feat(SaveImageLM): add add_loras_to_prompt toggle to restore legacy lora syntax line in metadata

py/nodes/save_image.py

@@ -252,6 +252,13 @@ class SaveImageLM:
                         "tooltip": "When enabled, embeds generation parameters into the saved image metadata. Disable to skip writing generation metadata.",
                     },
                 ),
+                "add_loras_to_prompt": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "When enabled, appends the LoRA syntax line (e.g. <lora:name:strength>) after the positive prompt in the saved metadata.",
+                    },
+                ),
                 "add_counter_to_filename": (
                     "BOOLEAN",
                     {
@@ -348,7 +355,7 @@ class SaveImageLM:
         type_lower = model_type.lower() if model_type else "other"
         return f"urn:air:{slug}:{type_lower}:civitai:{model_id}@{version_id}"
 
-    def format_metadata(self, metadata_dict: dict) -> str:
+    def format_metadata(self, metadata_dict: dict, add_loras_to_prompt: bool = False) -> str:
         """Format metadata as A1111-compatible parameters string with Hashes JSON and Civitai resources."""
         if not metadata_dict: return ""
 
@@ -458,7 +465,10 @@ class SaveImageLM:
         scheduler_name = scheduler_mapping.get(scheduler, scheduler) if scheduler else None
 
         # Build output lines
-        lines = [prompt] if prompt else [""]
+        prompt_line = prompt if prompt else ""
+        if add_loras_to_prompt and loras_text:
+            prompt_line = f"{prompt_line}\n{loras_text}" if prompt_line else loras_text
+        lines = [prompt_line] if prompt_line else [""]
         if negative_prompt:
             lines.append(f"Negative prompt: {negative_prompt}")
 
@@ -793,6 +803,7 @@ class SaveImageLM:
         save_with_metadata=True,
         add_counter_to_filename=True,
         save_as_recipe=False,
+        add_loras_to_prompt=False,
     ):
         """Save images with metadata"""
         results = []
@@ -801,7 +812,7 @@ class SaveImageLM:
         raw_metadata = get_metadata()
         metadata_dict = MetadataProcessor.to_dict(raw_metadata, id)
 
-        metadata = self.format_metadata(metadata_dict)
+        metadata = self.format_metadata(metadata_dict, add_loras_to_prompt)
 
         # Process filename_prefix with pattern substitution
         filename_prefix = self.format_filename(filename_prefix, metadata_dict)
@@ -943,6 +954,7 @@ class SaveImageLM:
         save_with_metadata=True,
         add_counter_to_filename=True,
         save_as_recipe=False,
+        add_loras_to_prompt=False,
     ):
         """Process and save image with metadata"""
         # Make sure the output directory exists
@@ -974,6 +986,7 @@ class SaveImageLM:
             save_with_metadata,
             add_counter_to_filename,
             save_as_recipe,
+            add_loras_to_prompt,
         )
 
         return {

tests/nodes/test_save_image.py

@@ -86,6 +86,41 @@ def test_save_image_skips_png_parameters_when_metadata_disabled_and_keeps_workfl
         assert img.info["workflow"] == json.dumps(workflow)
 
 
+def test_save_image_does_not_append_loras_to_prompt_by_default(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(
+        monkeypatch,
+        {"prompt": "prompt text", "seed": 123, "loras": "<lora:foo:0.7>"},
+    )
+
+    node = SaveImageLM()
+    node.save_images([_make_image()], "ComfyUI", "png", id="node-1")
+
+    image_path = tmp_path / "sample_00001_.png"
+    with Image.open(image_path) as img:
+        assert "<lora:" not in img.info["parameters"]
+        assert img.info["parameters"] == "prompt text\nSeed: 123, Version: ComfyUI"
+
+
+def test_save_image_appends_loras_to_prompt_when_enabled(monkeypatch, tmp_path):
+    _configure_save_paths(monkeypatch, tmp_path)
+    _configure_metadata(
+        monkeypatch,
+        {"prompt": "prompt text", "seed": 123, "loras": "<lora:foo:0.7>"},
+    )
+
+    node = SaveImageLM()
+    node.save_images(
+        [_make_image()], "ComfyUI", "png", id="node-1", add_loras_to_prompt=True
+    )
+
+    image_path = tmp_path / "sample_00001_.png"
+    with Image.open(image_path) as img:
+        assert img.info["parameters"] == (
+            "prompt text\n<lora:foo:0.7>\nSeed: 123, Version: ComfyUI"
+        )
+
+
 def test_save_image_skips_jpeg_metadata_when_disabled(monkeypatch, tmp_path):
     _configure_save_paths(monkeypatch, tmp_path)
     _configure_metadata(monkeypatch, {"prompt": "prompt text", "seed": 123})
@@ -451,6 +486,14 @@ class TestParameterDefaultConsistency:
         assert SaveImageLM.save_images.__defaults__[5] == 0
         assert SaveImageLM.process_image.__defaults__[7] == 0
 
