diff --git a/nodes.py b/nodes.py
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+++ b/nodes.py
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+import json
+import os
+import hashlib
+import time
+import torch
+import torch.nn.functional as F
+
+"""
+最简批量帧文本编码（仅正向），增强兼容性：
+- 解决：不同 prompt 生成的 cond 长度不一致导致 torch.cat 报错 -> 统一 padding
+- 解决：某些 clip.encode_from_tokens 在 return_pooled=True 时返回 (cond, None) 或根本不支持 pooled 的情况
+- 若 pooled 全部为 None，则不放 pooled_output 或放占位空字符串
+"""
+
+# ComfyUI 节点注册字典（在文件末尾进行 update 之前，必须先定义）
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+def parse_frame_prompts(raw_text: str):
+    """
+    解析输入为 {frame_index: prompt} 的升序字典。
+    允许缺少最外层 {}；允许末尾多余逗号。
+    """
+    txt = raw_text.strip()
+    if not txt:
+        return {}
+    if not txt.startswith("{"):
+        txt = "{" + txt
+    if not txt.endswith("}"):
+        txt = txt + "}"
+    txt = txt.replace(",}", "}")
+    data = json.loads(txt)
+    out = {}
+    for k, v in data.items():
+        try:
+            idx = int(k)
+        except:
+            continue
+            # 非数字 key 忽略
+        out[idx] = str(v)
+    return dict(sorted(out.items(), key=lambda x: x[0]))
+
+
+def encode_prompts_sequential(clip, ordered_items):
+    """
+    逐条编码；对 cond 做长度统一；pooled 可能为 None。
+    ordered_items: List[(frame_index, prompt)]
+    返回: cond_final, pooled_final(或 None), has_pooled(bool)
+    """
+    cond_list = []
+    pooled_list = []
+    pooled_available = True  # 先假设有 pooled
+    max_tokens = 0
+
+    with torch.no_grad():
+        for frame_idx, prompt in ordered_items:
+            tokens = clip.tokenize(prompt)
+            # 兼容：有的实现如果 return_pooled=True 仍返回单值
+            enc = clip.encode_from_tokens(tokens, return_pooled=True)
+            if isinstance(enc, (list, tuple)) and len(enc) == 2:
+                cond, pooled = enc
+            else:
+                cond, pooled = enc, None
+
+            if cond.dim() != 3:
+                # 期望 shape: [B(=1), T, C]; 若是 [T, C] 补一个 batch 维
+                if cond.dim() == 2:
+                    cond = cond.unsqueeze(0)
+                else:
+                    raise ValueError(f"Unexpected cond tensor shape: {cond.shape}")
+
+            if cond.shape[1] > max_tokens:
+                max_tokens = cond.shape[1]
+
+            cond_list.append(cond)
+
+            if pooled is None:
+                pooled_available = False
+                pooled_list.append(None)
+            else:
+                # 期望 [B(=1), D] 或 [D]; 若 [D] -> [1,D]
+                if pooled.dim() == 1:
+                    pooled = pooled.unsqueeze(0)
+                pooled_list.append(pooled)
+
+    if not cond_list:
+        dummy = torch.zeros(1, 1)
+        return dummy, None, False
+
+    # 对 cond 做 padding
+    padded = []
+    for c in cond_list:
+        if c.shape[1] < max_tokens:
+            pad_len = max_tokens - c.shape[1]
+            # shape [1, T, C]，pad 第二维 (tokens)：(last_dim_left, last_dim_right, second_dim_left, second_dim_right)
+            c = F.pad(c, (0, 0, 0, pad_len))
+        padded.append(c)
+    cond_final = torch.cat(padded, dim=0)  # [N, max_tokens, C]
+
+    pooled_final = None
+    if pooled_available:
+        # 校验 pooled 维度并 cat
+        processed = []
+        for p in pooled_list:
+            if p is None:
+                pooled_available = False
+                break
+            if p.dim() == 1:
+                p = p.unsqueeze(0)
+            processed.append(p)
+        if pooled_available and processed:
+            try:
+                pooled_final = torch.cat(processed, dim=0)  # [N, D]
+            except Exception:
+                # 如果维度不一致，放弃 pooled
+                pooled_final = None
+                pooled_available = False
+
+    return cond_final, pooled_final, pooled_available
+
+
+## 删除了 BatchPrompt 节点，保留 PromptQueue
+
+
+def _read_lines_from_source(multiline_text: str, file_path: str = "", use_file: bool = False):
+    """
+    从多行文本或文件读取，每行一个提示词；自动去除空行和首尾空白。
+    """
+    lines = []
+    if use_file and file_path:
+        try:
+            with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
+                lines = f.readlines()
+        except Exception:
+            # 回退到多行文本
+            lines = multiline_text.splitlines()
+    else:
+        lines = multiline_text.splitlines()
+
+    cleaned = []
+    for raw in lines:
+        s = str(raw).strip()
+        if not s:
+            continue
+        cleaned.append(s)
+    return cleaned
+
+
+def _apply_template_to_lines(lines, template: str):
+    """
+    将每行替换进模板。若模板为空或不含 {p}，则直接返回行本身。
+    """
+    if not template or "{p}" not in template:
+        return lines
+    return [template.replace("{p}", line) for line in lines]
+
+
+class PromptQueue:
+    """
+    将多行提示词视为队列，或从 txt 文件读取（逐行）。
+    输出按批次堆叠的 CONDITIONING，便于一次性批量生成。
+    可选模板，将每行填充到模板中的 {prompt} 占位符。
+    """
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "clip": ("CLIP",),
