bi8 *dZddlZddlZddlmZddlmZerddlZeeZ GddejZ dd d d d d dddddd Z ddddddddddddd Z ddddddddddd Zidd dd!dd"dd#dd$dd%dd&dd'd(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7Zd d!d"d#d$d%d&d'd8d9d:d;d< Zd=d>d?Ze j$ee j&e iZe j$ee j*eiZe j*eiZd@dAiZdBZdKdCZdKdDZdEZdFZdKdGZdKdHZdIe fdJZ!y)LzI State dict utilities: utility methods for converting state dicts easily N)is_torch_available) get_loggerc eZdZdZdZdZdZdZy) StateDictTypez6 The mode to use when converting state dicts. diffusers_oldkohya_sspeft diffusersN)__name__ __module__ __qualname____doc__ DIFFUSERS_OLDKOHYA_SSPEFT DIFFUSERS[/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/utils/state_dict_utils.pyrr s$MH DIrrz.to_out.0.lora_Bz.to_out.0.lora_Az .to_q.lora_Az .to_q.lora_Bz .to_k.lora_Az .to_k.lora_Bz .to_v.lora_Az .to_v.lora_Bz.lora_B.lora_Az.to_out.0.lora_magnitude_vector) .to_out_lora.up.to_out_lora.down.to_q_lora.down .to_q_lora.up.to_k_lora.down .to_k_lora.up.to_v_lora.down .to_v_lora.upz.lora.upz .lora.down.to_out.lora_magnitude_vectorz.q_proj.lora_Bz.q_proj.lora_Az.k_proj.lora_Bz.k_proj.lora_Az.v_proj.lora_Bz.v_proj.lora_Az.out_proj.lora_Bz.out_proj.lora_Aztext_projection.lora_A.weightztext_projection.lora_B.weight) .q_proj.lora_linear_layer.up.q_proj.lora_linear_layer.down.k_proj.lora_linear_layer.up.k_proj.lora_linear_layer.down.v_proj.lora_linear_layer.up.v_proj.lora_linear_layer.down.out_proj.lora_linear_layer.up .out_proj.lora_linear_layer.down.lora_linear_layer.up.lora_linear_layer.downz text_projection.lora.down.weightztext_projection.lora.up.weight) rrrrrrrrr)r*r!r"r#r$r%r&r'r(z to_k.lora_Azto_k.lora.downz to_k.lora_Bz to_k.lora.upz to_q.lora_Azto_q.lora.downz to_q.lora_Bz to_q.lora.upz to_v.lora_Azto_v.lora.downz to_v.lora_Bz to_v.lora.upzto_out.0.lora_Azto_out.0.lora.downzto_out.0.lora_Bzto_out.0.lora.upz.k_proj.lora_magnitude_vectorz.v_proj.lora_magnitude_vectorz.q_proj.lora_magnitude_vectorz.out_proj.lora_magnitude_vector) rrrrrrrrz.to_k.lora_magnitude_vectorz.to_v.lora_magnitude_vectorz.to_q.lora_magnitude_vectorr lora_downlora_up)lora_Alora_Bz .processor..ci}|jD]u\}}tjD]"}||vst|}|j||}$|jD]}||vs||}|j||}n|||<w|S)a& Simply iterates over the state dict and replaces the patterns in `mapping` with the corresponding values. Args: state_dict (`dict[str, torch.Tensor]`): The state dict to convert. mapping (`dict[str, str]`): The mapping to use for conversion, the mapping should be a dictionary with the following structure: - key: the pattern to replace - value: the pattern to replace with Returns: converted_state_dict (`dict`) The converted state dict. )itemsKEYS_TO_ALWAYS_REPLACEkeysreplace) state_dictmappingconverted_state_dictkvpattern new_patterns rconvert_state_dictr<s   " $1-224 4G!|4W= IIg{3 4 ||~ G!|%g. IIg{3   #$Q $ rc T|mtd|jDrtj}nz-convert_state_dict_to_peft..=a}!=c3$K|]}d|v ywlora_linear_layerNrrAs rrCz-convert_state_dict_to_peft..Ea$)ErE-Could not automatically infer state dict typeOriginal type  is not supported)anyr3rrr ValueErrorPEFT_STATE_DICT_MAPPINGSr<)r5 original_typekwargsr6s rconvert_state_dict_to_peftrRs =:??+<= =)77M E:??3DE E)33MLM M499;;>-8IJKK&}5G j' 22rc |jdddznd|td|jDrtj}n`tfd|jDrtj }n-td|jDr|St d|tjvrt d |d t|}t||S) a Converts a state dict to new diffusers format. The state dict can be from previous diffusers format (`OLD_DIFFUSERS`), or PEFT format (`PEFT`) or new diffusers format (`DIFFUSERS`). In the last case the method will return the state dict as is. The method only supports the conversion from diffusers old, PEFT to diffusers new for now. Args: state_dict (`dict[str, torch.Tensor]`): The state dict to convert. original_type (`StateDictType`, *optional*): The original type of the state dict, if not provided, the method will try to infer it automatically. kwargs (`dict`, *args*): Additional arguments to pass to the method. - **adapter_name**: For example, in case of PEFT, some keys will be prepended with the adapter name, therefore needs a special