# BSAI ComfyUI Prism **Native 2K Joint Video-Audio Generation with Block Sparse Attention — ComfyUI Plugin** **原生 2K 联合视频-音频生成 + 动态块稀疏注意力 —— ComfyUI 插件** [![BSAI](https://img.shields.io/badge/BSAI-ComfyUI-blue?style=flat-square)]() [![Prism](https://img.shields.io/badge/Model-Prism-green?style=flat-square)]() [![License](https://img.shields.io/badge/License-MIT-yellow?style=flat-square)](LICENSE) [![Python](https://img.shields.io/badge/Python-3.10+-blue?style=flat-square)]() [![CUDA](https://img.shields.io/badge/CUDA-12.4+-green?style=flat-square)]() **Reference Image + Text → Synchronized Video + Audio · Native 720p / 1080p / 2K** **参考图 + 文本 → 同步生成视频 + 音频 · 原生支持 720p / 1080p / 2K** --- [**中文**](#中文) | [**English**](#english)

--- ## 📖 中文 ### 介绍 BSAI ComfyUI Prism 是基于 **Prism** 模型打造的 ComfyUI 插件。Prism 是由腾讯混元、复旦大学、浙江大学联合发布的原生联合视频-音频生成扩散模型（arXiv:2610.05416），其核心创新是 **BSA（Block Sparse Attention，动态块稀疏注意力）**，相比全注意力实现了 **2.5 倍训练加速**，同时质量更优。本插件将官方 Prism 推理运行时封装为 7+ 个易用的 ComfyUI 节点，完整暴露 BSA 全部参数并内置互斥校验，并支持 **音频注入 / Lip-sync 对口型** 和 **Turbo 加速（Cache-DiT + CFG Rescale）**。 ### ✨ 特性 - 🎬 **原生联合视频-音频生成** — 视频和音频在同一去噪流水线中同步生成 - ⚡ **动态块稀疏注意力 (BSA)** — IVPQ 自适应块形状、Top-k + Top-p 混合稀疏 - 🎯 **7+ 个 ComfyUI 节点** — 模型加载、采样、Turbo 加速、音视频合成、权重下载、模型卸载 - 🗣️ **音频注入 / Lip-sync 对口型** — 外部 TTS 音频注入采样，自动匹配唇形 - 🏃 **Turbo 加速节点** — Cache-DiT 块级缓存 + CFG Rescale 保质 + 自适应跳步 - 📐 **分辨率档位预设** — 480p / 720p / 1080p / 2K，自动配置 shift 和 VAE tiling - 💾 **智能显存管理** — CPU offload、VAE 分块解码、模型缓存、切换后释放专家 - 🔊 **结构化音频提示词** — 支持 ` ` 标签 - 🇨🇳 **内置中文负向提示** — 官方默认中文负向提示词 ### 📦 节点说明 | 节点 | 说明 | |------|------| | **BSAIPrismLoader** | 加载 MOVA 基底 + preview 检查点，完整 BSA 配置 | | **BSAIPrismSampler** | 参考图 + 文本 → 视频 + 音频，支持音频注入/Lip-sync | | **BSAIPrismTurboOptimizer** | Cache-DiT 块级缓存优化器（加速采样） | | **BSAIPrismTurboSampler** | Turbo 采样器：CFG Rescale 保质 + 自适应跳步 | | **BSAIPrismVideoEncode** | IMAGE + AUDIO → MP4（独立音视频合成） | | **BSAIPrismWeightsDownload** | 选择性权重下载，支持 hf-mirror 镜像 | | **BSAIPrismUnload** | 释放 GPU 中全部模型缓存 | ### 🔧 安装 #### 环境要求 | 项目 | 要求 | |------|------| | 操作系统 | Windows / Linux | | Python | ≥ 3.10 | | GPU | NVIDIA CUDA ≥ 12.4（flash-attn 必须） | | 显存 | 480p/720p：24GB+ 配合 CPU offload；1080p/2K：推荐 80GB+ | | 磁盘空间 | 完整权条约 208 GB | #### 第一步：安装插件 ```bash cd ComfyUI/custom_nodes git clone https://github.com/xm6018924/BSAI-ComfyUI-Prism.git ``` 重启 ComfyUI。节点位于菜单 **BSAI/Prism** 下。 #### 第二步：安装依赖标准 ComfyUI CUDA 环境通常已包含大部分依赖。使用 `install.py` 校验： ```bash cd BSAI-ComfyUI-Prism python install.py ``` 核心依赖： ``` diffusers>=0.33.0 transformers>=4.57.0 accelerate safetensors einops flash-attn>=2.8.0 triton>=3.3.0 descript-audiotools>=0.7.2 imageio[ffmpeg] omegaconf ftfy huggingface_hub ``` #### 第三步：下载权重（约 208 GB） **方式 A — 插件内下载：** 使用 `BSAIPrismWeightsDownload` 节点选择性下载。 **方式 B — 命令行：** ```bash python -m huggingface_hub download --resume-download FrancisRing/Prism ^ --local-dir ComfyUI\models\diffusers\BSAI-Prism ^ --include "pretrained_models/MOVA-360p/*" "preview_alpha/*" "preview_beta/*" ``` 预期目录结构： ``` ComfyUI/models/diffusers/BSAI-Prism/ ├── pretrained_models/MOVA-360p/ │ ├── video_dit/ video_dit_2/ audio_dit/ dual_tower_bridge/ │ ├── video_vae/ audio_vae/ text_encoder/ tokenizer/ scheduler/ │ └── model_index.json ├── preview_alpha/diffusion_pytorch_model.safetensors # 65.3GB └── preview_beta/diffusion_pytorch_model.safetensors # 65.3GB ``` ### 🧩 模型说明 Prism 采用 **基底 + 微调** 的双层结构。