Merge remote-tracking branch 'origin/beta' into restart-needed

CHANGES.md

@@ -22,6 +22,7 @@
 Our focus continues to remain on an easy installation experience, and an easy user-interface. While still remaining pretty powerful, in terms of features and speed.
 
 ### Detailed changelog
+* 2.5.44 - 15 Jul 2023 - (beta-only) Support for multiple LoRA files.
 * 2.5.43 - 9 Jul 2023 - (beta-only) Support for loading Textual Inversion embeddings. You can find the option in the Image Settings panel. Thanks @JeLuf.
 * 2.5.43 - 9 Jul 2023 - Improve the startup time of the UI.
 * 2.5.42 - 4 Jul 2023 - Keyboard shortcuts for the Image Editor. Thanks @JeLuf.

scripts/Developer Console.cmd

@@ -41,6 +41,10 @@ call python --version
 
 echo PYTHONPATH=%PYTHONPATH%
 
+if exist "%cd%\profile" (
+    set HF_HOME=%cd%\profile\.cache\huggingface
+)
+
 @rem done
 echo.
 

scripts/check_modules.py

@@ -18,7 +18,7 @@ os_name = platform.system()
 modules_to_check = {
     "torch": ("1.11.0", "1.13.1", "2.0.0"),
     "torchvision": ("0.12.0", "0.14.1", "0.15.1"),
-    "sdkit": "1.0.116",
+    "sdkit": "1.0.125",
     "stable-diffusion-sdkit": "2.1.4",
     "rich": "12.6.0",
     "uvicorn": "0.19.0",

scripts/on_sd_start.bat

@@ -104,18 +104,21 @@ call python --version
 
 @FOR /F "tokens=* USEBACKQ" %%F IN (`python scripts\get_config.py --default=False net listen_to_network`) DO (
     if "%%F" EQU "True" (
-        @SET ED_BIND_IP=0.0.0.0    
+        @FOR /F "tokens=* USEBACKQ" %%G IN (`python scripts\get_config.py --default=0.0.0.0 net bind_ip`) DO (
+            @SET ED_BIND_IP=%%G
+        )
     ) else (
         @SET ED_BIND_IP=127.0.0.1
     )
 )
 
+
 @cd stable-diffusion
 
 @rem set any overrides
 set HF_HUB_DISABLE_SYMLINKS_WARNING=true
 
-@uvicorn main:server_api --app-dir "%SD_UI_PATH%" --port %ED_BIND_PORT% --host %ED_BIND_IP% --log-level error
+@python -m uvicorn main:server_api --app-dir "%SD_UI_PATH%" --port %ED_BIND_PORT% --host %ED_BIND_IP% --log-level error
 
 
 @pause

scripts/on_sd_start.sh

@@ -72,7 +72,7 @@ export SD_UI_PATH=`pwd`/ui
 export ED_BIND_PORT="$( python scripts/get_config.py --default=9000 net listen_port )"
 case "$( python scripts/get_config.py --default=False net listen_to_network )" in
     "True")
-        export ED_BIND_IP=0.0.0.0
+        export ED_BIND_IP=$( python scripts/get_config.py --default=0.0.0.0 net bind_ip)
         ;;
     "False")
         export ED_BIND_IP=127.0.0.1

ui/easydiffusion/model_manager.py

@@ -2,6 +2,7 @@ import os
 import shutil
 from glob import glob
 import traceback
+from typing import Union
 
 from easydiffusion import app
 from easydiffusion.types import TaskData
@@ -93,7 +94,14 @@ def unload_all(context: Context):
             del context.model_load_errors[model_type]
 
 
-def resolve_model_to_use(model_name: str = None, model_type: str = None, fail_if_not_found: bool = True):
+def resolve_model_to_use(model_name: Union[str, list] = None, model_type: str = None, fail_if_not_found: bool = True):
+    model_names = model_name if isinstance(model_name, list) else [model_name]
+    model_paths = [resolve_model_to_use_single(m, model_type, fail_if_not_found) for m in model_names]
+
+    return model_paths[0] if len(model_paths) == 1 else model_paths
+
+
+def resolve_model_to_use_single(model_name: str = None, model_type: str = None, fail_if_not_found: bool = True):
     model_extensions = MODEL_EXTENSIONS.get(model_type, [])
     default_models = DEFAULT_MODELS.get(model_type, [])
     config = app.getConfig()

