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S+\ \@\4S'\@S,\ \@S-\ \@S.\BS/\ \ S0\BS\ \@S\ \ \ \@4S1\ \@S%\ \@S2\ \ \@\44S3jj5rHSN)Path)DictListOptionalUnion)uuid4) DDUFEntry ModelCard ModelCardData create_repohf_hub_download model_infosnapshot_download upload_folder)HF_HUB_DISABLE_TELEMETRYHF_HUB_OFFLINE)REGEX_COMMIT_HASH)EntryNotFoundErrorRepositoryNotFoundErrorRevisionNotFoundErroris_jinja_availablevalidate_hf_hub_args)version) HTTPError) __version__)DEPRECATED_REVISION_ARGSHUGGINGFACE_CO_RESOLVE_ENDPOINTSAFETENSORS_WEIGHTS_NAME WEIGHTS_NAME)ENV_VARS_TRUE_VALUES _flax_version _jax_version_onnxruntime_version_torch_versionis_flax_availableis_onnx_availableis_torch_available) get_loggerzmodel_card_template.md user_agentreturncS[S[RR5SS[3n[ (d [ (aUS-$[5(a US[3- n[5(aUS[3- nUS[3- n[5(a US [3- n[RR!S S 5R#5[$;aUS - n['U[(5(a.US S R+SUR-555-- nU$['U[.5(aUS U-- nU$)z> Formats a user-agent string with basic info about a request. z diffusers/z ; python/rz ; session_id/z; telemetry/offz; torch/z; jax/z; flax/z; onnxruntime/DIFFUSERS_IS_CIz ; is_ci/truez; c34# UHupUSU3v M g7f)/N).0kvs S/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/utils/hub_utils.py "http_user_agent..]sI6Hda!AaSz6Hs)rsysrsplit SESSION_IDrrr)r&r'r$r#r(r%osenvirongetupperr" isinstancedictjoinitemsstr)r+uas r6http_user_agentrFKs3 k])CKK,=,=,?,B+C=Q[P\ ]B>>%%% ()) |n%%  '' 3455 zz~~',2248LL n*d## dTYYIj6F6F6HIIII I J $ $ dZ IFrepo_id_or_pathtoken is_pipeline from_trainingmodel_description base_modelpromptlicensewidget inferencec V[5(d [S5e[R"XS9n U $![[ 4af U(a)[R "[USU UUUS9[US9n U $[5n U(aSOSn UcSU S 3n[R "XS 9n U $f=f) a4 Loads or creates a model card. Args: repo_id_or_path (`str`): The repo id (e.g., "runwayml/stable-diffusion-v1-5") or local path where to look for the model card. token (`str`, *optional*): Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more details. is_pipeline (`bool`): Boolean to indicate if we're adding tag to a [`DiffusionPipeline`]. from_training: (`bool`): Boolean flag to denote if the model card is being created from a training script. model_description (`str`, *optional*): Model description to add to the model card. Helpful when using `load_or_create_model_card` from a training script. base_model (`str`): Base model identifier (e.g., "stabilityai/stable-diffusion-xl-base-1.0"). Useful for DreamBooth-like training. prompt (`str`, *optional*): Prompt used for training. Useful for DreamBooth-like training. license: (`str`, *optional*): License of the output artifact. Helpful when using `load_or_create_model_card` from a training script. widget (`List[dict]`, *optional*): Widget to accompany a gallery template. inference: (`bool`, optional): Whether to turn on inference widget. Helpful when using `load_or_create_model_card` from a training script. zModelcard rendering is based on Jinja templates. Please make sure to have `jinja` installed before using `load_or_create_model_card`. To install it, please run `pip install Jinja2`.rI diffusers)rO library_namerQrMinstance_promptrP) card_data template_pathrLpipelinemodelu+This is the model card of a 🧨 diffusers zS that has been pushed on the Hub. 