运行推理需要 **1 个基底 + 1 个微调权重**。 | 目录 | 大小 | 角色 | 说明 | |------|------|------|------| | **pretrained_models/MOVA-360p** | ~47 GB | 🏗️ 基底（骨架） | 必须有。提供模型整体结构：video_dit、audio_dit、VAE、text_encoder 等。相当于"毛坯房"。 | | **preview_alpha** | ~65 GB | 🎨 微调版本 A | 二选一。第一代预览权重，稳定可靠。相当于"标准装修"。 | | **preview_beta** | ~65 GB | 🎨 微调版本 B | 二选一。第二代预览权重，画质更精细、更有电影感。相当于"轻奢装修"。 | **正确搭配：** - ✅ `MOVA-360p` + `preview_alpha` — 可用，推荐先试这个 - ✅ `MOVA-360p` + `preview_beta` — 可用，质量更高 - ⚠️ 只有 `MOVA-360p`（无微调） — 能跑但效果很差，不推荐 - ❌ 只有 `preview_alpha/beta`（无基底） — 完全不能用 **alpha 和 beta 怎么选？** | 对比项 | preview_alpha | preview_beta | |--------|---------------|--------------| | 版本 | 第一代预览 | 第二代预览 | | 画质 | 稳定、可靠 | 通常更精细、更有电影感 | | 风格 | 偏真实感 | 可能更有风格化/胶片感 | | 推荐 | 先试这个 | 追求最佳效果时再试 | > 💡 **小贴士：** 两个微调权重共用同一个基底。在下拉框切换 alpha/beta 时，基底会复用缓存，不需要重新加载。 ### 🚀 快速开始 1. 下载权重（见上文） 2. 将 `examples/BSAI_Prism_i2v_720p_workflow.json` 拖入 ComfyUI 3. 在 `LoadImage` 中选择参考图 4. 在 `BSAI Prism 模型加载` 中配置模型路径 5. 在 `BSAI Prism 采样` 中输入提示词并运行 6. 输出：`output/BSAI_Prism/*.mp4`（含音频），以及 IMAGE 帧序列和 AUDIO 张量 **提示词示例（官方风格）：** ``` Continuous rhythmic percussive drum beats. 一名交警在阳光明媚的城市街道上指挥交通，动作充满活力。低沉有节奏的男性发声：hm, ooh, ah, ha-ya. ``` ### 🗣️ Lip-sync 音频注入本插件支持将外部 TTS 生成的语音注入采样过程，实现人物对口型效果： 1. 使用任意 TTS 节点（如 IndexTTS、CosyVoice 等）生成语音，输出为 `AUDIO` 类型 2. 将 AUDIO 连接到 `BSAIPrismSampler` 的 `audio` 可选输入 3. 调整 `audio_strength`（默认 0.8）控制音频对画面唇形的影响强度 4. 开启 `auto_duration` 可根据音频长度自动计算所需帧数 > 💡 **提示：** 音频注入通过 monkey-patch scheduler 的 `prepare_audio_latents` 实现，将干净音频 latents 与噪声按 `(1-σ)` 比例混合，使模型在去噪过程中"听"到目标语音，从而生成匹配的口型动作。 ### 🏃 Turbo 加速使用 Turbo 节点可显著提升采样速度： - **BSAIPrismTurboOptimizer**：对 transformer 做 Cache-DiT 块级缓存，输入差异小时直接复用上一步输出 - **BSAIPrismTurboSampler**：加入 CFG Rescale 保质 + 自适应跳步，步数可从 50 步压缩到 20-30 步使用方式：`Loader → TurboOptimizer → TurboSampler → 输出` ### ⚙️ 参数速查 | 档位 | 分辨率 (高×宽) | visual_shift | audio_shift | VAE 分块 | 推荐配置 | |------|----------------|--------------|-------------|----------|----------| | 480p | 480×848 | 7.0 | 7.0 | 关 | 任意 24GB+ 显卡（offload） | | 720p | 720×1280 | 7.0 | 7.0 | 关 | 80GB + offload | | 1080p | 1072×1920 | 9~13 | 9.0 | 开 | 4×80GB FSDP | | 2K | 1440×2560 | 13~17 | 11.0 | 开 | 4+×80GB FSDP | BSA 稀疏率：`0.93`（最快）→ `0.85` → `0.75`（官方默认，均衡）。 ### 🧠 架构 ``` 参考图 → 视频VAE编码 ─────────────┐ ├──▶ MOVABridge（双塔DiT + BSA稀疏注意力）文本提示 → UMT5 → 嵌入向量 ──────┬┘ │ 音频提示 → UMT5 → 嵌入向量 ──────┤ 50步去噪 + CFG 负向提示 → UMT5 → 嵌入向量 ──────┘ │ ▼ 视频VAE解码 + DAC音频解码 │ ▼ PIL帧序列 + 音频 → FFmpeg → MP4 ``` ### 🗺️ 路线图 - [x] 单卡加载 / 采样 / 合成 / 下载 / 卸载节点 - [x] BSA 全参数透出（含互斥校验） - [x] 分辨率档位预设 + 自动 shift/tiling - [x] 音频注入 / Lip-sync 对口型支持 - [x] Turbo 加速节点（Cache-DiT + CFG Rescale） - [ ] FSDP 多卡 1080p/2K 推理 - [ ] XPU/CPU VAE 解码路由（多硬件协调） - [ ] Preview 检查点热切换（alpha ↔ beta 不重载基底） ### 📄 许可证 - 插件代码：MIT License - Prism 模型与官方代码：MIT License（腾讯混元 / 复旦大学 / 浙江大学） - 模型权重：MIT License（FrancisRing/Prism @ Hugging Face） ### 🙏 致谢 - **Prism 团队** — 腾讯混元 / 复旦大学 / 浙江大学 - [论文](https://arxiv.org/abs/2610.05416) · [代码](https://github.com/Tencent-Hunyuan/Prism) · [项目主页](https://francis-rings.github.io/Prism/) - **BSAI ComfyUI 团队** — 插件适配与整合 --- ## 📖 English ### Introduction BSAI ComfyUI Prism is a ComfyUI plugin built on top of the **Prism** model — a native joint video-audio generation diffusion model jointly developed by Tencent Hunyuan, Fudan University, and Zhejiang University (arXiv:2610.05416). Prism's core innovation is **BSA (Block Sparse Attention)**, a dynamic sparse attention mechanism that achieves **2.5× training speedup** with improved quality compared to full attention. This plugin wraps the official Prism inference runtime into 7+ easy-to-use ComfyUI nodes, exposing the full BSA parameter space with mutual-exclusion validation. It also supports **audio injection / lip-sync** and **Turbo acceleration (Cache-DiT + CFG Rescale)**. ### ✨ Features - 🎬 **Native Video-Audio Joint Generation** — Video and audio generated simultaneously in a single denoising pipeline - ⚡ **Block Sparse Attention (BSA)** — Dynamic sparse attention with IVPQ adaptive block shapes, Top-k + Top-p hybrid sparsity - 🎯 **7+ ComfyUI Nodes** — Model loader, sampler, turbo optimizer, turbo sampler, video encoder, weight downloader, model unloader - 🗣️ **Audio Injection / Lip-sync** — Inject external TTS audio into sampling for synchronized lip movements - 🏃 **Turbo Acceleration** — Cache-DiT block caching + CFG Rescale quality preservation + adaptive step skipping - 📐 **Resolution Presets** — 480p / 720p / 1080p / 2K with automatic shift & VAE tiling configuration - 💾 **Smart Memory Management** — CPU offload, VAE tiling, model caching, expert freeing after switch - 🔊 **Structured Audio Prompts** — Support ` ` tags - 🇨🇳 **Built-in Chinese Negative Prompt** — Official default negative prompt in Chinese ### 📦 Nodes | Node | Description | |------|-------------| | **BSAIPrismLoader** | Load MOVA base + preview checkpoint, full BSA configuration | | **BSAIPrismSampler** | Reference image + text → video + audio, supports audio injection/lip-sync | | **BSAIPrismTurboOptimizer** | Cache-DiT block-level cache optimizer (speeds up sampling) | | **BSAIPrismTurboSampler** | Turbo sampler: CFG Rescale quality + adaptive step skipping | | **BSAIPrismVideoEncode** | IMAGE + AUDIO → MP4 (standalone video encoding) | | **BSAIPrismWeightsDownload** | Selective weight download with hf-mirror support | | **BSAIPrismUnload** | Release all model cache from GPU memory | ### 🔧 Installation #### Requirements | Item | Requirement | |------|-------------| | OS | Windows / Linux | | Python | ≥ 3.10 | | GPU | NVIDIA CUDA ≥ 12.4 (required for flash-attn) | | VRAM | 480p/720p: 24GB+ with CPU offload; 1080p/2K: 80GB+ recommended | | Disk | ~208 GB for full weights | #### Step 1: Install the Plugin ```bash cd ComfyUI/custom_nodes git clone https://github.com/xm6018924/BSAI-ComfyUI-Prism.git ``` Then restart ComfyUI. Nodes are located under **BSAI/Prism** menu. #### Step 2: Install Dependencies Most dependencies are already included in standard ComfyUI CUDA environments. Use `install.py` to verify: ```bash cd