375 lines
17 KiB
Python
375 lines
17 KiB
Python
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# Copyright 2025 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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import math
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from dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import torch
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.schedulers.scheduling_utils import SchedulerMixin
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from diffusers.utils import BaseOutput, deprecate
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@dataclass
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# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
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class DDIMSchedulerOutput(BaseOutput):
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"""
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Output class for the scheduler's `step` function output.
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Args:
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prev_sample (`torch.Tensor` 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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pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
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The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
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`pred_original_sample` can be used to preview progress or for guidance.
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"""
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prev_sample: torch.Tensor
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pred_original_sample: Optional[torch.Tensor] = None
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# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
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def betas_for_alpha_bar(
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num_diffusion_timesteps,
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max_beta=0.999,
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alpha_transform_type="cosine",
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):
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"""
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Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
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(1-beta) over time from t = [0,1].
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Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
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to that part of the diffusion process.
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Args:
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num_diffusion_timesteps (`int`): the number of betas to produce.
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max_beta (`float`): the maximum beta to use; use values lower than 1 to
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prevent singularities.
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alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
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Choose from `cosine` or `exp`
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Returns:
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betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
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"""
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if alpha_transform_type == "cosine":
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def alpha_bar_fn(t):
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return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
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elif alpha_transform_type == "exp":
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def alpha_bar_fn(t):
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return math.exp(t * -12.0)
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else:
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raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}")
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betas = []
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for i in range(num_diffusion_timesteps):
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t1 = i / num_diffusion_timesteps
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t2 = (i + 1) / num_diffusion_timesteps
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betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
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return torch.tensor(betas, dtype=torch.float32)
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# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
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def rescale_zero_terminal_snr(betas):
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"""
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Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
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Args:
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betas (`torch.Tensor`):
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the betas that the scheduler is being initialized with.
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Returns:
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`torch.Tensor`: rescaled betas with zero terminal SNR
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"""
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# Convert betas to alphas_bar_sqrt
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alphas = 1.0 - betas
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alphas_cumprod = torch.cumprod(alphas, dim=0)
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alphas_bar_sqrt = alphas_cumprod.sqrt()
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# Store old values.
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alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
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alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
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# Shift so the last timestep is zero.
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alphas_bar_sqrt -= alphas_bar_sqrt_T
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# Scale so the first timestep is back to the old value.
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alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
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# Convert alphas_bar_sqrt to betas
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alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
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alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod
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alphas = torch.cat([alphas_bar[0:1], alphas])
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betas = 1 - alphas
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return betas
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class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
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"""
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`DDIMInverseScheduler` is the reverse scheduler of [`DDIMScheduler`].
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This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
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methods the library implements for all schedulers such as loading and saving.
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Args:
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num_train_timesteps (`int`, defaults to 1000):
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The number of diffusion steps to train the model.
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beta_start (`float`, defaults to 0.0001):
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The starting `beta` value of inference.
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beta_end (`float`, defaults to 0.02):
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The final `beta` value.
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beta_schedule (`str`, defaults to `"linear"`):
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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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Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
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clip_sample (`bool`, defaults to `True`):
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Clip the predicted sample for numerical stability.
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clip_sample_range (`float`, defaults to 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`, defaults to `True`):
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Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
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there is no previous alpha. When this option is `True` the previous alpha product is fixed to 0, otherwise
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it uses the alpha value at step `num_train_timesteps - 1`.
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steps_offset (`int`, defaults to 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`, defaults to `epsilon`, *optional*):
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Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
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`sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
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Video](https://imagen.research.google/video/paper.pdf) paper).
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timestep_spacing (`str`, defaults to `"leading"`):
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The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
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Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
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rescale_betas_zero_snr (`bool`, defaults to `False`):
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Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
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dark samples instead of limiting it to samples with medium brightness. Loosely related to
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[`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
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"""
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order = 1
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ignore_for_config = ["kwargs"]
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_deprecated_kwargs = ["set_alpha_to_zero"]
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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[Union[np.ndarray, List[float]]] = None,
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clip_sample: bool = True,
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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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clip_sample_range: float = 1.0,
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timestep_spacing: str = "leading",
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rescale_betas_zero_snr: bool = False,
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**kwargs,
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):
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if kwargs.get("set_alpha_to_zero", None) is not None:
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deprecation_message = (
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"The `set_alpha_to_zero` argument is deprecated. Please use `set_alpha_to_one` instead."
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)
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deprecate("set_alpha_to_zero", "1.0.0", deprecation_message, standard_warn=False)
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set_alpha_to_one = kwargs["set_alpha_to_zero"]
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if trained_betas is not None:
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self.betas = torch.tensor(trained_betas, dtype=torch.float32)
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elif beta_schedule == "linear":
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self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
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elif beta_schedule == "scaled_linear":
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# this schedule is very specific to the latent diffusion model.
