+h2:SSKrSSKJrJrJrJrJrJrJr SSK r SSK J r SSK J r SSKJr SSKJrJr \(aSSKJr S r\"\5r"S S \\5rS S jrg)N) TYPE_CHECKINGAnyDictListOptionalTupleUnion)validate_hf_hub_args)Self) ConfigMixin)PushToHubMixin get_logger BlockStatezguider_config.jsonc v\rSrSrSr\rSrSrS'S\ S\ 4Sjjr Sr S r S \ S \ S \RS S4SjrS\\\\\\\444S S4SjrS\R,R.S S4SjrS\R,R.S S4SjrSSS \S4SjrS\SS \4SjrS \4Sjr\S \ 4Sj5r!\S \ 4Sj5r"\S \ 4Sj5r#\$S\\\\\\\444SSS\ S\S S4 Sj5r%\$\&S(S \'\\\(RR4S!\'\S \*4S"jj55r+S)S#\\\(RR4S$\ 4S%jjr,S&r-g)* BaseGuidance$zGBase class providing the skeleton for implementing guidance techniques.N__guidance_identifier__startstopcZXlX lSUlSUlSUlSUlSUlSUlSUs=::aS:dO [SUS35eXs=::aS::dO [SUSUS35eURb[UR[5(d [S 5eg) NrT?z4Expected `start` to be between 0.0 and 1.0, but got .zExpected `stop` to be between z and 1.0, but got z\`_input_predictions` must be a list of required prediction names for the guidance technique.) _start_stop_step_num_inference_steps _timestep_count_prepared _input_fields_enabled ValueError_input_predictions isinstancelist)selfrrs X/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/guiders/guider_utils.py__init__BaseGuidance.__init__+s   )-!+/ EI u"s"STYSZZ[\] ]$$=eWDVW[V\\]^_ _  " " **T=T=TVZ2[2[n 3\cSUlg)NFr#r(s r)disableBaseGuidance.disable?s  r,cSUlg)NTr.r/s r)enableBaseGuidance.enableBs  r,stepnum_inference_stepstimestepreturnc6XlX lX0lSUlg)Nr)rrr r!)r(r5r6r7s r) set_stateBaseGuidance.set_stateEs $7!! r,kwargsc 4UR5H~up#[U[5n[U[5=(a' [ U5S:H=(a [ SU55nU(aM\U(aMe[ S[U5SUS35e Xlg)a Set the input fields for the guidance technique. The input fields are used to specify the names of the returned attributes containing the prepared data after `prepare_inputs` is called. The prepared data is obtained from the values of the provided keyword arguments to this method. Args: **kwargs (`Dict[str, Union[str, Tuple[str, str]]]`): A dictionary where the keys are the names of the fields that will be used to store the data once it is prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used to look up the required data provided for preparation. If a string is provided, it will be used as the conditional data (or unconditional if used with a guidance method that requires it). If a tuple of length 2 is provided, the first element must be the conditional data identifier and the second element must be the unconditional data identifier or None. Example: ``` data = {"prompt_embeds": , "negative_prompt_embeds": , "latents": } BaseGuidance.set_input_fields( latents="latents", prompt_embeds=("prompt_embeds", "negative_prompt_embeds"), ) ``` r c3B# UHn[U[5v M g7fN)r&str).0vs r) 0BaseGuidance.set_input_fields..hsDgaf\]ZPQSVEWEWafszcExpected `set_input_fields` to be called with a string or a tuple of string with length 2, but got z for key rN) itemsr&r@tuplelenallr$typer")r(r<keyvalue is_stringis_tuple_of_str_with_len_2s r)set_input_fieldsBaseGuidance.set_input_fieldsKs4!,,.JC"5#.I5%(gSZ1_gDgafDgAg 'I!;!; yz~@E{FzGGPQTPUUVW )$r,denoiserc.U=RS- slg)z Prepares the models for the guidance technique on a given batch of data. This method should be overridden in subclasses to implement specific model preparation logic. N)r!r(rPs r)prepare_modelsBaseGuidance.prepare_modelsps !r,cg)a  Cleans up the models for the guidance technique after a given batch of data. This method should be overridden in subclasses to implement specific model cleanup logic. It is useful for removing any hooks or other stateful modifications made during `prepare_models`. NrSs r)cleanup_modelsBaseGuidance.cleanup_modelsws r,datarc[S5e)Nz?BaseGuidance::prepare_inputs must be implemented in subclasses.NotImplementedError)r(rZs r)prepare_inputsBaseGuidance.prepare_inputss!"cddr,cP[SU55(d [S5e[U5UR:wa%[SURS[U5S35eUVs0sH#n[ X R 5UR _M% nnUR"S0UD6$s snf)Nc3:# UHn[US5v M g7f) noise_predN)hasattr)rAds r)rC(BaseGuidance.