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import torch
import comfy.utils
def convert_lora_bfl_control(sd): #BFL loras for Flux
sd_out = {}
for k in sd:
k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.scale.set_weight"))
sd_out[k_to] = sd[k]
sd_out["diffusion_model.img_in.reshape_weight"] = torch.tensor([sd["img_in.lora_B.weight"].shape[0], sd["img_in.lora_A.weight"].shape[1]])
return sd_out
def convert_lora_wan_fun(sd): #Wan Fun loras
return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"})
def convert_uso_lora(sd):
sd_out = {}
for k in sd:
tensor = sd[k]
k_to = "diffusion_model.{}".format(k.replace(".down.weight", ".lora_down.weight")
.replace(".up.weight", ".lora_up.weight")
.replace(".qkv_lora2.", ".txt_attn.qkv.")
.replace(".qkv_lora1.", ".img_attn.qkv.")
.replace(".proj_lora1.", ".img_attn.proj.")
.replace(".proj_lora2.", ".txt_attn.proj.")
.replace(".qkv_lora.", ".linear1_qkv.")
.replace(".proj_lora.", ".linear2.")
.replace(".processor.", ".")
)
sd_out[k_to] = tensor
return sd_out
def convert_lora(sd):
if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd:
return convert_lora_bfl_control(sd)
if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd:
return convert_lora_wan_fun(sd)
if "single_blocks.37.processor.qkv_lora.up.weight" in sd and "double_blocks.18.processor.qkv_lora2.up.weight" in sd:
return convert_uso_lora(sd)
return sd
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