ui/easydiffusion/task_manager.py

@@ -473,15 +473,15 @@ def start_render_thread(device):
         render_threads.append(rthread)
     finally:
         manager_lock.release()
-    # timeout = DEVICE_START_TIMEOUT
+    timeout = DEVICE_START_TIMEOUT
-    # while not rthread.is_alive() or not rthread in weak_thread_data or not "device" in weak_thread_data[rthread]:
+    while not rthread.is_alive() or not rthread in weak_thread_data or not "device" in weak_thread_data[rthread]:
-    #     if rthread in weak_thread_data and "error" in weak_thread_data[rthread]:
+        if rthread in weak_thread_data and "error" in weak_thread_data[rthread]:
-    #         log.error(f"{rthread}, {device}, error: {weak_thread_data[rthread]['error']}")
+            log.error(f"{rthread}, {device}, error: {weak_thread_data[rthread]['error']}")
-    #         return False
+            return False
-    #     if timeout <= 0:
+        if timeout <= 0:
-    #         return False
+            return False
-    #     timeout -= 1
+        timeout -= 1
-    #     time.sleep(1)
+        time.sleep(1)
     return True
 
 
@@ -535,12 +535,12 @@ def update_render_threads(render_devices, active_devices):
         if not start_render_thread(device):
             log.warn(f"{device} failed to start.")
 
-    # if is_alive() <= 0:  # No running devices, probably invalid user config.
+    if is_alive() <= 0:  # No running devices, probably invalid user config.
-    #     raise EnvironmentError(
+        raise EnvironmentError(
-    #         'ERROR: No active render devices! Please verify the "render_devices" value in config.json'
+            'ERROR: No active render devices! Please verify the "render_devices" value in config.json'
-    #     )
+        )
 
-    # log.debug(f"active devices: {get_devices()['active']}")
+    log.debug(f"active devices: {get_devices()['active']}")
 
 
 def shutdown_event():  # Signal render thread to close on shutdown

ui/easydiffusion/types.py

@@ -1,4 +1,4 @@
-from typing import Any
+from typing import Any, List, Union
 
 from pydantic import BaseModel
 
@@ -22,7 +22,7 @@ class GenerateImageRequest(BaseModel):
 
     sampler_name: str = None  # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
     hypernetwork_strength: float = 0
-    lora_alpha: float = 0
+    lora_alpha: Union[float, List[float]] = 0
     tiling: str = "none"  # "none", "x", "y", "xy"
 
 
@@ -32,15 +32,14 @@ class TaskData(BaseModel):
     save_to_disk_path: str = None
     vram_usage_level: str = "balanced"  # or "low" or "medium"
 
-    use_face_correction: str = None  # or "GFPGANv1.3"
+    use_face_correction: Union[str, List[str]] = None  # or "GFPGANv1.3"
-    use_upscale: str = None  # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B" or "latent_upscaler"
+    use_upscale: Union[str, List[str]] = None
     upscale_amount: int = 4  # or 2
     latent_upscaler_steps: int = 10
-    use_stable_diffusion_model: str = "sd-v1-4"
+    use_stable_diffusion_model: Union[str, List[str]] = "sd-v1-4"
-    # use_stable_diffusion_config: str = "v1-inference"
+    use_vae_model: Union[str, List[str]] = None
-    use_vae_model: str = None
+    use_hypernetwork_model: Union[str, List[str]] = None
-    use_hypernetwork_model: str = None
+    use_lora_model: Union[str, List[str]] = None
-    use_lora_model: str = None
 
     show_only_filtered_image: bool = False
     block_nsfw: bool = False

ui/easydiffusion/utils/save_utils.py

@@ -1,6 +1,8 @@
 import os
 import re
 import time
+import regex
+
 from datetime import datetime
 from functools import reduce
 
@@ -30,11 +32,12 @@ TASK_TEXT_MAPPING = {
     "lora_alpha": "LoRA Strength",
     "use_hypernetwork_model": "Hypernetwork model",
     "hypernetwork_strength": "Hypernetwork Strength",
+    "use_embedding_models": "Embedding models",
     "tiling": "Seamless Tiling",
     "use_face_correction": "Use Face Correction",
     "use_upscale": "Use Upscaling",
     "upscale_amount": "Upscale By",
-    "latent_upscaler_steps": "Latent Upscaler Steps"
+    "latent_upscaler_steps": "Latent Upscaler Steps",
 }
 
 time_placeholders = {
@@ -202,6 +205,9 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
     req_metadata = req.dict()
     task_data_metadata = task_data.dict()
 
