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-PuLID-Flux-Enhanced/online_train2.py

@@ -0,0 +1,71 @@
+# self-supervised learning, one of the embedding acts as the target, the other as the support
+# works nicely
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch
+from tqdm import tqdm
+import random
+
+class Transform(nn.Module):
+    def __init__(self, n=2, token_size=32, input_dim=2048):
+        super().__init__()
+        
+        self.n=n
+        self.token_size=token_size
+        
+        self.weight = nn.Parameter(torch.ones(self.n,self.token_size),requires_grad=True)
+        
+    def encode(self, x):
+        x = torch.einsum('bij,bi->ij', x, self.weight)
+        return x
+    
+    def forward(self, x):
+        x = self.encode(x)
+        return x
+    
+def criterion(output, target, token_sample_rate=0.25):
+    t=target-output
+    t=torch.norm(t,dim=1)
+    s=random.sample(range(t.shape[0]),int(token_sample_rate*t.shape[0]))
+    return torch.mean(t[s])
+
+def online_train(cond, device="cuda:1",step=1000):
+    old_device=cond.device
+    dtype=cond.dtype
+    cond = cond.clone().to(device,torch.float32)
+    # cond.requires_grad=False
+    # torch.set_grad_enabled(True)
+    
+    y=cond[0,:,:]
+    cond=cond[1:,:,:]
+    
+    print("online training, initializing model...")
+    n=cond.shape[0]
+    model=Transform(n=n)
+    optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.0001)
+    model.to(device)
+    model.train()
+    
+    random.seed(42)
+    bar=tqdm(range(step))
+    for s in bar:
+        optimizer.zero_grad()
+        x=cond
+        output = model(x)
+        loss = criterion(output, y)
+        loss.backward()
+        optimizer.step()
+        bar.set_postfix(loss=loss.item())
+        
+    weight=model.weight
+    print(weight)
+    cond=weight[:,:,None]*cond+y[None,:,:]*(1.0/n)
+    
+    print("online training, ending...")
+    del model
+    del optimizer
+    
+    cond=torch.mean(cond,dim=0).unsqueeze(0)
+    return cond.to(old_device,dtype=dtype)