biZddlZddlmZddlmZddlmZddlmZm Z m Z ddl Z ddl Z ddl mcmZddlmZddlmZmZmZmZmZmZdd lmZdd lmZmZdd lm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)dd l*m+Z+d dl,m-Z-d dl.m/Z/m0Z0m1Z1m2Z2d dlm3Z3e)jhe5Z6dZ7dZ8GddZ9y)N) defaultdict) nullcontext)Path)CallableDictUnion)validate_hf_hub_args)ImageProjectionIPAdapterFaceIDImageProjection"IPAdapterFaceIDPlusImageProjectionIPAdapterFullImageProjectionIPAdapterPlusImageProjectionMultiIPAdapterImageProjection)load_model_dict_into_meta)_LOW_CPU_MEM_USAGE_DEFAULTload_state_dict) USE_PEFT_BACKEND_get_model_fileconvert_unet_state_dict_to_peft deprecateget_adapter_nameget_peft_kwargsis_accelerate_availableis_peft_versionis_torch_versionlogging)empty_device_cache#_func_optionally_disable_offloading)LORA_WEIGHT_NAMELORA_WEIGHT_NAME_SAFETEXT_ENCODER_NAME UNET_NAME)AttnProcsLayersz$pytorch_custom_diffusion_weights.binz,pytorch_custom_diffusion_weights.safetensorsc eZdZdZeZeZede e e e e jfffdZdZdZedZ dde e ej(fd ed e d ed ef d ZdZefdZefdZefdZdZy)UNet2DConditionLoadersMixinz: Load LoRA layers into a [`UNet2DCondtionModel`]. %pretrained_model_name_or_path_or_dictc jddlm}|jdd}|jdd}|jdd}|jdd}|jd d}|jd d} |jd d} |jd d} |jd d} |jdd} |jdd}|jdd}|jdt}d}|rt ddr t d| d} d}ddd}d}t |ts| r| | P| jdr? t|| xst|||||| | | }tjj|d}|+t|| xst|||||| | | }t!|}n|}t#dj%D}t'd|j%D}d}d}d}|rd}t)d d!||r|j+|"}n6|r&|j-||j.|| ||#\}}}nt |d$|rP|N|j1|%\}}}|j3|j5|j6|j8&|r|j;y|r|j=y|rM|j>jAD]/}t |tjBjDs(||1yy#t$r}|s|Yd}~d}~wwxYw)'a Load pretrained attention processor layers into [`UNet2DConditionModel`]. Attention processor layers have to be defined in [`attention_processor.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py) and be a `torch.nn.Module` class. Currently supported: LoRA, Custom Diffusion. For LoRA, one must install `peft`: `pip install -U peft`. Parameters: pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`): Can be either: - A string, the model id (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on the Hub. - A path to a directory (for example `./my_model_directory`) containing the model weights saved with [`ModelMixin.save_pretrained`]. - A [torch state dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict). cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory where a downloaded pretrained model configuration is cached if the standard cache is not used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. local_files_only (`bool`, *optional*, defaults to `False`): Whether to only load local model weights and configuration files or not. If set to `True`, the model won't be downloaded from the Hub. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from `diffusers-cli login` (stored in `~/.huggingface`) is used. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier allowed by Git. subfolder (`str`, *optional*, defaults to `""`): The subfolder location of a model file within a larger model repository on the Hub or locally. network_alphas (`Dict[str, float]`): The value of the network alpha used for stable learning and preventing underflow. This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script. Refer to [this link](https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning). adapter_name (`str`, *optional*, defaults to None): Adapter name to be used for referencing the loaded adapter model. If not specified, it will use `default_{i}` where i is the total number of adapters being loaded. weight_name (`str`, *optional*, defaults to None): Name of the serialized state dict file. low_cpu_mem_usage (`bool`, *optional*): Speed up model loading by only loading the pretrained LoRA weights and not initializing the random weights. Example: ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 ).to("cuda") pipeline.unet.load_attn_procs( "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic" ) ``` r )*_maybe_remove_and_reapply_group_offloading cache_dirNforce_downloadFproxieslocal_files_onlytokenrevision subfolder weight_nameuse_safetensors adapter_name _pipelinenetwork_alphaslow_cpu_mem_usage<=z0.13.0zq`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`.Tattn_procs_weightspytorch) file_type frameworkz .safetensors) weights_namer,r-r.r/r0r1r2 user_agentcpu)devicec3$K|]}d|v yw)custom_diffusionN.0ks Q/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/loaders/unet.py z>UNet2DConditionLoadersMixin.load_attn_procs..s!Ua"4"9!Uc3JK|]}d|vxs|jdyw)loraz.alphaN)endswithrEs rHrIz>UNet2DConditionLoadersMixin.load_attn_procs..s%Wv{:ajj&::Ws!#zUsing the `load_attn_procs()` method has been deprecated and will be removed in a future version. Please use `load_lora_adapter()`.load_attn_procs0.40.0) state_dict)rPunet_identifier_keyr7r5r6r8zQ does not seem to be in the correct format expected by Custom Diffusion training.r6dtyperA)#hooks.group_offloadingr+poprr ValueError isinstancedictrMrr# safetensorstorch load_fileIOErrorr"ranykeysallr_process_custom_diffusion _process_lora unet_name_optionally_disable_offloadingset_attn_processortorTrAenable_model_cpu_offloadenable_sequential_cpu_offload componentsvaluesnnModule)selfr)kwargsr+r,r-r.r/r0r1r2r3r4r5r6r7r8 allow_pickler? model_filerPeis_custom_diffusionis_lorais_model_cpu_offloadis_sequential_cpu_offloadis_group_offloaddeprecation_messageattn_processors components rHrNz+UNet2DConditionLoadersMixin.load_attn_procsDsH XJJ{D1 $4e<**Y-!::&8$? 7D)::j$/JJ{D1 jj5  **%6=zz.$7 JJ{D1 $4d;"JJ':J!!U:??CT!UUWZ__EVWW$$)!  #h  '3F G "<< <SO PTPbPb%$(NN-)#"3 QcQ M ";=M,op  9#8PTPsPs#QtQ M ";=M  # #O 4 GG$**T[[G 9  . . 0 &  3 3 5 &1188: J i9>yI JG' s>L L2#L--L2cddlm}i}tt}|j D]\}}t |dk(ri||<d|vrGdj |jddddj |jddd}}nFdj |jddddj |jddd}}||||<|j D]r\}} t | dk(r|dddd ||<$| d jd } | d jd} d | vrd nd} |d | | | ||<||j| t|S)Nr )CustomDiffusionAttnProcessorrto_out.F)train_kv train_q_out hidden_sizecross_attention_dimzto_k_custom_diffusion.weightrzto_q_custom_diffusion.weightT) models.attention_processorr{rrYitemslenjoinsplitshaper) rmrPr{rxcustom_diffusion_grouped_dictkeyvalueattn_processor_keysub_key value_dictrrrs rHraz5UNet2DConditionLoadersMixin._process_custom_diffusionsM(3D(9%$**, SJC5zQ57-c2s?25((399S>#2;N2OQTQYQYZ]ZcZcdgZhikilZmQn&25((399S>#2;N2OQTQYQYZ]ZcZcdgZhikilZmQn&MR-.