+    def test_add_loras_to_prompt_defaults_are_consistent(self):
+        input_types = SaveImageLM.INPUT_TYPES()
+        optional = input_types["optional"]
+
+        assert optional["add_loras_to_prompt"][1]["default"] is False
+        assert SaveImageLM.save_images.__defaults__[-1] is False
+        assert SaveImageLM.process_image.__defaults__[-1] is False
+
 
 def test_png_does_not_pass_webp_method_or_jpeg_subsampling(monkeypatch, tmp_path):
     _configure_save_paths(monkeypatch, tmp_path)

vue-widgets/src/components/AutocompleteTextWidget.vue

@@ -45,7 +45,7 @@ export interface AutocompleteTextWidgetInterface {
 const props = defineProps<{
   widget: AutocompleteTextWidgetInterface
   node: { id: number }
-  modelType?: 'loras' | 'embeddings' | 'custom_words' | 'prompt'
+  modelType?: 'loras' | 'prompt'
   placeholder?: string
   showPreview?: boolean
   spellcheck?: boolean

vue-widgets/src/components/LoraInfoWidget.vue

@@ -98,7 +98,7 @@ interface LoraInfoWidget {
   onSetValue?: (v: unknown) => void
   callback?: unknown
   options?: {
-    getValue?: () => LoraInfoWidgetValue
+    getValue?: () => unknown
     setValue?: (v: unknown) => void
   }
   node?: { widgets?: Array<{ id?: string }>; widgets_values?: Array<unknown> }
@@ -299,8 +299,12 @@ onMounted(() => {
 
   // ComponentWidgetImpl.value getter/setter delegates to options.getValue/options.setValue.
   // These must be set for workflow JSON persistence (LGraphNode.serialize/configure) to work.
+  if (props.widget.options) {
     props.widget.options.getValue = buildValue
     props.widget.options.setValue = applyValue
+  } else {
+    console.warn('[LoraInfoWidget] widget.options missing, value persistence disabled')
+  }
 
   // Also set serializeValue for prompt/API serialization path (executionUtil.ts)
   props.widget.serializeValue = async () => buildValue()

vue-widgets/src/composables/useAutocomplete.ts

@@ -3,7 +3,7 @@ import { ref, onMounted, onUnmounted, type Ref } from 'vue'
 // Dynamic import type for AutoComplete class
 type AutoCompleteClass = new (
   inputElement: HTMLTextAreaElement,
-  modelType: 'loras' | 'embeddings' | 'custom_words' | 'prompt',
+  modelType: 'loras' | 'prompt',
   options?: AutocompleteOptions
 ) => AutoCompleteInstance
 
@@ -29,7 +29,7 @@ export interface UseAutocompleteOptions {
 
 export function useAutocomplete(
   textareaRef: Ref<HTMLTextAreaElement | null>,
-  modelType: 'loras' | 'embeddings' | 'custom_words' | 'prompt' = 'loras',
+  modelType: 'loras' | 'prompt' = 'loras',
   options: UseAutocompleteOptions = {}
 ) {
   const autocompleteInstance = ref<AutoCompleteInstance | null>(null)

vue-widgets/src/main.ts

@@ -36,6 +36,9 @@ const AUTOCOMPLETE_TEXT_MIN_HEIGHT_DEFAULT = 300
 const AUTOCOMPLETE_METADATA_VERSION = 1
 const LORA_MANAGER_WIDGET_IDS_PROPERTY = '__lm_widget_ids'
 