handling. By default PEFT also takes care of that in `get_peft_model_state_dict` method: https://github.com/huggingface/peft/blob/ba0477f2985b1ba311b83459d29895c809404e99/src/peft/utils/save_and_load.py#L92 but we add it here in case we don't want to rely on that method. adapter_nameNr/c3$K|]}d|v ywr?rrAs rrCz2convert_state_dict_to_diffusers..rDrEc3.K|] }dd|vywrz.weightNrrBr8peft_adapter_names rrCz2convert_state_dict_to_diffusers..s!Vq7,-W5:Vc3$K|]}d|v ywrGrrAs rrCz2convert_state_dict_to_diffusers..rIrErJrKrL) poprMr3rrrrNDIFFUSERS_STATE_DICT_MAPPINGSr<)r5rPrQr6rZs @rconvert_state_dict_to_diffusersr_s, >48$"33 =:??+<= =)77M VJOODUV V)..M E:??3DE E LM M9>>@@>-8IJKK+M:G j' 22rc&t}t||S)za Converts a state dict from UNet format to diffusers format - i.e. by removing some keys )UNET_TO_DIFFUSERSr<)r5r6s rconvert_unet_state_dict_to_peftrbs G j' 22rc t|}t d|j Ds t d|S#t$r%}t|dk(r t|}nYd}~Vd}~wwxYw)z Attempts to first `convert_state_dict_to_peft`, and if it doesn't detect `lora_linear_layer` for a valid `DIFFUSERS` LoRA for example, attempts to exclusively convert the Unet `convert_unet_state_dict_to_peft` rJNc30K|]}d|vxsd|vyw)r-r.Nr)rBkeys rrCz1convert_all_state_dict_to_peft..s Ncx31(c/1Nsz#Your LoRA was not converted to PEFT)rR ExceptionstrrbrMr3rN)r5 peft_dictes rconvert_all_state_dict_to_peftrjsk .z:  NY^^=MN N>??   q6D D7 CI  s : A(A##A(c Z ddl}|j dd d z nd |2t fd|j Drtj}|tj vrtd|d t|ttj}i}|jD]\}}d |vr|jd d }nDd |vr|jd d }n-d|vr|jdd}nd|vr|jdd}|jdd|jddz }|j d}|||<d|vs|jddd}|j t#|||<|S#t$rtjdwxYw)a Converts a `PEFT` state dict to `Kohya` format that can be used in AUTOMATIC1111, ComfyUI, SD.Next, InvokeAI, etc. The method only supports the conversion from PEFT to Kohya for now. Args: state_dict (`dict[str, torch.Tensor]`): The state dict to convert. original_type (`StateDictType`, *optional*): The original type of the state dict, if not provided, the method will try to infer it automatically. kwargs (`dict`, *args*): Additional arguments to pass to the method. - **adapter_name**: For example, in case of PEFT, some keys will be prepended with the adapter name, therefore needs a special handling. By default PEFT also takes care of that in `get_peft_model_state_dict` method: https://github.com/huggingface/peft/blob/ba0477f2985b1ba311b83459d29895c809404e99/src/peft/utils/save_and_load.py#L92 but we add it here in case we don't want to rely on that method. rNzDConverting PEFT state dicts to Kohya requires torch to be installed.rTr/rUc3.K|] }dd|vywrXrrYs rrCz.convert_state_dict_to_kohya..9s!TQ*+73q8Tr[rKrLztext_encoder_2.z lora_te2.z text_encoder.z lora_te1.unet lora_unetlora_magnitude_vector dora_scale_r+z.alpha)torch ImportErrorloggererrorr]rMr3rrKOHYA_STATE_DICT_MAPPINGSrNr<r1r4countsplittensorlen) r5rPrQrskohya_ss_partial_state_dictkohya_ss_state_dict kohya_keyweight alpha_keyrZs @rconvert_state_dict_to_kohyars&  >48$"33 T*//BST T)..M5::<<>-8IJKK#5ZAZ[h[m[mAn"o9>>@G 6  )!))*;[II  )!))/;GI y !))&+>I $ 1!))*A<PI%%c3 0Dq0HI %%&7< )/I& ) #$??3/236:I-9U\\#f+-F  *G" O  [\ s F F*c|Lt|tr|g}|jDcic]\}tfd|Ds|}}}t d|j DScc}}w)Nc3&K|]}|v ywNr)rBfr8s rrCz&state_dict_all_zero..\s@\Aa@\sc3hK|]*}tj|dk(j,yw)rN)rsallitem)rBparams rrCz&state_dict_all_zero..^s&Muyy!$))+Ms02) isinstancergr1rMrvalues)r5 filter_strr8r9s ` rstate_dict_all_zerorXso j# &$J'1'7'7'9]]tq!S@\Q[@\=\ad] ] M9J9J9LM MM^s A0A0 model_filec"ddl}ddlm}|jj |dd5}|j xsi}dddj dd|r*|j|}|rtj|SdSy#1swYHxYw)Nrrr)LORA_ADAPTER_METADATA_KEYptcpu) frameworkdeviceformat) safetensors.torchloaders.lora_baserrs safe_openmetadatar]getjsonloads)r safetensorsrrrraws r_load_sft_state_dict_metadataras=    $ $Z4 $ N&RS::<%2& LL4 ll45"%tzz#/4/&&s BBr)"renumr import_utilsrloggingrrsr ruEnumrraDIFFUSERS_TO_PEFTDIFFUSERS_OLD_TO_PEFTPEFT_TO_DIFFUSERSDIFFUSERS_OLD_TO_DIFFUSERSPEFT_TO_KOHYA_SSrrrOrr^rwr2r<rRr_rbrjrrrgrrrrrsi , H DII*+%#%#%#%F  %5&6$4&6$4&6&8(:&((G&E  &'%'%')+&( 4646  4  6 8:#>#>#>+ )!(4737377;#B#B#B%F  !6. !;)! +//1AB3  B36,3^3&<~N c r