(ti}FddlmZddlZddlmZmZddlZddlmZddl m Z ddl m Z ddl mZmZmZerdd lmZd ZeeZGd d e eZGd deZddZy)) annotationsN) TYPE_CHECKINGAny)validate_hf_hub_args)Self) ConfigMixin) BaseOutputPushToHubMixin get_logger BlockStatezguider_config.jsoncNeZdZdZeZdZdZdddZdZ dZ dZ ddZ dd Z dfd Zdd Zdd Zdd Z ddZd dZd!dZed"dZed"dZed#dZe d$dZe d%dZee d& d'dZd(d)dZxZS)* BaseGuidancezGBase class providing the skeleton for implementing guidance techniques.N__guidance_identifier__cvtjd||_||_d|_d|_d|_d|_d|_||_ d|cxkrdksntd|d||cxkrdksntd|d|d|jt|jts td y) NzGuiders are currently an experimental feature under active development. The API is subject to breaking changes in future releases.r?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.)loggerwarning_start_stop_step_num_inference_steps _timestep_count_prepared _input_fields_enabled ValueError_input_predictions isinstancelist)selfstartstopenableds m/Volumes/fast/ai/experiments/MLX_z-image/.venv/lib/python3.12/site-packages/diffusers/guiders/guider_utils.py__init__zBaseGuidance.__init__-s Q    )-!+/ ?C u"s"STYSZZ[\] ]$$=eWDVW[V\\]^_ _  " " **T=T=TVZ2[n 3\c P|jj|jfi|S)a Creates a copy of this guider instance, optionally with modified configuration parameters. Args: **kwargs: Configuration parameters to override in the new instance. If no kwargs are provided, returns an exact copy with the same configuration. Returns: A new guider instance with the same (or updated) configuration. Example: ```python # Create a CFG guider guider = ClassifierFreeGuidance(guidance_scale=3.5) # Create an exact copy same_guider = guider.new() # Create a copy with different start step, keeping other config the same new_guider = guider.new(guidance_scale=5) ``` ) __class__ from_configconfig)r$kwargss r(newzBaseGuidance.newEs$.*t~~))$++@@@r*cd|_y)NFrr$s r(disablezBaseGuidance.disable^s  r*cd|_y)NTr2r3s r(enablezBaseGuidance.enableas  r*c<||_||_||_d|_y)Nr)rrrr)r$stepnum_inference_stepstimesteps r( set_statezBaseGuidance.set_stateds  $7!! r*c|j|j|j|j|j|j d}|S)an Returns the current state of the guidance technique as a dictionary. The state variables will be included in the __repr__ method. Returns: `dict[str, Any]`: A dictionary containing the current state variables including: - step: Current inference step - num_inference_steps: Total number of inference steps - timestep: Current timestep tensor - count_prepared: Number of times prepare_models has been called - enabled: Whether the guidance is enabled - num_conditions: Number of conditions )r8r9r:count_preparedr'num_conditions)rrrrrr>)r$states r( get_statezBaseGuidance.get_statejsBJJ#'#<#<"22}}"11   r*c xt |}|j}g}|jD]j\}}t |}d|vr?|j d}|ddzdj |ddDcgc]}d|z c}z}|jd|d|ldj |} |d| Scc}w) zq Returns a string representation of the guidance object including both config and current state.  rNz z z: z State: )super__repr__r@itemsstrsplitjoinappend) r$str_reprr? state_lineskvv_strv_linesline state_strr,s r(rEzBaseGuidance.__repr__s 7#%  KKMDAqFEu}++d+ T)DIIQXYZY[Q\6]Q\v}Q\6],^^   A3b0 1"IIk* :i[11 7^s1 B7 c.|xjdz c_y)z Prepares the models for the guidance technique on a given batch of data. This method should be overridden in subclasses to implement specific model preparation logic. rCN)rr$denoisers r(prepare_modelszBaseGuidance.prepare_modelss !r*cy)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`. NrTs r(cleanup_modelszBaseGuidance.cleanup_modelss r*ctd)Nz?BaseGuidance::prepare_inputs must be implemented in subclasses.NotImplementedError)r$datas r(prepare_inputszBaseGuidance.prepare_inputss!"cddr*ctd)NzPBaseGuidance::prepare_inputs_from_block_state must be implemented in subclasses.r[)r$r] input_fieldss r(prepare_inputs_from_block_statez,BaseGuidance.prepare_inputs_from_block_states""tuur*c@td|Ds tdt||jk7r%td|jdt|d|Dcic]#}t ||j |j %}}|jdi|Scc}w)Nc34K|]}t|dyw) noise_predN)hasattr).0ds r( z(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.rX)allr lenr>getattr_identifier_keyrdforward)r$r]rgforward_inputss r(__call__zBaseGuidance.