) {
-
+            res = await fetch('/ping?session_id=' + sessionId)
-        if (res[0] == 'OK') {
-            setStatus('server', 'online', 'success')
         } else {
-            setStatus('server', 'offline', 'error')
+            res = await fetch('/ping')
         }
+        serverState = await res.json()
+        if (typeof serverState !== 'object' || typeof serverState.status !== 'string') {
+            serverState = {'status': 'Offline', 'time': Date.now()}
+            setServerStatus('error', 'offline')
+            return
+        }
+        // Set status
+        switch(serverState.status) {
+            case 'Init':
+                // Wait for init to complete before updating status.
+                break
+            case 'Online':
+                setServerStatus('online', 'ready')
+                break
+            case 'LoadingModel':
+                setServerStatus('busy', 'loading model')
+                break
+            case 'Rendering':
+                setServerStatus('busy', 'rendering')
+                break
+            default: // Unavailable
+                setServerStatus('error', serverState.status.toLowerCase())
+                break
+        }
+        serverState.time = Date.now()
     } catch (e) {
-        setStatus('server', 'offline', 'error')
+        serverState = {'status': 'Offline', 'time': Date.now()}
+        setServerStatus('error', 'offline')
     }
 }
 function resizeInpaintingEditor() {
@@ -329,7 +375,7 @@ function showImages(reqBody, res, outputContainer, livePreview) {
     if(typeof res != 'object') return
     res.output.reverse()
     res.output.forEach((result, index) => {
-        const imageData = result?.data || result?.path + '?t=' + new Date().getTime(),
+        const imageData = result?.data || result?.path + '?t=' + Date.now(),
             imageSeed = result?.seed,
             imagePrompt = reqBody.prompt,
             imageInferenceSteps = reqBody.num_inference_steps,
@@ -440,8 +486,8 @@ function getSaveImageHandler(imageItemElem, outputFormat) {
 }
 function getStartNewTaskHandler(reqBody, imageItemElem, mode) {
     return function() {
-        if (serverStatus !== 'online') {
+        if (!isServerAvailable()) {
-            alert('The server is still starting up..')
+            alert('The server is not available.')
             return
         }
         const imageElem = imageItemElem.querySelector('img')
@@ -507,37 +553,76 @@ async function doMakeImage(task) {
     const progressBar = task['progressBar']
 
     let res = undefined
-    let stepUpdate = undefined
     try {
-        res = await fetch('/image', {
+        const lastTask = serverState.task
+        let renderRequest = undefined
+        do {
+            res = await fetch('/render', {
                 method: 'POST',
                 headers: {
                     'Content-Type': 'application/json'
                 },
                 body: JSON.stringify(reqBody)
             })
+            renderRequest = await res.json()
+            // status_code 503, already a task running.
+        } while (renderRequest.status_code === 503 && await asyncDelay(30 * 1000))
+        if (typeof renderRequest?.stream !== 'string') {
+            console.log('Endpoint response: ', renderRequest)
+            throw new Error('Endpoint response does not contains a response stream url.')
+        }
+        task['taskStatusLabel'].innerText = "Waiting"
+        task['taskStatusLabel'].classList.add('waitingTaskLabel')
+        task['taskStatusLabel'].classList.remove('activeTaskLabel')
 
+        do { // Wait for server status to update.
+            await asyncDelay(250)
+            if (!isServerAvailable()) {
+                throw new Error('Connexion with server lost.')
+            }
+        } while (serverState.time > (Date.now() - (10 * 1000)) && serverState.task !== renderRequest.task)
+        if (serverState.session !== 'pending' && serverState.session !== 'running' && serverState.session !== 'buffer') {
+            if (serverState.session === 'stopped') {
+                return false
+            }
+
+            throw new Error('Unexpected server task state: ' + serverState.session || 'Undefined')
+        }
+        while (serverState.task === renderRequest.task && serverState.session === 'pending') {
+            // Wait for task to start on server.
+            await asyncDelay(1500)
+        }
+
+        // Task started!
+        res = await fetch(renderRequest.stream, {
+            headers: {
+                'Content-Type': 'application/json'
+            },
+        })
+
+        task['taskStatusLabel'].innerText = "Processing"
+        task['taskStatusLabel'].classList.add('activeTaskLabel')
+        task['taskStatusLabel'].classList.remove('waitingTaskLabel')
+
+        let stepUpdate = undefined
         let reader = res.body.getReader()
         let textDecoder = new TextDecoder()
         let finalJSON = ''
         let prevTime = -1
         let readComplete = false
-        while (true) {
+        while (!readComplete || finalJSON.length > 0) {
-            let t = new Date().getTime()
+            let t = Date.now()
-
             let jsonStr = ''
             if (!readComplete) {
                 const {value, done} = await reader.read()
                 if (done) {
                     readComplete = true
                 }
-                if (done && finalJSON.length <= 0 && !value) {
-                    break
-                }
                 if (value) {
                     jsonStr = textDecoder.decode(value)
                 }
             }
+            stepUpdate = undefined
             try {
                 // hack for a middleman buffering all the streaming updates, and unleashing them on the poor browser in one shot.
                 // this results in having to parse JSON like {"step": 1}{"step": 2}{"step": 3}{"ste...
@@ -571,9 +656,6 @@ async function doMakeImage(task) {
                     throw e
                 }
             }
-            if (readComplete && finalJSON.length <= 0) {
-                break
-            }
             if (typeof stepUpdate === 'object' && 'step' in stepUpdate) {
                 let batchSize = stepUpdate.total_steps
                 let overallStepCount = stepUpdate.step + task.batchesDone * batchSize
@@ -598,6 +680,23 @@ async function doMakeImage(task) {
                     showImages(reqBody, stepUpdate, outputContainer, true)
                 }
             }
+            if (stepUpdate?.status) {
+                break
+            }
+            if (readComplete && finalJSON.length <= 0) {
+                if (res.status === 200) {
+                    await asyncDelay(5000)
+                    res = await fetch(renderRequest.stream, {
+                        headers: {
+                            'Content-Type': 'application/json'
+                        },
+                    })
+                    reader = res.body.getReader()
+                    readComplete = false
+                } else {
+                    console.log('Stream stopped: ', res)
+                }
+            }
             prevTime = t
         }
 