+    app_config = app.getConfig()
+    using_diffusers = app_config.get("test_diffusers", False)
+
     # Save the metadata in the order defined in TASK_TEXT_MAPPING
     metadata = {}
     for key in TASK_TEXT_MAPPING.keys():
@@ -209,6 +215,24 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
             metadata[key] = req_metadata[key]
         elif key in task_data_metadata:
             metadata[key] = task_data_metadata[key]
+        elif key is "use_embedding_models" and using_diffusers:
+            embeddings_extensions = {".pt", ".bin", ".safetensors"}
+            def scan_directory(directory_path: str):
+                used_embeddings = []
+                for entry in os.scandir(directory_path):
+                    if entry.is_file():
+                        entry_extension = os.path.splitext(entry.name)[1]
+                        if entry_extension not in embeddings_extensions:
+                            continue
+
+                        embedding_name_regex = regex.compile(r"(^|[\s,])" + regex.escape(os.path.splitext(entry.name)[0]) + r"([+-]*$|[\s,]|[+-]+[\s,])")
+                        if embedding_name_regex.search(req.prompt) or embedding_name_regex.search(req.negative_prompt):
+                            used_embeddings.append(entry.path)
+                    elif entry.is_dir():
+                        used_embeddings.extend(scan_directory(entry.path))
+                return used_embeddings
+            used_embeddings = scan_directory(os.path.join(app.MODELS_DIR, "embeddings"))
+            metadata["use_embedding_models"] = ", ".join(used_embeddings) if len(used_embeddings) > 0 else None
     
     # Clean up the metadata
     if req.init_image is None and "prompt_strength" in metadata:
@@ -222,8 +246,7 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
     if task_data.use_upscale != "latent_upscaler" and "latent_upscaler_steps" in metadata:
         del metadata["latent_upscaler_steps"]
 
-    app_config = app.getConfig()
+    if not using_diffusers:
-    if not app_config.get("test_diffusers", False):
         for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip", "tiling", "latent_upscaler_steps"] if x in metadata):
             del metadata[key]
 

ui/index.html

@@ -31,7 +31,7 @@
             <h1>
                 <img id="logo_img" src="/media/images/icon-512x512.png" >
                 Easy Diffusion
-                <small><span id="version">v2.5.43</span> <span id="updateBranchLabel"></span></small>
+                <small><span id="version">v2.5.44</span> <span id="updateBranchLabel"></span></small>
             </h1>
         </div>
         <div id="server-status">
@@ -162,9 +162,10 @@
                             <option value="dpm2_a">DPM2 Ancestral</option>
                             <option value="lms">LMS</option>
                             <option value="dpm_solver_stability">DPM Solver (Stability AI)</option>
-                            <option value="dpmpp_2s_a" class="k_diffusion-only">DPM++ 2s Ancestral (Karras)</option>
+                            <option value="dpmpp_2s_a">DPM++ 2s Ancestral (Karras)</option>
                             <option value="dpmpp_2m">DPM++ 2m (Karras)</option>
-                            <option value="dpmpp_sde" class="k_diffusion-only">DPM++ SDE (Karras)</option>
+                            <option value="dpmpp_2m_sde" class="diffusers-only">DPM++ 2m SDE (Karras)</option>
+                            <option value="dpmpp_sde">DPM++ SDE (Karras)</option>
                             <option value="dpm_fast" class="k_diffusion-only">DPM Fast (Karras)</option>
                             <option value="dpm_adaptive" class="k_diffusion-only">DPM Adaptive (Karras)</option>
                             <option value="ddpm" class="diffusers-only">DDPM</option>
@@ -224,21 +225,14 @@
                         <label for="height"><small>(height)</small></label>
                         <div id="small_image_warning" class="displayNone">Small image sizes can cause bad image quality</div>
                     </td></tr>
-                    <tr class="pl-5"><td><label for="num_inference_steps">Inference Steps:</label></td><td> <input id="num_inference_steps" name="num_inference_steps" size="4" value="25" onkeypress="preventNonNumericalInput(event)"></td></tr>
+                    <tr class="pl-5"><td><label for="num_inference_steps">Inference Steps:</label></td><td> <input id="num_inference_steps" name="num_inference_steps" type="number" min="1" step="1" style="width: 42pt" value="25" onkeypress="preventNonNumericalInput(event)"></td></tr>
                     <tr class="pl-5"><td><label for="guidance_scale_slider">Guidance Scale:</label></td><td> <input id="guidance_scale_slider" name="guidance_scale_slider" class="editor-slider" value="75" type="range" min="11" max="500"> <input id="guidance_scale" name="guidance_scale" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"></td></tr>
                     <tr id="prompt_strength_container" class="pl-5"><td><label for="prompt_strength_slider">Prompt Strength:</label></td><td> <input id="prompt_strength_slider" name="prompt_strength_slider" class="editor-slider" value="80" type="range" min="0" max="99"> <input id="prompt_strength" name="prompt_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"><br/></td></tr>
                     <tr id="lora_model_container" class="pl-5">
-                        <td><label for="lora_model">LoRA:</label></td>
+                        <td>
-                        <td class="diffusers-restart-needed">
+                            <label for="lora_model">LoRA:</label>
-                            <input id="lora_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
-                        </td>
-                    </tr>
-                    <tr id="lora_alpha_container" class="pl-5">
-                        <td><label for="lora_alpha_slider">LoRA Strength:</label></td>
-                        <td class="diffusers-restart-needed">
-                            <small>-2</small> <input id="lora_alpha_slider" name="lora_alpha_slider" class="editor-slider" value="50" type="range" min="-200" max="200"> <small>2</small> &nbsp;
-                            <input id="lora_alpha" name="lora_alpha" size="4" pattern="^-?[0-9]*\.?[0-9]*$" onkeypress="preventNonNumericalInput(event)"><br/>
                         </td>
+                        <td class="model_entries diffusers-restart-needed"></td>
                     </tr>
                     <tr class="pl-5"><td><label for="hypernetwork_model">Hypernetwork:</label></td><td>
                         <input id="hypernetwork_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />

