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