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self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
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elif beta_schedule == "squaredcos_cap_v2":
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# Glide cosine schedule
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self.betas = betas_for_alpha_bar(num_train_timesteps)
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else:
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raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
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# Rescale for zero SNR
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if rescale_betas_zero_snr:
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self.betas = rescale_zero_terminal_snr(self.betas)
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self.alphas = 1.0 - self.betas
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self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
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# At every step in inverted ddim, we are looking into the next alphas_cumprod
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# For the initial step, there is no current alphas_cumprod, and the index is out of bounds
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# `set_alpha_to_one` decides whether we set this parameter simply to one
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# in this case, self.step() just output the predicted noise
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# or whether we use the initial alpha used in training the diffusion model.
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self.initial_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
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# standard deviation of the initial noise distribution
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self.init_noise_sigma = 1.0
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# setable values
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self.num_inference_steps = None
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self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps).copy().astype(np.int64))
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# Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.scale_model_input
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def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor:
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"""
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Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
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current timestep.
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Args:
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sample (`torch.Tensor`):
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The input sample.
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timestep (`int`, *optional*):
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The current timestep in the diffusion chain.
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Returns:
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`torch.Tensor`:
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A scaled input sample.
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"""
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return sample
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def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
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"""
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Sets the discrete timesteps used for the diffusion chain (to be run before inference).
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Args:
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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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if num_inference_steps > self.config.num_train_timesteps:
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raise ValueError(
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f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
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f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
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f" maximal {self.config.num_train_timesteps} timesteps."
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)
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self.num_inference_steps = num_inference_steps
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# "leading" and "trailing" corresponds to annotation of Table 2. of https://huggingface.co/papers/2305.08891
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if self.config.timestep_spacing == "leading":
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step_ratio = self.config.num_train_timesteps // self.num_inference_steps
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# creates integer timesteps by multiplying by ratio
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# casting to int to avoid issues when num_inference_step is power of 3
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timesteps = (np.arange(0, num_inference_steps) * step_ratio).round().copy().astype(np.int64)
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timesteps += self.config.steps_offset
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elif self.config.timestep_spacing == "trailing":
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step_ratio = self.config.num_train_timesteps / self.num_inference_steps
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# creates integer timesteps by multiplying by ratio
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# casting to int to avoid issues when num_inference_step is power of 3
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timesteps = np.round(np.arange(self.config.num_train_timesteps, 0, -step_ratio)[::-1]).astype(np.int64)
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timesteps -= 1
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else:
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raise ValueError(
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f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'leading' or 'trailing'."
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)
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self.timesteps = torch.from_numpy(timesteps).to(device)
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def step(
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self,
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model_output: torch.Tensor,
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timestep: int,
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sample: torch.Tensor,
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return_dict: bool = True,
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) -> Union[DDIMSchedulerOutput, Tuple]:
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"""
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Predict the sample from the previous timestep by reversing the SDE. This function propagates 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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model_output (`torch.Tensor`):
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The direct output from learned diffusion model.
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timestep (`float`):
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The current discrete timestep in the diffusion chain.
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sample (`torch.Tensor`):
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A current instance of a sample created by the diffusion process.
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eta (`float`):
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The weight of noise for added noise in diffusion step.
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use_clipped_model_output (`bool`, defaults to `False`):
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If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
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because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
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clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
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`use_clipped_model_output` has no effect.
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variance_noise (`torch.Tensor`):
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Alternative to generating noise with `generator` by directly providing the noise for the variance
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itself. Useful for methods such as [`CycleDiffusion`].
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] or
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`tuple`.
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Returns:
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[`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] or `tuple`:
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If return_dict is `True`, [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] is
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returned, otherwise a tuple is returned where the first element is the sample tensor.
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"""
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# 1. get previous step value (=t+1)
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prev_timestep = timestep
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timestep = min(
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timestep - self.config.num_train_timesteps // self.num_inference_steps, self.config.num_train_timesteps - 1
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)
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# 2. compute alphas, betas
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# change original implementation to exactly match noise levels for analogous forward process
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alpha_prod_t = self.alphas_cumprod[timestep] if timestep >= 0 else self.initial_alpha_cumprod
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alpha_prod_t_prev = self.alphas_cumprod[prev_timestep]
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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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||
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pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
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||
|
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else:
|
||
|
|
raise ValueError(
|
||
|
|
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
|
||
|
|
" `v_prediction`"
|
||
|
|
)
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||
|
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|
||
|
|
# 4. Clip or threshold "predicted x_0"
|
||
|
|
if self.config.clip_sample:
|
||
|
|
pred_original_sample = pred_original_sample.clamp(
|
||
|
|
-self.config.clip_sample_range, self.config.clip_sample_range
|
||
|
|
)
|
||
|
|
|
||
|
|
# 5. compute "direction pointing to x_t" of formula (12) from https://huggingface.co/papers/2010.02502
|
||
|
|
pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * pred_epsilon
|
||
|
|
|
||
|
|
# 6. compute x_t without "random noise" of formula (12) from https://huggingface.co/papers/2010.02502
|
||
|
|
prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (prev_sample, pred_original_sample)
|
||
|
|
return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.config.num_train_timesteps
|