@A'J S =BBD AOC:!#'C"4]a($'11O&P&V&VWX&Y#()GHNNqQ &D &RdX] 'C! + +(; ($  $44Z@ A"cRts tdddlm}m}m} t |j} | D cgc]} | j|s| } } |jD cic] \} } | | vs | j|dd| "}} } |j|jD cgc]} | j|s| }} |jD cic] \} } | |vs | j|dd| "}} } d}d}d}t|dkDr|n|}t|dkDr|t|divrtd|d t|}| t|}i}|jD]\}}d |vs |jd ||<t|||d }d|vr9|drt!ddr(tdt!ddr|j#dd|vr9|drt!ddr(tdt!ddr|j#d|d!i|}| t%|}|j'|\}}}i}t!ddr||d<|||fd|i|| |||fi|}d}|t|dd}|r0|D cgc]} d| vs|| vs | }} |rddj)|d}t|dd}|r3|D cgc]} d| vs|| vs | }} |r|d dj)|dz }|rt*j-||||fScc} wcc} } wcc} wcc} } wcc} wcc} w)"Nz)PEFT backend is required for this method.r) LoraConfiginject_adapter_in_modelset_peft_model_state_dictr}F peft_configz Adapter name z? already in use in the Unet - please select a new adapter name.lora_BrT)is_unetuse_dora=z0.13.1r8r5unexpected_keyslora_zSLoading adapter weights from state_dict led to unexpected keys found in the model: z, z. missing_keyszJLoading adapter weights from state_dict led to missing keys in the model: rD)rrWpeftrrrlistr_ startswithrreplacergetattrrrrrrVrrdrloggerwarning) rmrPrQr7r5r6r8rrrr_rG unet_keysvunet_state_dict alpha_keysrtrurvstate_dict_to_be_usedrankrvallora_config_kwargs lora_config peft_kwargsincompatible_keyswarn_msgrlora_unexpected_keysrlora_missing_keyss rHrbz)UNet2DConditionLoadersMixin._process_lora#s HI IWWJOO%& $J1 5H(IQJ JDNDTDTDV <@AqZ[_hZhAII,-Q/ 4a 7    %%3%8%8%:`allK^>_!`J`HVH\H\H^@D1bcgqbq 013R8!;N %$)! 363G!3KQ[ $ % )wt]B?? #L>1pq99NOJ)"A!PD&,,. -Ss? # ! DI -"1~z[_!` //%j1&sG4(D'sG4*..z:00%k2&tX6(B'tX6*..{;$:'9:K#/5 QUPsPsQ M ";=MKtX.3D /0 #K `L `T_ ` 9$ L h\g h H ,")*;=NPT"U"7F+m!'UV,[gkl[lA+m(+m+ $ *> ?@D! ''8.$O 4@(gqGqLUaefUf(g%(g(  $ *; <=Q@ x(#%>@PPPEK aN,n)hsSL L ) L6L%L<L L"L? L LL> L$L$ L$ct|S)NrRr )clsr6s rHrdz:UNet2DConditionLoadersMixin._optionally_disable_offloadings3YGGrNsave_directoryis_main_processr3 save_functionsafe_serializationc ddlmmmtj j |rtjd|dytfd|jjD}|r|j}||r|jD cic]#\} } t| tjr!| | %} } } t!| dkDr&tj#d| j%|jD cic]#\} } t| tjs!| | %}} } n.d } t'd d | t(s t+d dd lm} | |}||rd}ntj0}t j2|d||r|rt4nt6}n|rt8nt:}t=||j?}|||tjAd|ycc} } wcc} } w)az Save attention processor layers to a directory so that it can be reloaded with the [`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method. Arguments: save_directory (`str` or `os.PathLike`): Directory to save an attention processor to (will be created if it doesn't exist). is_main_process (`bool`, *optional*, defaults to `True`): Whether the process calling this is the main process or not. Useful during distributed training and you need to call this function on all processes. In this case, set `is_main_process=True` only on the main process to avoid race conditions. save_function (`Callable`): The function to use to