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