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-rw-r--r--extensions-builtin/Lora/network.py2
-rw-r--r--extensions-builtin/Lora/network_full.py4
-rw-r--r--extensions-builtin/Lora/network_glora.py10
-rw-r--r--extensions-builtin/Lora/network_hada.py12
-rw-r--r--extensions-builtin/Lora/network_ia3.py2
-rw-r--r--extensions-builtin/Lora/network_lokr.py18
-rw-r--r--extensions-builtin/Lora/network_lora.py6
-rw-r--r--extensions-builtin/Lora/network_norm.py4
-rw-r--r--extensions-builtin/Lora/network_oft.py37
-rw-r--r--extensions-builtin/Lora/networks.py21
10 files changed, 55 insertions, 61 deletions
diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py
index 6021fd8d..a62e5eff 100644
--- a/extensions-builtin/Lora/network.py
+++ b/extensions-builtin/Lora/network.py
@@ -137,7 +137,7 @@ class NetworkModule:
def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
if self.bias is not None:
updown = updown.reshape(self.bias.shape)
- updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown += self.bias.to(orig_weight.device, dtype=updown.dtype)
updown = updown.reshape(output_shape)
if len(output_shape) == 4:
diff --git a/extensions-builtin/Lora/network_full.py b/extensions-builtin/Lora/network_full.py
index bf6930e9..f221c95f 100644
--- a/extensions-builtin/Lora/network_full.py
+++ b/extensions-builtin/Lora/network_full.py
@@ -18,9 +18,9 @@ class NetworkModuleFull(network.NetworkModule):
def calc_updown(self, orig_weight):
output_shape = self.weight.shape
- updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown = self.weight.to(orig_weight.device)
if self.ex_bias is not None:
- ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
+ ex_bias = self.ex_bias.to(orig_weight.device)
else:
ex_bias = None
diff --git a/extensions-builtin/Lora/network_glora.py b/extensions-builtin/Lora/network_glora.py
index 492d4870..efe5c681 100644
--- a/extensions-builtin/Lora/network_glora.py
+++ b/extensions-builtin/Lora/network_glora.py
@@ -22,12 +22,12 @@ class NetworkModuleGLora(network.NetworkModule):
self.w2b = weights.w["b2.weight"]
def calc_updown(self, orig_weight):
- w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
- w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
- w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
- w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1a = self.w1a.to(orig_weight.device)
+ w1b = self.w1b.to(orig_weight.device)
+ w2a = self.w2a.to(orig_weight.device)
+ w2b = self.w2b.to(orig_weight.device)
output_shape = [w1a.size(0), w1b.size(1)]
- updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a))
+ updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a))
return self.finalize_updown(updown, orig_weight, output_shape)
diff --git a/extensions-builtin/Lora/network_hada.py b/extensions-builtin/Lora/network_hada.py
index 5fcb0695..d95a0fd1 100644
--- a/extensions-builtin/Lora/network_hada.py
+++ b/extensions-builtin/Lora/network_hada.py
@@ -27,16 +27,16 @@ class NetworkModuleHada(network.NetworkModule):
self.t2 = weights.w.get("hada_t2")
def calc_updown(self, orig_weight):
- w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
- w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
- w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
- w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1a = self.w1a.to(orig_weight.device)
+ w1b = self.w1b.to(orig_weight.device)
+ w2a = self.w2a.to(orig_weight.device)
+ w2b = self.w2b.to(orig_weight.device)
output_shape = [w1a.size(0), w1b.size(1)]
if self.t1 is not None:
output_shape = [w1a.size(1), w1b.size(1)]
- t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
+ t1 = self.t1.to(orig_weight.device)
updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
output_shape += t1.shape[2:]
else:
@@ -45,7 +45,7 @@ class NetworkModuleHada(network.NetworkModule):
updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
if self.t2 is not None:
- t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+ t2 = self.t2.to(orig_weight.device)
updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
else:
updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
diff --git a/extensions-builtin/Lora/network_ia3.py b/extensions-builtin/Lora/network_ia3.py
index 7edc4249..96faeaf3 100644
--- a/extensions-builtin/Lora/network_ia3.py
+++ b/extensions-builtin/Lora/network_ia3.py
@@ -17,7 +17,7 @@ class NetworkModuleIa3(network.NetworkModule):
self.on_input = weights.w["on_input"].item()
def calc_updown(self, orig_weight):
- w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
+ w = self.w.to(orig_weight.device)
output_shape = [w.size(0), orig_weight.size(1)]
if self.on_input:
diff --git a/extensions-builtin/Lora/network_lokr.py b/extensions-builtin/Lora/network_lokr.py
index 340acdab..fcdaeafd 100644
--- a/extensions-builtin/Lora/network_lokr.py
+++ b/extensions-builtin/Lora/network_lokr.py
@@ -37,22 +37,22 @@ class NetworkModuleLokr(network.NetworkModule):
def calc_updown(self, orig_weight):
if self.w1 is not None:
- w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1 = self.w1.to(orig_weight.device)
else:
- w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
- w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1a = self.w1a.to(orig_weight.device)
+ w1b = self.w1b.to(orig_weight.device)
w1 = w1a @ w1b
if self.w2 is not None:
- w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2 = self.w2.to(orig_weight.device)
elif self.t2 is None:
- w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
- w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2a = self.w2a.to(orig_weight.device)
+ w2b = self.w2b.to(orig_weight.device)
w2 = w2a @ w2b
else:
- t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
- w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
