feat: integrate Qwen3-TTS support into podcast nodes (v1.11.0)

README.md

@@ -971,11 +971,35 @@ hf download digital-avatar/ditto-talkinghead --local-dir ditto
 | **克隆能力 (Cloning)**             | **SOTA** (Zero-Shot)`<br>`只需 3-10秒，对**音色质感**还原极高。                                                                                                  | **SOTA** (稳定性)`<br>`对**说话韵律/口音**的捕捉最准。         | **良好** `<br>`适合克隆特定语气，而非纯粹音色。         |
 | **多语言/方言**                    | **中/英** (双语优化)                                                                                                                                                     | **👑 霸主** (9种语言 + 18种方言)                                       | **中/英**                                                 |
 | **语音转换 (VC)** (Audio-to-Audio) | ❌**不支持** `<br>`仅支持 TTS (Text-to-Speech)。无法改变已有音频的音色。                                                                                               | ✅**支持** `<br>`可以将任意音频转换为任意音色 (保留语调/停顿)。      | ❌**不支持** `<br>`纯 TTS 模型。仅支持 Text-to-Speech。 |
+| **Qwen3-TTS** (1.7B/0.6B)           | ✅**支持** `<br>`支持 CustomVoice (内置) 和 VoiceDesign (描述)。                                                                                                         | ✅**支持** `<br>`支持 3秒极速 Zero-shot 克隆。                               | ✅**支持** `<br>`支持 10 种语言。                         |
+
+#### 3.13 Qwen3-TTS (New! 🔥)
+
+- **用途**: 阿里巴巴 Qwen 团队推出的最新旗舰级 TTS 模型，支持 10 种主要语言及多种方言，具备极高的稳定性和表现力。
+- **核心能力**:
+  - **CustomVoice**: 使用内置的高品质音色进行语音合成。提供 1.7B 和 0.6B 两种规格。
+  - **VoiceDesign**: 通过自然语言描述（如“活泼的少女音，带点羞涩”）从零设计音色。
+  - **VoiceClone**: 顶级的 3秒快速音色克隆，支持 X-Vector 模式提升稳定性。
+- **环境要求**:
+  - **qwen-tts**: `pip install qwen-tts` (插件会自动尝试安装)。
+  - **Flash Attention 2**: 强烈推荐以获得最佳推理性能。
+- **节点**:
+  - `🤖 Qwen3-TTS Loader`: 加载模型。支持 `Base` (克隆)、`CustomVoice` (内置音色) 和 `VoiceDesign` (音色设计) 模型。
+  - `🗣️ Qwen3-TTS Synthesis`: 执行合成。根据加载的模型类型自动切换功能。
+- **模型下载**:
+  - `Qwen/Qwen3-TTS-12Hz-1.7B-Base` (或 0.6B-Base)
+  - `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` (或 0.6B-CustomVoice)
+  - `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`
+
+---
+
+#### 💡 用户实测与选型指南 (Model Comparison & Selection)
 
 **选型建议**:
 
 - **追求“听起来最像真人” (音质+音色)**: 选 **VoxCPM 1.5**。它的 Tokenizer-free 架构带来了质的飞跃。
 - **追求“方言/多语言/稳定性”**: 选 **CosyVoice 3.0**。目前依然是生产环境最稳的选择。
+- **追求“多样化音色设计/最新 Qwen 生态/长语音流畅度”**: 选 **Qwen3-TTS**。其 VoiceDesign 功能能让你用描述语“捏”出从未听过的声音。
 - **要做“长篇广播剧/播客”**: 选 **VibeVoice**。它的长窗口上下文优势依然不可替代。
 
