52 lines
1.5 KiB
Markdown
52 lines
1.5 KiB
Markdown
---
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tags:
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- SamplerScheduler
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- Sampling
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---
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# KSamplerSelect
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## Documentation
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- Class name: `KSamplerSelect`
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- Category: `sampling/custom_sampling/samplers`
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- Output node: `False`
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The KSamplerSelect node is designed to select a specific sampler based on the provided sampler name. It abstracts the complexity of sampler selection, allowing users to easily switch between different sampling strategies for their tasks.
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## Input types
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### Required
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- **`sampler_name`**
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- Specifies the name of the sampler to be selected. This parameter determines which sampling strategy will be used, impacting the overall sampling behavior and results.
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- Comfy dtype: `COMBO[STRING]`
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- Python dtype: `str`
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## Output types
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- **`sampler`**
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- Comfy dtype: `SAMPLER`
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- Returns the selected sampler object, ready to be used for sampling tasks.
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- Python dtype: `comfy.samplers.Sampler`
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## Usage tips
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- Infra type: `CPU`
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- Common nodes:
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- [SamplerCustom](../../Comfy/Nodes/SamplerCustom.md)
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- Reroute
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## Source code
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```python
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class KSamplerSelect:
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"sampler_name": (comfy.samplers.SAMPLER_NAMES, ),
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}
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}
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RETURN_TYPES = ("SAMPLER",)
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CATEGORY = "sampling/custom_sampling/samplers"
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FUNCTION = "get_sampler"
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def get_sampler(self, sampler_name):
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sampler = comfy.samplers.sampler_object(sampler_name)
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return (sampler, )
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```
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