314 lines
13 KiB
Python
314 lines
13 KiB
Python
# Copyright 2025 Stanford University 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 code is strongly influenced by https://github.com/pesser/pytorch_diffusion
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# and https://github.com/hojonathanho/diffusion
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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.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 DDIMSchedulerState:
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common: CommonSchedulerState
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final_alpha_cumprod: jnp.ndarray
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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(
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cls,
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common: CommonSchedulerState,
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final_alpha_cumprod: jnp.ndarray,
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init_noise_sigma: jnp.ndarray,
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timesteps: jnp.ndarray,
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):
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return cls(
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common=common,
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final_alpha_cumprod=final_alpha_cumprod,
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init_noise_sigma=init_noise_sigma,
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timesteps=timesteps,
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)
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@dataclass
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class FlaxDDIMSchedulerOutput(FlaxSchedulerOutput):
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state: DDIMSchedulerState
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class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
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"""
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Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
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diffusion probabilistic models (DDPMs) with non-Markovian guidance.
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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/2010.02502
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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 (`jnp.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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clip_sample (`bool`, default `True`):
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option to clip predicted sample between for numerical stability. The clip range is determined by
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`clip_sample_range`.
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clip_sample_range (`float`, default `1.0`):
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the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
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set_alpha_to_one (`bool`, default `True`):
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each diffusion step uses the value of alphas product at that step and at the previous one. For the final
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step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
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otherwise it uses the value of alpha at step 0.
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steps_offset (`int`, default `0`):
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An offset added to the inference steps, as required by some model families.
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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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clip_sample: bool = True,
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clip_sample_range: float = 1.0,
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set_alpha_to_one: bool = True,
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steps_offset: int = 0,
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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) -> DDIMSchedulerState:
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if common is None:
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common = CommonSchedulerState.create(self)
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# At every step in ddim, we are looking into the previous alphas_cumprod
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# For the final step, there is no previous alphas_cumprod because we are already at 0
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# `set_alpha_to_one` decides whether we set this parameter simply to one or
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# whether we use the final alpha of the "non-previous" one.
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final_alpha_cumprod = (
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jnp.array(1.0, dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
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)
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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 DDIMSchedulerState.create(
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common=common,
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final_alpha_cumprod=final_alpha_cumprod,
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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: DDIMSchedulerState, 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: DDIMSchedulerState, num_inference_steps: int, shape: Tuple = ()
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) -> DDIMSchedulerState:
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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 `FlaxDDIMScheduler` 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] + self.config.steps_offset
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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: DDIMSchedulerState, timestep, prev_timestep):
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alpha_prod_t = state.common.alphas_cumprod[timestep]
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alpha_prod_t_prev = jnp.where(
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prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod
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)
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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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variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
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return variance
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def step(
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self,
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state: DDIMSchedulerState,
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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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eta: float = 0.0,
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return_dict: bool = True,
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) -> Union[FlaxDDIMSchedulerOutput, 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 (`DDIMSchedulerState`): the `FlaxDDIMScheduler` 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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return_dict (`bool`): option for returning tuple rather than FlaxDDIMSchedulerOutput class
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Returns:
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[`FlaxDDIMSchedulerOutput`] or `tuple`: [`FlaxDDIMSchedulerOutput`] 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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if state.num_inference_steps is None:
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raise ValueError(
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"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
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)
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# See formulas (12) and (16) of DDIM paper https://huggingface.co/papers/2010.02502
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# Ideally, read DDIM paper in-detail understanding
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# Notation (<variable name> -> <name in paper>
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# - pred_noise_t -> e_theta(x_t, t)
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# - pred_original_sample -> f_theta(x_t, t) or x_0
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# - std_dev_t -> sigma_t
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# - eta -> η
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# - pred_sample_direction -> "direction pointing to x_t"
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# - pred_prev_sample -> "x_t-1"
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# 1. get previous step value (=t-1)
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prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps
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alphas_cumprod = state.common.alphas_cumprod
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final_alpha_cumprod = state.final_alpha_cumprod
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# 2. compute alphas, betas
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alpha_prod_t = alphas_cumprod[timestep]
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alpha_prod_t_prev = jnp.where(prev_timestep >= 0, alphas_cumprod[prev_timestep], final_alpha_cumprod)
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beta_prod_t = 1 - alpha_prod_t
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# 3. compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (12) from https://huggingface.co/papers/2010.02502
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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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pred_epsilon = model_output
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elif self.config.prediction_type == "sample":
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pred_original_sample = model_output
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pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
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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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pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
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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`, or"
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" `v_prediction`"
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)
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# 4. Clip or threshold "predicted x_0"
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if self.config.clip_sample:
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pred_original_sample = pred_original_sample.clip(
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-self.config.clip_sample_range, self.config.clip_sample_range
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)
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# 4. compute variance: "sigma_t(η)" -> see formula (16)
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# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
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variance = self._get_variance(state, timestep, prev_timestep)
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std_dev_t = eta * variance ** (0.5)
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# 5. compute "direction pointing to x_t" of formula (12) from https://huggingface.co/papers/2010.02502
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pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
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# 6. compute x_t without "random noise" of formula (12) from https://huggingface.co/papers/2010.02502
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prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
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if not return_dict:
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return (prev_sample, state)
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return FlaxDDIMSchedulerOutput(prev_sample=prev_sample, state=state)
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def add_noise(
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self,
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state: DDIMSchedulerState,
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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: DDIMSchedulerState,
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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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