| # Generation Parameters |
|
|
| In the demo UI, command-line tool (`kimodo_gen` / `python -m kimodo.scripts.generate`), and low-level Python API, Kimodo allows some advanced configuration for motion generation. |
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|
| ## Classifier-Free Guidance |
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| Control the strength of text and constraint guidance: |
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|
| ```python |
| output = model( |
| prompt="A person jumps", |
| num_frames=150, |
| cfg_weight=[2.0, 2.0], # [text_weight, constraint_weight] |
| cfg_type="separated", # Options: "nocfg", "regular", "separated" |
| num_denoising_steps=100, |
| ) |
| ``` |
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| These are helpful when there is a tradeoff between following the prompt and hitting constraints. |
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|
| The CFG options are: |
| - `cfg_type="nocfg"`: No guidance (faster, less controllable) |
| - `cfg_type="regular"`: "Standard" classifier-free guidance |
| - Equation: `out_uncond + w * (out_text_and_constraint - out_uncond)` |
| - `cfg_type="separated"`: Separate weights for text and constraints |
| - Equation: `out_uncond + w_text * (out_text - out_uncond) + w_constraint * (out_constraint - out_uncond)` |
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|
| ### CLI |
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| The same options are available from the command line as `--cfg_type` and `--cfg_weight`. See the {ref}`CLI user guide (CFG) <classifier-free-guidance-cfg>` for examples, validation rules, and how `meta.json` interacts with explicit flags when using `--input_folder`. |
|
|
| ## Denoising Steps |
| The number of denoising steps used in DDIM sampling can be used to control the speed vs. quality trade-off: |
| - Fewer steps (50-100): Faster inference, slightly lower quality |
| - More steps (100-200): Higher quality, slower inference |
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|