+// Access LiteGraph global for Vue DOM mode detection (matches AutocompleteTextWidget.vue)
+declare const LiteGraph: { vueNodesMode?: boolean } | undefined
+
 // @ts-ignore - ComfyUI external module
 import { app } from '../../../scripts/app.js'
 // @ts-ignore - ComfyUI external module
@@ -718,7 +721,7 @@ function createLoraInfoWidget(node: any) {
 function createAutocompleteTextWidgetFactory(
   node: any,
   widgetName: string,
-  modelType: 'loras' | 'embeddings' | 'prompt',
+  modelType: 'loras' | 'prompt',
   inputOptions: { placeholder?: string } = {}
 ) {
   const metadataWidgetName = `__lm_autocomplete_meta_${widgetName}`
@@ -835,7 +838,7 @@ function createAutocompleteTextWidgetFactory(
     applyAutocompleteTextLayoutFix(
       widget,
       container,
-      typeof LiteGraph !== 'undefined' && LiteGraph.vueNodesMode
+      typeof LiteGraph !== 'undefined' && LiteGraph.vueNodesMode === true
     )
   }
 
@@ -964,13 +967,7 @@ app.registerExtension({
         const options = widgetInputOptions.get(`${node.comfyClass}:text`) || {}
         return createAutocompleteTextWidgetFactory(node, 'text', 'loras', options)
       },
-      // Autocomplete text widget for embeddings (used by Prompt node)
+      // Autocomplete text widget for prompt (used by Prompt and Text nodes)
-      // @ts-ignore
-      AUTOCOMPLETE_TEXT_EMBEDDINGS(node) {
-        const options = widgetInputOptions.get(`${node.comfyClass}:text`) || {}
-        return createAutocompleteTextWidgetFactory(node, 'text', 'embeddings', options)
-      },
-      // Autocomplete text widget for prompt (supports both embeddings and custom words)
       // @ts-ignore
       AUTOCOMPLETE_TEXT_PROMPT(node) {
         const options = widgetInputOptions.get(`${node.comfyClass}:text`) || {}

web/comfyui/vue-widgets/lora-manager-widgets.js

@@ -2118,14 +2118,14 @@ to { transform: rotate(360deg);
   padding: 20px 0;
 }
 
-.autocomplete-text-widget[data-v-3f3d7a1a] {
+.autocomplete-text-widget[data-v-55e3316e] {
   background: transparent;
   height: 100%;
   display: flex;
   flex-direction: column;
   box-sizing: border-box;
 }
-.input-wrapper[data-v-3f3d7a1a] {
+.input-wrapper[data-v-55e3316e] {
   position: relative;
   flex: 1;
   display: flex;
@@ -2133,7 +2133,7 @@ to { transform: rotate(360deg);
 }
 
 /* Canvas mode styles (default) - matches built-in comfy-multiline-input */
-.text-input[data-v-3f3d7a1a] {
+.text-input[data-v-55e3316e] {
   flex: 1;
   width: 100%;
   background-color: var(--comfy-input-bg, #222);
@@ -2152,7 +2152,7 @@ to { transform: rotate(360deg);
 }
 
 /* Vue DOM mode styles - matches built-in p-textarea in Vue DOM mode */
-.text-input.vue-dom-mode[data-v-3f3d7a1a] {
+.text-input.vue-dom-mode[data-v-55e3316e] {
   background-color: var(--color-charcoal-400, #313235);
   color: #fff;
   padding: 8px 12px 30px 12px;  /* Reserve bottom space for clear button */
@@ -2161,12 +2161,12 @@ to { transform: rotate(360deg);
   font-size: 12px;
   font-family: inherit;
 }
-.text-input[data-v-3f3d7a1a]:focus {
+.text-input[data-v-55e3316e]:focus {
   outline: none;
 }
 
 /* Clear button styles */
-.clear-button[data-v-3f3d7a1a] {
+.clear-button[data-v-55e3316e] {
   position: absolute;
   right: 6px;
   bottom: 6px;  /* Changed from top to bottom */
@@ -2189,31 +2189,31 @@ to { transform: rotate(360deg);
 }
 
 /* Show clear button when hovering over input wrapper */
-.input-wrapper:hover .clear-button[data-v-3f3d7a1a] {
+.input-wrapper:hover .clear-button[data-v-55e3316e] {
   opacity: 0.7;
   pointer-events: auto;
 }
-.clear-button[data-v-3f3d7a1a]:hover {
+.clear-button[data-v-55e3316e]:hover {
   opacity: 1;
   background: rgba(255, 100, 100, 0.8);
 }
-.clear-button svg[data-v-3f3d7a1a] {
+.clear-button svg[data-v-55e3316e] {
   width: 12px;
   height: 12px;
 }
 