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Please check the DDUF structurer/r1zError no file named z found in archive rtz found in directory z0.22.0) filenameryrzr{r|rIr+rxr}zLoading the variant z from z via `revision='zG'` is deprecated. Loading instead from `revision='main'` with `variant=z)`. Loading model variants via `revision='z9'` will be removed in diffusers v1. Please use `variant='z '` instead.zYou are loading the variant z^'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='z%'` instead. However, it appears that z currently does not have a z file in the 'main' branch of z. 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M<;M M<"M77M<c ^U (aX;a[SUSUS35eO6[RRU5(d[SUSUS35eU (a'[R "XR 55n O9[US5n [R "U R55n SSS5 [[W SR555n U Sn [U SR55U S'U SR5U S'[RRX5n[RR!U5(dU (aU V s/sH"n [RRX5PM$ nn UHXmU (aTU ;a[#US TS 35eM#[RR%T5(aMI[#US TS 35e X4$U nUb0UVs/sH#n[RRUU5PM% nnS S /nU(dJ['XUS 9nU H:m[)U4SjUR*55nU(aM+[-US TS 35e [/UUUUUUUUUS9 nUb [RRUU5nU V s/sH#n [RRUU 5PM% nn UH8n[RRU5(aM)[-USUS 35e UU 4$!,(df  GNy=fs sn fs snf![0an[-S[2SUS35UeSnAff=fs sn f)a For a given model: - download and cache all the shards of a sharded checkpoint if `pretrained_model_name_or_path` is a model ID on the Hub - returns the list of paths to all the shards, as well as some metadata. 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Modelz Upload model SchedulerzUpload schedulerzUpload zUploading the files of z to rt)r folder_pathrIrr path_in_repo) __class____name__loggerinfor)selfrrrIrrrxs r6_upload_folderPushToHubMixin._upload_folders  !$..111!/ 7 77!3#*4>>+B+B*C!D -k]$wiqIJ#)"   rGprivatesafe_serializationrrr,c [XUSS9RnU(d[XS9n [U 5n SU0n SURR ;aU R SU05 [R"5n UR"U 40U D6 U(d/W R[RRU S55 URU UUUUUS9sS S S 5 $!,(df  g =f) u Upload model, scheduler, or pipeline files to the 🤗 Hugging Face Hub. Parameters: repo_id (`str`): The name of the repository you want to push your model, scheduler, or pipeline files to. It should contain your organization name when pushing to an organization. `repo_id` can also be a path to a local directory. commit_message (`str`, *optional*): Message to commit while pushing. Default to `"Upload {object}"`. private (`bool`, *optional*): Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists. token (`str`, *optional*): The token to use as HTTP bearer authorization for remote files. The token generated when running `hf auth login` (stored in `~/.huggingface`). create_pr (`bool`, *optional*, defaults to `False`): Whether or not to create a PR with the uploaded files or directly commit. safe_serialization (`bool`, *optional*, defaults to `True`): Whether or not to convert the model weights to the `safetensors` format. variant (`str`, *optional*): If specified, weights are saved in the format `pytorch_model..bin`. Examples: ```python from diffusers import UNet2DConditionModel unet = UNet2DConditionModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="unet") # Push the `unet` to your namespace with the name "my-finetuned-unet". unet.push_to_hub("my-finetuned-unet") # Push the `unet` to an organization with the name "my-finetuned-unet". unet.push_to_hub("your-org/my-finetuned-unet") ``` T)rrIexist_okrSrrrrz README.md)rIrrrxN)r rrargrrupdatetempfileTemporaryDirectorysave_pretrainedsaver<rrBr) rrrrrIrrrrrxr_ save_kwargstmpdirs r6 push_to_hubPushToHubMixin.push_to_hubs`gedS[[27HJ,Z8J,-?@ dnn55 5    73 4  ( ( *f   7; 7 V[ AB&&-## '+ * *s 7AC C,r2)NNFN)NNNFTNN)r __module__ __qualname____firstlineno____doc__rrDr<PathLikerboolrr__static_attributes__r2rGr6rrs $(,#' 3 +,  }  !    C= B)-"&##'!%#'JJ! 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