o Gi @sddlZddlZddlmZddlmZdddddd d d d d ddddddZddddZddZddZ e dkr]ej ddZ e j ded d!d"e j d#ed d$d"e Ze ejejdSdS)%N) load_file)Kandinsky3UNetztime_embedding.linear_1ztime_embedding.linear_2conv_in conv_norm_outconv_out down_blocks up_blocksz"encoder_hid_proj.projection_linearz encoder_hid_proj.projection_normadd_time_conditionto_qto_kto_vzto_out.0z attentions.0)zto_time_embed.1zto_time_embed.3in_layerz out_layer.0z out_layer.2 down_samples up_samplesprojection_lin projection_lnfeature_poolingto_queryto_keyto_value output_layerself_attention_blockz resnets_in.*)z attentions.*z resnets_out.*)zresnet_attn_blocks.*.0zresnet_attn_blocks.*.1zresnet_attn_blocks.*.2c Csi}|D]m}|}tD] \}}|||}q tD]O\}}d}t|d|drj|sjt||ddddd} t|trP| |d} |d}n| } |dt | }|dt | }|||}d }q||||<q|S) aR Args: Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model. unet_model (torch.nn.Module): The original U-Net model. unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with. Returns: OrderedDict: The converted state dictionary. Fz*.z.*.rr*T) MAPPINGitemsreplace DYNAMIC_MAPfnmatchintsplit isinstancetuplestr) unet_state_dictconverted_state_dictkeynew_keypattern new_pattern dyn_patterndyn_new_pattern has_matchedstarnew_starr1j/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/kandinsky3/convert_kandinsky3_unet.pyconvert_state_dict$s( &    r3cCsBt|}i}t|}t|}||||td|dS)NzConverted model saved to )rr3rload_state_dictsave_pretrainedprint) model_path output_pathr&configr'unetr1r1r2mainKs  r;__main__z4Convert U-Net PyTorch model to Kandinsky3UNet format) descriptionz --model_pathTz(Path to the original U-Net PyTorch model)typerequiredhelpz --output_pathz Path to save the converted model)argparser safetensors.torchr diffusersrrrr3r;__name__ArgumentParserparser add_argumentr% parse_argsargsr7r8r1r1r1r2sB  '