 /* Vue DOM mode adjustments for clear button */
-.text-input.vue-dom-mode ~ .clear-button[data-v-3f3d7a1a] {
+.text-input.vue-dom-mode ~ .clear-button[data-v-55e3316e] {
   right: 8px;
   bottom: 10px;  /* Changed from top to bottom, adjusted for Vue DOM padding */
   width: 20px;
   height: 20px;
   background: rgba(107, 114, 128, 0.6);
 }
-.text-input.vue-dom-mode ~ .clear-button[data-v-3f3d7a1a]:hover {
+.text-input.vue-dom-mode ~ .clear-button[data-v-55e3316e]:hover {
   background: oklch(62% 0.18 25);
 }
-.text-input.vue-dom-mode ~ .clear-button svg[data-v-3f3d7a1a] {
+.text-input.vue-dom-mode ~ .clear-button svg[data-v-55e3316e] {
   width: 14px;
   height: 14px;
 }
@@ -2224,7 +2224,7 @@ to { transform: rotate(360deg);
   resize: vertical !important;
 }
 
-.lora-info-widget[data-v-a99cc1ab] {
+.lora-info-widget[data-v-d7692b6f] {
   padding: 12px;
   background: rgba(40, 44, 52, 0.6);
   border-radius: 4px;
@@ -2240,45 +2240,45 @@ to { transform: rotate(360deg);
    determined solely by CSS — not by descendant content. This breaks the
    feedback loop where content grows → ResizeObserver resizes → content
    reflows → repeat. Same technique used by tags_widget.js + lm_styles.css. */
-.lora-info-widget.lm-vue-node[data-v-a99cc1ab] {
+.lora-info-widget.lm-vue-node[data-v-d7692b6f] {
   contain: layout size;
 }
 
 /* ── Tab bar ── */
-.lora-info-tabs[data-v-a99cc1ab] {
+.lora-info-tabs[data-v-d7692b6f] {
   display: flex;
   gap: 0;
   margin-bottom: 10px;
   border-bottom: 1px solid var(--border-color, #444);
   flex-shrink: 0;
 }
-.lora-info-tab[data-v-a99cc1ab] {
+.lora-info-tab[data-v-d7692b6f] {
   flex: 1;
   text-align: center;
   cursor: pointer;
   padding: 6px 0;
   position: relative;
 }
-.lora-info-tab-input[data-v-a99cc1ab] {
+.lora-info-tab-input[data-v-d7692b6f] {
   position: absolute;
   opacity: 0;
   width: 0;
   height: 0;
 }
-.lora-info-tab-label[data-v-a99cc1ab] {
+.lora-info-tab-label[data-v-d7692b6f] {
   font-size: 12px;
   font-weight: 500;
   color: var(--fg-color, #fff);
   opacity: 0.5;
   transition: opacity 0.15s;
 }
-.lora-info-tab:hover .lora-info-tab-label[data-v-a99cc1ab] {
+.lora-info-tab:hover .lora-info-tab-label[data-v-d7692b6f] {
   opacity: 0.75;
 }
-.lora-info-tab.active .lora-info-tab-label[data-v-a99cc1ab] {
+.lora-info-tab.active .lora-info-tab-label[data-v-d7692b6f] {
   opacity: 1;
 }
-.lora-info-tab.active[data-v-a99cc1ab]::after {
+.lora-info-tab.active[data-v-d7692b6f]::after {
   content: '';
   position: absolute;
   bottom: -1px;
@@ -2290,16 +2290,16 @@ to { transform: rotate(360deg);
 }
 
 /* ── Tab content ── */
-.tab-content[data-v-a99cc1ab] {
+.tab-content[data-v-d7692b6f] {
   flex: 1;
   min-height: 0;
   overflow: hidden;
 }
-.notes-tab[data-v-a99cc1ab] {
+.notes-tab[data-v-d7692b6f] {
   display: flex;
   flex-direction: column;
 }
-.description-tab[data-v-a99cc1ab] {
+.description-tab[data-v-d7692b6f] {
   display: flex;
   flex-direction: column;
   overflow-y: auto;
@@ -2307,12 +2307,12 @@ to { transform: rotate(360deg);
 }
 
