o Gi!@sddlZddlZddlmZddlmZddlZddlmZddl m Z ddl m Z m Z dZGd d d eZgd gd gd gdgddZeGddde ZGddde ZdS)N) dataclass)Enum)validate_hf_hub_args)Self) BaseOutputPushToHubMixinzscheduler_config.jsonc@sHeZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdZdZdS)KarrasDiffusionSchedulersr N)__name__ __module__ __qualname__ DDIMScheduler DDPMScheduler PNDMSchedulerLMSDiscreteSchedulerEulerDiscreteSchedulerHeunDiscreteSchedulerEulerAncestralDiscreteSchedulerDPMSolverMultistepSchedulerDPMSolverSinglestepSchedulerKDPM2DiscreteSchedulerKDPM2AncestralDiscreteSchedulerDEISMultistepSchedulerUniPCMultistepSchedulerDPMSolverSDESchedulerEDMEulerSchedulerr*r*Y/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/schedulers/scheduling_utils.pyr !s r ) iRiii!iiW|) {G:-@gfffff@gS@g~jt@gV-?gtV?g7A`?gw/?gK7?g~jt?) r,iMiiKii6t5r) r0g(\B@g^I +@g r@gFx?g/$?g(\?gRQ?gʡE?g!rh?r1) g@g@K@gʡ/@g r@gˡE@g9v?g1Zd?gn?g/$?g rh?r1)StableDiffusionTimestepsStableDiffusionSigmasStableDiffusionXLTimestepsStableDiffusionXLSigmasStableDiffusionVideoSigmasc@seZdZUdZejed<dS)SchedulerOutputaH Base class for the output of a scheduler's `step` function. Args: prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. prev_sampleN)rrr__doc__torchTensor__annotations__r*r*r*r+r:<s  r:c @seZdZdZeZgZdZee   dde e j BdBde dBde fdd Zdd e e j Bd efd d ZeddZeddZdS)SchedulerMixina Base class for all schedulers. [`SchedulerMixin`] contains common functions shared by all schedulers such as general loading and saving functionalities. [`ConfigMixin`] takes care of storing the configuration attributes (like `num_train_timesteps`) that are passed to the scheduler's `__init__` function, and the attributes can be accessed by `scheduler.config.num_train_timesteps`. Class attributes: - **_compatibles** (`list[str]`) -- A list of scheduler classes that are compatible with the parent scheduler class. Use [`~ConfigMixin.from_config`] to load a different compatible scheduler class (should be overridden by parent class). TNFpretrained_model_name_or_path subfolderreturncKs6|jd||ddd|\}}}|j|fd|i|S)a( Instantiate a scheduler 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 scheduler configuration saved with [`~SchedulerMixin.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)rArBreturn_unused_kwargsreturn_commit_hashrDNr*) load_config from_config)clsrArBrDkwargsconfig commit_hashr*r*r+from_pretrained^s4 zSchedulerMixin.from_pretrainedsave_directory push_to_hubcKs|jd||d|dS)a? Save a scheduler configuration object to a directory so that it can be reloaded using the [`~SchedulerMixin.from_pretrained`] class method. Args: save_directory (`str` or `os.PathLike`): Directory where the configuration JSON file will be saved (will be created if it does not exist). push_to_hub (`bool`, *optional*, defaults to `False`): Whether or not to push your model to the Hugging Face Hub after saving it. You can specify the repository you want to push to with `repo_id` (will default to the name of `save_directory` in your namespace). kwargs (`dict[str, Any]`, *optional*): Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method. )rMrNNr*) save_config)selfrMrNrIr*r*r+save_pretrainedszSchedulerMixin.save_pretrainedcCs|S)z Returns all schedulers that are compatible with this scheduler Returns: `list[SchedulerMixin]`: list of compatible schedulers )_get_compatibles)rPr*r*r+ compatiblesszSchedulerMixin.compatiblescs@tt|jg|j}ttddfdd|D}|S)N.rcs g|] }t|rt|qSr*)hasattrgetattr).0cdiffusers_libraryr*r+ s  z3SchedulerMixin._get_compatibles..)listsetr _compatibles importlib import_modulesplit)rHcompatible_classes_strcompatible_classesr*rYr+rRs  zSchedulerMixin._get_compatibles)NNF)F)rrrr<SCHEDULER_CONFIG_NAME config_namer^has_compatibles classmethodrstrosPathLikerrLboolrQpropertyrSrRr*r*r*r+r@Js, ;  r@)r_ri dataclassesrenumrr=huggingface_hub.utilsrtyping_extensionsrutilsrrrdr AysSchedulesr:r@r*r*r*r+s&