ui/main.py

@@ -1,12 +1,12 @@
 from easydiffusion import model_manager, app, server
 from easydiffusion.server import server_api  # required for uvicorn
 
+server.init()
+
 # Init the app
 model_manager.init()
 app.init()
-server.init()
+app.init_render_threads()
 
 # start the browser ui
 app.open_browser()
-
-app.init_render_threads()

ui/media/css/main.css

@@ -5,6 +5,8 @@
 
 html {
     position: relative;
+    overscroll-behavior-y: none;
+    color-scheme: dark !important;
 }
 
 body {
@@ -1677,6 +1679,10 @@ body.wait-pause {
   background: var(--background-color3);
 }
 
+.model_entry .model_name {
+    width: 70%;
+}
+
 .diffusers-disabled-on-startup .diffusers-restart-needed {
     font-size: 0;
 }

ui/media/js/auto-save.js

@@ -16,7 +16,9 @@ const SETTINGS_IDS_LIST = [
     "clip_skip",
     "vae_model",
     "hypernetwork_model",
-    "lora_model",
+    "lora_model_0",
+    "lora_model_1",
+    "lora_model_2",
     "sampler_name",
     "width",
     "height",
@@ -24,7 +26,9 @@ const SETTINGS_IDS_LIST = [
     "guidance_scale",
     "prompt_strength",
     "hypernetwork_strength",
-    "lora_alpha",
+    "lora_alpha_0",
+    "lora_alpha_1",
+    "lora_alpha_2",
     "tiling",
     "output_format",
     "output_quality",
@@ -176,13 +180,14 @@ function loadSettings() {
         // So this is likely the first time Easy Diffusion is running.
         // Initialize vram_usage_level based on the available VRAM
         function initGPUProfile(event) {
-            if (    "detail" in event 
+            if (
-                && "active" in event.detail
+                "detail" in event &&
-                && "cuda:0" in event.detail.active
+                "active" in event.detail &&
-                && event.detail.active["cuda:0"].mem_total <4.5 )
+                "cuda:0" in event.detail.active &&
-            {
+                event.detail.active["cuda:0"].mem_total < 4.5
-               vramUsageLevelField.value = "low"
+            ) {
-               vramUsageLevelField.dispatchEvent(new Event("change"))
+                vramUsageLevelField.value = "low"
+                vramUsageLevelField.dispatchEvent(new Event("change"))
             }
             document.removeEventListener("system_info_update", initGPUProfile)
         }

ui/media/js/dnd.js

@@ -292,29 +292,58 @@ const TASK_MAPPING = {
     use_lora_model: {
         name: "LoRA model",
         setUI: (use_lora_model) => {
-            const oldVal = loraModelField.value
+            use_lora_model.forEach((model_name, i) => {
-            use_lora_model =
+                let field = loraModels[i][0]
-                use_lora_model === undefined || use_lora_model === null || use_lora_model === "None"
+                const oldVal = field.value
-                    ? ""
-                    : use_lora_model
 
