303 lines
12 KiB
Python
303 lines
12 KiB
Python
# Copyright 2025 UC Berkeley Team and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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import flax
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import jax
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import jax.numpy as jnp
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from ..configuration_utils import ConfigMixin, register_to_config
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from .scheduling_utils_flax import (
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CommonSchedulerState,
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FlaxKarrasDiffusionSchedulers,
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FlaxSchedulerMixin,
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FlaxSchedulerOutput,
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add_noise_common,
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get_velocity_common,
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)
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@flax.struct.dataclass
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class DDPMSchedulerState:
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common: CommonSchedulerState
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# setable values
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init_noise_sigma: jnp.ndarray
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timesteps: jnp.ndarray
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num_inference_steps: Optional[int] = None
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@classmethod
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def create(cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray):
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return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps)
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@dataclass
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class FlaxDDPMSchedulerOutput(FlaxSchedulerOutput):
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state: DDPMSchedulerState
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class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
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"""
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Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
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Langevin dynamics sampling.
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[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
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function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
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[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
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[`~SchedulerMixin.from_pretrained`] functions.
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For more details, see the original paper: https://huggingface.co/papers/2006.11239
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Args:
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num_train_timesteps (`int`): number of diffusion steps used to train the model.
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beta_start (`float`): the starting `beta` value of inference.
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beta_end (`float`): the final `beta` value.
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beta_schedule (`str`):
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the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
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`linear`, `scaled_linear`, or `squaredcos_cap_v2`.
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trained_betas (`np.ndarray`, optional):
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option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
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variance_type (`str`):
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options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
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`fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
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clip_sample (`bool`, default `True`):
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option to clip predicted sample between -1 and 1 for numerical stability.
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prediction_type (`str`, default `epsilon`):
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indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`.
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`v-prediction` is not supported for this scheduler.
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dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
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the `dtype` used for params and computation.
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"""
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_compatibles = [e.name for e in FlaxKarrasDiffusionSchedulers]
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dtype: jnp.dtype
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@property
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def has_state(self):
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return True
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@register_to_config
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def __init__(
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self,
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num_train_timesteps: int = 1000,
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beta_start: float = 0.0001,
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beta_end: float = 0.02,
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beta_schedule: str = "linear",
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trained_betas: Optional[jnp.ndarray] = None,
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variance_type: str = "fixed_small",
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clip_sample: bool = True,
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prediction_type: str = "epsilon",
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dtype: jnp.dtype = jnp.float32,
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):
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self.dtype = dtype
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def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDPMSchedulerState:
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if common is None:
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common = CommonSchedulerState.create(self)
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# standard deviation of the initial noise distribution
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init_noise_sigma = jnp.array(1.0, dtype=self.dtype)
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timesteps = jnp.arange(0, self.config.num_train_timesteps).round()[::-1]
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return DDPMSchedulerState.create(
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common=common,
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init_noise_sigma=init_noise_sigma,
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timesteps=timesteps,
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)
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def scale_model_input(
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self, state: DDPMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
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) -> jnp.ndarray:
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"""
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Args:
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state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.
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sample (`jnp.ndarray`): input sample
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timestep (`int`, optional): current timestep
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Returns:
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`jnp.ndarray`: scaled input sample
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"""
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return sample
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def set_timesteps(
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self, state: DDPMSchedulerState, num_inference_steps: int, shape: Tuple = ()
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) -> DDPMSchedulerState:
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"""
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Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
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Args:
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state (`DDIMSchedulerState`):
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the `FlaxDDPMScheduler` state data class instance.
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num_inference_steps (`int`):
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the number of diffusion steps used when generating samples with a pre-trained model.
