---
tags:
- MotionData
---

# MotionDiff Simple Sampler
## Documentation
- Class name: `MotionDiffSimpleSampler`
- Category: `MotionDiff`
- Output node: `False`

The MotionDiffSimpleSampler node is designed to facilitate the sampling process within a motion diffusion framework, specifically targeting the generation or transformation of motion data. It abstracts the complexities involved in selecting and applying different sampling strategies, such as uniform or loss-aware sampling, to produce or modify motion sequences based on given conditions and inputs.
## Input types
### Required
- **`sampler_name`**
    - Specifies the name of the sampling strategy to be used, affecting the choice of algorithm for generating or transforming motion data.
    - Comfy dtype: `COMBO[STRING]`
    - Python dtype: `str`
- **`md_model`**
    - A wrapper around the motion diffusion model, providing the necessary interface for the sampling process.
    - Comfy dtype: `MD_MODEL`
    - Python dtype: `MotionDiffModelWrapper`
- **`md_clip`**
    - Represents the clip information required for motion generation or transformation, encapsulating the model and its associated data.
    - Comfy dtype: `MD_CLIP`
    - Python dtype: `MotionDiffCLIPWrapper`
- **`md_cond`**
    - The conditions under which the motion data is to be generated or transformed, guiding the sampling process. This includes text and motion data processed by the MotionCLIPTextEncode node.
    - Comfy dtype: `MD_CONDITIONING`
    - Python dtype: `Dict[str, torch.Tensor]`
- **`motion_data`**
    - The input motion data that will be transformed or generated anew, depending on the sampling strategy. Includes motion, motion mask, and motion length.
    - Comfy dtype: `MOTION_DATA`
    - Python dtype: `Dict[str, torch.Tensor]`
- **`seed`**
    - A seed value to ensure reproducibility of the generated or transformed motion data.
    - Comfy dtype: `INT`
    - Python dtype: `int`
## Output types
- **`motion_data`**
    - Comfy dtype: `MOTION_DATA`
    - The output motion data after sampling, including the generated or transformed motion sequence, motion mask, and motion length.
    - Python dtype: `Dict[str, torch.Tensor]`
## Usage tips
- Infra type: `GPU`
- Common nodes: unknown


## Source code
```python
class MotionDiffSimpleSampler:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "sampler_name": (["ddpm", "ddim"], {"default": "ddim"}),
                "md_model": ("MD_MODEL", ),
                "md_clip": ("MD_CLIP", ),
                "md_cond": ("MD_CONDITIONING", ),
                "motion_data": ("MOTION_DATA",),
                "seed": ("INT", {"default": 123,"min": 0, "max": 0xffffffffffffffff, "step": 1}),
            }
        }

    RETURN_TYPES = ("MOTION_DATA",)
    CATEGORY = "MotionDiff"
    FUNCTION = "sample"

    def sample(self, sampler_name, md_model: MotionDiffModelWrapper, md_clip, md_cond, motion_data, seed):
        md_model.to(get_torch_device())
        md_clip.to(get_torch_device())
        for key in motion_data:
            motion_data[key] = to_gpu(motion_data[key])

        kwargs = {
            **motion_data,
            'inference_kwargs': {},
            'sampler': sampler_name,
            'seed': seed
        }

        with torch.no_grad():
            output = md_model(md_clip.model, cond_dict=md_cond, **kwargs)[0]['pred_motion']
            pred_motion = output * md_model.dataset.std + md_model.dataset.mean
            pred_motion = pred_motion.cpu().detach()
        
        md_model.cpu(), md_clip.cpu()
        for key in motion_data:
            motion_data[key] = to_cpu(motion_data[key])
        return ({
            'motion': pred_motion,
            'motion_mask': motion_data['motion_mask'],
            'motion_length': motion_data['motion_length'],
        }, )

```