BSAI-ComfyUI-Prism python install.py ``` Key dependencies: ``` diffusers>=0.33.0 transformers>=4.57.0 accelerate safetensors einops flash-attn>=2.8.0 triton>=3.3.0 descript-audiotools>=0.7.2 imageio[ffmpeg] omegaconf ftfy huggingface_hub ``` #### Step 3: Download Weights (~208 GB) **Option A — From ComfyUI:** Use the `BSAIPrismWeightsDownload` node to download components selectively. **Option B — Command line:** ```bash python -m huggingface_hub download --resume-download FrancisRing/Prism \ --local-dir ComfyUI/models/diffusers/BSAI-Prism \ --include "pretrained_models/MOVA-360p/*" "preview_alpha/*" "preview_beta/*" ``` Expected directory structure: ``` ComfyUI/models/diffusers/BSAI-Prism/ ├── pretrained_models/MOVA-360p/ │ ├── video_dit/ video_dit_2/ audio_dit/ dual_tower_bridge/ │ ├── video_vae/ audio_vae/ text_encoder/ tokenizer/ scheduler/ │ └── model_index.json ├── preview_alpha/diffusion_pytorch_model.safetensors # 65.3GB └── preview_beta/diffusion_pytorch_model.safetensors # 65.3GB ``` ### 🧩 Model Overview Prism uses a **base + fine-tune** two-layer structure. You need **1 base + 1 checkpoint** to run inference. | Folder | Size | Role | Description | |--------|------|------|-------------| | **pretrained_models/MOVA-360p** | ~47 GB | 🏗️ Base (skeleton) | Required. Provides the model architecture: video_dit, audio_dit, VAE, text_encoder, etc. Think of it as the "house frame". | | **preview_alpha** | ~65 GB | 🎨 Fine-tune A | Choose one. First preview release, stable and reliable. The "standard finish". | | **preview_beta** | ~65 GB | 🎨 Fine-tune B | Choose one. Second preview release, typically higher quality and more cinematic. The "premium finish". | **Valid combinations:** - ✅ `MOVA-360p` + `preview_alpha` — works, recommended starting point - ✅ `MOVA-360p` + `preview_beta` — works, higher quality - ⚠️ `MOVA-360p` only (no fine-tune) — works but very poor quality, not recommended - ❌ `preview_alpha/beta` only (no base) — won't work at all **alpha vs beta — which to choose?** | Aspect | preview_alpha | preview_beta | |--------|---------------|--------------| | Version | 1st preview | 2nd preview | | Quality | Solid, reliable | Often more refined & cinematic | | Style | Realistic leaning | Can be more stylized / filmic | | Recommendation | Start here | Try when chasing best quality | > 💡 **Tip:** Both checkpoints share the same base. You can switch between alpha and beta in the dropdown without reloading the base model (it stays cached). ### 🚀 Quick Start 1. Download weights (see above) 2. Load `examples/BSAI_Prism_i2v_720p_workflow.json` into ComfyUI 3. Select a reference image in `LoadImage` 4. Configure model paths in `BSAI Prism Loader` 5. Enter your prompt in `BSAI Prism Sampler` and run 6. Output: `output/BSAI_Prism/*.mp4` (with audio), plus IMAGE frames and AUDIO tensor **Prompt example (official style):** ``` Continuous rhythmic percussive drum beats. A traffic police officer on a sunlit city street, directing cars with dynamic movements. Deep rhythmic male vocalizations: hm, ooh, ah, ha-ya. ``` ### 🗣️ Lip-sync Audio Injection This plugin supports injecting