Add custom nodes, Civitai loras (LFS), and vast.ai setup script

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>

custom_nodes/comfyui-image-saver/nodes_loaders.py

@@ -0,0 +1,60 @@
+import torch
+import folder_paths
+import comfy.sd
+
+class CheckpointLoaderWithName:
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE", "STRING")
+    RETURN_NAMES = ("MODEL", "CLIP", "VAE", "model_name")
+    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")
+    FUNCTION = "load_checkpoint"
+
+    CATEGORY = "ImageSaver/utils"
+    DESCRIPTION = "Loads U-Net model, CLIP model and VAE model from a checkpoint file"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "checkpoint"}),
+            }
+        }
+
+    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, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+
+        # add checkpoint name to the output tuple (without the ClipVisionModel)
+        out = (*out[:3], ckpt_name)
+        return out
+
+class UNETLoaderWithName:
+    RETURN_TYPES = ("MODEL", "STRING")
+    RETURN_NAMES = ("model", "filename")
+    OUTPUT_TOOLTIPS = ("U-Net model (denoising latents)", "model filename")
+    FUNCTION = "load_unet"
+
+    CATEGORY = "ImageSaver/utils"
+    DESCRIPTION = "Loads U-Net model and outputs it's filename"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "unet_name": (folder_paths.get_filename_list("diffusion_models"),),
+                "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],)
+            }
+        }
+
+    def load_unet(self, unet_name, weight_dtype):
+        model_options = {}
+        if weight_dtype == "fp8_e4m3fn":
+            model_options["dtype"] = torch.float8_e4m3fn
+        elif weight_dtype == "fp8_e4m3fn_fast":
+            model_options["dtype"] = torch.float8_e4m3fn
+            model_options["fp8_optimizations"] = True
+        elif weight_dtype == "fp8_e5m2":
+            model_options["dtype"] = torch.float8_e5m2
+
+        unet_path = folder_paths.get_full_path_or_raise("diffusion_models", unet_name)
+        model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
+        return (model, unet_name)