Wan-Move Motion-Control Image to Video
Generate videos from a single image using Wan-Move, with fine-grained point-level motion control via trajectory guidance.
简介
The Wan-Move Motion-Control Image to Video workflow is a sophisticated process designed to transform a single image into a dynamic video. Utilizing the Wan2.1 and Wan models, this workflow allows users to apply fine-grained motion control to specific points within an image, creating a seamless video output.
At its core, the workflow leverages nodes such as WanMoveVisualizeTracks and GenerateTracks to define and visualize motion trajectories. The user can guide these motions using trajectory guidance, ensuring precise and controlled animation of image elements. The CreateVideo and SaveVideo nodes then compile these movements into a cohesive video file.
This workflow is particularly useful for creators looking to animate still images with detailed motion paths, offering a high level of customization and control over the final video output.
你需要提供
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- ComfyUI
需要自定义节点
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通过代码使用此工作流
尽可能根据工作流图和输入自动生成代码，否则显示已保存的示例。运行代码可能产生费用，请先核对凭据和端点。
What you need
- Runs on
- Comfy Cloud
- 密钥
- COMFY_API_KEY
需要付费的 Cloud 方案和额度。
# Python 3.10+: pip install comfy-sdk==0.4.0
import os
from comfy_sdk import Comfy
api_key = os.environ.get("COMFY_API_KEY", "").strip()
if not api_key: raise RuntimeError("Set COMFY_API_KEY")
os.environ.setdefault("COMFY_BASE_URL", "https://cloud.comfy.org")
client = Comfy(api_key=api_key)
workflow = client.workflows.from_json({
"228": {
"class_type": "ImageScale",
"inputs": {
"crop": "center",
"height": 480,
"image": [
"238",
0
],
"upscale_method": "lanczos",
"width": 720
},
"_meta": {
"title": "Upscale Image"
}
},
"230": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"228",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"235": {
"class_type": "SaveVideo",
"inputs": {
"filename_prefix": "video/ComfyUI_WanMove",
"format": "auto",
"format.codec": "auto",
"video": [
"257:234",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"238": {
"class_type": "LoadImage",
"inputs": {
"image": "snowboard_rush.png"
},
"_meta": {
"title": "Load Image"
}
},
"244": {
"class_type": "GenerateTracks",
"inputs": {
"bezier": True,
"end_x": 0.22,
"end_y": 0.45,
"height": 480,
"interpolation": "linear",
"mid_x": 0.46,
"mid_y": 0.44,
"num_frames": 81,
"num_tracks": 5,
"start_x": 0.6,
"start_y": 0.27,
"track_spread": 0.007,
"width": 720
},
"_meta": {
"title": "Generate Video Tracks"
}
},
"245": {
"class_type": "GenerateTracks",
"inputs": {
"bezier": True,
"end_x": 0.96,
"end_y": 0.16,
"height": 480,
"interpolation": "linear",
"mid_x": 0.88,
"mid_y": 0.72,
"num_frames": 81,
"num_tracks": 7,
"start_x": 0.48,
"start_y": 0.97,
"track_spread": 0.017,
"width": 720
},
"_meta": {
"title": "Generate Video Tracks"
}
},
"246": {
"class_type": "GenerateTracks",
"inputs": {
"bezier": True,
"end_x": 0.55,
"end_y": 0.63,
"height": 480,
"interpolation": "linear",
"mid_x": 0.42,
"mid_y": 0.96,
"num_frames": 81,
"num_tracks": 5,
"start_x": 0.12,
"start_y": 1,
"track_spread": 0.012,
"width": 720
},
"_meta": {
"title": "Generate Video Tracks"
}
},
"247": {
"class_type": "WanMoveConcatTrack",
"inputs": {
"tracks_1": [
"244",
0
],
"tracks_2": [
"245",
0
]
},
"_meta": {
"title": "WanMoveConcatTrack"
}
},
"248": {
"class_type": "WanMoveConcatTrack",
"inputs": {
"tracks_1": [
"247",
0
],
"tracks_2": [
"246",
0
]
},
"_meta": {
"title": "WanMoveConcatTrack"
}
},
"257:10": {
"class_type": "VAELoader",
"inputs": {
"vae_name": "wan_2.1_vae.safetensors"
},
"_meta": {
"title": "Load VAE"
}
},
"257:131": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"257:144",
0
],
"vae": [
"257:10",
0
]
},
"_meta": {
"title": "VAE Decode"
}
},
"257:144": {
"class_type": "KSampler",
"inputs": {
"cfg": 1,
"denoise": 1,
"latent_image": [
"257:195",
2
],
"model": [
"257:61",
0
],
"negative": [
"257:195",
1
],
"positive": [
"257:195",
0
],
"sampler_name": "er_sde",
"scheduler": "simple",
"seed": 128,
"steps": 6
},
"_meta": {
"title": "KSampler"
}
},
"257:151": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"257:193",
0
],
"text": "cartoon character walking in a grassy meadow surrounded by trees"
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"257:193": {
"class_type": "CLIPLoader",
"inputs": {
"clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
"device": "default",
"type": "wan"
},
"_meta": {
"title": "Load CLIP"
}
},
"257:195": {
"class_type": "WanMoveTrackToVideo",
"inputs": {
"batch_size": 1,
"clip_vision_output": [
"257:83",
0
],
"height": [
"257:226",
1
],
"length": 81,
"negative": [
"257:34",
0
],
"positive": [
"257:151",
0
],
"start_image": [
"228",
0
],
"strength": 1,
"tracks": [
"248",
0
],
"vae": [
"257:10",
0
],
"width": [
"257:226",
0
]
},
"_meta": {
"title": "WanMoveTrackToVideo"
}
},
"257:226": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"228",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"257:231": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16.safetensors",
"model": [
"257:232",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"257:232": {
"class_type": "UNETLoader",
"inputs": {
"unet_name": "Wan21-WanMove_fp8_scaled_e4m3fn_KJ.safetensors",
"weight_dtype": "default"
},
"_meta": {
"title": "Load Diffusion Model"
}
},
"257:234": {
"class_type": "CreateVideo",
"inputs": {
"bit_depth": "auto",
"codec": "none",
"color_space": "sRGB",
"fps": 16,
"images": [
"257:131",
0
]
},
"_meta": {
"title": "Create Video"
}
},
"257:34": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"257:193",
0
],
"text": "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"257:61": {
"class_type": "ModelSamplingSD3",
"inputs": {
"model": [
"257:231",
0
],
"shift": 5
},
"_meta": {
"title": "ModelSamplingSD3"
}
},
"257:83": {
"class_type": "CLIPVisionEncode",
"inputs": {
"clip_vision": [
"257:84",
0
],
"crop": "center",
"image": [
"228",
0
]
},
"_meta": {
"title": "CLIP Vision Encode"
}
},
"257:84": {
"class_type": "CLIPVisionLoader",
"inputs": {
"clip_name": "clip_vision_h.safetensors"
},
"_meta": {
"title": "Load CLIP Vision"
}
}
})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)常见问答
You need the Wan2.1 and Wan models to effectively use this workflow.
Yes, you can use any image, but results may vary based on the complexity and detail of the motion paths you create.
You can create motion paths using the GenerateTracks node and visualize them with the WanMoveVisualizeTracks node. For more control, use the WanMoveTracksFromCoords node.
Yes, you can use the PreviewImage node to see how your motion paths will affect the image before creating the final video.