2.2 KiB
2.2 KiB
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unCLIPCheckpointLoader
Documentation
- Class name:
unCLIPCheckpointLoader - Category:
loaders - Output node:
False
The unCLIPCheckpointLoader node is designed for loading checkpoints specifically tailored for unCLIP models. It facilitates the retrieval and initialization of models, CLIP vision modules, and VAEs from a specified checkpoint, streamlining the setup process for further operations or analyses.
Input types
Required
ckpt_name- The 'ckpt_name' parameter specifies the name of the checkpoint to be loaded. It is crucial for identifying and retrieving the correct checkpoint file from a predefined directory of checkpoints, thereby determining the models and configurations to be initialized.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
Output types
model- Comfy dtype:
MODEL - Represents the primary model loaded from the checkpoint.
- Python dtype:
torch.nn.Module
- Comfy dtype:
clip- Comfy dtype:
CLIP - Represents the CLIP module loaded from the checkpoint, if available.
- Python dtype:
torch.nn.Module
- Comfy dtype:
vae- Comfy dtype:
VAE - Represents the VAE module loaded from the checkpoint, if available.
- Python dtype:
torch.nn.Module
- Comfy dtype:
clip_vision- Comfy dtype:
CLIP_VISION - Represents the CLIP vision module loaded from the checkpoint, if available.
- Python dtype:
torch.nn.Module
- Comfy dtype:
Usage tips
- Infra type:
GPU - Common nodes: unknown
Source code
class unCLIPCheckpointLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
}}
RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION")
FUNCTION = "load_checkpoint"
CATEGORY = "loaders"
def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return out