Repository: princepainter/ComfyUI-PainterI2V Branch: main Commit: 652565fc032c Files: 6 Total size: 93.5 KB Directory structure: gitextract_9op3e33m/ ├── README.md ├── __init__.py ├── __manifest__.py ├── nodes.py ├── workflows.json └── 对比测试工作流2025-11-9.json ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # ComfyUI-PainterI2V 此节点由抖音博主：绘画小子 制作。 Wan2.2 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。 2026-2-13 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 2025-12-30，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 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) （FirstLastFrameToVideo）首尾帧节点 PainterFLF2V：https://github.com/princepainter/Comfyui-PainterFLF2V ## 🛠 解决的问题 - ✅ 1.减少慢动作：提升运动幅度 15-50% - ✅ 2.增强运镜：让运镜提示词更听话，运镜幅度更大 ## 和wan2.2官方节点效果对比
图1 图2 图3
motion_amplitude=1.2 motion_amplitude=1.15 motion_amplitude=1.15
图4 图5 图6
motion_amplitude=1.5 motion_amplitude=1.2 motion_amplitude=1.15
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 ![我使用的模型和lora](image/QQ截图20251111171920.jpg) ## 节点特点 - 单帧输入优化：专为wan2.2单帧图生视频设计 - 即插即用：完全兼容原版 Wan2.2 工作流 ## 📦 安装 ### 方法 1: ComfyUI Manager（推荐） 1. 打开 ComfyUI Manager 2. 搜索 PainterI2V 3. 点击安装 ### 方法 2: 手动安装 ```bash # 进入ComfyUI的custom_nodes目录 cd ComfyUI/custom_nodes # 克隆仓库 git clone https://github.com/princepainter/ComfyUI-PainterI2V.git ``` # 重启ComfyUI ## 💡 使用方法 ### 替换节点 在工作流中将 `WanImageToVideo` 替换为 `PainterI2V` ![替换原节点](image/QQ截图20251110095134.jpg) ### 参数设置 - `motion_amplitude`: 1.15（推荐起始值） - 其他参数与原版保持一致 ### 场景参数推荐 | 运动类型 | 推荐参数 | 示例提示词 | |------------------|------------|------------------------| | 快速（跑步 / 跳跃） | 1.25–1.35 | "快速向前奔跑" | | 正常（走路 / 挥手） | 1.10–1.20 | "流畅地行走" | | 动态增强 | 1.00–1.10 | "略微增强动态和运镜" | ### 提示词优化 - 明确描述运动节奏，如 “快速奔跑”、“流畅行走” - 避免模糊描述如 “移动”、“走动” ## 🔬 技术细节 | 参数值 | 运动提升 | 亮度变化 | 适用场景 | |--------|----------|----------|--------------| | 1.0（原版） | 0% | 无 | 和WAN原版节点无区别 | | 1.15（默认）| +15% | 无 | 通用场景 | | 1.3 | +30% | 无 | 体育运动 | | 1.5 | +50% | 无 | 极限运动 | ### 核心算法原理 - **亮度保护的运动缩放**：放大运动向量前分离亮度均值 - **零 latent 初始化**：严格保持 4 步 LoRA 的时序依赖链 - **参考帧增强**：使用 `reference_latents` 保持主体一致性，不约束运动 ## ⚡ 进阶技巧 - 最佳效果：配合强运动提示词使用 - 运动过快：每次减少 `motion_amplitude` 0.05 - 仍然偏慢：可适当增大到 1.4 ## 🙏 致谢 - Wan2.2 团队：提供惊人的视频生成模型 - ComfyUI 社区：灵活的节点系统 🙏如果这个项目对你有帮助，请给颗星 ⭐️ 支持一下！ - # ComfyUI-Painterl2V This node is created by Douyin creator: 绘画小子 PainterI2V node that specifically fixes the slow-motion issue in 4-step LoRAs (e.g., lightx2v). 2025-12-30现在已经推出升级版PainterI2Vadvanced，建议使用。项目地址：https://github.com/princepainter/PainterI2Vadvanced The 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 . If 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 ## 🛠 Problems Solved - ✅ Reduces Slow-Motion Drag: Increases motion amplitude by 15-50% - ✅ Enhance camera movement: Make the camera movement prompts more responsive and increase the movement amplitude. - ✅ Optimized for Single Frame: Designed specifically for single-frame image-to-video workflows - ✅ Plug & Play: Fully compatible with original Wan2.2 workflows ## 📦 Installation ### Method 1: ComfyUI Manager (Recommended) 1. Open ComfyUI Manager 2. Search for **PainterI2V** 3. Click **Install** ### Method 2: Manual Installation ```bash # Navigate to ComfyUI's custom_nodes directory cd ComfyUI/custom_nodes # Clone the repository git clone https://github.com/princepainter/ComfyUI-PainterI2V.git ``` ## 💡 Usage Guide ### Replace the Node In your workflow, replace `WanImageToVideo` with `PainterI2V`. ### Parameter Settings - `motion_amplitude`: 1.15 (recommended starting value) - Keep all other parameters consistent with the original node. ### Recommended Parameters by Scene | Motion Type | Recommended Value | Example Prompt | |------------------------|-------------------|-----------------------------| | Fast (running / jumping) | 1.25–1.35 | "Running forward quickly" | | Normal (walking / waving)| 1.10–1.20 | "Walking smoothly" | | Motion Enhancement | 1.00–1.10 | "Slightly enhance motion and camera movement" | ### Prompt Optimization Tips - Clearly describe motion rhythm, e.g., “run fast”, “walk smoothly” - Avoid vague terms like “move” or “walk around” ## 🔬 Technical Details | Parameter | Motion Boost | Brightness Change | Best For | |-----------|--------------|-------------------|--------------------| | 1.0 (original) | 0% | None | No difference from the original WAN node | | 1.15 (default) | +15% | None | General use | | 1.3 | +30% | None | Sports action | | 1.5 | +50% | None | Extreme motion | ### Core Algorithm Principles - **Brightness-Protected Motion Scaling**: Separates luminance mean before scaling motion vectors - **Zero Latent Initialization**: Strictly preserves the temporal dependency chain required by 4-step LoRAs - **Reference Frame Enhancement**: Uses `reference_latents` to maintain subject consistency without constraining motion ## ⚡ Advanced Tips - Best results: Use with strong motion-related prompts - Motion too fast? Reduce `motion_amplitude` by 0.05 increments - Still too slow? Try increasing up to 1.4 ## 🙏 Acknowledgements - Wan2.2 Team: For the amazing video generation model - ComfyUI Community: For the flexible node system - Contributors & testers: For helping refine this node
