[
  {
    "path": "README.md",
    "content": "# ComfyUI-PainterI2V  此节点由抖音博主：绘画小子 制作。\r\n\r\nWan2.2 图生视频增强节点，专门针对4步LoRA（如 lightx2v）的慢动作问题进行优化。\r\n\r\n2026-2-13 我制作了一个PainterHumoAI2V节点，已经实现WAN2.2+Humo 直接 音频+图片 4步生成音频驱动的图生视频（AI2V）并支持首尾帧，欢迎尝试（项目内有工作流）！https://github.com/princepainter/ComfyUI-PainterNodes\r\n\r\n2025-12-30，我更新了一个PainterI2V的优化版PainterI2Vadvanced，建议尝试使用。项目地址：https://github.com/princepainter/ComfyUI-PainterI2Vadvanced\r\n\r\n如果你用的是KJ的wan2.2-i2v工作流，请使用这个专门为KJ开发的节点，效果一致 (https://github.com/princepainter/ComfyUI-PainterI2VforKJ)\r\n\r\n（FirstLastFrameToVideo）首尾帧节点 PainterFLF2V：https://github.com/princepainter/Comfyui-PainterFLF2V\r\n\r\n## 🛠 解决的问题\r\n\r\n- ✅ 1.减少慢动作：提升运动幅度 15-50%\r\n- ✅ 2.增强运镜：让运镜提示词更听话，运镜幅度更大\r\n\r\n  ## 和wan2.2官方节点效果对比\r\n\r\n<table>\r\n  <tr>\r\n    <td><img src=\"image/wan2-2_00003.gif\" alt=\"图1\" width=\"300\"></td>\r\n    <td><img src=\"image/wan2-2_00004.gif\" alt=\"图2\" width=\"300\"></td>\r\n    <td><img src=\"image/wan2-2_00005.gif\" alt=\"图3\" width=\"300\"></td>\r\n  </tr>\r\n  <tr>\r\n    <td>motion_amplitude=1.2</td>\r\n    <td>motion_amplitude=1.15</td>\r\n    <td>motion_amplitude=1.15</td>\r\n  </tr>\r\n  <tr>\r\n    <td><img src=\"image/wan2-2_00006.gif\" alt=\"图4\" width=\"300\"></td>\r\n    <td><img src=\"image/wan2-2_00007.gif\" alt=\"图5\" width=\"300\"></td>\r\n    <td><img src=\"image/wan2-2_00008.gif\" alt=\"图6\" width=\"300\"></td>\r\n  </tr>\r\n  <tr>\r\n    <td>motion_amplitude=1.5</td>\r\n    <td>motion_amplitude=1.2</td>\r\n    <td>motion_amplitude=1.15</td>\r\n  </tr>\r\n</table>\r\n\r\n\r\n以上视频对比效果，我是使用wan2.2官方模型+light2v 4步i2v lora V1.0做的测试（见下图），其他模型使用效果有待各位自行测试\r\n\r\n ![我使用的模型和lora](image/QQ截图20251111171920.jpg)\r\n## 节点特点\r\n- 单帧输入优化：专为wan2.2单帧图生视频设计\r\n- 即插即用：完全兼容原版 Wan2.2 工作流\r\n\r\n## 📦 安装\r\n\r\n### 方法 1: ComfyUI Manager（推荐）\r\n\r\n1. 打开 ComfyUI Manager  \r\n2. 搜索 PainterI2V  \r\n3. 点击安装  \r\n\r\n### 方法 2: 手动安装\r\n\r\n```bash\r\n# 进入ComfyUI的custom_nodes目录\r\ncd ComfyUI/custom_nodes\r\n\r\n# 克隆仓库\r\ngit clone https://github.com/princepainter/ComfyUI-PainterI2V.git\r\n```\r\n# 重启ComfyUI\r\n\r\n## 💡 使用方法\r\n\r\n### 替换节点\r\n\r\n在工作流中将 `WanImageToVideo` 替换为 `PainterI2V`\r\n![替换原节点](image/QQ截图20251110095134.jpg)\r\n\r\n### 参数设置\r\n\r\n- `motion_amplitude`: 1.15（推荐起始值）\r\n- 其他参数与原版保持一致\r\n\r\n### 场景参数推荐\r\n\r\n| 运动类型         | 推荐参数   | 示例提示词             |\r\n|------------------|------------|------------------------|\r\n| 快速（跑步 / 跳跃） | 1.25–1.35  | \"快速向前奔跑\"         |\r\n| 正常（走路 / 挥手） | 1.10–1.20  | \"流畅地行走\"           |\r\n| 动态增强          | 1.00–1.10  | \"略微增强动态和运镜\"    |\r\n\r\n### 提示词优化\r\n\r\n- 明确描述运动节奏，如 “快速奔跑”、“流畅行走”\r\n- 避免模糊描述如 “移动”、“走动”\r\n\r\n## 🔬 技术细节\r\n\r\n| 参数值 | 运动提升 | 亮度变化 | 适用场景     |\r\n|--------|----------|----------|--------------|\r\n| 1.0（原版） | 0%       | 无       | 和WAN原版节点无区别   |\r\n| 1.15（默认）| +15%     | 无       | 通用场景     |\r\n| 1.3    | +30%     | 无       | 体育运动     |\r\n| 1.5    | +50%     | 无       | 极限运动     |\r\n\r\n### 核心算法原理\r\n\r\n- **亮度保护的运动缩放**：放大运动向量前分离亮度均值  \r\n- **零 latent 初始化**：严格保持 4 步 LoRA 的时序依赖链  \r\n- **参考帧增强**：使用 `reference_latents` 保持主体一致性，不约束运动\r\n\r\n## ⚡ 进阶技巧\r\n\r\n- 最佳效果：配合强运动提示词使用  \r\n- 运动过快：每次减少 `motion_amplitude` 0.05  \r\n- 仍然偏慢：可适当增大到 1.4\r\n\r\n## 🙏 致谢\r\n\r\n- Wan2.2 团队：提供惊人的视频生成模型  \r\n- ComfyUI 社区：灵活的节点系统  \r\n🙏如果这个项目对你有帮助，请给颗星 ⭐️ 支持一下！\r\n\r\n- # ComfyUI-Painterl2V\r\n\r\nThis node is created by Douyin creator: 绘画小子\r\n\r\nPainterI2V node that specifically fixes the slow-motion issue in 4-step LoRAs (e.g., lightx2v).