 ### 4. 播客与对话生成 (Podcast & Dialogue Generation)
@@ -1005,6 +1029,7 @@ https://github.com/user-attachments/assets/9a5502c5-79e3-4fc8-8a2d-2cbdbdbbc860
 - **TTS Engine**: 后端引擎选择。
   - **CosyVoice**: 精准控制型。
   - **VibeVoice**: 自然演绎型。
+  - **Qwen3-TTS**: 万能旗舰型。支持音色设计与内置高质量音色。
 - **Speaker A/B/C**:
   - **Ref Audio**: 参考音频 (用于 Zero-Shot 克隆)。
   - **ID**: 内置音色 ID (如 CosyVoice 的 `Chinese Female`)。
@@ -1026,6 +1051,7 @@ https://github.com/user-attachments/assets/9a5502c5-79e3-4fc8-8a2d-2cbdbdbbc860
 - **原理**:
   - **CosyVoice**: 使用生成时的精确时长。
   - **VibeVoice**: 使用**智能插值算法 (Smart Interpolation)**，根据字符长度自动计算长音频段内的单句时间轴。
+  - **Qwen3-TTS**: 基于生成的音频振幅精准断句，支持多角色时间轴导出。
 
 #### 4.4 AIIA Subtitle to Segments (字幕转分段)
 
@@ -1069,13 +1095,13 @@ https://github.com/user-attachments/assets/9a5502c5-79e3-4fc8-8a2d-2cbdbdbbc860
 
 #### 💡 引擎选型与最佳实践 (Best Practices)
 
-| 特性               | **CosyVoice**                        | **VibeVoice**                                                                              |
-| :----------------- | :----------------------------------------- | :----------------------------------------------------------------------------------------------- |
-| **核心优势** | **精准控制 (Instruction)**           | **自然演绎 (Context-Aware)**                                                               |
-| **情感控制** | ✅**支持** (使用 `[Happy]` 等标签) | ❌ 不支持显式标签 (依赖上下文)                                                                   |
-| **生成逻辑** | **逐句生成** (严格遵循每句话的指令)  | **混合批处理** (Hybrid Batching)                                                           |
-| **最佳场景** | 需要精确指定某句话语气、方言时             | 长篇对话、广播剧、闲聊                                                                           |
-| **使用建议** | 可以在剧本中详细标注情感。                 | **尽量减少 `(Pause)`**！`<br>`让多句对话连在一起，模型能更好地联系上下文产生自然语气。 |
+| 特性               | **CosyVoice**                        | **VibeVoice**                                                                              | **Qwen3-TTS**                               |
+| :----------------- | :----------------------------------------- | :----------------------------------------------------------------------------------------------- | :------------------------------------------ |
+| **核心优势** | **精准控制 (Instruction)**           | **自然演绎 (Context-Aware)**                                                               | **万能旗舰 (Voice Design)**                 |
+| **情感控制** | ✅**支持** (使用 `[Happy]` 等标签) | ❌ 不支持显式标签 (依赖上下文)                                                                   | ✅**支持** (通过 `instruct` 或标签) |
+| **生成逻辑** | **逐句生成** (严格遵循每句话的指令)  | **混合批处理** (Hybrid Batching)                                                           | **动态引擎** (支持流式与批处理)             |
+| **最佳场景** | 需要精确指定某句话语气、方言时             | 长篇对话、广播剧、闲聊                                                                           | 音色定制、高质量配音、极速克隆              |
+| **使用建议** | 可以在剧本中详细标注情感。                 | **尽量减少 `(Pause)`**！`<br>`让多句对话连在一起，模型能更好地联系上下文产生自然语气。 | 尝试使用其 Voice Design 进行创意捏人。      |
 
