Remove character image analysis flow

README.md

@@ -50,13 +50,7 @@ All of my nodes are created with the help of AI, so there may or may not be redu
 
 This is a Modded LTX director node from "WhatDreamsCost" with some extra options to help you create videos with references sheets
 
-This node uses Ollama Locally : you will need to install it (very lightweight) and 
+- Character descriptions are entered manually in the MSR character fields and are appended to the director prompt as tagged character references.
-
-- In your Ollama model folder run this command to install qwen 3.5 2b q4 (1.9gb) "ollama run huihui_ai/qwen3.5-abliterated:2B"
-
-- The model won't eat memory while generating since there is an auto clear VRAM when you hit Run or after 5min.
-
-- You can still enter your description manually if you don't want to install it but it works very well!
 
 - Reference mode included: Licon MSR.
 

js/ltx_director.js

@@ -11352,28 +11352,6 @@ const APPENDED_WIDGET_DEFAULTS = [
 
 app.registerExtension({
   name: "LTXDirectorCS",
-  async setup() {
-    // On Run, ask Ollama to release the analysis model from VRAM so it doesn't
-    // compete with LTX generation. Fully tolerant: failures are swallowed.
-    if (app._ltxDirectorUnloadHookInstalled) return;
-    app._ltxDirectorUnloadHookInstalled = true;
-    const origQueuePrompt = app.queuePrompt;
-    app.queuePrompt = async function (...args) {
-      try {
-        const nodes = app.graph?._nodes || [];
-        const director = nodes.find(n => n && (n.comfyClass === "LTXDirectorCS" || n.type === "LTXDirectorCS"));
-        if (director) {
-          try {
-            await api.fetchApi("/ltx_director/unload_ollama", {
-              method: "POST",
-              body: JSON.stringify({ provider: "ollama" }),
-            });
-          } catch (e) {}
-        }
-      } catch (e) {}
-      return origQueuePrompt.apply(this, args);
-    };
-  },
   async beforeRegisterNodeDef(nodeType, nodeData, app) {
     if (nodeData.name === "LTXDirectorCS") {
 

js/msr_character.js

@@ -1,6 +1,5 @@
 const { app } = window.comfyAPI.app;
 const { api } = window.comfyAPI.api;
-const DEFAULT_ANALYZE_PROMPT = "Describe the character's physical appearance in two concise sentences. Specify their hair color/style, face details, and their clothing type/color. Keep the entire response very brief.";
 
 function findWidget(node, name) {
   return (node.widgets || []).find((widget) => widget.name === name);
@@ -58,139 +57,6 @@ function resolveCandidateImageUrls(originNode) {
   return urls;
 }
 