\r\n\r\n2025-12-30现在已经推出升级版PainterI2Vadvanced，建议使用。项目地址：https://github.com/princepainter/PainterI2Vadvanced\r\n\r\nThe comparative effects in the above video are from my test using the official Wan2.2 model + Light2v 4-step I2V LoRA V1.0. The performance of other models awaits your own testing .\r\n\r\nIf you're using KJ's wan2.2-i2v workflow, use this node specifically developed for KJ — the effect is consistent.https://github.com/princepainter/ComfyUI-PainterI2VforKJ\r\n\r\n## 🛠 Problems Solved\r\n\r\n- ✅ Reduces Slow-Motion Drag: Increases motion amplitude by 15-50% \r\n- ✅ Enhance camera movement: Make the camera movement prompts more responsive and increase the movement amplitude. \r\n- ✅ Optimized for Single Frame: Designed specifically for single-frame image-to-video workflows  \r\n- ✅ Plug & Play: Fully compatible with original Wan2.2 workflows \r\n\r\n## 📦 Installation\r\n\r\n### Method 1: ComfyUI Manager (Recommended)\r\n\r\n1. Open ComfyUI Manager  \r\n2. Search for **PainterI2V**  \r\n3. Click **Install**  \r\n\r\n### Method 2: Manual Installation\r\n\r\n```bash\r\n# Navigate to ComfyUI's custom_nodes directory\r\ncd ComfyUI/custom_nodes\r\n\r\n# Clone the repository\r\ngit clone https://github.com/princepainter/ComfyUI-PainterI2V.git\r\n```\r\n## 💡 Usage Guide\r\n\r\n### Replace the Node\r\n\r\nIn your workflow, replace `WanImageToVideo` with `PainterI2V`.\r\n\r\n### Parameter Settings\r\n\r\n- `motion_amplitude`: 1.15 (recommended starting value)  \r\n- Keep all other parameters consistent with the original node.\r\n\r\n### Recommended Parameters by Scene\r\n\r\n| Motion Type            | Recommended Value | Example Prompt              |\r\n|------------------------|-------------------|-----------------------------|\r\n| Fast (running / jumping) | 1.25–1.35         | \"Running forward quickly\"   |\r\n| Normal (walking / waving)| 1.10–1.20         | \"Walking smoothly\"          |\r\n| Motion Enhancement      | 1.00–1.10         | \"Slightly enhance motion and camera movement\" |\r\n\r\n### Prompt Optimization Tips\r\n\r\n- Clearly describe motion rhythm, e.g., “run fast”, “walk smoothly”  \r\n- Avoid vague terms like “move” or “walk around”\r\n\r\n## 🔬 Technical Details\r\n\r\n| Parameter | Motion Boost | Brightness Change | Best For           |\r\n|-----------|--------------|-------------------|--------------------|\r\n| 1.0 (original) | 0%           | None              | No difference from the original WAN node |\r\n| 1.15 (default) | +15%         | None              | General use        |\r\n| 1.3       | +30%         | None              | Sports action      |\r\n| 1.5       | +50%         | None              | Extreme motion     |\r\n\r\n### Core Algorithm Principles\r\n\r\n- **Brightness-Protected Motion Scaling**: Separates luminance mean before scaling motion vectors  \r\n- **Zero Latent Initialization**: Strictly preserves the temporal dependency chain required by 4-step LoRAs  \r\n- **Reference Frame Enhancement**: Uses `reference_latents` to maintain subject consistency without constraining motion  \r\n\r\n## ⚡ Advanced Tips\r\n\r\n- Best results: Use with strong motion-related prompts  \r\n- Motion too fast? Reduce `motion_amplitude` by 0.05 increments  \r\n- Still too slow? Try increasing up to 1.4  \r\n\r\n## 🙏 Acknowledgements\r\n\r\n- Wan2.2 Team: For the amazing video generation model  \r\n- ComfyUI Community: For the flexible node system  \r\n- Contributors & testers: For helping refine this node  \r\n\r\n<div align=\"center\">\r\nIf you find this project helpful, please give it a star ⭐ to show your support!\r\n</div>\r\n# Restart ComfyUI\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n"