-            if (use_lora_model !== "") {
+                if (model_name !== "") {
-                use_lora_model = getModelPath(use_lora_model, [".ckpt", ".safetensors"])
+                    model_name = getModelPath(model_name, [".ckpt", ".safetensors"])
-                use_lora_model = use_lora_model !== "" ? use_lora_model : oldVal
+                    model_name = model_name !== "" ? model_name : oldVal
+                }
+                field.value = model_name
+            })
+
+            // clear the remaining entries
+            for (let i = use_lora_model.length; i < loraModels.length; i++) {
+                loraModels[i][0].value = ""
             }
-            loraModelField.value = use_lora_model
         },
-        readUI: () => loraModelField.value,
+        readUI: () => {
-        parse: (val) => val,
+            let values = loraModels.map((e) => e[0].value)
+            values = values.filter((e) => e.trim() !== "")
+            values = values.length > 0 ? values : "None"
+            return values
+        },
+        parse: (val) => {
+            val = !val || val === "None" ? "" : val
+            val = Array.isArray(val) ? val : [val]
+            return val
+        },
     },
     lora_alpha: {
         name: "LoRA Strength",
         setUI: (lora_alpha) => {
-            loraAlphaField.value = lora_alpha
+            lora_alpha.forEach((model_strength, i) => {
-            updateLoraAlphaSlider()
+                let field = loraModels[i][1]
+                field.value = model_strength
+            })
+
+            // clear the remaining entries
+            for (let i = lora_alpha.length; i < loraModels.length; i++) {
+                loraModels[i][1].value = 0
+            }
+        },
+        readUI: () => {
+            let models = loraModels.filter((e) => e[0].value.trim() !== "")
+            let values = models.map((e) => e[1].value)
+            values = values.length > 0 ? values : 0
+            return values
+        },
+        parse: (val) => {
+            val = Array.isArray(val) ? val : [val]
+            val = val.map((e) => parseFloat(e))
+            return val
         },
-        readUI: () => parseFloat(loraAlphaField.value),
-        parse: (val) => parseFloat(val),
     },
     use_hypernetwork_model: {
         name: "Hypernetwork model",
@@ -426,8 +455,11 @@ function restoreTaskToUI(task, fieldsToSkip) {
     }
 
     if (!("use_lora_model" in task.reqBody)) {
-        loraModelField.value = ""
+        loraModels.forEach((e) => {
-        loraModelField.dispatchEvent(new Event("change"))
+            e[0].value = ""
+            e[1].value = 0
+            e[0].dispatchEvent(new Event("change"))
+        })
     }
 
     // restore the original prompt if provided (e.g. use settings), fallback to prompt as needed (e.g. copy/paste or d&d)

ui/media/js/main.js

@@ -103,9 +103,6 @@ let vaeModelField = new ModelDropdown(document.querySelector("#vae_model"), "vae
 let hypernetworkModelField = new ModelDropdown(document.querySelector("#hypernetwork_model"), "hypernetwork", "None")
 let hypernetworkStrengthSlider = document.querySelector("#hypernetwork_strength_slider")
 let hypernetworkStrengthField = document.querySelector("#hypernetwork_strength")
-let loraModelField = new ModelDropdown(document.querySelector("#lora_model"), "lora", "None")
-let loraAlphaSlider = document.querySelector("#lora_alpha_slider")
-let loraAlphaField = document.querySelector("#lora_alpha")
 let outputFormatField = document.querySelector("#output_format")
 let outputLosslessField = document.querySelector("#output_lossless")
 let outputLosslessContainer = document.querySelector("#output_lossless_container")
@@ -159,6 +156,8 @@ let undoButton = document.querySelector("#undo")
 let undoBuffer = []
 const UNDO_LIMIT = 20
 
+let loraModels = []
+
 imagePreview.addEventListener("drop", function(ev) {
     const data = ev.dataTransfer?.getData("text/plain")
     if (!data) {
@@ -1292,13 +1291,31 @@ function getCurrentUserRequest() {
         newTask.reqBody.use_hypernetwork_model = hypernetworkModelField.value
         newTask.reqBody.hypernetwork_strength = parseFloat(hypernetworkStrengthField.value)
     }
-    if (testDiffusers.checked && loraModelField.value) {
+    if (testDiffusers.checked) {
-        newTask.reqBody.use_lora_model = loraModelField.value
+        let [modelNames, modelStrengths] = getModelInfo(loraModels)
-        newTask.reqBody.lora_alpha = parseFloat(loraAlphaField.value)
+
+        if (modelNames.length > 0) {
+            modelNames = modelNames.length == 1 ? modelNames[0] : modelNames
+            modelStrengths = modelStrengths.length == 1 ? modelStrengths[0] : modelStrengths
+
+            newTask.reqBody.use_lora_model = modelNames
+            newTask.reqBody.lora_alpha = modelStrengths
+        }
     }
     return newTask
 }
 