- w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ t2 = self.t2.to(orig_weight.device)
+ w2a = self.w2a.to(orig_weight.device)
+ w2b = self.w2b.to(orig_weight.device)
w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
diff --git a/extensions-builtin/Lora/network_lora.py b/extensions-builtin/Lora/network_lora.py
index 26c0a72c..4cc40295 100644
--- a/extensions-builtin/Lora/network_lora.py
+++ b/extensions-builtin/Lora/network_lora.py
@@ -61,13 +61,13 @@ class NetworkModuleLora(network.NetworkModule):
return module
def calc_updown(self, orig_weight):
- up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
- down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ up = self.up_model.weight.to(orig_weight.device)
+ down = self.down_model.weight.to(orig_weight.device)
output_shape = [up.size(0), down.size(1)]
if self.mid_model is not None:
# cp-decomposition
- mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ mid = self.mid_model.weight.to(orig_weight.device)
updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
output_shape += mid.shape[2:]
else:
diff --git a/extensions-builtin/Lora/network_norm.py b/extensions-builtin/Lora/network_norm.py
index ce450158..d25afcbb 100644
--- a/extensions-builtin/Lora/network_norm.py
+++ b/extensions-builtin/Lora/network_norm.py
@@ -18,10 +18,10 @@ class NetworkModuleNorm(network.NetworkModule):
def calc_updown(self, orig_weight):
output_shape = self.w_norm.shape
- updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown = self.w_norm.to(orig_weight.device)
if self.b_norm is not None:
- ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+ ex_bias = self.b_norm.to(orig_weight.device)
else:
ex_bias = None
diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index 05c37811..fa647020 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -21,6 +21,8 @@ class NetworkModuleOFT(network.NetworkModule):
self.lin_module = None
self.org_module: list[torch.Module] = [self.sd_module]
+ self.scale = 1.0
+
# kohya-ss
if "oft_blocks" in weights.w.keys():
self.is_kohya = True
@@ -53,12 +55,18 @@ class NetworkModuleOFT(network.NetworkModule):
self.constraint = None
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
- def calc_updown_kb(self, orig_weight, multiplier):
+ def calc_updown(self, orig_weight):
oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
- oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
+ eye = torch.eye(self.block_size, device=self.oft_blocks.device)
+
+ if self.is_kohya:
+ block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
+ norm_Q = torch.norm(block_Q.flatten())
+ new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
+ block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
+ oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
- R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
# This errors out for MultiheadAttention, might need to be handled up-stream
merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
@@ -72,26 +80,3 @@ class NetworkModuleOFT(network.NetworkModule):
updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
output_shape = orig_weight.shape
return self.finalize_updown(updown, orig_weight, output_shape)
-
- def calc_updown(self, orig_weight):
- # if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it
- multiplier = self.multiplier()
- return self.calc_updown_kb(orig_weight, multiplier)
-
- # override to remove the multiplier/scale factor; it's already multiplied in get_weight
- def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
- if self.bias is not None:
- updown = updown.reshape(self.bias.shape)
- updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
- updown = updown.reshape(output_shape)
-
- if len(output_shape) == 4:
- updown = updown.reshape(output_shape)
-
- if orig_weight.size().numel() == updown.size().numel():
- updown = updown.reshape(orig_weight.shape)
-
- if ex_bias is not None:
- ex_bias = ex_bias * self.multiplier()
-
- return updown, ex_bias
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 7f814706..985b2753 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -159,7 +159,8 @@ def load_network(name, network_on_disk):
bundle_embeddings = {}
for key_network, weight in sd.items():
- key_network_without_network_parts, network_part = key_network.split(".", 1)
+ key_network_without_network_parts, _, network_part = key_network.partition(".")
+
if key_network_without_network_parts == "bundle_emb":
emb_name, vec_name = network_part.split(".", 1)
emb_dict = bundle_embeddings.get(emb_name, {})
@@ -388,18 +389,26 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
if module is not None and hasattr(self, 'weight'):
try:
with torch.no_grad():
- updown, ex_bias = module.calc_updown(self.weight)
+ if getattr(self, 'fp16_weight', None) is None:
+ weight = self.weight
+ bias = self.bias
+ else:
+ weight = self.fp16_weight.clone().to(self.weight.device)
+ bias = getattr(self, 'fp16_bias', None)
+ if bias is not None:
+ bias = bias.clone().to(self.bias.device)
+ updown, ex_bias = module.calc_updown(weight)
- if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+ if len(weight.shape) == 4 and weight.shape[1] == 9:
# inpainting model. zero pad updown to make channel[1] 4 to 9
updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
- self.weight += updown
+ self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
if ex_bias is not None and hasattr(self, 'bias'):
if self.bias is None:
- self.bias = torch.nn.Parameter(ex_bias)
+ self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
else:
- self.bias += ex_bias
+ self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
except RuntimeError as e:
logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 17:44:15 +0000