  },
  {
    "path": "__init__.py",
    "content": "from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS\r\n\r\n__version__ = \"1.0.6\"\r\n\r\n__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']\r\n\r\n"
  },
  {
    "path": "__manifest__.py",
    "content": "MANIFEST = {\r\n    \"name\": \"ComfyUI-PainterI2V\",\r\n    \"version\": \"1.0.0\",\r\n    \"author\": \"Painter (社区贡献)\",\r\n    \"description\": \"Wan2.2图生视频增强节点，修复4步LoRA慢动作问题，保持亮度稳定\",\r\n    \"tags\": [\"wan\", \"video\", \"i2v\", \"lora\", \"slow-motion\"],\r\n    \"requirements\": [],\r\n    \"custom_nodes\": {\r\n        \"PainterI2V\": {\r\n            \"category\": \"conditioning/video_models\",\r\n            \"display_name\": \"🎨 PainterI2V (Wan2.2)\",\r\n            \"description\": \"增强版图生视频，解决慢动作问题\"\r\n        }\r\n    }\r\n}\r\n"
  },
  {
    "path": "nodes.py",
    "content": "import torch\r\nimport comfy.model_management\r\nimport comfy.utils\r\nimport node_helpers\r\nfrom comfy_api.latest import io, ComfyExtension\r\nfrom typing_extensions import override\r\n\r\nclass PainterI2V(io.ComfyNode):\r\n    \"\"\"\r\n    An enhanced Wan2.2 Image-to-Video node specifically designed to fix the slow-motion issue in 4-step LoRAs (like lightx2v).\r\n    \"\"\"\r\n    \r\n    @classmethod\r\n    def define_schema(cls):\r\n        return io.Schema(\r\n            node_id=\"PainterI2V\",\r\n            category=\"conditioning/video_models\",\r\n            inputs=[\r\n                io.Conditioning.Input(\"positive\"),\r\n                io.Conditioning.Input(\"negative\"),\r\n                io.Vae.Input(\"vae\"),\r\n                io.Int.Input(\"width\", default=832, min=16, max=4096, step=16),\r\n                io.Int.Input(\"height\", default=480, min=16, max=4096, step=16),\r\n                io.Int.Input(\"length\", default=81, min=1, max=4096, step=4),\r\n                io.Int.Input(\"batch_size\", default=1, min=1, max=4096),\r\n                io.Float.Input(\"motion_amplitude\", default=1.15, min=1.0, max=2.0, step=0.05),\r\n                io.ClipVisionOutput.Input(\"clip_vision_output\", optional=True),\r\n                io.Image.Input(\"start_image\", optional=True),\r\n            ],\r\n            outputs=[\r\n                io.Conditioning.Output(display_name=\"positive\"),\r\n                io.Conditioning.Output(display_name=\"negative\"),\r\n                io.Latent.Output(display_name=\"latent\"),\r\n            ]\r\n        )\r\n\r\n    @classmethod\r\n    def execute(cls, positive, negative, vae, width, height, length, batch_size,\r\n                motion_amplitude=1.15, start_image=None, clip_vision_output=None) -> io.NodeOutput:\r\n        # 1. 严格的零latent初始化（4步LoRA的生命线）\r\n        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], \r\n                           device=comfy.model_management.intermediate_device())\r\n        \r\n        if start_image is not None:\r\n            # 单帧输入处理\r\n            start_image = start_image[:1]\r\n            start_image = comfy.utils.common_upscale(\r\n                start_image.movedim(-1, 1), width, height, \"bilinear\", \"center\"\r\n            ).movedim(1, -1)\r\n            \r\n            # 创建序列：首帧真实，后续0.5灰\r\n            image = torch.ones((length, height, width, start_image.shape[-1]), \r\n                             