+function getModelInfo(models) {
+    let modelInfo = models.map((e) => [e[0].value, e[1].value])
+    modelInfo = modelInfo.filter((e) => e[0].trim() !== "")
+    modelInfo = modelInfo.map((e) => [e[0], parseFloat(e[1])])
+
+    let modelNames = modelInfo.map((e) => e[0])
+    let modelStrengths = modelInfo.map((e) => e[1])
+
+    return [modelNames, modelStrengths]
+}
+
 function getPrompts(prompts) {
     if (typeof prompts === "undefined") {
         prompts = promptField.value
@@ -1346,7 +1363,8 @@ function getPromptsNumber(prompts) {
 
     let promptsToMake = []
     let numberOfPrompts = 0
-    if (prompts.trim() !== "") { // this needs to stay sort of the same, as the prompts have to be passed through to the other functions
+    if (prompts.trim() !== "") {
+        // this needs to stay sort of the same, as the prompts have to be passed through to the other functions
         prompts = prompts.split("\n")
         prompts = prompts.map((prompt) => prompt.trim())
         prompts = prompts.filter((prompt) => prompt !== "")
@@ -1354,7 +1372,11 @@ function getPromptsNumber(prompts) {
         // estimate number of prompts
         let estimatedNumberOfPrompts = 0
         prompts.forEach((prompt) => {
-            estimatedNumberOfPrompts += (prompt.match(/{[^}]*}/g) || []).map((e) => (e.match(/,/g) || []).length + 1).reduce( (p,a) => p*a, 1) * (2**(prompt.match(/\|/g) || []).length)
+            estimatedNumberOfPrompts +=
+                (prompt.match(/{[^}]*}/g) || [])
+                    .map((e) => (e.match(/,/g) || []).length + 1)
+                    .reduce((p, a) => p * a, 1) *
+                2 ** (prompt.match(/\|/g) || []).length
         })
 
         if (estimatedNumberOfPrompts >= 10000) {
@@ -1394,7 +1416,8 @@ function applySetOperator(prompts) {
     return promptsToMake
 }
 
-function applyPermuteOperator(prompts) { // prompts is array of input, trimmed, filtered and split by \n
+function applyPermuteOperator(prompts) {
+    // prompts is array of input, trimmed, filtered and split by \n
     let promptsToMake = []
     prompts.forEach((prompt) => {
         let promptMatrix = prompt.split("|")
@@ -1414,13 +1437,14 @@ function applyPermuteOperator(prompts) { // prompts is array of input, trimmed,
 }
 
 // returns how many prompts would have to be made with the given prompts
-function applyPermuteOperatorNumber(prompts) { // prompts is array of input, trimmed, filtered and split by \n
+function applyPermuteOperatorNumber(prompts) {
+    // prompts is array of input, trimmed, filtered and split by \n
     let numberOfPrompts = 0
     prompts.forEach((prompt) => {
         let promptCounter = 1
         let promptMatrix = prompt.split("|")
         promptMatrix.shift()
-        
+
         promptMatrix = promptMatrix.map((p) => p.trim())
         promptMatrix = promptMatrix.filter((p) => p !== "")
 
@@ -1510,8 +1534,12 @@ clearAllPreviewsBtn.addEventListener("click", (e) => {
 })
 
 /* Download images popup */
-showDownloadDialogBtn.addEventListener("click", (e) => { saveAllImagesDialog.showModal() }) 
+showDownloadDialogBtn.addEventListener("click", (e) => {
-saveAllImagesCloseBtn.addEventListener("click", (e) => { saveAllImagesDialog.close() })
+    saveAllImagesDialog.showModal()
+})
+saveAllImagesCloseBtn.addEventListener("click", (e) => {
+    saveAllImagesDialog.close()
+})
 modalDialogCloseOnBackdropClick(saveAllImagesDialog)
 makeDialogDraggable(saveAllImagesDialog)
 
@@ -1629,15 +1657,11 @@ function renameMakeImageButton() {
         imageLabel = totalImages + " Images"
     }
     if (SD.activeTasks.size == 0) {
-        if (totalImages >= 10000)
+        if (totalImages >= 10000) makeImageBtn.innerText = "Make 10000+ images"
-            makeImageBtn.innerText = "Make 10000+ images"
+        else makeImageBtn.innerText = "Make " + imageLabel
-        else
-            makeImageBtn.innerText = "Make " + imageLabel
     } else {
-        if (totalImages >= 10000)
+        if (totalImages >= 10000) makeImageBtn.innerText = "Enqueue 10000+ images"
-            makeImageBtn.innerText = "Enqueue 10000+ images"
+        else makeImageBtn.innerText = "Enqueue Next " + imageLabel
-        else
-            makeImageBtn.innerText = "Enqueue Next " + imageLabel
     }
 }
 numOutputsTotalField.addEventListener("change", renameMakeImageButton)
@@ -1829,36 +1853,6 @@ function updateHypernetworkStrengthContainer() {
 hypernetworkModelField.addEventListener("change", updateHypernetworkStrengthContainer)
 updateHypernetworkStrengthContainer()
 