external TTS-generated speech into the sampling process for synchronized lip movements: 1. Use any TTS node (e.g., IndexTTS, CosyVoice) to generate speech as `AUDIO` output 2. Connect the AUDIO output to the optional `audio` input of `BSAIPrismSampler` 3. Adjust `audio_strength` (default 0.8) to control how strongly the audio drives lip movement 4. Enable `auto_duration` to automatically calculate frame count from audio length > 💡 **Tip:** Audio injection works by monkey-patching the scheduler's `prepare_audio_latents`, mixing clean audio latents with noise at `(1-σ)` ratio, so the model "hears" the target speech during denoising and generates matching lip movements. ### 🏃 Turbo Acceleration Use Turbo nodes to significantly speed up sampling: - **BSAIPrismTurboOptimizer**: Cache-DiT block-level caching — reuses previous block output when input difference is small - **BSAIPrismTurboSampler**: CFG Rescale quality preservation + adaptive step skipping, reducing steps from 50 to 20-30 Usage: `Loader → TurboOptimizer → TurboSampler → output` ### ⚙️ Parameter Reference | Preset | Resolution (H×W) | visual_shift | audio_shift | VAE Tiling | Recommendation | |--------|-------------------|--------------|-------------|------------|----------------| | 480p | 480×848 | 7.0 | 7.0 | off | Any 24GB+ GPU (offload) | | 720p | 720×1280 | 7.0 | 7.0 | off | 80GB + offload | | 1080p | 1072×1920 | 9~13 | 9.0 | on | 4×80GB FSDP | | 2K | 1440×2560 | 13~17 | 11.0 | on | 4+×80GB FSDP | BSA sparsity: `0.93` (fastest) → `0.85` → `0.75` (official default, balanced). ### 🧠 Architecture ``` Reference Image → Video VAE Encode ──┐ ├──▶ MOVABridge (Dual-Tower DiT + BSA) Text Prompt → UMT5 → Embeddings ────┬┘ │ Audio Prompt → UMT5 → Embeddings ───┤ 50-step denoising + CFG Negative Prompt → UMT5 → Embeddings ─┘ │ ▼ Video VAE Decode + DAC Audio Decode │ ▼ PIL Frames + Audio → FFmpeg → MP4 ``` ### 🗺️ Roadmap - [x] Single-GPU load / sample / encode / download / unload nodes - [x] Full BSA parameter exposure with mutual-exclusion validation - [x] Resolution presets with automatic shift & tiling - [x] Audio injection / lip-sync support - [x] Turbo acceleration nodes (Cache-DiT + CFG Rescale) - [ ] FSDP multi-GPU 1080p/2K inference - [ ] XPU/CPU VAE decode routing (multi-hardware coordination) - [ ] Preview checkpoint hot-swap (alpha ↔ beta without reloading base) ### 📄 License - Plugin code: MIT License - Prism model & official code: MIT License (Tencent Hunyuan / Fudan / Zhejiang University) - Weights: MIT License (FrancisRing/Prism @ Hugging Face) ### 🙏 Acknowledgments - **Prism Team** — Tencent Hunyuan / Fudan University / Zhejiang University - [Paper](https://arxiv.org/abs/2610.05416) · [Code](https://github.com/Tencent-Hunyuan/Prism) · [Project Page](https://francis-rings.github.io/Prism/) - **BSAI ComfyUI Team** — Plugin adaptation and integration ---

**Made with ❤️ by BSAI Team** [BSAI-ComfyUI-Prism](https://github.com/xm6018924/BSAI-ComfyUI-Prism) · ComfyUI 整合包

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BSAI Prism Unload Models

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