device=start_image.device, dtype=start_image.dtype) * 0.5\r\n            image[0] = start_image[0]\r\n            \r\n            concat_latent_image = vae.encode(image[:, :, :, :3])\r\n            \r\n            # 单帧mask：仅约束首帧\r\n            mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], \r\n                             concat_latent_image.shape[-1]), \r\n                            device=start_image.device, dtype=start_image.dtype)\r\n            mask[:, :, 0] = 0.0\r\n            \r\n            # 2. 运动幅度增强（亮度保护核心算法）\r\n            if motion_amplitude > 1.0:\r\n                base_latent = concat_latent_image[:, :, 0:1]      # 首帧\r\n                gray_latent = concat_latent_image[:, :, 1:]       # 灰帧\r\n                \r\n                diff = gray_latent - base_latent\r\n                diff_mean = diff.mean(dim=(1, 3, 4), keepdim=True)\r\n                diff_centered = diff - diff_mean\r\n                scaled_latent = base_latent + diff_centered * motion_amplitude + diff_mean\r\n                \r\n                # Clamp & 组合\r\n                scaled_latent = torch.clamp(scaled_latent, -6, 6)\r\n                concat_latent_image = torch.cat([base_latent, scaled_latent], dim=2)\r\n            \r\n            # 3. 注入到conditioning\r\n            positive = node_helpers.conditioning_set_values(\r\n                positive, {\"concat_latent_image\": concat_latent_image, \"concat_mask\": mask}\r\n            )\r\n            negative = node_helpers.conditioning_set_values(\r\n                negative, {\"concat_latent_image\": concat_latent_image, \"concat_mask\": mask}\r\n            )\r\n\r\n            # 4. 参考帧增强\r\n            ref_latent = vae.encode(start_image[:, :, :, :3])\r\n            positive = node_helpers.conditioning_set_values(positive, {\"reference_latents\": [ref_latent]}, append=True)\r\n            negative = node_helpers.conditioning_set_values(negative, {\"reference_latents\": [torch.zeros_like(ref_latent)]}, append=True)\r\n\r\n        if clip_vision_output is not None:\r\n            positive = node_helpers.conditioning_set_values(positive, {\"clip_vision_output\": clip_vision_output})\r\n            negative = node_helpers.conditioning_set_values(negative, {\"clip_vision_output\": clip_vision_output})\r\n\r\n        out_latent = {}\r\n        out_latent[\"samples\"] = latent\r\n        return io.NodeOutput(positive, negative, out_latent)\r\n\r\n\r\nclass PainterI2VExtension(ComfyExtension):\r\n    @override\r\n    async def get_node_list(self) -> list[type[io.ComfyNode]]:\r\n        return [PainterI2V]\r\n\r\nasync def comfy_entrypoint() -> PainterI2VExtension:\r\n    return PainterI2VExtension()\r\n\r\n\r\n# 节点注册映射\r\nNODE_CLASS_MAPPINGS = {\r\n    \"PainterI2V\": PainterI2V,\r\n}\r\n\r\nNODE_DISPLAY_NAME_MAPPINGS = {\r\n    \"PainterI2V\": \"PainterI2V (Wan2.2 Slow-Motion Fix)\",\r\n}\r\n\r\n"
  },
  {
    "path": "workflows.json",