 #### 📝 综合测试剧本 (Example Script)
 
@@ -1175,6 +1201,15 @@ B: 太神奇了！那我们快去生成试试吧！
 
 ## Changelog
 
+### [1.11.0] - 2026-02-04
+
+- **Qwen3-TTS**: 新增阿里巴巴 **Qwen3-TTS** 全系列支持。
+  - **🤖 Qwen3-TTS Loader**: 支持加载 Base, CustomVoice, VoiceDesign 及其 1.7B/0.6B 版本。
+  - **🗣️ Qwen3-TTS Synthesis**: 实现全功能生成，包括 Zero-shot 克隆、音色设计和内置音色合成。
+- **Podcast Integration**: **AIIA Dialogue TTS** 节点现在正式集成 Qwen3-TTS 引擎。
+  - 支持多角色混合场景下的 Qwen3 驱动，支持使用脚本标签触发 `instruct`。
+- **Auto-Dependency**: 首次运行 Qwen3 节点会自动检测并安装 `qwen-tts` 库。
+
 ### [1.10.17] - 2026-02-03
 
 - **Subtitle**: 引入“说话人 ID 为了映射 (Speaker Mapping)”机制。

__init__.py

@@ -136,6 +136,9 @@ else:
     # 25. 处理 aiia_debug_nodes.py (新增调试节点)
     _load_nodes_from_module(".aiia_debug_nodes", "aiia_debug_nodes")
 
+    # 26. 处理 aiia_qwen_nodes.py (新增 Qwen3-TTS)
+    _load_nodes_from_module(".aiia_qwen_nodes", "aiia_qwen_nodes")
+
     # 告诉 ComfyUI 这个节点包有一个包含网页资源的 'js' 目录
     WEB_DIRECTORY = "js"
 

aiia_podcast_nodes.py

@@ -176,7 +176,7 @@ class AIIA_Dialogue_TTS:
         return {
             "required": {
                 "dialogue_json": ("STRING", {"forceInput": True}),
-                "tts_engine": (["CosyVoice", "VibeVoice"], {"default": "CosyVoice"}),
+                "tts_engine": (["CosyVoice", "VibeVoice", "Qwen3-TTS"], {"default": "CosyVoice"}),
                 "pause_duration": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 5.0, "step": 0.1}),
                 "speed_global": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 2.0}),
                 "batch_mode": (["Natural (Hybrid)", "Strict (Per-Speaker)", "Whole (Single Batch)"], {"default": "Natural (Hybrid)"}),
@@ -190,6 +190,7 @@ class AIIA_Dialogue_TTS:
             "optional": {
                 "cosyvoice_model": ("COSYVOICE_MODEL",),
                 "vibevoice_model": ("VIBEVOICE_MODEL",),
+                "qwen_model": ("QWEN_MODEL",),
                 
                 # Speaker A
                 "speaker_A_ref": ("AUDIO",),
@@ -248,7 +249,7 @@ class AIIA_Dialogue_TTS:
             return None
 
     def process_dialogue(self, dialogue_json, tts_engine, pause_duration, speed_global, 
-                         cosyvoice_model=None, vibevoice_model=None, 
+                         cosyvoice_model=None, vibevoice_model=None, qwen_model=None,
                          cfg_scale=1.5, temperature=0.8, top_k=20, top_p=0.95, **kwargs):
         import json
         import torch
@@ -260,6 +261,8 @@ class AIIA_Dialogue_TTS:
             raise ValueError("选择 CosyVoice 引擎时，必须连接 'cosyvoice_model'！")
         if tts_engine == "VibeVoice" and vibevoice_model is None:
             raise ValueError("选择 VibeVoice 引擎时，必须连接 'vibevoice_model'！")
+        if tts_engine == "Qwen3-TTS" and qwen_model is None:
+            raise ValueError("选择 Qwen3-TTS 引擎时，必须连接 'qwen_model'！")
 
         dialogue = json.loads(dialogue_json)
         full_waveform = []
@@ -267,9 +270,11 @@ class AIIA_Dialogue_TTS:
         
         from .aiia_cosyvoice_nodes import AIIA_CosyVoice_TTS
         from .aiia_vibevoice_nodes import AIIA_VibeVoice_TTS
+        from .aiia_qwen_nodes import AIIA_Qwen_TTS
         
         cosy_gen = AIIA_CosyVoice_TTS()
         vibe_gen = AIIA_VibeVoice_TTS()
+        qwen_gen = AIIA_Qwen_TTS()
 
         print(f"[AIIA Podcast] 开始处理对话，共 {len(dialogue)} 个片段。引擎: {tts_engine}")
 
