| import torch |
| import comfy.utils |
|
|
|
|
| def convert_lora_bfl_control(sd): |
| sd_out = {} |
| for k in sd: |
| k_to = "diffusion_model.{}".format(k.replace(".lora_B.bias", ".diff_b").replace("_norm.scale", "_norm.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): |
| 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 |
|
|
