65 lines
2.4 KiB
Markdown
65 lines
2.4 KiB
Markdown
---
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tags:
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- Checkpoint
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- Loader
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- ModelIO
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- ModelLoader
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---
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# CheckpointSave
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## Documentation
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- Class name: `CheckpointSave`
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- Category: `advanced/model_merging`
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- Output node: `True`
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The CheckpointSave node is designed for saving the state of various model components, including models, CLIP, and VAE, into a checkpoint file. This functionality is crucial for preserving the training progress or configuration of models for later use or sharing.
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## Input types
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### Required
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- **`model`**
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- The model parameter represents the primary model whose state is to be saved. It is essential for capturing the current state of the model for future restoration or analysis.
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- Comfy dtype: `MODEL`
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- Python dtype: `torch.nn.Module`
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- **`clip`**
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- The clip parameter is intended for the CLIP model associated with the primary model, allowing its state to be saved alongside the main model.
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- Comfy dtype: `CLIP`
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- Python dtype: `torch.nn.Module`
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- **`vae`**
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- The vae parameter is for the Variational Autoencoder (VAE) model, enabling its state to be saved for future use or analysis alongside the main model and CLIP.
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- Comfy dtype: `VAE`
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- Python dtype: `torch.nn.Module`
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- **`filename_prefix`**
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- This parameter specifies the prefix for the filename under which the checkpoint will be saved, providing a means to organize and identify saved checkpoints.
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- Comfy dtype: `STRING`
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- Python dtype: `str`
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## Output types
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The node doesn't have output types
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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 CheckpointSave:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"clip": ("CLIP",),
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"vae": ("VAE",),
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"filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
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RETURN_TYPES = ()
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FUNCTION = "save"
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OUTPUT_NODE = True
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CATEGORY = "advanced/model_merging"
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def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None):
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save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
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return {}
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```
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