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refactor: simplify lora_text to plain text input with name resolution

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Remove the autocomplete JS class (was ~155 lines). Instead, resolve
short LoRA names server-side: typing "Milena_20260216180133" matches
against installed LoRAs by filename stem, so it resolves to e.g.
"ZImageTurbo/character/Milena_20260216180133.safetensors". No Lora
Manager dependency needed — just plain text input with smart matching.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-03-04 19:34:42 +01:00
co-authored by Claude Opus 4.6
parent 3e1eb45065
commit 6a60059cf0
2 changed files with 71 additions and 214 deletions
Download Patch File Download Diff File Expand all files Collapse all files
js/lora_stack_dynamic.js
+1 -194
View File
@@ -84,7 +84,7 @@ function updateVisibility(node) {
const LM_LORAS_LIST_URL = "/api/lm/loras/list";
const LM_BASE_MODELS_URL = "/api/lm/loras/base-models?limit=100";
const LM_AUTOCOMPLETE_URL = "/api/lm/loras/relative-paths";
const PAGE_SIZE = 100;
async function fetchJson(url) {
@@ -227,187 +227,6 @@ function applyLoraFilter(node, baseModel, fullLoraList, loraBaseModelMap) {
app.canvas?.setDirty?.(true, true);
}
// --- LoRA Text Autocomplete (requires ComfyUI-Lora-Manager) ---
class LoraTextAutocomplete {
constructor(textareaEl) {
this.textarea = textareaEl;
this.items = [];
this.selectedIdx = -1;
this._debounceTimer = null;
this.dropdown = document.createElement("div");
this.dropdown.style.cssText =
"position:absolute;z-index:9999;background:#1a1a2e;border:1px solid #555;" +
"max-height:200px;overflow-y:auto;display:none;font-size:13px;" +
"border-radius:4px;box-shadow:0 4px 12px rgba(0,0,0,0.4);";
document.body.appendChild(this.dropdown);
this._onInput = this._handleInput.bind(this);
this._onKeyDown = this._handleKeyDown.bind(this);
this._onBlur = () => setTimeout(() => this._hide(), 150);
this.textarea.addEventListener("input", this._onInput);
this.textarea.addEventListener("keydown", this._onKeyDown);
this.textarea.addEventListener("blur", this._onBlur);
}
_getCurrentLine() {
const val = this.textarea.value;
const pos = this.textarea.selectionStart;
const start = val.lastIndexOf("\n", pos - 1) + 1;
let end = val.indexOf("\n", pos);
if (end === -1) end = val.length;
return { text: val.slice(start, end), start, end };
}
_handleInput() {
clearTimeout(this._debounceTimer);
this._debounceTimer = setTimeout(async () => {
const { text } = this._getCurrentLine();
// Strip trailing :N or :N:N for search term
const term = text.replace(/:[^:]*$/, "").replace(/:[^:]*$/, "").trim();
if (term.length < 2) {
this._hide();
return;
}
await this._search(term);
}, 200);
}
async _search(term) {
try {
const params = new URLSearchParams({ search: term, limit: "20" });
const data = await fetchJson(`${LM_AUTOCOMPLETE_URL}?${params}`);
this.items = data.relative_paths || data || [];
if (!Array.isArray(this.items)) this.items = [];
} catch {
this.items = [];
}
if (this.items.length === 0) {
this._hide();
return;
}
this.selectedIdx = 0;
this._render(term);
this._show();
}
_render(searchTerm) {
this.dropdown.innerHTML = "";
const lowerTerm = searchTerm.toLowerCase();
this.items.forEach((path, idx) => {
const row = document.createElement("div");
row.style.cssText =
"padding:4px 8px;cursor:pointer;white-space:nowrap;color:#e0e0e0;";
if (idx === this.selectedIdx) {
row.style.background = "#3a3a5e";
}
// Highlight matching portion
const lowerPath = path.toLowerCase();
const matchIdx = lowerPath.indexOf(lowerTerm);
if (matchIdx >= 0) {
const before = path.slice(0, matchIdx);
const match = path.slice(matchIdx, matchIdx + searchTerm.length);
const after = path.slice(matchIdx + searchTerm.length);
row.innerHTML =