@@ -402,6 +407,74 @@ class AIIA_Dialogue_TTS:
                         })
                     time_ptr[0] += 1.0
                     
+            elif tts_engine == "Qwen3-TTS":
+                # Qwen3-TTS (Iterative)
+                for i, item in enumerate(batch_items):
+                    spk_name = item["speaker"]
+                    spk_key = get_speaker_key(spk_name)
+                    text = item["text"]
+                    emotion = item.get("emotion", "None")
+                    
+                    # Mapping logic for Qwen
+                    spk_id = kwargs.get(f"speaker_{spk_key}_id", "Vivian") # Default to Vivian if empty
+                    if not spk_id.strip(): spk_id = "Vivian"
+                    
+                    ref_audio = get_ref_audio(spk_key)
+                    instruct = f"{emotion}." if emotion and emotion != "None" else ""
+                    
+                    print(f"  [Qwen Processing] {spk_name} (ID: {spk_id}): {text[:15]}...")
+                    try:
+                        # Call Qwen TTS
+                        res = qwen_gen.generate(
+                            qwen_model=qwen_model,
+                            text=text,
+                            language="Auto",
+                            speaker=spk_id,
+                            instruct=instruct,
+                            reference_audio=ref_audio,
+                            seed=42+i,
+                            speed=speed_global
+                        )
+                        
+                        generated = res[0]
+                        wav = generated["waveform"]
+                        sr = generated["sample_rate"]
+                        
+                        if sr_ptr[0] != sr:
+                            if current_full_wav:
+                                wav = torchaudio.transforms.Resample(sr, sr_ptr[0])(wav)
+                            else:
+                                sr_ptr[0] = sr
+                        
+                        if wav.ndim == 3: wav = wav.squeeze(0)
+                        if wav.ndim == 1: wav = wav.unsqueeze(0)
+                        current_full_wav.append(wav)
+
+                        # --- Timestamp Tracking ---
+                        seg_duration = wav.shape[-1] / sr
+                        seg_start = time_ptr[0]
+                        seg_end = seg_start + seg_duration
+                        
+                        segments_info.append({
+                            "start": round(seg_start, 3),
+                            "end": round(seg_end, 3),
+                            "text": text,
+                            "speaker": spk_name,
+                            "visual": item.get("visual")
+                        })
+                        time_ptr[0] += seg_duration
+                        
+                        # Add a small gap between segments
+                        gap = 0.2
+                        gap_samples = int(gap * sr_ptr[0])
+                        current_full_wav.append(torch.zeros(1, gap_samples))
+                        time_ptr[0] += gap
+                            
+                    except Exception as e:
+                        print(f"[Error] Qwen item generation failed: {e}")
+                        current_full_wav.append(torch.zeros(1, 24000))
+                        time_ptr[0] += 1.0
+
             else:
                 # CosyVoice (Iterative)
                 for i, item in enumerate(batch_items):