-async function blobToOptimizedDataUrl(blob, maxDim = 1024, quality = 0.82) {
-  const objectUrl = URL.createObjectURL(blob);
-  try {
-    const image = await new Promise((resolve, reject) => {
-      const img = new Image();
-      img.onload = () => resolve(img);
-      img.onerror = () => reject(new Error("Failed to decode image blob"));
-      img.src = objectUrl;
-    });
-
-    const width = image.naturalWidth || image.width || 0;
-    const height = image.naturalHeight || image.height || 0;
-    const scale = width > 0 && height > 0 ? Math.min(1, maxDim / Math.max(width, height)) : 1;
-    const targetWidth = Math.max(1, Math.round(width * scale)) || width || 1;
-    const targetHeight = Math.max(1, Math.round(height * scale)) || height || 1;
-
-    const canvas = document.createElement("canvas");
-    canvas.width = targetWidth;
-    canvas.height = targetHeight;
-    const ctx = canvas.getContext("2d");
-    if (!ctx) {
-      throw new Error("Could not acquire canvas context");
-    }
-    ctx.drawImage(image, 0, 0, targetWidth, targetHeight);
-
-    const optimizedBlob = await new Promise((resolve, reject) => {
-      canvas.toBlob((result) => {
-        if (result) resolve(result);
-        else reject(new Error("Canvas toBlob failed"));
-      }, "image/jpeg", quality);
-    });
-
-    const dataUrl = await new Promise((resolve) => {
-      const reader = new FileReader();
-      reader.onloadend = () => resolve(reader.result);
-      reader.readAsDataURL(optimizedBlob);
-    });
-
-    return {
-      dataUrl,
-      originalWidth: width,
-      originalHeight: height,
-      outputWidth: targetWidth,
-      outputHeight: targetHeight,
-      outputBytes: optimizedBlob.size || 0,
-    };
-  } finally {
-    URL.revokeObjectURL(objectUrl);
-  }
-}
-
-async function imageInputToDataUrl(node, inputName) {
-  const originNode = getOriginNodeForInput(node, inputName);
-  const candidateUrls = resolveCandidateImageUrls(originNode);
-  if (!candidateUrls.length) {
-    return {
-      dataUrl: null,
-      debug: {
-        inputName,
-        originNodeId: originNode?.id ?? null,
-        originNodeType: originNode?.type ?? null,
-        candidateUrls: [],
-        selectedUrl: null,
-        mimeType: null,
-        blobBytes: 0,
-        dataUrlLength: 0,
-        originalWidth: 0,
-        originalHeight: 0,
-        outputWidth: 0,
-        outputHeight: 0,
-        outputBytes: 0,
-        error: "No candidate image URLs found",
-      },
-    };
-  }
-
-  for (const imageUrl of candidateUrls) {
-    try {
-      const response = await fetch(imageUrl);
-      if (!response.ok) {
-        throw new Error(`HTTP ${response.status}`);
-      }
-      const blob = await response.blob();
-      if (!blob.type.startsWith("image/")) {
-        throw new Error(`Unexpected blob type: ${blob.type || "unknown"}`);
-      }
-      const optimized = await blobToOptimizedDataUrl(blob);
-      const dataUrl = optimized.dataUrl;
-      return {
-        dataUrl,
-        debug: {
-          inputName,
-          originNodeId: originNode?.id ?? null,
-          originNodeType: originNode?.type ?? null,
-          candidateUrls,
-          selectedUrl: imageUrl,
-          mimeType: blob.type || null,
-          blobBytes: blob.size || 0,
-          dataUrlLength: typeof dataUrl === "string" ? dataUrl.length : 0,
-          originalWidth: optimized.originalWidth,
-          originalHeight: optimized.originalHeight,
-          outputWidth: optimized.outputWidth,
-          outputHeight: optimized.outputHeight,
-          outputBytes: optimized.outputBytes,
-          error: null,
-        },
-      };
-    } catch (error) {
-      console.warn("[MSRCharacter] Failed candidate image source", imageUrl, error);
-    }
-  }
-
-  return {
-    dataUrl: null,
-    debug: {
-      inputName,
-      originNodeId: originNode?.id ?? null,
-      originNodeType: originNode?.type ?? null,
-      candidateUrls,
-      selectedUrl: null,
-      mimeType: null,
-      blobBytes: 0,
-      dataUrlLength: 0,
-      originalWidth: 0,
-      originalHeight: 0,
-      outputWidth: 0,
-      outputHeight: 0,
-      outputBytes: 0,
-      error: "All candidate image URLs failed",
-    },
-  };
-}
-
 function setWidgetValue(node, widget, value) {
   if (!widget) return;
   const previous = widget.value;
@@ -220,9 +86,6 @@ function setWidgetValue(node, widget, value) {
 function syncFormFromWidgets(node) {
   const description = findWidget(node, "description")?.value || "";
   const alias = findWidget(node, "alias")?.value || "";
-  const provider = findWidget(node, "analyze_provider")?.value || "ollama";
-  const model = findWidget(node, "analyze_model")?.value || "";
-  const baseUrl = findWidget(node, "analyze_base_url")?.value || "";
 
   if (node._msrAliasInput && node._msrAliasInput.value !== alias) {
     node._msrAliasInput.value = alias;
@@ -235,8 +98,7 @@ function syncFormFromWidgets(node) {
       alias
         ? `Alias: <span style="color:#e8e8e8">@${String(alias).replace(/^@/, "")}</span>`
         : "Alias: <span style=\"color:#666\">none</span>",
-      `<div style="margin-top:4px;color:#888">Analyze: ${provider}${model ? ` / ${model}` : ""}${baseUrl ? ` / ${baseUrl}` : ""}</div>`,
+      `<div style="margin-top:4px;color:#666">Description is stored in the field below and appended to the director prompt as a tagged character reference.</div>`,
-      `<div style="margin-top:4px;color:#666">Description is stored in the field below.</div>`,
     ].join("");
   }
 }
@@ -323,70 +185,6 @@ function buildCharacterUi(node) {
     gap: "6px",
   });
 