save the state dictionary. Useful during distributed training when you need to replace `torch.save` with another method. Can be configured with the environment variable `DIFFUSERS_SAVE_MODE`. safe_serialization (`bool`, *optional*, defaults to `True`): Whether to save the model using `safetensors` or with `pickle`. Example: ```py import torch from diffusers import DiffusionPipeline pipeline = DiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, ).to("cuda") pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin") pipeline.unet.save_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin") ``` r r{CustomDiffusionAttnProcessor2_0$CustomDiffusionXFormersAttnProcessorzProvided path (z#) should be a directory, not a fileNc3BK|]\}}t|fyw)N)rX)rF_xr{rrs rHrIz>UNet2DConditionLoadersMixin.save_attn_procs..s3" A -/NPtu " srzfSafetensors does not support saving dicts with non-tensor values. The following keys will be ignored: zUsing the `save_attn_procs()` method has been deprecated and will be removed in a future version. Please use `save_lora_adapter()`.save_attn_procsrOzOPEFT backend is required for saving LoRAs using the `save_attn_procs()` method.)get_peft_model_state_dictcJtjj||ddiS)Nformatpt)metadata)rZr[ save_file)weightsfilenames rHrzBUNet2DConditionLoadersMixin.save_attn_procs..save_functions'&,,66wT\^bSc6ddrT)exist_okzModel weights saved in )!rr{rrospathisfilererrorr^rxr _get_custom_diffusion_state_dictrXr[Tensorrrr_rrrW peft.utilsrsavemakedirs!CUSTOM_DIFFUSION_WEIGHT_NAME_SAFEr#CUSTOM_DIFFUSION_WEIGHT_NAMEr"ras_posixinfo)rmrrr3rrrnrrrPrGrempty_state_dictrwr save_pathr{rrs @@@rHrz+UNet2DConditionLoadersMixin.save_attn_procssR  77>>. ) LL?>*::]^ _ !" ..446 "   >>@J$);5?5E5E5G#kTQzZ[]b]i]iOjAqD#k #k'(1,NN??O?T?T?V>WY0:/?/?/Aatq!ZPQSXS_S_E`ada a#h  '3F G# !rss <248J  !e!&  NT2  !CV?\q >Q:Wg 5>>@ j), -i[9:M$l bs"G6G6"G<3G<c Zddlm}m}m}t |j j Dcic]\}}t||||fr||c}}}|j}|j j D]'\}} t| jdk(s#i||<)|Scc}}w)Nr rr) rr{rrr&rxrrXrPr) rmr{rryr model_to_saverPnameattns rHrzd?} | jd@dA} | jdBdC} | jdDdE} | jdFdG} | jdHdI} dJ| vr| || jdKdL<dM| vr| || jdNdO<dP| vrE| jdQdR}|d|| jdPdS<|d|| jdPdT<dU| vr| || jdUdV<-d| k(r| |dW<9dX| k(r| |dY<EdZ| k(r| |d[<Qd\| k(r| |d]<]| || <dnd^|vr|djd}|djd}||z}d^}||jd} |dZjd| z}|5t| |||_}ddd|jD].\} } | jdd} | jdd} | || <0n#|d`jd}|dajd}|dbjd}|d`jdQ}t!dc|D}|r|ddjddzn|dejddz}|5t#|||||f}ddd|jD]i\} } | jd dg} | jdhdi} | jdjdk} | jdldm} | jdndo} | jdpdq} | jdrds} | jdtdu} | jdvdw} dP| vrl| j%dx}dy|dQ<dxj'|} | jdQdR}|d|| jdPdS<|d|| jdPdT<dz| vr.| j%dx}dy|dQ<dxj'|} | || <KdU| vr>| j%dx}dy|dQ<dxj'|} | || jdUdV<| jd{d|} | jd}d~} | jdd} | jdd} | jdd} | jdd} | jdd} | jdd} | jdd} | jdd} | jdd} | jdd} | || <l|s|j)|d|Sd|j*i}t-||||j.t1|S#1swYxYw#1swYxYw#1swYxYw#1swYxYw#1swYxYw)Nrinit_empty_weightsF,Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: ``` pip install accelerate ``` .Tr1.9.0~Low memory initialization requires torch >= 1.9.0. 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