`${this._esc(before)}<b style="color:#7ca8ff">${this._esc(match)}</b>${this._esc(after)}`;
} else {
row.textContent = path;
}
row.addEventListener("mousedown", (e) => {
e.preventDefault();
this._insert(path);
});
row.addEventListener("mouseenter", () => {
this.selectedIdx = idx;
this._render(searchTerm);
});
this.dropdown.appendChild(row);
});
}
_esc(str) {
const d = document.createElement("span");
d.textContent = str;
return d.innerHTML;
}
_show() {
const rect = this.textarea.getBoundingClientRect();
this.dropdown.style.left = `${rect.left + window.scrollX}px`;
this.dropdown.style.top = `${rect.bottom + window.scrollY + 2}px`;
this.dropdown.style.width = `${rect.width}px`;
this.dropdown.style.display = "block";
}
_hide() {
this.dropdown.style.display = "none";
this.items = [];
this.selectedIdx = -1;
}
_insert(path) {
const { start, end } = this._getCurrentLine();
const val = this.textarea.value;
this.textarea.value = val.slice(0, start) + path + val.slice(end);
const newPos = start + path.length;
this.textarea.setSelectionRange(newPos, newPos);
this.textarea.dispatchEvent(new Event("input", { bubbles: true }));
this._hide();
}
_handleKeyDown(e) {
if (this.dropdown.style.display === "none") return;
if (e.key === "ArrowDown") {
e.preventDefault();
this.selectedIdx = (this.selectedIdx + 1) % this.items.length;
this._render(this._getCurrentLine().text.replace(/:[^:]*$/, "").replace(/:[^:]*$/, "").trim());
} else if (e.key === "ArrowUp") {
e.preventDefault();
this.selectedIdx = (this.selectedIdx - 1 + this.items.length) % this.items.length;
this._render(this._getCurrentLine().text.replace(/:[^:]*$/, "").replace(/:[^:]*$/, "").trim());
} else if (e.key === "Enter" && this.selectedIdx >= 0) {
e.preventDefault();
this._insert(this.items[this.selectedIdx]);
} else if (e.key === "Escape") {
e.preventDefault();
this._hide();
}
}
destroy() {
clearTimeout(this._debounceTimer);
this.textarea.removeEventListener("input", this._onInput);
this.textarea.removeEventListener("keydown", this._onKeyDown);
this.textarea.removeEventListener("blur", this._onBlur);
this.dropdown.remove();
}
}
async function initLoraTextAutocomplete(node, retries = 0) {
const textWidget = findWidget(node, "lora_text");
if (!textWidget) return;
// widget.inputEl may not exist yet (lazy DOM creation)
if (!textWidget.inputEl) {
if (retries < 20) {
setTimeout(() => initLoraTextAutocomplete(node, retries + 1), 500);
}
return;
}
// Probe Lora Manager availability
try {
await fetchJson(LM_BASE_MODELS_URL);
} catch {
return; // No Lora Manager — text input works, just no autocomplete
}
const ac = new LoraTextAutocomplete(textWidget.inputEl);
node._loraTextAutocomplete = ac;
}
// --- Node Registration ---
app.registerExtension({
@@ -426,19 +245,7 @@ app.registerExtension({
updateVisibility(node);
// Initialize base model filter after visibility is set
initBaseModelFilter(node);
// Initialize autocomplete for lora_text widget
initLoraTextAutocomplete(node);
}, 100);
// Cleanup on node removal
const origOnRemoved = node.onRemoved;
node.onRemoved = function () {
if (this._loraTextAutocomplete) {
this._loraTextAutocomplete.destroy();
this._loraTextAutocomplete = null;
}
if (origOnRemoved) origOnRemoved.call(this);
};
},
});
lora_optimizer.py
+70 -20
View File
@@ -122,14 +122,12 @@ class LoRAStackDynamic:
"lora_text": ("STRING", {
"multiline": True,
"default": "",
"placeholder": "path/to/lora.safetensors:0.8\npath/to/another.safetensors:0.8:0.5",
"placeholder": "Milena_20260216180133:0.8\npath/to/lora.safetensors:0.8:0.5",
"pysssss.autocomplete": False,
"dynamicPrompts": False,
"tooltip": "Type LoRA paths directly, one per line. "
"Format: path.safetensors (strength 1.0), "
"path.safetensors:0.8 (both strengths), or "
"path.safetensors:0.8:0.5 (model:clip). "
"Autocomplete available when ComfyUI-Lora-Manager is installed."