-  const actionsRow = document.createElement("div");
-  Object.assign(actionsRow.style, {
-    display: "flex",
-    gap: "8px",
-    alignItems: "center",
-    flexWrap: "wrap",
-  });
-
-  const analyzeButton = document.createElement("button");
-  analyzeButton.type = "button";
-  analyzeButton.textContent = "Analyze";
-  Object.assign(analyzeButton.style, {
-    alignSelf: "flex-start",
-    background: "#2b4f38",
-    color: "#f3f3f3",
-    border: "1px solid #496d56",
-    borderRadius: "6px",
-    padding: "6px 10px",
-    fontSize: "11px",
-    cursor: "pointer",
-  });
-  analyzeButton.addEventListener("click", () => {
-    const widgetButton = (node.widgets || []).find((widget) => widget.msrAnalyze);
-    if (!widgetButton) {
-      alert("Analyze button widget is missing on this node.");
-      return;
-    }
-    analyzeCharacterNode(node, widgetButton);
-  });
-
-  const settingsButton = document.createElement("button");
-  settingsButton.type = "button";
-  settingsButton.textContent = "Analyze Settings";
-  Object.assign(settingsButton.style, {
-    alignSelf: "flex-start",
-    background: "#252525",
-    color: "#ddd",
-    border: "1px solid #444",
-    borderRadius: "6px",
-    padding: "6px 10px",
-    fontSize: "11px",
-    cursor: "pointer",
-  });
-  settingsButton.addEventListener("click", () => {
-    const providerWidget = findWidget(node, "analyze_provider");
-    const baseUrlWidget = findWidget(node, "analyze_base_url");
-    const modelWidget = findWidget(node, "analyze_model");
-    const promptWidget = findWidget(node, "analyze_prompt");
-    const provider = window.prompt("Analyze provider: ollama, lmstudio, custom, off", providerWidget?.value || "ollama");
-    if (provider == null) return;
-    const baseUrl = window.prompt("Analyze base URL (blank = default)", baseUrlWidget?.value || "");
-    if (baseUrl == null) return;
-    const model = window.prompt("Analyze model (blank = provider default)", modelWidget?.value || "");
-    if (model == null) return;
-    const prompt = window.prompt("Analyze prompt (blank = default)", promptWidget?.value || DEFAULT_ANALYZE_PROMPT);
-    if (prompt == null) return;
-    setWidgetValue(node, providerWidget, provider.trim() || "ollama");
-    setWidgetValue(node, baseUrlWidget, baseUrl.trim());
-    setWidgetValue(node, modelWidget, model.trim());
-    setWidgetValue(node, promptWidget, prompt.trim());
-    syncFormFromWidgets(node);
-    node.setDirtyCanvas?.(true, true);
-  });
-
   const aliasLabel = document.createElement("label");
   aliasLabel.textContent = "Alias";
   Object.assign(aliasLabel.style, {
@@ -439,13 +237,10 @@ function buildCharacterUi(node) {
     syncFormFromWidgets(node);
   });
 
-  actionsRow.appendChild(analyzeButton);
-  actionsRow.appendChild(settingsButton);
   form.appendChild(aliasLabel);
   form.appendChild(aliasInput);
   form.appendChild(descLabel);
   form.appendChild(descInput);
-  form.appendChild(actionsRow);
 
   container.appendChild(previewRow);
   container.appendChild(meta);
@@ -456,79 +251,9 @@ function buildCharacterUi(node) {
   node._msrMeta = meta;
   node._msrAliasInput = aliasInput;
   node._msrDescriptionInput = descInput;
-  node._msrAnalyzeButton = analyzeButton;
   return container;
 }
 
