mirror of
https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-16 08:52:45 +00:00
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import torch
|
|
|
|
def generate_synthetic_weights(org_weight, seed=42):
|
|
generator = torch.manual_seed(seed)
|
|
|
|
# Base random normal distribution
|
|
weights = torch.randn_like(org_weight)
|
|
|
|
# Add structured variance to mimic real-world weight matrices
|
|
# Techniques to create more realistic weight distributions:
|
|
|
|
# 1. Block-wise variation
|
|
block_size = max(1, org_weight.shape[0] // 4)
|
|
for i in range(0, org_weight.shape[0], block_size):
|
|
block_end = min(i + block_size, org_weight.shape[0])
|
|
block_variation = torch.randn(1, generator=generator) * 0.3 # Local scaling
|
|
weights[i:block_end, :] *= (1 + block_variation)
|
|
|
|
# 2. Sparse connectivity simulation
|
|
sparsity_mask = torch.rand(org_weight.shape, generator=generator) > 0.2 # 20% sparsity
|
|
weights *= sparsity_mask.float()
|
|
|
|
# 3. Magnitude decay
|
|
magnitude_decay = torch.linspace(1.0, 0.5, org_weight.shape[0]).unsqueeze(1)
|
|
weights *= magnitude_decay
|
|
|
|
# 4. Add structured noise
|
|
structural_noise = torch.randn_like(org_weight) * 0.1
|
|
weights += structural_noise
|
|
|
|
# Normalize to have similar statistical properties to trained weights
|
|
weights = (weights - weights.mean()) / weights.std()
|
|
|
|
return weights
|