 /* ── Info fields (shared) ── */
-.info-field[data-v-a99cc1ab] {
+.info-field[data-v-d7692b6f] {
   display: flex;
   flex-direction: column;
   gap: 4px;
 }
-.info-label[data-v-a99cc1ab] {
+.info-label[data-v-d7692b6f] {
   font-size: 10px;
   font-weight: 600;
   text-transform: uppercase;
@@ -2320,7 +2320,7 @@ to { transform: rotate(360deg);
   color: var(--fg-color, #fff);
   opacity: 0.6;
 }
-.lora-filename[data-v-a99cc1ab] {
+.lora-filename[data-v-d7692b6f] {
   font-size: 13px;
   font-weight: 500;
   color: var(--fg-color, #fff);
@@ -2331,11 +2331,11 @@ to { transform: rotate(360deg);
   user-select: text;
   -webkit-user-select: text;
 }
-.notes-field[data-v-a99cc1ab] {
+.notes-field[data-v-d7692b6f] {
   flex: 1;
   min-height: 0;
 }
-.lora-notes[data-v-a99cc1ab] {
+.lora-notes[data-v-d7692b6f] {
   width: 100%;
   flex: 1;
   min-height: 60px;
@@ -2350,14 +2350,14 @@ to { transform: rotate(360deg);
   font-family: inherit;
   outline: none;
 }
-.lora-notes[data-v-a99cc1ab]:focus {
+.lora-notes[data-v-d7692b6f]:focus {
   border-color: var(--comfy-input-border, #444);
 }
-.lora-notes[data-v-a99cc1ab]:disabled {
+.lora-notes[data-v-d7692b6f]:disabled {
   opacity: 0.6;
   cursor: not-allowed;
 }
-.save-btn[data-v-a99cc1ab] {
+.save-btn[data-v-d7692b6f] {
   width: 100%;
   margin-top: 8px;
   padding: 6px 12px;
@@ -2371,11 +2371,11 @@ to { transform: rotate(360deg);
   box-sizing: border-box;
   flex-shrink: 0;
 }
-.save-btn[data-v-a99cc1ab]:hover:not(:disabled) {
+.save-btn[data-v-d7692b6f]:hover:not(:disabled) {
   background: rgba(66, 153, 225, 0.25);
   border-color: rgba(66, 153, 225, 0.6);
 }
-.save-btn[data-v-a99cc1ab]:disabled {
+.save-btn[data-v-d7692b6f]:disabled {
   opacity: 0.4;
   cursor: not-allowed;
   background: rgba(66, 153, 225, 0.05);
@@ -2383,7 +2383,7 @@ to { transform: rotate(360deg);
 }
 
 /* ── Description states ── */
-.description-state[data-v-a99cc1ab] {
+.description-state[data-v-d7692b6f] {
   display: flex;
   align-items: center;
   justify-content: center;
@@ -2395,22 +2395,22 @@ to { transform: rotate(360deg);
   min-height: 0;
   flex-shrink: 0;
 }
-.description-state.error[data-v-a99cc1ab] {
+.description-state.error[data-v-d7692b6f] {
   opacity: 0.7;
   color: #f87171;
 }
 
 /* ── Description content ── */
-.description-content[data-v-a99cc1ab] {
+.description-content[data-v-d7692b6f] {
   min-height: 0;
 }
-.description-section[data-v-a99cc1ab] {
+.description-section[data-v-d7692b6f] {
   margin-bottom: 14px;
 }
-.description-section[data-v-a99cc1ab]:last-child {
+.description-section[data-v-d7692b6f]:last-child {
   margin-bottom: 0;
 }
-.description-text[data-v-a99cc1ab] {
+.description-text[data-v-d7692b6f] {
   padding: 8px 0;
   font-size: 12px;
   line-height: 1.5;
@@ -2422,41 +2422,41 @@ to { transform: rotate(360deg);
   user-select: text;
   -webkit-user-select: text;
 }
-.description-text[data-v-a99cc1ab] p {
+.description-text[data-v-d7692b6f] p {
   margin: 0 0 8px 0;
 }
-.description-text[data-v-a99cc1ab] p:last-child {
+.description-text[data-v-d7692b6f] p:last-child {
   margin-bottom: 0;
 }
-.description-text[data-v-a99cc1ab] a {
+.description-text[data-v-d7692b6f] a {
   color: rgba(66, 153, 225, 0.9);
 }
-.description-text[data-v-a99cc1ab] ul,
+.description-text[data-v-d7692b6f] ul,
-.description-text[data-v-a99cc1ab] ol {
+.description-text[data-v-d7692b6f] ol {
   padding-left: 20px;
   margin: 4px 0;
 }
-.description-text[data-v-a99cc1ab] h1,
+.description-text[data-v-d7692b6f] h1,
-.description-text[data-v-a99cc1ab] h2,
+.description-text[data-v-d7692b6f] h2,
-.description-text[data-v-a99cc1ab] h3 {
+.description-text[data-v-d7692b6f] h3 {
   font-size: 13px;
   margin: 10px 0 4px 0;
   font-weight: 600;
   opacity: 0.95;
 }
-.description-text[data-v-a99cc1ab] code {
+.description-text[data-v-d7692b6f] code {
   background: rgba(255, 255, 255, 0.08);
   padding: 1px 4px;
   border-radius: 3px;
   font-size: 11px;
 }
-.description-text[data-v-a99cc1ab] img {
+.description-text[data-v-d7692b6f] img {
   max-width: 100%;
   border-radius: 4px;
 }
 
