ComfyUI Extension: ComfyUI-PainterLongVideo

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Powerful node for generating long-form videos with consistent motion, global scene coherence, and slow-motion correction in Wan 2.2-based workflows.

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    README

    PainterLongVideo Node for ComfyUI

    ComfyUI 的 PainterLongVideo 节点

    A powerful node for generating long-form videos with consistent motion, global scene coherence, and slow-motion correction in Wan 2.2-based workflows.
    一个强大的节点，用于在基于 Wan 2.2 的工作流中生成长视频，具备运动一致性、全局场景连贯性以及慢动作修复功能。

    ✨ Features / 功能亮点

    • Long Video Continuation: Seamlessly continues from the last frame of a previous video segment.
      长视频接续：无缝接续上一段视频的最后一帧。

    • Slow-Motion Fix: Built-in motion_amplitude control to enhance motion intensity and fix sluggish movement in 4-step LoRAs (e.g., lightx2v).
      慢动作修复：内置 motion_amplitude 参数，增强运动幅度，修复 4 步 LoRA（如 lightx2v）中的迟缓问题。

    • Global Consistency Anchor: Optional initial_reference_image input allows the model to remember the original character/scene layout from the first segment, preventing drift when the camera returns.
      全局一致性锚定：可选的 initial_reference_image 输入，让模型记住第一段的初始人物与场景布局，防止镜头回溯时内容漂移。

    • Compact UI: Clean, official-style node size with short name PainterLongVideo.
      紧凑界面：简洁、官方风格的节点尺寸，名称简短为 PainterLongVideo。

    📥 Installation / 安装方法

    1. Place this folder into your ComfyUI custom nodes directory:
      将本文件夹放入 ComfyUI 的自定义节点目录中：

    2. Ensure you have the required dependencies (usually included with standard ComfyUI):
      确保已安装所需依赖（通常随标准 ComfyUI 自带）：

    • torch
    • comfyui (latest)
    1. Restart ComfyUI. The node will appear under video/painter category.
      重启 ComfyUI。该节点将出现在 video/painter 分类下。

    ⚙️ Inputs / 输入参数

    | Input | Type | Description | |------|------|-------------| | positive | CONDITIONING | Positive prompt conditioning. | | negative | CONDITIONING | Negative prompt conditioning. | | vae | VAE | VAE model for latent encoding/decoding. | | width | INT | Output width (multiple of 16). Default: 832. | | height | INT | Output height (multiple of 16). Default: 480. | | length | INT | Number of output frames. Default: 81. | | batch_size | INT | Batch size for generation. Default: 1. | | previous_video | IMAGE | The full output video from the previous segment (used for continuity). | | motion_frames | INT | Number of trailing frames from previous_video used as motion reference. Default: 5. | | motion_amplitude | FLOAT | Motion intensity multiplier (1.0 = normal, 1.15 = recommended). Range: 1.0–2.0. | | initial_reference_image (optional) | IMAGE | The first frame of the very first video segment. Helps maintain global consistency across segments. | | clip_vision_output (optional) | CLIP_VISION_OUTPUT | Optional CLIP vision embedding for image-guided generation. |

    | 输入 | 类型 | 说明 | |------|------|------| | positive | CONDITIONING | 正向提示词条件。 | | negative | CONDITIONING | 负向提示词条件。 | | vae | VAE | 用于 latent 编解码的 VAE 模型。 | | width | INT | 输出宽度（需为 16 的倍数）。默认：832。 | | height | INT | 输出高度（需为 16 的倍数）。默认：480。 | | length | INT | 输出帧数。默认：81。 | | batch_size | INT | 生成批次大小。默认：1。 | | previous_video | IMAGE | 上一段视频的完整输出（用于连续性）。 | | motion_frames | INT | 从 previous_video 末尾提取的参考帧数量。默认：5。 | | motion_amplitude | FLOAT | 运动强度倍率（1.0=正常，1.15=推荐）。范围：1.0–2.0。 | | initial_reference_image (可选) | IMAGE | 整个视频序列的第一帧。用于跨段落保持全局一致性。 | | clip_vision_output (可选) | CLIP_VISION_OUTPUT | 可选的 CLIP 视觉嵌入，用于图像引导生成。 |

    💡 Usage Tips / 使用建议

    • For best results, always provide the first frame of Segment 1 as initial_reference_image to all subsequent segments.
      为获得最佳效果，请将第一段的第一帧作为 initial_reference_image 输入到所有后续段落中。

    • Set motion_amplitude = 1.15 as default. Increase to 1.2–1.3 if motion still feels too slow.
      默认设为 motion_amplitude = 1.15。若仍觉动作太慢，可增至 1.2–1.3。

    • Keep motion_frames small (3–7) unless complex motion is needed.
      除非需要复杂运镜，否则保持 motion_frames 较小（3–7）。

    • This node works best with Wan 2.2 + 4-step LoRA pipelines.
      本节点最适合搭配 Wan 2.2 + 4 步 LoRA 流程使用。

    🧠 How It Works / 工作原理

    The node:

    1. Encodes the last frame of previous_video as the starting point.
    2. Constructs a latent sequence with the first frame fixed and others initialized to gray.
    3. Applies motion enhancement via latent difference scaling (motion_amplitude).
    4. Injects both last-frame and initial-frame latents into reference_latents for dual-reference guidance.

    该节点：

    1. 将 previous_video 的最后一帧编码为起点；
    2. 构建 latent 序列：首帧固定，其余初始化为灰色；
    3. 通过 latent 差值缩放实现运动增强（motion_amplitude）；
    4. 将结尾帧和起始帧同时注入 reference_latents，实现双重参考引导。

    📜 License / 许可证

    MIT License – Free to use, modify, and distribute.
    MIT 许可证 – 免费使用、修改和分发。