o ۷i0@sZddlZddlZddlZddlmZddlmZddlZddlm Z ddl m Z ddl mZmZmZeeZdZGdd d eZeGd d d eZGd d d eZde jdeede jfddZde jfdede jfddZejjGdddZdede jde jde jfddZ dede jde jde jfdd Z!ded!e jde jde jfd"d#Z"dS)$N) dataclass)Enum)validate_hf_hub_args) BaseOutputPushToHubMixinloggingzscheduler_config.jsonc@s$eZdZdZdZdZdZdZdZdS)FlaxKarrasDiffusionSchedulersrN) __name__ __module__ __qualname__FlaxDDIMSchedulerFlaxDDPMSchedulerFlaxPNDMSchedulerFlaxLMSDiscreteSchedulerFlaxDPMSolverMultistepSchedulerFlaxEulerDiscreteSchedulerrr`/home/ubuntu/vllm_env/lib/python3.10/site-packages/diffusers/schedulers/scheduling_utils_flax.pyr $sr c@seZdZUdZejed<dS)FlaxSchedulerOutputa> Base class for the scheduler's step function output. Args: prev_sample (`jnp.ndarray` 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__jnpndarray__annotations__rrrrr-s  rc@seZdZdZeZdgZgZdZe e   dde e j BdBde dBfdd Zdd e e j Bd efd d ZeddZe ddZdS)FlaxSchedulerMixinaY Mixin containing common functions for the schedulers. Class attributes: - **_compatibles** (`list[str]`) -- A list of classes that are compatible with the parent class, so that `from_config` can be used from a class different than the one used to save the config (should be overridden by parent class). dtypeTNFpretrained_model_name_or_path subfolderc Ksttd|jd ||dd|\}}|j|fddi|\}}t|dr/t|ddr/|}|r6|||fS||fS) a Instantiate a Scheduler class from a pre-defined JSON-file. Parameters: pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): Can be either: - A string, the *model id* of a model repo on huggingface.co. Valid model ids should have an organization name, like `google/ddpm-celebahq-256`. - A path to a *directory* containing model weights saved using [`~SchedulerMixin.save_pretrained`], e.g., `./my_model_directory/`. subfolder (`str`, *optional*): In case the relevant files are located inside a subfolder of the model repo (either remote in huggingface.co or downloaded locally), you can specify the folder name here. 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 in which a downloaded pretrained model configuration should be cached if the standard cache should not be 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, e.g., `{'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 or not to only look at local files (i.e., do not try to download the model). token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `transformers-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. > [!TIP] > It is required to be logged in (`hf auth login`) when you want to use private or [gated > models](https://huggingface.co/docs/hub/models-gated#gated-models). > [!TIP] > Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to > use this method in a firewalled environment. zFlax classes are deprecated and will be removed in Diffusers v1.0.0. 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Returns: betas (`jnp.ndarray`): the betas used by the scheduler to step the model outputs cSs t|ddtjddS)NgMb?gT㥛 ?r)mathcospi) time_steprrr alpha_bars z&betas_for_alpha_bar..alpha_barr r!)rangeappendminrarray)r[max_betar!r`betasit1t2rrrbetas_for_alpha_bars  "rkc@s8eZdZUejed<ejed<ejed<eddZdS)CommonSchedulerStatealphasrgalphas_cumprodcCs|j}|jdurtj|j|jd}nG|jdkr%tj|j|j|j |jd}n4|jdkr>tj|jd|jd|j |jdd}n|jdkrLt |j |jd}n t d|jd|j j d |}tj|d d }||||d S) Nralinear scaled_linear?rsquaredcos_cap_v2zbeta_schedule z" is not implemented for scheduler g?r)axis)rmrgrn)r/ trained_betasrasarrayr! beta_schedulelinspace beta_startbeta_endnum_train_timestepsrkNotImplementedError __class__rcumprod)r-r0r/rgrmrnrrrcreates0    zCommonSchedulerState.createN)rrrrrrrMr~rrrrrls   rlr2original_samplesnoise timestepscCsR|j}||d}|}t||j}d||d}|}t||j}||fS)Nrqr )rnflattenrZrT)r2rrrrnsqrt_alpha_prodsqrt_one_minus_alpha_prodrrrget_sqrt_alpha_prods   rcCs&t||||\}}||||}|SNr)r2rrrrr noisy_samplesrrradd_noise_commonsrsamplecCs&t||||\}}||||}|Srr)r2rrrrrvelocityrrrget_velocity_commonsr)#rDr\rO dataclassesrenumrflax jax.numpynumpyrhuggingface_hub.utilsrutilsrrr get_loggerrr'rIr rr rtupleintrZfloat32rkstructrlrrrrrrrsN        (  $