52 lines
1.7 KiB
Markdown
52 lines
1.7 KiB
Markdown
---
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tags:
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- MotionData
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---
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# Empty Motion Data
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## Documentation
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- Class name: `EmptyMotionData`
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- Category: `MotionDiff`
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- Output node: `False`
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The EmptyMotionData node is designed to generate a template for motion data with specified frame lengths. It primarily serves the purpose of creating a base structure for motion data that can be further processed or manipulated, providing a standardized format for initializing motion-related tasks within the MotionDiff framework.
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## Input types
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### Required
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- **`frames`**
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- Specifies the number of frames for the generated motion data. This parameter directly influences the shape of the motion tensor, thereby determining the temporal length of the motion data.
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- Comfy dtype: `INT`
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- Python dtype: `int`
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## Output types
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- **`motion_data`**
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- Comfy dtype: `MOTION_DATA`
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- Outputs a dictionary containing tensors for motion, motion mask, and motion length, establishing a foundational structure for motion data with zeroed motion values and appropriate masking.
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- Python dtype: `Dict[str, torch.Tensor]`
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class EmptyMotionData:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"frames": ("INT", {"default": 196, "min": 1, "max": 196})
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}
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}
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RETURN_TYPES = ("MOTION_DATA", )
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CATEGORY = "MotionDiff"
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FUNCTION = "encode_text"
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def encode_text(self, frames):
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return ({
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'motion': torch.zeros(1, frames, 263),
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'motion_mask': torch.ones(1, frames),
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'motion_length': torch.Tensor([frames]).long(),
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}, )
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```
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