-async function analyzeCharacterNode(node, buttonWidget) {
-  const descriptionWidget = findWidget(node, "description");
-  if (!descriptionWidget) return;
-  const providerWidget = findWidget(node, "analyze_provider");
-  const baseUrlWidget = findWidget(node, "analyze_base_url");
-  const modelWidget = findWidget(node, "analyze_model");
-  const promptWidget = findWidget(node, "analyze_prompt");
-
-  buttonWidget.label = "Analyzing...";
-  if (node._msrAnalyzeButton) {
-    node._msrAnalyzeButton.textContent = "Analyzing...";
-    node._msrAnalyzeButton.disabled = true;
-  }
-  node.setDirtyCanvas?.(true, true);
-
-  try {
-    const imageResults = await Promise.all([
-      imageInputToDataUrl(node, "image_1"),
-      imageInputToDataUrl(node, "image_2"),
-    ]);
-    const imageDebug = imageResults.map((result) => result?.debug || null).filter(Boolean);
-    const images = (
-      imageResults.map((result) => result?.dataUrl || null)
-    ).filter(Boolean);
-
-    if (!images.length) {
-      throw new Error("Connect at least one image input before analyzing.");
-    }
-
-    const response = await api.fetchApi("/ltx_director/analyze_character", {
-      method: "POST",
-      body: JSON.stringify({
-        provider: providerWidget?.value || "ollama",
-        base_url: baseUrlWidget?.value || "",
-        model: modelWidget?.value || "",
-        prompt: promptWidget?.value || "",
-        image_b64: images,
-        image_debug: imageDebug,
-      }),
-    });
-    const result = await response.json();
-    if (result.status !== "success") {
-      console.warn("[MSRCharacter] analyze debug", {
-        sentImages: imageDebug,
-        response: result,
-      });
-      throw new Error(result.message || "Unknown analysis error");
-    }
-
-    const description = result.description || "";
-    setWidgetValue(node, descriptionWidget, description);
-    if (node._msrDescriptionInput) {
-      node._msrDescriptionInput.value = description;
-    }
-    syncFormFromWidgets(node);
-    refreshCharacterPreview(node);
-  } catch (error) {
-    console.error("[MSRCharacter] analyze failed", error);
-    alert(`MSR Character analyze failed: ${error.message || error}`);
-  } finally {
-    buttonWidget.label = "Analyze with Ollama";
-    if (node._msrAnalyzeButton) {
-      node._msrAnalyzeButton.textContent = "Analyze";
-      node._msrAnalyzeButton.disabled = false;
-    }
-    node.setDirtyCanvas?.(true, true);
-  }
-}
-
 function hideNodeWidget(widget) {
   if (!widget) return;
   widget.hidden = true;
@@ -547,30 +272,6 @@ app.registerExtension({
       nodeType.prototype.onNodeCreated = function () {
         if (originalOnNodeCreated) originalOnNodeCreated.apply(this, arguments);
 
-        if (!(this.widgets || []).find((widget) => widget.msrAnalyze)) {
-          this.addWidget("button", "Analyze with Ollama", null, () => {
-            analyzeCharacterNode(this, (this.widgets || []).find((widget) => widget.msrAnalyze));
-          }, { serialize: false });
-          const buttonWidget = this.widgets[this.widgets.length - 1];
-          buttonWidget.msrAnalyze = true;
-          buttonWidget.label = "Analyze with Ollama";
-        }
-
-        if (!findWidget(this, "analyze_provider")) {
-          this.addWidget("combo", "analyze_provider", "ollama", null, {
-            values: ["ollama", "lmstudio", "custom", "off"],
-          });
-        }
-        if (!findWidget(this, "analyze_base_url")) {
-          this.addWidget("text", "analyze_base_url", "");
-        }
-        if (!findWidget(this, "analyze_model")) {
-          this.addWidget("text", "analyze_model", "");
-        }
-        if (!findWidget(this, "analyze_prompt")) {
-          this.addWidget("text", "analyze_prompt", DEFAULT_ANALYZE_PROMPT);
-        }
-
         if (!this._msrPreviewWidget) {
           const previewContainer = buildCharacterUi(this);
           this._msrPreviewWidget = this.addDOMWidget("msr_character_ui", "msr_character_ui", previewContainer, {
@@ -597,10 +298,9 @@ app.registerExtension({
           return result;
         };
 
-        ["alias", "description", "analyze_provider", "analyze_base_url", "analyze_model", "analyze_prompt"].forEach((widgetName) => {
+        ["alias", "description"].forEach((widgetName) => {
           hideNodeWidget(findWidget(this, widgetName));
         });
-        hideNodeWidget((this.widgets || []).find((widget) => widget.msrAnalyze));
 
         this.size[0] = Math.max(this.size[0] || 0, 330);
         syncFormFromWidgets(this);

ltx_director.py

@@ -366,50 +366,32 @@ def _build_character_tag_groups(characters: list[dict]) -> list[tuple[str, ...]]
     return groups
 
