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Add twinflow_z_image_lora_to_diffusers function
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@ -32,7 +32,14 @@ def convert_uso_lora(sd):
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sd_out[k_to] = tensor
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return sd_out
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def twinflow_z_image_lora_to_diffusers(state_dict):
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"""Convert TwinFlow LoRA state dict for diffusers compatibility."""
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for key in list(state_dict.keys()):
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if "t_embedder_2" not in key and key.startswith("t_embedder."):
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new_key = key.replace("t_embedder.", "t_embedder_2.", 1)
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state_dict[new_key] = state_dict.pop(key)
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return state_dict
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def convert_lora(sd):
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if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd:
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return convert_lora_bfl_control(sd)
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@ -40,4 +47,6 @@ def convert_lora(sd):
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return convert_lora_wan_fun(sd)
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if "single_blocks.37.processor.qkv_lora.up.weight" in sd and "double_blocks.18.processor.qkv_lora2.up.weight" in sd:
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return convert_uso_lora(sd)
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if any(k.startswith("t_embedder.") for k in sd.keys()):
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return twinflow_z_image_lora_to_diffusers(sd)
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return sd
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