-/********************* LoRA alpha **********************/
-function updateLoraAlpha() {
-    loraAlphaField.value = loraAlphaSlider.value / 100
-    loraAlphaField.dispatchEvent(new Event("change"))
-}
-
-function updateLoraAlphaSlider() {
-    if (loraAlphaField.value < -2) {
-        loraAlphaField.value = -2
-    } else if (loraAlphaField.value > 2) {
-        loraAlphaField.value = 2
-    }
-
-    loraAlphaSlider.value = loraAlphaField.value * 100
-    loraAlphaSlider.dispatchEvent(new Event("change"))
-}
-
-loraAlphaSlider.addEventListener("input", updateLoraAlpha)
-loraAlphaField.addEventListener("input", updateLoraAlphaSlider)
-updateLoraAlpha()
-
-function updateLoraAlphaContainer() {
-    const loraModelContainer = document.querySelector("#lora_model_container")
-    if (loraModelContainer && window.getComputedStyle(loraModelContainer).display !== "none") {
-        document.querySelector("#lora_alpha_container").style.display = loraModelField.value === "" ? "none" : ""
-    }
-}
-loraModelField.addEventListener("change", updateLoraAlphaContainer)
-updateLoraAlphaContainer()
-
 /********************* JPEG/WEBP Quality **********************/
 function updateOutputQuality() {
     outputQualityField.value = 0 | outputQualitySlider.value
@@ -2076,9 +2070,8 @@ function resumeClient() {
     })
 }
 
-
 function splashScreen(force = false) {
-    const splashVersion = splashScreenPopup.dataset['version']
+    const splashVersion = splashScreenPopup.dataset["version"]
     const lastSplash = localStorage.getItem("lastSplashScreenVersion") || 0
     if (testDiffusers.checked) {
         if (force || lastSplash < splashVersion) {
@@ -2088,8 +2081,9 @@ function splashScreen(force = false) {
     }
 }
 
-
+document.getElementById("logo_img").addEventListener("click", (e) => {
-document.getElementById("logo_img").addEventListener("click", (e) => { splashScreen(true) })
+    splashScreen(true)
+})
 
 promptField.addEventListener("input", debounce(renameMakeImageButton, 1000))
 
@@ -2142,21 +2136,21 @@ document.getElementById("toggle-cloudflare-tunnel").addEventListener("click", as
 
 /* Embeddings */
 
-function updateEmbeddingsList(filter="") {
+function updateEmbeddingsList(filter = "") {
-    function html(model, prefix="", filter="") {
+    function html(model, prefix = "", filter = "") {
         filter = filter.toLowerCase()
-        let toplevel=""
+        let toplevel = ""
-        let folders=""
+        let folders = ""
-       
+
-        model?.forEach( m => {
+        model?.forEach((m) => {
-            if (typeof(m) == "string") {
+            if (typeof m == "string") {
-                if (m.toLowerCase().search(filter)!=-1) {
+                if (m.toLowerCase().search(filter) != -1) {
                     toplevel += `<button data-embedding="${m}">${m}</button> `
                 }
             } else {
-                let subdir = html(m[1], prefix+m[0]+"/", filter)
+                let subdir = html(m[1], prefix + m[0] + "/", filter)
                 if (subdir != "") {
-                   folders += `<h4>${prefix}${m[0]}</h4>` + subdir
+                    folders += `<h4>${prefix}${m[0]}</h4>` + subdir
                 }
             }
         })
@@ -2174,7 +2168,7 @@ function updateEmbeddingsList(filter="") {
                 insertAtCursor(promptField, text)
             }
         } else {
-            let pad=""
+            let pad = ""
             if (e.shiftKey) {
                 if (!negativePromptField.value.endsWith(" ")) {
                     pad = " "
@@ -2189,14 +2183,26 @@ function updateEmbeddingsList(filter="") {
         }
     }
 
-    embeddingsList.innerHTML = html(modelsOptions.embeddings, "", filter)
+    // Remove after fixing https://github.com/huggingface/diffusers/issues/3922
-    embeddingsList.querySelectorAll("button").forEach( (b) => { b.addEventListener("click", onButtonClick)})
+    let warning = ""
+    if (vramUsageLevelField.value == "low") {
+        warning = `
+            <div style="border-color: var(--accent-color); border-width: 4px; border-radius: 1em; border-style: solid; background: black; text-align: center; padding: 1em; margin: 1em; ">
+                <i class="fa fa-fire" style="color:#f7630c;"></i> Warning: Your GPU memory profile is set to "Low". Embeddings currently only work in "Balanced" mode!
+            </div>`
+    }
+    // END of remove block
+
+    embeddingsList.innerHTML = warning + html(modelsOptions.embeddings, "", filter)
+    embeddingsList.querySelectorAll("button").forEach((b) => {
+        b.addEventListener("click", onButtonClick)
+    })
 }
 