aiia_qwen_nodes.py

@@ -0,0 +1,189 @@
+import os
+import sys
+import torch
+import torchaudio
+import numpy as np
+import folder_paths
+import subprocess
+
+# --- Robust Package Installation ---
+def _install_qwen_tts_if_needed():
+    try:
+        from qwen_tts import Qwen3TTSModel
+        return
+    except ImportError:
+        print("[AIIA] qwen-tts missing. Attempting installation...")
+        try:
+            subprocess.check_call([sys.executable, "-m", "pip", "install", "-U", "qwen-tts"])
+            print("[AIIA] qwen-tts installed successfully.")
+        except Exception as e:
+            print(f"[AIIA] Failed to install qwen-tts: {e}")
+
+class AIIA_Qwen_Loader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model_name": ([
+                    "Qwen/Qwen3-TTS-12Hz-1.7B-Base",
+                    "Qwen/Qwen3-TTS-12Hz-0.6B-Base",
+                    "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
+                    "Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice",
+                    "Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign"
+                ], {"default": "Qwen/Qwen3-TTS-12Hz-1.7B-Base"}),
+                "device": (["cuda", "cpu", "auto", "mps"], {"default": "auto"}),
+                "dtype": (["bf16", "fp16", "fp32"], {"default": "bf16"}),
+            },
+            "optional": {
+                "local_path": ("STRING", {"default": ""}),
+            }
+        }
+
+    RETURN_TYPES = ("QWEN_MODEL",)
+    RETURN_NAMES = ("qwen_model",)
+    FUNCTION = "load_model"
+    CATEGORY = "AIIA/Loaders"
+
+    def load_model(self, model_name, device, dtype, local_path=""):
+        _install_qwen_tts_if_needed()
+        from qwen_tts import Qwen3TTSModel
+
+        # Resolve device
+        if device == "auto":
+            if torch.cuda.is_available(): device = "cuda"
+            elif torch.backends.mps.is_available(): device = "mps"
+            else: device = "cpu"
+        
+        # Resolve dtype
+        torch_dtype = torch.bfloat16 if dtype == "bf16" else (torch.float16 if dtype == "fp16" else torch.float32)
+        
+        # Resolve path
+        path = local_path if local_path and os.path.exists(local_path) else model_name
+        
+        print(f"[AIIA] Loading Qwen3-TTS: {path} on {device} with {dtype}")
+        
+        # Flash Attention check
+        attn_impl = "flash_attention_2" if (device == "cuda" and torch.cuda.get_device_capability()[0] >= 8) else "sdpa"
+        
+        model = Qwen3TTSModel.from_pretrained(
+            path,
+            device_map=device,
+            torch_dtype=torch_dtype,
+            attn_implementation=attn_impl
+        )
+        
+        model_type = "Base"
+        if "CustomVoice" in path: model_type = "CustomVoice"
+        elif "VoiceDesign" in path: model_type = "VoiceDesign"
+        
+        return ({"model": model, "type": model_type, "name": path, "device": device},)
+
+class AIIA_Qwen_TTS:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "qwen_model": ("QWEN_MODEL",),
+                "text": ("STRING", {"multiline": True, "default": "你好，这是 Qwen3-TTS 的测试。"}),
+                "language": (["Auto", "Chinese", "English", "Japanese", "Korean", "German", "French", "Russian", "Portuguese", "Spanish", "Italian"], {"default": "Chinese"}),
+            },
+            "optional": {
+                "speaker": ("STRING", {"default": "Vivian"}),
+                "instruct": ("STRING", {"multiline": True, "default": ""}),
+                "reference_audio": ("AUDIO",),
+                "reference_text": ("STRING", {"multiline": True, "default": ""}),
+                "x_vector_only": ("BOOLEAN", {"default": False}),
+                "seed": ("INT", {"default": 42, "min": -1, "max": 2147483647}),
+                "speed": ("FLOAT", {"default": 1.0, "min": 0.5, "max": 2.0}),
+            }
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("audio",)
+    FUNCTION = "generate"
+    CATEGORY = "AIIA/Synthesis"
+
+    def generate(self, qwen_model, text, language, speaker="Vivian", instruct="", reference_audio=None, reference_text="", x_vector_only=False, seed=42, speed=1.0):
+        model = qwen_model["model"]
+        m_type = qwen_model["type"]
+        
+        if seed >= 0:
