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Support resizing ControlLoRA

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woctordho
2025-08-15 03:00:40 +08:00
parent 3ce0c6e71f
commit c6fab554f4
1 changed files with 11 additions and 5 deletions
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16
networks/resize_lora.py
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@@ -24,6 +24,7 @@ MIN_SV = 1e-6
LORAFMT1 = ["lora_down", "lora_up"]
LORAFMT2 = ["lora.down", "lora.up"]
LORAFMT3 = ["lora_A", "lora_B"]
LORAFMT4 = ["down", "up"]
LORAFMT = LORAFMT1
# Model save and load functions
@@ -209,13 +210,15 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
if network_alpha is None and "alpha" in key:
network_alpha = value
if (network_dim is None and len(value.size()) == 2
and (LORAFMT1[0] in key or LORAFMT2[0] in key or LORAFMT3[0] in key)):
and (LORAFMT1[0] in key or LORAFMT2[0] in key or LORAFMT3[0] in key or LORAFMT4[0] in key)):
if LORAFMT1[0] in key:
LORAFMT = LORAFMT1
elif LORAFMT2[0] in key:
LORAFMT = LORAFMT2
elif LORAFMT3[0] in key:
LORAFMT = LORAFMT3
elif LORAFMT4[0] in key:
LORAFMT = LORAFMT4
network_dim = value.size()[0]
if network_alpha is not None and network_dim is not None:
break
@@ -241,14 +244,17 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
weight_name = None
if LORAFMT[0] in key:
block_down_name = key.rsplit(f".{LORAFMT[0]}", 1)[0]
weight_name = key.rsplit(".", 1)[-1]
if key.endswith(f".{LORAFMT[0]}"):
weight_name = ""
else:
weight_name = key.rsplit(f".{LORAFMT[0]}", 1)[-1]
lora_down_weight = value
else:
continue
# find corresponding lora_up and alpha
block_up_name = block_down_name
lora_up_weight = lora_sd.get(block_up_name + f".{LORAFMT[1]}." + weight_name, None)
lora_up_weight = lora_sd.get(block_up_name + f".{LORAFMT[1]}" + weight_name, None)
lora_alpha = lora_sd.get(block_down_name + ".alpha", None)
weights_loaded = lora_down_weight is not None and lora_up_weight is not None
@@ -286,8 +292,8 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
verbose_str += "\n"
new_alpha = param_dict["new_alpha"]
o_lora_sd[block_down_name + f".{LORAFMT[0]}.weight"] = param_dict[LORAFMT[0]].to(save_dtype).contiguous()
o_lora_sd[block_up_name + f".{LORAFMT[1]}.weight"] = param_dict[LORAFMT[1]].to(save_dtype).contiguous()
o_lora_sd[block_down_name + f".{LORAFMT[0]}" + weight_name] = param_dict[LORAFMT[0]].to(save_dtype).contiguous()
o_lora_sd[block_up_name + f".{LORAFMT[1]}" + weight_name] = param_dict[LORAFMT[1]].to(save_dtype).contiguous()
o_lora_sd[block_up_name + ".alpha"] = torch.tensor(param_dict["new_alpha"]).to(save_dtype)
block_down_name = None