+                "multiline_text": ("STRING", {"multiline": True, "default": "A cat\nA dog\nA bird"}),
+                "use_file": ("BOOLEAN", {"default": False}),
+                "file_path": ("STRING", {"multiline": False, "default": ""}),
+                "template": ("STRING", {"multiline": False, "default": "{p}"}),
+                "mode": (["all", "incremental"],),
+                "label": ("STRING", {"multiline": False, "default": "Prompt Queue 001"}),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    RETURN_NAMES = ("COND",)
+    FUNCTION = "run"
+    CATEGORY = "SimpleBatch"
+
+    def run(self, clip, multiline_text, use_file, file_path, template, mode="all", label="Prompt Queue 001"):
+        lines = _read_lines_from_source(multiline_text, file_path, use_file)
+        lines = _apply_template_to_lines(lines, template)
+
+        if not lines:
+            dummy = torch.zeros(1, 1)
+            return ([[dummy, {"pooled_output": ""}]],)
+
+        # Mode: incremental consumes one line per call using a persistent counter per label
+        if mode == "incremental":
+            idx = _pq_get_next_index(label, _pq_total=len(lines), source_descriptor=_pq_source_descriptor(use_file, file_path, multiline_text))
+            line = lines[idx]
+            ordered = [(0, line)]
+            cond, pooled, has_pooled = encode_prompts_sequential(clip, ordered)
+            meta = {"pooled_output": pooled if has_pooled and pooled is not None else ""}
+            return ([[cond, meta]],)
+
+        # Default: all lines batched together
+        ordered = list(enumerate(lines))
+        cond, pooled, has_pooled = encode_prompts_sequential(clip, ordered)
+        meta = {"pooled_output": pooled if has_pooled and pooled is not None else ""}
+        return ([[cond, meta]],)
+
+    @classmethod
+    def IS_CHANGED(cls, clip, multiline_text, use_file, file_path, template, mode="all", label="Prompt Queue 001"):
+        # Ensure incremental mode re-executes each time to advance the queue
+        if mode == "incremental":
+            return str(time.time())
+        # For deterministic modes, hash inputs so caching works as expected
+        try:
+            desc = _pq_source_descriptor(bool(use_file), str(file_path), str(multiline_text))
+            key = f"{desc}|{template}"
+            return hashlib.sha1(key.encode("utf-8", errors="ignore")).hexdigest()
+        except Exception:
+            return ""
+
+
+# --- Persistent queue state (per label) ---
+class _JsonKVStore:
+    def __init__(self, file_path: str):
+        self.file_path = file_path
+        self.data = {}
+        self._load()
+
+    def _load(self):
+        try:
+            if os.path.exists(self.file_path):
+                with open(self.file_path, "r", encoding="utf-8") as f:
+                    self.data = json.load(f)
+            else:
+                self.data = {}
+        except Exception:
+            self.data = {}
+
+    def _save(self):
+        try:
+            os.makedirs(os.path.dirname(self.file_path), exist_ok=True)
+            with open(self.file_path, "w", encoding="utf-8") as f:
+                json.dump(self.data, f, indent=2)
+        except Exception:
+            pass
+
+    def get(self, category: str, key: str, default=None):
+        return self.data.get(category, {}).get(key, default)
+
+    def put(self, category: str, key: str, value) -> None:
+        if category not in self.data:
+            self.data[category] = {}
+        self.data[category][key] = value
+        self._save()
+
+
+_pq_state_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "_prompt_queue_state")
+_pq_store = _JsonKVStore(os.path.join(_pq_state_dir, "state.json"))
+
+
+def _pq_source_descriptor(use_file: bool, file_path: str, multiline_text: str) -> str:
+    if use_file and file_path:
+        try:
+            return "FILE:" + os.path.abspath(file_path)
+        except Exception:
+            return "FILE:" + file_path
+    # Text hash as descriptor; content changes won't reset index to allow continuing when appending
+    h = hashlib.sha1(multiline_text.encode("utf-8", errors="ignore")).hexdigest()
+    return "TEXT:" + h
+
+
+def _pq_get_next_index(label: str, _pq_total: int, source_descriptor: str) -> int:
+    if _pq_total <= 0:
+        return 0
+    # Reset only when source type switches between FILE and TEXT, or file path changes
+    stored_src = _pq_store.get("PromptQueue Sources", label, None)
+    if stored_src != source_descriptor:
+        _pq_store.put("PromptQueue Sources", label, source_descriptor)
+        _pq_store.put("PromptQueue Counters", label, 0)
+
+    current = _pq_store.get("PromptQueue Counters", label, 0) or 0
+    next_index = current % _pq_total
+    _pq_store.put("PromptQueue Counters", label, (current + 1) % _pq_total)
+    return next_index
+
+
+# 注册新节点
+NODE_CLASS_MAPPINGS.update({
+    "PromptQueue": PromptQueue,
+})
+
+NODE_DISPLAY_NAME_MAPPINGS.update({
+    "PromptQueue": "Prompt Queue",
+})
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