 /* ── Placeholder (shared) ── */
-.placeholder[data-v-a99cc1ab] {
+.placeholder[data-v-d7692b6f] {
   font-style: italic;
   color: rgba(226, 232, 240, 0.5);
   text-align: center;
@@ -2465,10 +2465,10 @@ to { transform: rotate(360deg);
 }
 
 /* ── Spinner (Font Awesome) ── */
-.fa-spinner[data-v-a99cc1ab] {
+.fa-spinner[data-v-d7692b6f] {
-  animation: fa-spin-a99cc1ab 1s linear infinite;
+  animation: fa-spin-d7692b6f 1s linear infinite;
 }
-@keyframes fa-spin-a99cc1ab {
+@keyframes fa-spin-d7692b6f {
 0% { transform: rotate(0deg);
 }
 100% { transform: rotate(360deg);
@@ -15316,7 +15316,7 @@ const _sfc_main$1 = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const AutocompleteTextWidget = /* @__PURE__ */ _export_sfc(_sfc_main$1, [["__scopeId", "data-v-3f3d7a1a"]]);
+const AutocompleteTextWidget = /* @__PURE__ */ _export_sfc(_sfc_main$1, [["__scopeId", "data-v-55e3316e"]]);
 const _hoisted_1 = { class: "lora-info-tabs" };
 const _hoisted_2 = { class: "tab-content notes-tab" };
 const _hoisted_3 = { class: "info-field" };
@@ -15511,8 +15511,12 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
           if (data.filePath !== void 0) filePath.value = data.filePath;
         }
       };
+      if (props.widget.options) {
         props.widget.options.getValue = buildValue;
         props.widget.options.setValue = applyValue;
+      } else {
+        console.warn("[LoraInfoWidget] widget.options missing, value persistence disabled");
+      }
       props.widget.serializeValue = async () => buildValue();
       props.widget.onSetValue = applyValue;
       const widgetIndex = (_b = (_a2 = props.widget.node) == null ? void 0 : _a2.widgets) == null ? void 0 : _b.findIndex(
@@ -15641,7 +15645,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const LoraInfoWidget = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-a99cc1ab"]]);
+const LoraInfoWidget = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-d7692b6f"]]);
 function createVueWidgetCleanup(vueApp, onCleanup) {
   let didUnmount = false;
   return () => {
@@ -16637,7 +16641,7 @@ function createAutocompleteTextWidgetFactory(node, widgetName, modelType, inputO
     applyAutocompleteTextLayoutFix(
       widget,
       container,
-      typeof LiteGraph !== "undefined" && LiteGraph.vueNodesMode
+      typeof LiteGraph !== "undefined" && LiteGraph.vueNodesMode === true
     );
   }
   const vueCleanup = createVueWidgetCleanup(vueApp, () => {
@@ -16747,13 +16751,7 @@ app$1.registerExtension({
         const options = widgetInputOptions.get(`${node.comfyClass}:text`) || {};
         return createAutocompleteTextWidgetFactory(node, "text", "loras", options);
       },
-      // Autocomplete text widget for embeddings (used by Prompt node)
+      // Autocomplete text widget for prompt (used by Prompt and Text nodes)
-      // @ts-ignore
-      AUTOCOMPLETE_TEXT_EMBEDDINGS(node) {
-        const options = widgetInputOptions.get(`${node.comfyClass}:text`) || {};
-        return createAutocompleteTextWidgetFactory(node, "text", "embeddings", options);
-      },
-      // Autocomplete text widget for prompt (supports both embeddings and custom words)
       // @ts-ignore
       AUTOCOMPLETE_TEXT_PROMPT(node) {
         const options = widgetInputOptions.get(`${node.comfyClass}:text`) || {};