 
-def _character_prompt_replacements(characters: list[dict]) -> dict[str, str]:
+def _build_character_reference_block(characters: list[dict] | None = None) -> str:
-    replacements: dict[str, str] = {}
+    lines: list[str] = []
-    for character, tags in zip(characters or [], _build_character_tag_groups(characters)):
+    for idx, character in enumerate(characters or []):
-        replacement = character.get("description", "") or ""
+        description = (character.get("description", "") or "").strip()
-        for tag in tags:
+        if not description:
-            replacements[tag] = replacement
+            continue
-    return replacements
+
+        alias = _normalize_character_alias(character.get("alias", ""))
+        canonical_tag = f"@char{idx + 1}"
+        ref_label = canonical_tag if not alias else f"{canonical_tag} / @{alias}"
+        lines.append(f"{ref_label} - {description}")
+
+    if not lines:
+        return ""
+
+    return "Character references:\n" + "\n".join(lines)
 
 
-def _prepend_character_descriptions(global_prompt: str, characters: list[dict] | None = None) -> str:
+def _append_character_references(global_prompt: str, characters: list[dict] | None = None) -> str:
-    descriptions = [
+    prompt = (global_prompt or "").strip()
-        (character.get("description", "") or "").strip()
+    reference_block = _build_character_reference_block(characters)
-        for character in (characters or [])
+    if not reference_block:
-        if (character.get("description", "") or "").strip()
+        return prompt
-    ]
+    if not prompt:
-    if not descriptions:
+        return reference_block
-        return global_prompt or ""
+    return f"{prompt}\n\n{reference_block}"
-
-    prefix = ". ".join(descriptions)
-    if not (global_prompt or "").strip():
-        return prefix
-    return f"{prefix}. {global_prompt.strip()}"
-
-
-def _preprocess_prompts_with_characters(global_prompt, local_prompts, characters: list[dict] | None = None):
-    """Invisibly swaps out @characterN/@charN/@alias tags with their character descriptions."""
-    if "@" not in (global_prompt or "") and "@" not in (local_prompts or ""):
-        return global_prompt or "", local_prompts or ""
-
-    replacements = _character_prompt_replacements(characters or [])
-
-    def apply_replacements(text: str) -> str:
-        updated = text or ""
-        for tag, replacement in replacements.items():
-            if tag in updated:
-                updated = updated.replace(tag, replacement)
-        return updated
-
-    gp = apply_replacements(global_prompt or "")
-    if not local_prompts:
-        return gp, ""
-
-    processed_locals = [apply_replacements(part.strip()) for part in local_prompts.split("|")]
-    return gp, " | ".join(processed_locals)
 
 
 def _load_image_source(b64_or_url: str, filename: str = None, cache: dict | None = None,
@@ -512,312 +494,6 @@ async def ltx_director_check_file(request):
 