    "content": "{\n  \"id\": \"1d90e051-4a31-4d86-a7b7-83b0b324f79b\",\n  \"revision\": 0,\n  \"last_node_id\": 191,\n  \"last_link_id\": 394,\n  \"nodes\": [\n    {\n      \"id\": 161,\n      \"type\": \"PathchSageAttentionKJ\",\n      \"pos\": [\n        493.3536457504734,\n        1190.6989261251294\n      ],\n      \"size\": [\n        225.8580078125,\n        58\n      ],\n      \"flags\": {},\n      \"order\": 10,\n      \"mode\": 0,\n      \"inputs\": [\n        {\n          \"label\": \"模型\",\n          \"name\": \"model\",\n          \"type\": \"MODEL\",\n          \"link\": 387\n        }\n      ],\n      \"outputs\": [\n        {\n          \"name\": \"MODEL\",\n          \"type\": \"MODEL\",\n          \"links\": [\n            323\n          ]\n        }\n      ],\n      \"properties\": {\n        \"cnr_id\": \"comfyui-kjnodes\",\n        \"ver\": \"a6b867b63a29ca48ddb15c589e17a9f2d8530d57\",\n        \"Node name for S&R\": \"PathchSageAttentionKJ\",\n        \"ue_properties\": {\n          \"version\": \"7.0.1\",\n          \"widget_ue_connectable\": {}\n        }\n      },\n      \"widgets_values\": [\n        \"auto\"\n      ]\n    },\n    {\n      \"id\": 163,\n      \"type\": \"PathchSageAttentionKJ\",\n      \"pos\": [\n        493.3536457504734,\n        1060.6989261251294\n      ],\n      \"size\": [\n        225.8580078125,\n        58\n      ],\n      \"flags\": {},\n      \"order\": 11,\n      \"mode\": 0,\n      \"inputs\": [\n        {\n          \"label\": \"模型\",\n          \"name\": \"model\",\n          \"type\": \"MODEL\",\n          \"link\": 381\n        }\n      ],\n      \"outputs\": [\n        {\n          \"name\": \"MODEL\",\n          \"type\": \"MODEL\",\n          \"links\": [\n            322\n          ]\n        }\n      ],\n      \"properties\": {\n        \"cnr_id\": \"comfyui-kjnodes\",\n        \"ver\": \"a6b867b63a29ca48ddb15c589e17a9f2d8530d57\",\n        \"Node name for S&R\": \"PathchSageAttentionKJ\",\n        \"ue_properties\": {\n          \"version\": \"7.0.1\",\n          \"widget_ue_connectable\": {}\n        }\n      },\n      \"widgets_values\": [\n        \"auto\"\n      ]\n    },\n    {\n      \"id\": 165,\n      \"type\": \"ModelSamplingSD3\",\n      \"pos\": [\n        743.3536457504731,\n        1090.6989261251294\n      ],\n      \"size\": [\n        210,\n        58\n      ],\n      \"flags\": {\n        \"collapsed\": false\n      },\n      \"order\": 12,\n      \"mode\": 0,\n      \"inputs\": [\n        {\n          \"name\": \"model\",\n          \"type\": \"MODEL\",\n          \"link\": 323\n        }\n      ],\n      \"outputs\": [\n        {\n          \"name\": \"MODEL\",\n          \"type\": \"MODEL\",\n          \"slot_index\": 0,\n          \"links\": [\n            328\n          ]\n        }\n      ],\n      \"properties\": {\n        \"cnr_id\": \"comfy-core\",\n        \"ver\": \"0.3.45\",\n        \"Node name for S&R\": \"ModelSamplingSD3\",\n        \"ue_properties\": {\n          \"version\": \"7.0.1\",\n          \"widget_ue_connectable\": {\n            \"shift\": true\n          }\n        }\n      },\n      \"widgets_values\": [\n        5.000000000000001\n      ]\n    },\n    {\n      \"id\": 166,\n      \"type\": \"KSamplerAdvanced\",\n      \"pos\": [\n        743.3536457504731,\n        1180.6989261251294\n      ],\n      \"size\": [\n        210,\n        334\n      ],\n      \"flags\": {},\n      \"order\": 14,\n      \"mode\": 0,\n      \"inputs\": [\n        {\n          \"name\": \"model\",\n          \"type\": \"MODEL\",\n          \"link\": 324\n        },\n        {\n          \"name\": \"positive\",\n          \"type\": \"CONDITIONING\",\n          \"link\": 375\n        },\n        {\n          \"name\": \"negative\",\n          \"type\": \"CONDITIONING\",\n          \"link\": 376\n        },\n        {\n          \"name\": \"latent_image\",\n          \"type\": \"LATENT\",\n       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