@@ -614,27 +713,28 @@ async function doMakeImage(task) {
                             3. Try generating a smaller image.<br/>`
                 }
             } else {
-                msg = `Unexpected Read Error:<br/><pre>StepUpdate:${JSON.stringify(stepUpdate, undefined, 4)}</pre>`
+                msg = `Unexpected Read Error:<br/><pre>StepUpdate: ${JSON.stringify(stepUpdate, undefined, 4)}</pre>`
             }
             logError(msg, res, outputMsg)
             return false
         }
         if (typeof stepUpdate !== 'object' || !res || res.status != 200) {
-            if (serverStatus !== 'online') {
+            if (!isServerAvailable()) {
                 logError("Stable Diffusion is still starting up, please wait. If this goes on beyond a few minutes, Stable Diffusion has probably crashed. Please check the error message in the command-line window.", res, outputMsg)
             } else if (typeof res === 'object') {
                 let msg = 'Stable Diffusion had an error reading the response: '
                 try { // 'Response': body stream already read
                     msg += 'Read: ' + await res.text()
                 } catch(e) {
-                    msg += 'No error response. '
+                    msg += 'Unexpected end of stream. '
                 }
                 if (finalJSON) {
                     msg += 'Buffered data: ' + finalJSON
                 }
                 logError(msg, res, outputMsg)
             } else {
-                msg = `Unexpected Read Error:<br/><pre>Response:${res}<br/>StepUpdate:${typeof stepUpdate === 'object' ? JSON.stringify(stepUpdate, undefined, 4) : stepUpdate}</pre>`
+                let msg = `Unexpected Read Error:<br/><pre>Response: ${res}<br/>StepUpdate: ${typeof stepUpdate === 'object' ? JSON.stringify(stepUpdate, undefined, 4) : stepUpdate}</pre>`
+                logError(msg, res, outputMsg)
             }
             progressBar.style.display = 'none'
             return false
@@ -682,14 +782,14 @@ async function checkTasks() {
     let task = taskQueue.pop()
     currentTask = task
 
-    let time = new Date().getTime()
+    let time = Date.now()
 
     let successCount = 0
 
     task.isProcessing = true
     task['stopTask'].innerHTML = '<i class="fa-solid fa-circle-stop"></i> Stop'
-    task['taskStatusLabel'].innerText = "Processing"
+    task['taskStatusLabel'].innerText = "Starting"
-    task['taskStatusLabel'].className += " activeTaskLabel"
+    task['taskStatusLabel'].classList.add('waitingTaskLabel')
 
     const genSeeds = Boolean(typeof task.reqBody.seed !== 'number' || (task.reqBody.seed === task.seed && task.numOutputsTotal > 1))
     const startSeed = task.reqBody.seed || task.seed
@@ -724,7 +824,7 @@ async function checkTasks() {
     task['stopTask'].innerHTML = '<i class="fa-solid fa-trash-can"></i> Remove'
     task['taskStatusLabel'].style.display = 'none'
 
-    time = new Date().getTime() - time
+    time = Date.now() - time
     time /= 1000
 
     if (successCount === task.batchCount) {
@@ -814,8 +914,8 @@ function getCurrentUserRequest() {
 }
 
 function makeImage() {
-    if (serverStatus !== 'online') {
+    if (!isServerAvailable()) {
-        alert('The server is still starting up..')
+        alert('The server is not available.')
         return
     }
     const taskTemplate = getCurrentUserRequest()
@@ -868,7 +968,7 @@ function createTask(task) {
         if (task['isProcessing']) {
             task.isProcessing = false
             try {
-                let res = await fetch('/image/stop')
+                let res = await fetch('/image/stop?session_id=' + sessionId)
             } catch (e) {
                 console.log(e)
             }
@@ -1008,7 +1108,7 @@ async function stopAllTasks() {
     }
 
     try {
-        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()
 

ui/sd_internal/runtime.py

@@ -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]'
+        assert 0. <= req.prompt_strength <= 1., 'can only work with strength in [0.0, 1.0]'
-        t_enc = int(opt_strength * opt_ddim_steps)
+        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:
+    if req.save_to_disk_path is not None:
-        session_out_path = os.path.join(opt_save_to_disk_path, req.session_id)
+        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
 