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"""
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step_ratio = self.config.num_train_timesteps // num_inference_steps
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# creates integer timesteps by multiplying by ratio
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# rounding to avoid issues when num_inference_step is power of 3
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timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round()[::-1]
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return state.replace(
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num_inference_steps=num_inference_steps,
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timesteps=timesteps,
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)
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def _get_variance(self, state: DDPMSchedulerState, t, predicted_variance=None, variance_type=None):
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alpha_prod_t = state.common.alphas_cumprod[t]
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alpha_prod_t_prev = jnp.where(t > 0, state.common.alphas_cumprod[t - 1], jnp.array(1.0, dtype=self.dtype))
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# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://huggingface.co/papers/2006.11239)
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# and sample from it to get previous sample
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# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
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variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
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if variance_type is None:
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variance_type = self.config.variance_type
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# hacks - were probably added for training stability
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if variance_type == "fixed_small":
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variance = jnp.clip(variance, a_min=1e-20)
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# for rl-diffuser https://huggingface.co/papers/2205.09991
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elif variance_type == "fixed_small_log":
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variance = jnp.log(jnp.clip(variance, a_min=1e-20))
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elif variance_type == "fixed_large":
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variance = state.common.betas[t]
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elif variance_type == "fixed_large_log":
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# Glide max_log
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variance = jnp.log(state.common.betas[t])
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elif variance_type == "learned":
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return predicted_variance
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elif variance_type == "learned_range":
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min_log = variance
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max_log = state.common.betas[t]
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frac = (predicted_variance + 1) / 2
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variance = frac * max_log + (1 - frac) * min_log
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return variance
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def step(
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self,
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state: DDPMSchedulerState,
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model_output: jnp.ndarray,
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timestep: int,
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sample: jnp.ndarray,
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key: Optional[jax.Array] = None,
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return_dict: bool = True,
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) -> Union[FlaxDDPMSchedulerOutput, Tuple]:
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"""
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Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
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process from the learned model outputs (most often the predicted noise).
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Args:
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state (`DDPMSchedulerState`): the `FlaxDDPMScheduler` state data class instance.
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model_output (`jnp.ndarray`): direct output from learned diffusion model.
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timestep (`int`): current discrete timestep in the diffusion chain.
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sample (`jnp.ndarray`):
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current instance of sample being created by diffusion process.
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key (`jax.Array`): a PRNG key.
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return_dict (`bool`): option for returning tuple rather than FlaxDDPMSchedulerOutput class
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Returns:
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[`FlaxDDPMSchedulerOutput`] or `tuple`: [`FlaxDDPMSchedulerOutput`] if `return_dict` is True, otherwise a
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`tuple`. When returning a tuple, the first element is the sample tensor.
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"""
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t = timestep
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if key is None:
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key = jax.random.key(0)
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if (
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len(model_output.shape) > 1
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and model_output.shape[1] == sample.shape[1] * 2
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and self.config.variance_type in ["learned", "learned_range"]
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):
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model_output, predicted_variance = jnp.split(model_output, sample.shape[1], axis=1)
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else:
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predicted_variance = None
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# 1. compute alphas, betas
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alpha_prod_t = state.common.alphas_cumprod[t]
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alpha_prod_t_prev = jnp.where(t > 0, state.common.alphas_cumprod[t - 1], jnp.array(1.0, dtype=self.dtype))
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beta_prod_t = 1 - alpha_prod_t
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beta_prod_t_prev = 1 - alpha_prod_t_prev
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# 2. compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (15) from https://huggingface.co/papers/2006.11239
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if self.config.prediction_type == "epsilon":
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pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
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elif self.config.prediction_type == "sample":
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pred_original_sample = model_output
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elif self.config.prediction_type == "v_prediction":
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pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
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else:
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raise ValueError(
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f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
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" for the FlaxDDPMScheduler."
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)
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# 3. Clip "predicted x_0"
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if self.config.clip_sample:
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pred_original_sample = jnp.clip(pred_original_sample, -1, 1)
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# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
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# See formula (7) from https://huggingface.co/papers/2006.11239
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pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * state.common.betas[t]) / beta_prod_t
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current_sample_coeff = state.common.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
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# 5. Compute predicted previous sample µ_t
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# See formula (7) from https://huggingface.co/papers/2006.11239
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pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
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# 6. Add noise
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def random_variance():
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split_key = jax.random.split(key, num=1)[0]
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noise = jax.random.normal(split_key, shape=model_output.shape, dtype=self.dtype)
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return (self._get_variance(state, t, predicted_variance=predicted_variance) ** 0.5) * noise
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variance = jnp.where(t > 0, random_variance(), jnp.zeros(model_output.shape, dtype=self.dtype))
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pred_prev_sample = pred_prev_sample + variance
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if not return_dict:
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return (pred_prev_sample, state)
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return FlaxDDPMSchedulerOutput(prev_sample=pred_prev_sample, state=state)
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def add_noise(
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self,
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state: DDPMSchedulerState,
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original_samples: jnp.ndarray,
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noise: jnp.ndarray,
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timesteps: jnp.ndarray,
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) -> jnp.ndarray:
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return add_noise_common(state.common, original_samples, noise, timesteps)
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def get_velocity(
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self,
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state: DDPMSchedulerState,
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sample: jnp.ndarray,
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noise: jnp.ndarray,
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timesteps: jnp.ndarray,
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) -> jnp.ndarray:
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return get_velocity_common(state.common, sample, noise, timesteps)
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def __len__(self):
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return self.config.num_train_timesteps
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