+            torch.manual_seed(seed)
+            if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed)
+
+        lang_param = language if language != "Auto" else "Auto"
+        
+        wavs = None
+        sr = 24000 # Default if unknown
+        
+        try:
+            if m_type == "CustomVoice":
+                print(f"[AIIA] Qwen3-TTS CustomVoice: {speaker} | Instruct: {instruct}")
+                wavs, sr = model.generate_custom_voice(
+                    text=text,
+                    language=lang_param,
+                    speaker=speaker,
+                    instruct=instruct if instruct else None
+                )
+            elif m_type == "VoiceDesign":
+                print(f"[AIIA] Qwen3-TTS VoiceDesign: {instruct}")
+                wavs, sr = model.generate_voice_design(
+                    text=text,
+                    language=lang_param,
+                    instruct=instruct
+                )
+            else: # Base / Clone
+                if reference_audio is not None:
+                    # Convert ComfyUI Audio format to (numpy, sr) tuple
+                    device = qwen_model["device"]
+                    ref_wav = reference_audio["waveform"]
+                    ref_sr = reference_audio["sample_rate"]
+                    
+                    # Convert to mono if needed
+                    if ref_wav.ndim == 3: ref_wav = ref_wav[0]
+                    if ref_wav.shape[0] > 1: ref_wav = torch.mean(ref_wav, dim=0, keepdim=True)
+                    
+                    ref_audio_data = (ref_wav.squeeze().cpu().numpy(), ref_sr)
+                    
+                    print(f"[AIIA] Qwen3-TTS VoiceClone: Using provided reference.")
+                    wavs, sr = model.generate_voice_clone(
+                        text=text,
+                        language=lang_param,
+                        ref_audio=ref_audio_data,
+                        ref_text=reference_text if reference_text else None,
+                        x_vector_only_mode=x_vector_only
+                    )
+                else:
+                    # Fallback if no reference provided for Base model
+                    # Typically Base model MUST have reference.
+                    # We might want to provide a default one or error out.
+                    raise ValueError("Qwen3-TTS Base model requires 'reference_audio' and 'reference_text' for cloning.")
+
+            # Process output
+            if wavs is not None and len(wavs) > 0:
+                audio_out = torch.from_numpy(wavs[0]).float()
+                if audio_out.ndim == 1: audio_out = audio_out.unsqueeze(0)
+                
+                # Speed adj (Qwen3-TTS might not have native speed param in generate_* yet, so we use torchaudio if needed)
+                if speed != 1.0:
+                    # Simple speed change via resampling (pitch change) - matches CosyVoice fallback
+                    resampler = torchaudio.transforms.Resample(orig_freq=int(sr*speed), new_freq=sr)
+                    audio_out = resampler(audio_out)
+                
+                return ({"waveform": audio_out.unsqueeze(0), "sample_rate": sr},)
+            
+        except Exception as e:
+            print(f"[AIIA] Qwen3-TTS Generation Error: {e}")
+            import traceback
+            traceback.print_exc()
+            raise e
+
+NODE_CLASS_MAPPINGS = {
+    "AIIA_Qwen_Loader": AIIA_Qwen_Loader,
+    "AIIA_Qwen_TTS": AIIA_Qwen_TTS
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AIIA_Qwen_Loader": "🤖 Qwen3-TTS Loader",
+    "AIIA_Qwen_TTS": "🗣️ Qwen3-TTS Synthesis"
+}

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "aiia"
 description = "The Ultimate AI Audio/Video toolkit for ComfyUI. Features an enhanced Ditto (with optimizations that outperform official demos and other SOTA talking head models in lip-sync accuracy and natural motion), EchoMimic V3 & FLOAT, VibeVoice & CosyVoice 3.0 (Zero-Shot Voice Cloning), Multi-Role Podcast Generation, and a powerful Media Browser."
-version = "1.10.20"
+version = "1.11.0"
 license = {file = "LICENSE"}
 readme = "README.md"
 authors = [
@@ -17,6 +17,7 @@ dependencies = [
     "huggingface_hub",
     "opencv-python",
     "ffmpeg-python",
+    "qwen-tts",
 ]
 
 [project.urls]