"tooltip": "Type LoRA names or paths, one per line. "
"Short names (e.g. Milena_20260216180133) are matched against installed LoRAs. "
"Format: name (strength 1.0), name:0.8 (both strengths), or name:0.8:0.5 (model:clip)."
}),
"lora_stack": ("LORA_STACK", {"tooltip": "Connect another LoRA Stack node here to add even more LoRAs to the list."}),
"base_model_filter": (["All"], {
@@ -145,11 +143,57 @@ class LoRAStackDynamic:
CATEGORY = "loaders/lora"
DESCRIPTION = "Dynamic LoRA stacker with adjustable slot count and optional per-LoRA CLIP strength"
@staticmethod
def _resolve_lora_name(name):
"""Resolve a short LoRA name to its full relative path.
Accepts full relative paths (returned as-is) or short names like
'Milena_20260216180133' which are matched against installed LoRAs
by filename (with or without extension).
"""
lora_list = folder_paths.get_filename_list("loras")
# Exact match (full relative path)
if name in lora_list:
return name
# Match by filename stem (without extension)
name_lower = name.lower()
for lora_path in lora_list:
filename = os.path.basename(lora_path)
stem = os.path.splitext(filename)[0]
if stem.lower() == name_lower:
return lora_path
# Match by filename with extension
for lora_path in lora_list:
filename = os.path.basename(lora_path)
if filename.lower() == name_lower:
return lora_path
# Substring match on filename stem (if unique)
matches = []
for lora_path in lora_list:
stem = os.path.splitext(os.path.basename(lora_path))[0]
if name_lower in stem.lower():
matches.append(lora_path)
if len(matches) == 1:
return matches[0]
logger = logging.getLogger("LoRAOptimizer")
if len(matches) > 1:
logger.warning(f"lora_text: '{name}' matches multiple LoRAs: {matches[:5]}... using first match")
return matches[0]
logger.warning(f"lora_text: '{name}' not found in installed LoRAs, skipping")
return None
@staticmethod
def _parse_lora_text(lora_text):
"""Parse multiline text into LoRA entries.
"""Parse multiline text into LoRA entries with name resolution.
Formats (one per line):
Milena_20260216180133 -> resolved to full path, strength 1.0
path/to/lora.safetensors -> (path, 1.0, 1.0, "all")
path/to/lora.safetensors:0.8 -> (path, 0.8, 0.8, "all")
path/to/lora.safetensors:0.8:0.5 -> (path, 0.8, 0.5, "all")
@@ -163,29 +207,35 @@ class LoRAStackDynamic:
if not line or line.startswith("#"):
continue
name = line
model_str = 1.0
clip_str = 1.0
# Try 3-part split: path:model_str:clip_str
parts = line.rsplit(":", 2)
if len(parts) == 3:
try:
clip_str = float(parts[2])
model_str = float(parts[1])
entries.append((parts[0].strip(), model_str, clip_str, "all"))
continue
name = parts[0].strip()
except ValueError:
pass
# Try 2-part split: path:strength
parts = line.rsplit(":", 1)
if len(parts) == 2:
try:
strength = float(parts[1])
entries.append((parts[0].strip(), strength, strength, "all"))
continue
except ValueError:
pass
# Try 2-part split: path:strength (only if 3-part didn't match)
if name == line:
parts = line.rsplit(":", 1)
if len(parts) == 2:
try:
strength = float(parts[1])
model_str = strength
clip_str = strength
name = parts[0].strip()
except ValueError:
pass
# No numeric suffix — entire line is the path, strength 1.0
entries.append((line.strip(), 1.0, 1.0, "all"))
resolved = LoRAStackDynamic._resolve_lora_name(name)
if resolved:
entries.append((resolved, model_str, clip_str, "all"))
return entries