-embeddingsButton.addEventListener("click", () => { 
+embeddingsButton.addEventListener("click", () => {
     updateEmbeddingsList()
-    embeddingsSearchBox.value=""
+    embeddingsSearchBox.value = ""
-    embeddingsDialog.showModal() 
+    embeddingsDialog.showModal()
 })
 embeddingsDialogCloseBtn.addEventListener("click", (e) => {
     embeddingsDialog.close()
@@ -2208,7 +2214,6 @@ embeddingsSearchBox.addEventListener("input", (e) => {
 modalDialogCloseOnBackdropClick(embeddingsDialog)
 makeDialogDraggable(embeddingsDialog)
 
-
 if (testDiffusers.checked) {
     document.getElementById("embeddings-container").classList.remove("displayNone")
 }
@@ -2235,3 +2240,43 @@ prettifyInputs(document)
 // set the textbox as focused on start
 promptField.focus()
 promptField.selectionStart = promptField.value.length
+
+// multi-models
+function addModelEntry(i, modelContainer, modelsList, modelType, defaultValue, strengthStep) {
+    let nameId = modelType + "_model_" + i
+    let strengthId = modelType + "_alpha_" + i
+
+    const modelEntry = document.createElement("div")
+    modelEntry.className = "model_entry"
+    modelEntry.innerHTML = `
+        <input id="${nameId}" class="model_name" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
+        <input id="${strengthId}" class="model_strength" type="number" step="${strengthStep}" style="width: 50pt" value="${defaultValue}" pattern="^-?[0-9]*\.?[0-9]*$" onkeypress="preventNonNumericalInput(event)"><br/>
+    `
+
+    let modelName = new ModelDropdown(modelEntry.querySelector(".model_name"), modelType, "None")
+    let modelStrength = modelEntry.querySelector(".model_strength")
+
+    modelContainer.appendChild(modelEntry)
+    modelsList.push([modelName, modelStrength])
+}
+
+function createLoRAEntries() {
+    let container = document.querySelector("#lora_model_container .model_entries")
+    for (let i = 0; i < 3; i++) {
+        addModelEntry(i, container, loraModels, "lora", 0.5, 0.02)
+    }
+}
+createLoRAEntries()
+
+// chrome-like spinners only on hover
+function showSpinnerOnlyOnHover(e) {
+    e.addEventListener("mouseenter", () => {
+        e.setAttribute("type", "number")
+    })
+    e.addEventListener("mouseleave", () => {
+        e.removeAttribute("type")
+    })
+    e.removeAttribute("type")
+}
+
+document.querySelectorAll("input[type=number]").forEach(showSpinnerOnlyOnHover)

ui/media/js/parameters.js

@@ -436,7 +436,6 @@ async function getAppConfig() {
 
         if (!testDiffusersEnabled) {
             document.querySelector("#lora_model_container").style.display = "none"
-            document.querySelector("#lora_alpha_container").style.display = "none"
             document.querySelector("#tiling_container").style.display = "none"
 
             document.querySelectorAll("#sampler_name option.diffusers-only").forEach((option) => {
@@ -444,7 +443,6 @@ async function getAppConfig() {
             })
         } else {
             document.querySelector("#lora_model_container").style.display = ""
-            document.querySelector("#lora_alpha_container").style.display = loraModelField.value ? "" : "none"
             document.querySelector("#tiling_container").style.display = ""
 
             document.querySelectorAll("#sampler_name option.k_diffusion-only").forEach((option) => {

ui/media/js/utils.js

@@ -1074,6 +1074,12 @@ async function deleteKeys(keyToDelete) {
 
 function modalDialogCloseOnBackdropClick(dialog) {
     dialog.addEventListener('mousedown', function (event) {
+        // Firefox creates an event with clientX|Y = 0|0 when choosing an <option>.
+        // Test whether the element interacted with is a child of the dialog, but not the
+        // dialog itself (the backdrop would be a part of the dialog)
+        if (dialog.contains(event.target) && dialog != event.target) {
+            return
+        }
         var rect = dialog.getBoundingClientRect()
         var isInDialog=(rect.top <= event.clientY && event.clientY <= rect.top + rect.height
           && rect.left <= event.clientX && event.clientX <= rect.left + rect.width)