 
 # --- Provider defaults shared by the analyze + unload endpoints ---
-_PROVIDER_DEFAULTS = {
-    "ollama": {"url": "http://127.0.0.1:11434", "model": "huihui_ai/qwen3.5-abliterated:2b"},
-    "lmstudio": {"url": "http://127.0.0.1:1234", "model": ""},
-    "custom": {"url": "", "model": ""},
-}
-
-_ANALYZE_SYSTEM_PROMPT = ""
-
-_ANALYZE_PROMPT = (
-    "Describe the character's physical appearance in two concise sentences. "
-    "Specify their hair color/style, face details, and their clothing type/color. "
-    "Keep the entire response very brief."
-)
-
-
-def _resolve_analyze_prompt(data: dict) -> str:
-    prompt = (data.get("prompt") or "").strip()
-    return prompt or _ANALYZE_PROMPT
-
-
-def _extract_ollama_generated_text(resp_json: dict) -> str:
-    if not isinstance(resp_json, dict):
-        return ""
-
-    candidates = [resp_json.get("response"), resp_json.get("content"), resp_json.get("thinking")]
-    message = resp_json.get("message")
-    if isinstance(message, dict):
-        candidates.extend([
-            message.get("content"),
-            message.get("reasoning_content"),
-            message.get("thinking"),
-        ])
-
-    cleaned_candidates = []
-    for candidate in candidates:
-        if isinstance(candidate, str):
-            text = candidate.strip()
-            if "<think>" in text:
-                text = text.split("</think>")[-1].strip()
-            if text:
-                cleaned_candidates.append(text)
-
-    if not cleaned_candidates:
-        return ""
-
-    usable = [text for text in cleaned_candidates if _analysis_text_is_usable(text)]
-    if usable:
-        return max(usable, key=len)
-
-    return max(cleaned_candidates, key=len)
-
-
-def _collect_ollama_text_candidates(resp_json: dict) -> list[str]:
-    if not isinstance(resp_json, dict):
-        return []
-
-    candidates = [resp_json.get("response"), resp_json.get("content"), resp_json.get("thinking")]
-    message = resp_json.get("message")
-    if isinstance(message, dict):
-        candidates.extend([
-            message.get("content"),
-            message.get("reasoning_content"),
-            message.get("thinking"),
-        ])
-
-    cleaned = []
-    for candidate in candidates:
-        if isinstance(candidate, str):
-            text = candidate.strip()
-            if "<think>" in text:
-                text = text.split("</think>")[-1].strip()
-            if text:
-                cleaned.append(text)
-    return cleaned
-
-
-def _analysis_text_is_usable(text: str) -> bool:
-    text = (text or "").strip()
-    if not text:
-        return False
-    if len(text) < 24:
-        return False
-    if len(text.split()) < 6:
-        return False
-    return True
-
-
-def _compress_analysis_image_b64(b64_payload: str, max_dim: int = 768, quality: int = 82) -> str:
-    """Shrink analysis images so multimodal providers do not burn their full context on pixels."""
-    try:
-        raw = base64.b64decode(_normalise_b64_payload(b64_payload))
-        with Image.open(_io.BytesIO(raw)) as img:
-            img = img.convert("RGB")
-            w, h = img.size
-            if max(w, h) > max_dim:
-                scale = max_dim / float(max(w, h))
-                img = img.resize((max(1, int(round(w * scale))), max(1, int(round(h * scale)))), Image.LANCZOS)
-            out = _io.BytesIO()
-            img.save(out, format="JPEG", quality=quality, optimize=True)
-            return base64.b64encode(out.getvalue()).decode("ascii")
-    except Exception:
-        return _normalise_b64_payload(b64_payload)
-
-
-def _prepare_analysis_images(cleaned_b64_list: list[str], max_dim: int = 768, quality: int = 82) -> list[str]:
-    return [
-        _compress_analysis_image_b64(b64, max_dim=max_dim, quality=quality)
-        for b64 in cleaned_b64_list
-    ]
-
-
-def _resolve_provider(data):
-    provider = (data.get("provider") or "ollama").lower()
-    defs = _PROVIDER_DEFAULTS.get(provider, _PROVIDER_DEFAULTS["ollama"])
-    base_url = (data.get("base_url") or defs["url"]).rstrip("/")
-    model = data.get("model") or defs["model"]
-    return provider, base_url, model
-
-
-# --- Character reference analysis endpoint (Ollama / LM Studio / Custom OpenAI-compatible) ---
-@PromptServer.instance.routes.post("/ltx_director/analyze_character")
-async def analyze_character_endpoint(request):
-    try:
-        import aiohttp
-        data = await request.json()
-        image_b64 = data.get("image_b64", "")
-        image_debug = data.get("image_debug") or []
-        char_index = int(data.get("char_index", 0))
-        provider, base_url, model_name = _resolve_provider(data)
-        analyze_prompt = _resolve_analyze_prompt(data)
-
-        if provider == "off":
-            return web.json_response({"status": "error", "message": "Analyze is set to Off / Manual."})
-        if not image_b64:
-            return web.json_response({"status": "error", "message": "No image provided for analysis."})
-
-        b64_list = image_b64 if isinstance(image_b64, list) else [image_b64]
-        cleaned_b64_list = []
-        for b64 in b64_list:
-            if "," in b64:
-                b64 = b64.split(",", 1)[1]
-            cleaned_b64_list.append(b64)
-        if not cleaned_b64_list:
-            return web.json_response({"status": "error", "message": "No valid base64 images decoded."})
-        if provider in ("lmstudio", "custom") and not model_name:
-            return web.json_response({
-                "status": "error",
-                "message": f"No model name set for {provider}. Open the gear menu and enter your loaded model's name.",
-            })
-
-        log.info("[LTXDirector] Analyzing Character %d via %s (%s, model '%s')...",
-                 char_index + 1, provider, base_url, model_name)
-
-        try:
-            async with aiohttp.ClientSession() as session:
-                if provider == "ollama":
-                    debug_candidates = []
-                    analysis_images = cleaned_b64_list
-                    payload = {
-                        "model": model_name,
-                        "system": _ANALYZE_SYSTEM_PROMPT,
-                        "prompt": analyze_prompt,
-                        "images": analysis_images,
-                        "stream": False,
-                        "keep_alive": 0,
-                        "options": {
-                            "temperature": 0.2,
-                            "num_predict": 768,
-                        },
-                    }
-                    async with session.post(f"{base_url}/api/generate", json=payload, timeout=300) as response:
-                        if response.status != 200:
-                            err_txt = await response.text()
-                            if response.status == 400 and "exceeds the available context size" in err_txt:
-                                analysis_images = _prepare_analysis_images(cleaned_b64_list, max_dim=512, quality=70)
-                                payload["images"] = analysis_images
-                                async with session.post(f"{base_url}/api/generate", json=payload, timeout=300) as retry_response:
-                                    if retry_response.status != 200:
-                                        retry_err = await retry_response.text()
-                                        return web.json_response({"status": "error", "message": f"Ollama HTTP {retry_response.status}: {retry_err}"})
-                                    resp_json = await retry_response.json()
