(lora, emb_dict, verbose=False)
| 30 | |
| 31 | |
| 32 | def pack_bundle(lora, emb_dict, verbose=False): |
| 33 | for emb, emb_sd in emb_dict.items(): |
| 34 | for key, value in emb_sd.items(): |
| 35 | if isinstance(value, dict): |
| 36 | for subkey, subvalue in value.items(): |
| 37 | lora[f"bundle_emb.{emb}.{key}.{subkey}"] = subvalue |
| 38 | elif isinstance(value, torch.Tensor): |
| 39 | lora[f"bundle_emb.{emb}.{key}"] = value |
| 40 | if verbose: |
| 41 | print("The following content has been added to lora") |
| 42 | for key, value in lora.items(): |
| 43 | if key.startswith("bundle_emb"): |
| 44 | if isinstance(value, torch.Tensor): |
| 45 | print(f" {key}: tensor of shape {value.shape}") |
| 46 | else: |
| 47 | print(f" {key}: {value}") |
| 48 | return lora |
| 49 | |
| 50 | |
| 51 | def unpack_bundle(lora, verbose, step="", emb_format=".pt"): |
no outgoing calls
no test coverage detected