__call__s:T::PQ Q t9++ +D//00Ec$i[Pno SWWRVQ'!T%9%9:ALLHRVWt||-n--Xs(Bctd)Nz8BaseGuidance::forward must be implemented in subclasses.r[)r$argsr/s r(rmzBaseGuidance.forwards!"\]]r*ctd)Nz?BaseGuidance::is_conditional must be implemented in subclasses.r[r3s r(is_conditionalzBaseGuidance.is_conditional!"cddr*c|j S)N)rsr3s r(is_unconditionalzBaseGuidance.is_unconditionals&&&&r*ctd)Nz?BaseGuidance::num_conditions must be implemented in subclasses.r[r3s r(r>zBaseGuidance.num_conditionsrtr*cbddlm}i}|jD]V\}} t|tj r|||<n0t|t r ||||<ntdt|X|||j<|di|S#t$rtjd|dYwxYw) Prepares a batch of data for the guidance technique. This method is used in the `prepare_inputs` method of the `BaseGuidance` class. It prepares the batch based on the provided tuple index. Args: input_fields (`dict[str, str | tuple[str, str]]`): A dictionary where the keys are the names of the fields that will be used to store the data once it is prepared with `prepare_inputs`. The values can be either a string or a tuple of length 2, which is used to look up the required data provided for preparation. If a string is provided, it will be used as the conditional data (or unconditional if used with a guidance method that requires it). If a tuple of length 2 is provided, the first element must be the conditional data identifier and the second element must be the unconditional data identifier or None. data (`BlockState`): The input data to be prepared. tuple_index (`int`): The index to use when accessing input fields that are tuples. Returns: `BlockState`: The prepared batch of data. rr zInvalid value type: "`data` does not have attribute(s) , skipping.rX) "modular_pipelines.modular_pipelinerrFr"torchTensortupler typerdebugrl)clsr] tuple_index identifierr data_batchkeyvalues r(_prepare_batchzBaseGuidance._prepare_batchs6 D **,JC VeU\\2&+JsOu-&+K&8JsO$';DK=%IJJ'+5 3&&''J'' V A% TU VsAB  "B.-B.cJddlm}i}|jD]J\}} t|trt ||||<n$t|t rt |||||<n L|||j<|di|S#t$rtjd|dYwxYw)ryrr rzr{rX) r|rrFr"rGrkrAttributeErrorrrrl) rr`r]rrrrrrs r(_prepare_batch_from_block_statez,BaseGuidance._prepare_batch_from_block_states8 D &,,.JC VeS)&-dE&:JsOu-&-dE+4F&GJsO/+5 3&&''J''" V A% TU VsAA=="B"!B"c b|jd||ddd|\}}}|j|fd|i|S)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 (`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. > [!TIP] > 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)pretrained_model_name_or_path subfolderreturn_unused_kwargsreturn_commit_hashrrX) load_configr-)rrrrr/r. commit_hashs r(from_pretrainedzBaseGuidance.from_pretrained!sVh'6coo' *G!%# '  ' # sv[ classmethodrrrrr __classcell__)r,s@r(rr&sR$K/O0A2! ,22" ev v0Pv v .^ee''ee((:(((( ((  ((((T*(6*(*( *(  *(  *(*(XCG $" 9\'?9\9\  9\9\v[[r*rc,eZdZUded<ded<ded<y) GuiderOutputz torch.Tensorpredztorch.Tensor | None pred_cond pred_uncondN)rrr__annotations__rXr*r(rrps ""$$r*rc|jttd|jd}|jttd|jd}|||z z}||zd|z |zz}|S)a Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). Args: noise_cfg (`torch.Tensor`): The predicted noise tensor for the guided diffusion process. noise_pred_text (`torch.Tensor`): The predicted noise tensor for the text-guided diffusion process. guidance_rescale (`float`, *optional*, defaults to 0.0): A rescale factor applied to the noise predictions. Returns: noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor. rCT)dimkeepdim)stdr#rangendim) noise_cfgnoise_pred_textguidance_rescalestd_textstd_cfgnoise_pred_rescaleds r(rescale_noise_cfgrvs ""tE!_5I5I,J'KUY"ZHmmU1inn%= >mMG#x''9: #66!>N:NR[9[[I r*)r) __future__rostypingrrr}huggingface_hub.utilsrtyping_extensionsrconfiguration_utilsr utilsr r r r|rrrrrrrrXr*r(rsb# % 6"-::?* H G[;G[T %:% r*