-                                    debug_candidates.extend(_collect_ollama_text_candidates(resp_json))
-                                    generated_text = _extract_ollama_generated_text(resp_json)
-                            else:
-                                return web.json_response({"status": "error", "message": f"Ollama HTTP {response.status}: {err_txt}"})
-                        else:
-                            resp_json = await response.json()
-                            debug_candidates.extend(_collect_ollama_text_candidates(resp_json))
-                            generated_text = _extract_ollama_generated_text(resp_json)
-
-                    if not _analysis_text_is_usable(generated_text):
-                        chat_payload = {
-                            "model": model_name,
-                            "messages": [
-                                {
-                                    "role": "system",
-                                    "content": _ANALYZE_SYSTEM_PROMPT,
-                                },
-                                {
-                                    "role": "user",
-                                    "content": analyze_prompt,
-                                    "images": analysis_images,
-                                },
-                            ],
-                            "stream": False,
-                            "keep_alive": 0,
-                            "options": {
-                                "temperature": 0.2,
-                                "num_predict": 768,
-                            },
-                        }
-                        async with session.post(f"{base_url}/api/chat", json=chat_payload, timeout=300) as response:
-                            if response.status == 200:
-                                resp_json = await response.json()
-                                debug_candidates.extend(_collect_ollama_text_candidates(resp_json))
-                                chat_text = _extract_ollama_generated_text(resp_json)
-                                if _analysis_text_is_usable(chat_text):
-                                    generated_text = chat_text
-                    if not _analysis_text_is_usable(generated_text):
-                        return web.json_response({
-                            "status": "error",
-                            "message": "Ollama returned only a truncated analysis response.",
-                            "debug_candidates": debug_candidates,
-                            "image_debug": image_debug,
-                            "image_lengths": [len(b64) for b64 in cleaned_b64_list],
-                            "description": generated_text,
-                        })
-                else:
-                    # OpenAI-compatible vision chat (LM Studio / Custom).
-                    content = [{"type": "text", "text": analyze_prompt}]
-                    for b64 in cleaned_b64_list:
-                        content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64}"}})
-                    payload = {
-                        "model": model_name,
-                        "messages": [
-                            {"role": "system", "content": _ANALYZE_SYSTEM_PROMPT},
-                            {"role": "user", "content": content},
-                        ],
-                        "max_tokens": 4096, "stream": False,
-                    }
-                    async with session.post(f"{base_url}/v1/chat/completions", json=payload, timeout=120) as response:
-                        if response.status != 200:
-                            err_txt = await response.text()
-                            return web.json_response({"status": "error", "message": f"{provider} HTTP {response.status}: {err_txt}"})
-                        resp_json = await response.json()
-                        try:
-                            msg = resp_json["choices"][0]["message"]
-                            generated_text = (msg.get("content") or "").strip()
-                            # Reasoning/"thinking" models (Gemma, Qwen-thinking, etc.) may leave
-                            # content empty and put their output in reasoning_content instead.
-                            if not generated_text:
-                                generated_text = (msg.get("reasoning_content") or "").strip()
-                        except (KeyError, IndexError, TypeError):
-                            return web.json_response({"status": "error", "message": f"Unexpected response shape from {provider}."})
-        except aiohttp.ClientConnectorError:
-            return web.json_response({
-                "status": "error",
-                "message": f"Could not connect to {provider} at {base_url}. Make sure the server is running and reachable.",
-            })
-
-        if "<think>" in generated_text:
-            generated_text = generated_text.split("</think>")[-1].strip()
-
-        log.info("[LTXDirector] Analysis complete: %s", generated_text)
-        return web.json_response({"status": "success", "description": generated_text})
-
-    except Exception as e:
-        log.error(f"[LTXDirector] Failed to analyze character: {e}")
-        return web.json_response({"status": "error", "message": str(e)}, status=500)
-
-
-
-@PromptServer.instance.routes.post("/ltx_director/unload_ollama")
-async def unload_ollama_endpoint(request):
-    """Evict the analysis model from VRAM right before a generation run, so the VLM doesn't
-    compete with LTX for VRAM (the cause of intermittent CUDA offload crashes).
-
-    Ollama supports a clean instant unload (keep_alive=0). LM Studio / Custom have no reliable
-    cross-version HTTP unload, so for those this is a graceful no-op — users should set a short
-    JIT / auto-unload TTL in their server instead. Fully tolerant: never raises into the run.
-    """
-    try:
-        import aiohttp
-        try:
-            data = await request.json()
-        except Exception:
-            data = {}
-        provider, base_url, model_name = _resolve_provider(data)
-
-        if provider == "ollama":
-            payload = {"model": model_name, "keep_alive": 0}
-            try:
-                async with aiohttp.ClientSession() as session:
-                    async with session.post(f"{base_url}/api/generate", json=payload, timeout=8) as response:
-                        await response.text()
-                log.info("[LTXDirector] Asked Ollama to release '%s' from VRAM before the run.", model_name)
-            except Exception:
-                pass  # Ollama not running / unreachable -> nothing to free.
-            return web.json_response({"status": "ok", "provider": provider})
-
-        # LM Studio / Custom: no reliable HTTP unload across versions -> graceful no-op.
-        return web.json_response({"status": "ok", "provider": provider, "note": "no-op (set a JIT/TTL unload in your server)"})
-    except Exception as e:
-        return web.json_response({"status": "error", "message": str(e)})
-
-
 def read_wav_peaks(wav_path):
     import wave
     with wave.open(wav_path, 'rb') as w:
@@ -1635,6 +1311,8 @@ def _build_reference_mode_outputs(
     vae,
     global_prompt: str,
     local_prompts: str,
+    raw_global_prompt: str,
+    raw_local_prompts: str,
     segment_lengths: str,
     duration_frames: int,
     epsilon: float,
@@ -1667,7 +1345,7 @@ def _build_reference_mode_outputs(
                 tensor = _resize_image(tensor, latent_w, latent_h, "stretch to fit", divisible_by)
             return tensor
 