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# Restart ComfyUI ================================================ FILE: __init__.py ================================================ from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS __version__ = "1.0.6" __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] ================================================ FILE: __manifest__.py ================================================ MANIFEST = { "name": "ComfyUI-PainterI2V", "version": "1.0.0", "author": "Painter (社区贡献)", "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", "tags": ["wan", "video", "i2v", "lora", "slow-motion"], "requirements": [], "custom_nodes": { "PainterI2V": { "category": "conditioning/video_models", "display_name": "🎨 PainterI2V (Wan2.2)", "description": "增强版图生视频，解决慢动作问题" } } } ================================================ FILE: nodes.py ================================================ import torch import comfy.model_management import comfy.utils import node_helpers from comfy_api.latest import io, ComfyExtension from typing_extensions import override class PainterI2V(io.ComfyNode): """ An enhanced Wan2.2 Image-to-Video node specifically designed to fix the slow-motion issue in 4-step LoRAs (like lightx2v). """ @classmethod def define_schema(cls): return io.Schema( node_id="PainterI2V", category="conditioning/video_models", inputs=[ io.Conditioning.Input("positive"), io.Conditioning.Input("negative"), io.Vae.Input("vae"), io.Int.Input("width", default=832, min=16, max=4096, step=16), io.Int.Input("height", default=480, min=16, max=4096, step=16), io.Int.Input("length", default=81, min=1, max=4096, step=4), io.Int.Input("batch_size", default=1, min=1, max=4096), io.Float.Input("motion_amplitude", default=1.15, min=1.0, max=2.0, step=0.05), io.ClipVisionOutput.Input("clip_vision_output", optional=True), io.Image.Input("start_image", optional=True), ], outputs=[ io.Conditioning.Output(display_name="positive"), io.Conditioning.Output(display_name="negative"), io.Latent.Output(display_name="latent"), ] ) @classmethod def execute(cls, positive, negative, vae, width, height, length, batch_size, motion_amplitude=1.15, start_image=None, clip_vision_output=None) -> io.NodeOutput: # 1. 严格的零latent初始化（4步LoRA的生命线） latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) if start_image is not None: # 单帧输入处理 start_image = start_image[:1] start_image = comfy.utils.common_upscale( start_image.movedim(-1, 1), width, height, "bilinear", "center" ).movedim(1, -1) # 创建序列：首帧真实，后续0.5灰 image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5 image[0] = start_image[0] concat_latent_image = vae.encode(image[:, :, :, :3]) # 单帧mask：仅约束首帧 mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) mask[:, :, 0] = 0.0 # 2. 运动幅度增强（亮度保护核心算法） if motion_amplitude > 1.0: base_latent = concat_latent_image[:, :, 0:1] # 首帧 gray_latent = concat_latent_image[:, :, 1:] # 灰帧 diff = gray_latent - base_latent diff_mean = diff.mean(dim=(1, 3, 4), keepdim=True) diff_centered = diff - diff_mean scaled_latent = base_latent + diff_centered * motion_amplitude + diff_mean # Clamp & 组合 scaled_latent = torch.clamp(scaled_latent, -6, 6) concat_latent_image = torch.cat([base_latent, scaled_latent], dim=2) # 3. 注入到conditioning positive = node_helpers.conditioning_set_values( positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask} ) negative = node_helpers.conditioning_set_values( negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask} ) # 4. 参考帧增强 ref_latent = vae.encode(start_image[:, :, :, :3]) positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True) negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [torch.zeros_like(ref_latent)]}, append=True) if clip_vision_output is not None: positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) out_latent = {} out_latent["samples"] = latent return io.NodeOutput(positive, negative, out_latent) class PainterI2VExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: return [PainterI2V] async def comfy_entrypoint() -> PainterI2VExtension: return PainterI2VExtension() # 节点注册映射 NODE_CLASS_MAPPINGS = { "PainterI2V": PainterI2V, } NODE_DISPLAY_NAME_MAPPINGS = { "PainterI2V": "PainterI2V (Wan2.2 Slow-Motion Fix)", } ================================================ FILE: workflows.json ================================================ { "id": "1d90e051-4a31-4d86-a7b7-83b0b324f79b", "revision": 0, "last_node_id": 191, "last_link_id": 394, "nodes": [ { "id": 161, "type": "PathchSageAttentionKJ", "pos": [ 493.3536457504734, 1190.6989261251294 ], "size": [ 225.8580078125, 58 ], "flags": {}, "order": 10, "mode": 0, "inputs": [ { "label": "模型", "name": "model", 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