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Merge pull request #2 from v0xie/network-oft-change-impl 

Use same updown implementation for LyCORIS OFT as kohya-ss OFT
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v0xie 2023-11-04 15:06:04 -07:00 committed by GitHub
parent 1dd25be037 bbf00a96af
commit 7edd50f304
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1 changed files with 27 additions and 17 deletions
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42
extensions-builtin/Lora/network_oft.py
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@ -24,12 +24,14 @@ class NetworkModuleOFT(network.NetworkModule):
# kohya-ss
if "oft_blocks" in weights.w.keys():
self.is_kohya = True
self.oft_blocks = weights.w["oft_blocks"]
self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
self.alpha = weights.w["alpha"]
self.dim = self.oft_blocks.shape[0]
self.dim = self.oft_blocks.shape[0] # lora dim
#self.oft_blocks = rearrange(self.oft_blocks, 'k m ... -> (k m) ...')
elif "oft_diag" in weights.w.keys():
self.is_kohya = False
self.oft_blocks = weights.w["oft_diag"]
self.oft_blocks = weights.w["oft_diag"] # (num_blocks, block_size, block_size)
# alpha is rank if alpha is 0 or None
if self.alpha is None:
pass
@ -51,12 +53,11 @@ class NetworkModuleOFT(network.NetworkModule):
raise ValueError("sd_module must be Linear or Conv")
if self.is_kohya:
self.num_blocks = self.dim
self.block_size = self.out_dim // self.num_blocks
self.constraint = self.alpha * self.out_dim
self.num_blocks, self.block_size = factorization(self.out_dim, self.dim)
else:
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
self.constraint = None
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
def merge_weight(self, R_weight, org_weight):
R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)
@ -77,7 +78,8 @@ class NetworkModuleOFT(network.NetworkModule):
else:
new_norm_Q = norm_Q
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
m_I = torch.eye(self.num_blocks, device=oft_blocks.device).unsqueeze(0).repeat(self.block_size, 1, 1)
#m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())
block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
@ -97,25 +99,33 @@ class NetworkModuleOFT(network.NetworkModule):
is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention]
if not is_other_linear:
if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
orig_weight=orig_weight.permute(1, 0)
#if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
# orig_weight=orig_weight.permute(1, 0)
oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
# without this line the results are significantly worse / less accurate
oft_blocks = oft_blocks - oft_blocks.transpose(1, 2)
R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
R = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
merged_weight = torch.einsum(
'k n m, k n ... -> k m ...',
R * multiplier + torch.eye(self.block_size, device=orig_weight.device),
R,
merged_weight
)
merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
orig_weight=orig_weight.permute(1, 0)
#if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
# orig_weight=orig_weight.permute(1, 0)
updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
output_shape = orig_weight.shape
else:
# FIXME: skip MultiheadAttention for now
#up = self.lin_module.weight.to(orig_weight.device, dtype=orig_weight.dtype)
updown = torch.zeros([orig_weight.shape[1], orig_weight.shape[1]], device=orig_weight.device, dtype=orig_weight.dtype)
output_shape = (orig_weight.shape[1], orig_weight.shape[1])
@ -123,9 +133,9 @@ class NetworkModuleOFT(network.NetworkModule):
def calc_updown(self, orig_weight):
multiplier = self.multiplier() * self.calc_scale()
if self.is_kohya:
return self.calc_updown_kohya(orig_weight, multiplier)
else:
#if self.is_kohya:
# return self.calc_updown_kohya(orig_weight, multiplier)
#else:
return self.calc_updown_kb(orig_weight, multiplier)
# override to remove the multiplier/scale factor; it's already multiplied in get_weight