jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
import torch
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import folder_paths
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import comfy.sd
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class CheckpointLoaderWithName:
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RETURN_TYPES = ("MODEL", "CLIP", "VAE", "STRING")
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RETURN_NAMES = ("MODEL", "CLIP", "VAE", "model_name")
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OUTPUT_TOOLTIPS = ("U-Net model (denoising latents)", "CLIP (Contrastive Language-Image Pre-Training) model (encoding text prompts)", "VAE (Variational autoencoder) model (latent<->pixel encoding/decoding)", "checkpoint name")
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FUNCTION = "load_checkpoint"
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CATEGORY = "ImageSaver/utils"
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DESCRIPTION = "Loads U-Net model, CLIP model and VAE model from a checkpoint file"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "checkpoint"}),
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}
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}
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def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
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# add checkpoint name to the output tuple (without the ClipVisionModel)
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out = (*out[:3], ckpt_name)
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return out
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class UNETLoaderWithName:
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RETURN_TYPES = ("MODEL", "STRING")
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RETURN_NAMES = ("model", "filename")
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OUTPUT_TOOLTIPS = ("U-Net model (denoising latents)", "model filename")
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FUNCTION = "load_unet"
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CATEGORY = "ImageSaver/utils"
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DESCRIPTION = "Loads U-Net model and outputs it's filename"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"unet_name": (folder_paths.get_filename_list("diffusion_models"),),
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"weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],)
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}
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}
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def load_unet(self, unet_name, weight_dtype):
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model_options = {}
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if weight_dtype == "fp8_e4m3fn":
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model_options["dtype"] = torch.float8_e4m3fn
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elif weight_dtype == "fp8_e4m3fn_fast":
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model_options["dtype"] = torch.float8_e4m3fn
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model_options["fp8_optimizations"] = True
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elif weight_dtype == "fp8_e5m2":
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model_options["dtype"] = torch.float8_e5m2
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unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name)
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model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
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return (model, unet_name)
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