-        prompt_text = (global_prompt or "") + " " + (local_prompts or "")
+        prompt_text = (raw_global_prompt or "") + " " + (raw_local_prompts or "")
         tag_groups = _build_character_tag_groups(characters)
         referenced_slots = [i for i, tags in enumerate(tag_groups) if any(t in prompt_text for t in tags)]
         selected = []
@@ -2103,21 +1781,12 @@ class LTXDirector(io.ComfyNode):
             image_cache=image_cache,
             input_dir=input_dir,
         )
-        global_prompt = _prepend_character_descriptions(global_prompt, characters)
+        raw_global_prompt = global_prompt or ""
+        raw_local_prompts = local_prompts or ""
+        global_prompt = _append_character_references(raw_global_prompt, characters)
         char_slot_images = [list(character.get("images") or []) for character in characters]
         char_images = [img for slot_images in char_slot_images for img in slot_images]
-
+        local_prompts = raw_local_prompts
-        # --- @charN substitution ---
-        # OFF: swap @char tags for their VLM descriptions in the prompt text.
-        # Licon MSR: leave the tags raw — there the reference IMAGE drives identity, and the
-        #            tags are used only to pick which character slots feed the slideshow.
-        if reference_mode == "Licon MSR (Prefix)":
-            ref_global, ref_local = global_prompt, local_prompts
-        else:
-            ref_global, ref_local = _preprocess_prompts_with_characters(
-                global_prompt, local_prompts, characters
-            )
-        global_prompt, local_prompts = ref_global, ref_local
 
         guide_data, derived_w, derived_h = _build_guide_data_from_timeline(
             tdata=tdata,
@@ -2168,6 +1837,8 @@ class LTXDirector(io.ComfyNode):
             vae=vae,
             global_prompt=global_prompt,
             local_prompts=local_prompts,
+            raw_global_prompt=raw_global_prompt,
+            raw_local_prompts=raw_local_prompts,
             segment_lengths=segment_lengths,
             duration_frames=duration_frames,
             epsilon=epsilon,