__call__..s:T71l++Tsz1Expected all data to have `noise_pred` attribute.z Expected z data items, but got z. Please check the input data.rW)rHr$rGnum_conditionsgetattr_identifier_keyrbforward)r(rZrdforward_inputss r)__call__BaseGuidance.__call__s:T:::PQ Q t9++ +D//00Ec$i[Pno SWWRVQ'!%9%9:ALLHRVW||-n--Xs%*B#c[S5e)Nz8BaseGuidance::forward must be implemented in subclasses.r\)r(argsr<s r)riBaseGuidance.forwards!"\]]r,c[S5e)Nz?BaseGuidance::is_conditional must be implemented in subclasses.r\r/s r)is_conditionalBaseGuidance.is_conditional!"cddr,c$UR(+$r?)rqr/s r)is_unconditionalBaseGuidance.is_unconditionals&&&&r,c[S5e)Nz?BaseGuidance::num_conditions must be implemented in subclasses.r\r/s r)rfBaseGuidance.num_conditionsrsr, input_fields tuple_index identifiercxSSKJn Uc [S5e0nUR5HQupx[ U[ 5(a[ X(5Xg'M*[ U[5(a[ X(U5Xg'MQMS XFUR'U"S0UD6$![a [RSUS35 Mf=f)a= Prepares a batch of data for the guidance technique. This method is used in the `prepare_inputs` method of the `BaseGuidance` class. It prepares the batch based on the provided tuple index. Args: input_fields (`Dict[str, Union[str, Tuple[str, str]]]`): A dictionary where the keys are the names of the fields that will be used to store the data once it is prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used to look up the required data provided for preparation. If a string is provided, it will be used as the conditional data (or unconditional if used with a guidance method that requires it). If a tuple of length 2 is provided, the first element must be the conditional data identifier and the second element must be the unconditional data identifier or None. data (`BlockState`): The input data to be prepared. tuple_index (`int`): The index to use when accessing input fields that are tuples. Returns: `BlockState`: The prepared batch of data. r rzInput fields cannot be None. Please pass `input_fields` to `prepare_inputs` or call `set_input_fields` before preparing inputs.z"`data` does not have attribute(s) z , skipping.rW) "modular_pipelines.modular_pipelinerr$rEr&r@rgrFAttributeErrorloggerdebugrh) clsryrZrzr{r data_batchrJrKs r)_prepare_batchBaseGuidance._prepare_batchs8 D  R  &,,.JC VeS))&-d&:JOu--&-d+4F&GJO/+53&&''J''" V A% TU Vs"B%B#B98B9pretrained_model_name_or_path subfolderc `UR"SUUSSS.UD6upTnUR"U4SU0UD6$)a Instantiate a guider from a pre-defined JSON configuration file in a local directory or Hub repository. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): 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 guider configuration saved with [`~BaseGuidance.save_pretrained`]. subfolder (`str`, *optional*): The subfolder location of a model file within a larger model repository on the Hub or locally. return_unused_kwargs (`bool`, *optional*, defaults to `False`): Whether kwargs that are not consumed by the Python class should be returned or not. 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. output_loading_info(`bool`, *optional*, defaults to `False`): Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. 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. To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with `hf auth login`. You can also activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a firewalled environment. T)rrreturn_unused_kwargsreturn_commit_hashrrW) load_config from_config)rrrrr<config commit_hashs r)from_pretrainedBaseGuidance.from_pretrainedsPp'*oo' *G!%# '  ' # v[mMG#''9: 6!>N:NR[9[[I r,)r)rtypingrrrrrrr rhuggingface_hub.utilsr typing_extensionsr configuration_utilsr utilsrrr}rrrrrrrWr,r)rsV III 6"-.?* H x[;x[vr,