18 lines
590 B
Python
18 lines
590 B
Python
|
import torch
|
||
|
|
|
||
|
|
|
||
|
|
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(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)
|
||
|
|
return sd
|