281 lines
12 KiB
Python
281 lines
12 KiB
Python
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# Copyright 2025 Google Brain 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/yang-song/score_sde_pytorch
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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 jax import random
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from ..configuration_utils import ConfigMixin, register_to_config
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from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
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@flax.struct.dataclass
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class ScoreSdeVeSchedulerState:
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# setable values
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timesteps: Optional[jnp.ndarray] = None
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discrete_sigmas: Optional[jnp.ndarray] = None
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sigmas: Optional[jnp.ndarray] = None
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@classmethod
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def create(cls):
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return cls()
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@dataclass
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class FlaxSdeVeOutput(FlaxSchedulerOutput):
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"""
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Output class for the ScoreSdeVeScheduler's step function output.
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Args:
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state (`ScoreSdeVeSchedulerState`):
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prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
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denoising loop.
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prev_sample_mean (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
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Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps.
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"""
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state: ScoreSdeVeSchedulerState
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prev_sample: jnp.ndarray
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prev_sample_mean: Optional[jnp.ndarray] = None
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class FlaxScoreSdeVeScheduler(FlaxSchedulerMixin, ConfigMixin):
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"""
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The variance exploding stochastic differential equation (SDE) scheduler.
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For more information, see the original paper: https://huggingface.co/papers/2011.13456
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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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Args:
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num_train_timesteps (`int`): number of diffusion steps used to train the model.
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snr (`float`):
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coefficient weighting the step from the model_output sample (from the network) to the random noise.
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sigma_min (`float`):
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initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the
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distribution of the data.
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sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model.
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sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to
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epsilon.
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correct_steps (`int`): number of correction steps performed on a produced sample.
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"""
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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 = 2000,
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snr: float = 0.15,
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sigma_min: float = 0.01,
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sigma_max: float = 1348.0,
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sampling_eps: float = 1e-5,
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correct_steps: int = 1,
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):
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pass
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def create_state(self):
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state = ScoreSdeVeSchedulerState.create()
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return self.set_sigmas(
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state,
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self.config.num_train_timesteps,
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self.config.sigma_min,
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self.config.sigma_max,
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self.config.sampling_eps,
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)
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def set_timesteps(
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self, state: ScoreSdeVeSchedulerState, num_inference_steps: int, shape: Tuple = (), sampling_eps: float = None
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) -> ScoreSdeVeSchedulerState:
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"""
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Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
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Args:
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state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` 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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sampling_eps (`float`, optional):
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final timestep value (overrides value given at Scheduler instantiation).
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"""
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sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
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timesteps = jnp.linspace(1, sampling_eps, num_inference_steps)
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return state.replace(timesteps=timesteps)
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def set_sigmas(
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self,
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state: ScoreSdeVeSchedulerState,
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num_inference_steps: int,
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sigma_min: float = None,
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sigma_max: float = None,
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sampling_eps: float = None,
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) -> ScoreSdeVeSchedulerState:
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"""
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Sets the noise scales used for the diffusion chain. Supporting function to be run before inference.
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The sigmas control the weight of the `drift` and `diffusion` components of sample update.
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Args:
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state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` 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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sigma_min (`float`, optional):
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initial noise scale value (overrides value given at Scheduler instantiation).
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sigma_max (`float`, optional):
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final noise scale value (overrides value given at Scheduler instantiation).
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sampling_eps (`float`, optional):
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final timestep value (overrides value given at Scheduler instantiation).
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"""
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sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min
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sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max
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sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
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if state.timesteps is None:
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state = self.set_timesteps(state, num_inference_steps, sampling_eps)
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discrete_sigmas = jnp.exp(jnp.linspace(jnp.log(sigma_min), jnp.log(sigma_max), num_inference_steps))
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sigmas = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])
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return state.replace(discrete_sigmas=discrete_sigmas, sigmas=sigmas)
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def get_adjacent_sigma(self, state, timesteps, t):
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return jnp.where(timesteps == 0, jnp.zeros_like(t), state.discrete_sigmas[timesteps - 1])
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def step_pred(
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self,
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state: ScoreSdeVeSchedulerState,
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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: jax.Array,
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return_dict: bool = True,
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) -> Union[FlaxSdeVeOutput, 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 (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` 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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generator: random number generator.
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return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class
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Returns:
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[`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
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returning a tuple, the first element is the sample tensor.
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"""
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if state.timesteps is None:
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raise ValueError(
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"`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
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)
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timestep = timestep * jnp.ones(
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sample.shape[0],
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)
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timesteps = (timestep * (len(state.timesteps) - 1)).long()
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sigma = state.discrete_sigmas[timesteps]
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adjacent_sigma = self.get_adjacent_sigma(state, timesteps, timestep)
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drift = jnp.zeros_like(sample)
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diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5
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# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
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# also equation 47 shows the analog from SDE models to ancestral sampling methods
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diffusion = diffusion.flatten()
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diffusion = broadcast_to_shape_from_left(diffusion, sample.shape)
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drift = drift - diffusion**2 * model_output
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# equation 6: sample noise for the diffusion term of
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key = random.split(key, num=1)
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noise = random.normal(key=key, shape=sample.shape)
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prev_sample_mean = sample - drift # subtract because `dt` is a small negative timestep
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# TODO is the variable diffusion the correct scaling term for the noise?
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prev_sample = prev_sample_mean + diffusion * noise # add impact of diffusion field g
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if not return_dict:
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return (prev_sample, prev_sample_mean, state)
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return FlaxSdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean, state=state)
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def step_correct(
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self,
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state: ScoreSdeVeSchedulerState,
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model_output: jnp.ndarray,
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sample: jnp.ndarray,
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key: jax.Array,
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return_dict: bool = True,
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) -> Union[FlaxSdeVeOutput, Tuple]:
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"""
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Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
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after making the prediction for the previous timestep.
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Args:
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state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
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model_output (`jnp.ndarray`): direct output from learned diffusion model.
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sample (`jnp.ndarray`):
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current instance of sample being created by diffusion process.
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generator: random number generator.
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return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class
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Returns:
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[`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
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returning a tuple, the first element is the sample tensor.
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"""
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if state.timesteps is None:
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raise ValueError(
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"`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
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)
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# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
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# sample noise for correction
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key = random.split(key, num=1)
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noise = random.normal(key=key, shape=sample.shape)
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# compute step size from the model_output, the noise, and the snr
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grad_norm = jnp.linalg.norm(model_output)
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noise_norm = jnp.linalg.norm(noise)
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step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
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step_size = step_size * jnp.ones(sample.shape[0])
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# compute corrected sample: model_output term and noise term
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step_size = step_size.flatten()
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step_size = broadcast_to_shape_from_left(step_size, sample.shape)
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prev_sample_mean = sample + step_size * model_output
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prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
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if not return_dict:
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return (prev_sample, state)
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return FlaxSdeVeOutput(prev_sample=prev_sample, state=state)
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def __len__(self):
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return self.config.num_train_timesteps
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