@@ -447,68 +464,48 @@ 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 \
+                        has_filters =   (req.use_face_correction is not None and req.use_face_correction.startswith('GFPGAN')) or \
-                                        (opt_use_upscale is not None and opt_use_upscale.startswith('RealESRGAN'))
+                                        (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:
+                        if req.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 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)
-
+                            meta_out_path = get_base_path(req.save_to_disk_path, req.session_id, prompts[0], 'txt')
-                            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)
+                            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 req.use_face_correction:
-                            if opt_use_face_correction:
+                                x_sample = apply_filters('gfpgan', x_sample)
-                                _, _, output = model_gfpgan.enhance(x_sample[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
+                                filters_applied.append(req.use_face_correction)
-                                x_sample = output[:,:,::-1]
+                            if req.use_upscale:
-                                filters_applied.append(opt_use_face_correction)
+                                x_sample = apply_filters('real_esrgan', x_sample)
-
+                                filters_applied.append(req.use_upscale)
-                            if opt_use_upscale:
+                            if (len(filters_applied) > 0):
-                                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, req.output_format)
-                            filtered_img_data = img_to_base64_str(filtered_image, opt_format)
+                                response_image = ResponseImage(data=filtered_img_data, seed=req.seed)
-                            res_image_filtered = ResponseImage(data=filtered_img_data, seed=opt_seed)
+                                res.images.append(response_image)
-                            res.images.append(res_image_filtered)
+                                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))
-                            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
+                                    response_image.path_abs = filtered_img_out_path
-
                                 del filtered_image
 
                         seeds += str(opt_seed) + ","
@@ -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):
+def save_metadata(meta_out_path, req, prompt, opt_seed):
-    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'}"
+    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)

ui/sd_internal/task_manager.py

@@ -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.')

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')
+@app.post('/render')
-def image(req : ImageRequest):
+def render(req : task_manager.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)
-
     try:
-        if not req.stream_progress_updates:
+        save_model_to_config(req.use_stable_diffusion_model)
-            r.stream_image_progress = False
+        req.use_stable_diffusion_model = resolve_model_to_use(req.use_stable_diffusion_model)
-
+        new_task = task_manager.render(req)
-        res = runtime.mk_img(r)
+        response = {
-
+            'status': str(task_manager.current_state), 
-        if req.stream_progress_updates:
+            'queue': task_manager.tasks_queue.qsize(),
-            return StreamingResponse(res, media_type='application/json')
+            'stream': f'/image/stream/{req.session_id}/{id(new_task)}',
-        else: # compatibility mode: buffer the streaming responses, and return the last one
+            'task': id(new_task)
-            last_result = None
+        }
-
+        return JSONResponse(response, headers=NOCACHE_HEADERS)
-            for result in res:
+    except ChildProcessError as e: # Render thread is dead
-                last_result = result
+        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 json.loads(last_result)
+        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():
+def stop(session_id:str=None):
-    try:
+    if not session_id:
-        if model_is_loading:
+        if task_manager.current_state == task_manager.ServerStates.Online or task_manager.current_state == task_manager.ServerStates.Unavailable:
-            return {'ERROR'}
+            return HTTPException(status_code=409, detail='Not currently running any tasks.') # HTTP409 Conflict
-
+        task_manager.current_state_error = StopAsyncIteration('')
-        from sd_internal import runtime
+        return {'OK'}
-        runtime.stop_processing = True
+    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'}
-    except Exception as e:
-        print(traceback.format_exc())
-        return HTTPException(status_code=500, detail=str(e))
 
-@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
+    task = task_manager.task_cache.tryGet(session_id)
-    buf = runtime.temp_images[session_id + '/' + img_id]
+    if not task: return HTTPException(status_code=410, detail=f'Session {session_id} has not submitted a task.') # HTTP410 Gone
-    buf.seek(0)
+    if not task.temp_images[img_id]: return HTTPException(status_code=425, detail='Too Early, task data is not available yet.') # HTTP425 Too Early
-    return StreamingResponse(buf, media_type='image/jpeg')
+    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 getConfig(default_val={}):
-def getAppConfig():
     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))
+        return default_val
-
-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 {}
 
 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')
+@app.get('/get/{key:path}')
-def read_modifiers():
+def read_web_data(key:str=None):
-    headers = {"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"}
+    if not key: # /get without parameters, stable-diffusion easter egg.
-    return FileResponse(os.path.join(SD_UI_DIR, 'modifiers.json'), headers=headers)
+        return HTTPException(status_code=418, detail="StableDiffusion is drawing a teapot!") # HTTP418 I'm a teapot
-
+    elif key == 'app_config':
-@app.get('/output_dir')
+        config = getConfig(default_val=None)
-def read_home_dir():
+        if config is None:
-    return {outpath}
+            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')