schedulers/scheduling_flow_match_euler_discrete.py

# Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput, logging
from diffusers.schedulers.scheduling_utils import SchedulerMixin


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
    """
    Output class for the scheduler's `step` function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
    """

    prev_sample: torch.FloatTensor


class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
    """
    Euler scheduler.

    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
    methods the library implements for all schedulers such as loading and saving.

    Args:
        num_train_timesteps (`int`, defaults to 1000):
            The number of diffusion steps to train the model.
        timestep_spacing (`str`, defaults to `"linspace"`):
            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
        shift (`float`, defaults to 1.0):
            The shift value for the timestep schedule.
    """

    _compatibles = []
    order = 1

    @register_to_config
    def __init__(
        self,
        num_train_timesteps: int = 1000,
        shift: float = 1.0,
        use_dynamic_shifting=False,
        base_shift: Optional[float] = 0.5,
        max_shift: Optional[float] = 1.15,
        base_image_seq_len: Optional[int] = 256,
        max_image_seq_len: Optional[int] = 4096,
    ):
        timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
        timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)

        sigmas = timesteps / num_train_timesteps
        if not use_dynamic_shifting:
            # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)

        self.timesteps = sigmas * num_train_timesteps

        self._step_index = None
        self._begin_index = None

        self.sigmas = sigmas.to("cpu")  # to avoid too much CPU/GPU communication
        self.sigma_min = self.sigmas[-1].item()
        self.sigma_max = self.sigmas[0].item()

    @property
    def step_index(self):
        """
        The index counter for current timestep. It will increase 1 after each scheduler step.
        """
        return self._step_index

    @property
    def begin_index(self):
        """
        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
        """
        return self._begin_index

    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
    def set_begin_index(self, begin_index: int = 0):
        """
        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.

        Args:
            begin_index (`int`):
                The begin index for the scheduler.
        """
        self._begin_index = begin_index

    def scale_noise(
        self,
        sample: torch.FloatTensor,
        timestep: Union[float, torch.FloatTensor],
        noise: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        """
        Forward process in flow-matching

        Args:
            sample (`torch.FloatTensor`):
                The input sample.
            timestep (`int`, *optional*):
                The current timestep in the diffusion chain.

        Returns:
            `torch.FloatTensor`:
                A scaled input sample.
        """
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype)

        if sample.device.type == "mps" and torch.is_floating_point(timestep):
            # mps does not support float64
            schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)
            timestep = timestep.to(sample.device, dtype=torch.float32)
        else:
            schedule_timesteps = self.timesteps.to(sample.device)
            timestep = timestep.to(sample.device)

        # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
        if self.begin_index is None:
            step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep]
        elif self.step_index is not None:
            # add_noise is called after first denoising step (for inpainting)
            step_indices = [self.step_index] * timestep.shape[0]
        else:
            # add noise is called before first denoising step to create initial latent(img2img)
            step_indices = [self.begin_index] * timestep.shape[0]

        sigma = sigmas[step_indices].flatten()
        while len(sigma.shape) < len(sample.shape):
            sigma = sigma.unsqueeze(-1)

        sample = sigma * noise + (1.0 - sigma) * sample

        return sample

    def _sigma_to_t(self, sigma):
        return sigma * self.config.num_train_timesteps

    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)

    def set_timesteps(
        self,
        num_inference_steps: int = None,
        device: Union[str, torch.device] = None,
        sigmas: Optional[List[float]] = None,
        mu: Optional[float] = None,
    ):
        """
        Sets the discrete timesteps used for the diffusion chain (to be run before inference).

        Args:
            num_inference_steps (`int`):
                The number of diffusion steps used when generating samples with a pre-trained model.
            device (`str` or `torch.device`, *optional*):
                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        """

        if self.config.use_dynamic_shifting and mu is None:
            raise ValueError(" you have a pass a value for `mu` when `use_dynamic_shifting` is set to be `True`")

        if sigmas is None:
            self.num_inference_steps = num_inference_steps
            timesteps = np.linspace(
                self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps
            )

            sigmas = timesteps / self.config.num_train_timesteps

        if self.config.use_dynamic_shifting:
            sigmas = self.time_shift(mu, 1.0, sigmas)
        else:
            sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)

        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
        timesteps = sigmas * self.config.num_train_timesteps

        self.timesteps = timesteps.to(device=device)
        self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])

        self._step_index = None
        self._begin_index = None

    def index_for_timestep(self, timestep, schedule_timesteps=None):
        if schedule_timesteps is None:
            schedule_timesteps = self.timesteps

        indices = (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        pos = 1 if len(indices) > 1 else 0

        return indices[pos].item()

    def _init_step_index(self, timestep):
        if self.begin_index is None:
            if isinstance(timestep, torch.Tensor):
                timestep = timestep.to(self.timesteps.device)
            self._step_index = self.index_for_timestep(timestep)
        else:
            self._step_index = self._begin_index

    def step(
        self,
        model_output: torch.FloatTensor,
        timestep: Union[float, torch.FloatTensor],
        sample: torch.FloatTensor,
        s_churn: float = 0.0,
        s_tmin: float = 0.0,
        s_tmax: float = float("inf"),
        s_noise: float = 1.0,
        generator: Optional[torch.Generator] = None,
        return_dict: bool = True,
        omega: Union[float, np.array] = 0.0
    ) -> Union[FlowMatchEulerDiscreteSchedulerOutput, Tuple]:
        """
        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
        process from the learned model outputs (most often the predicted noise).

        Args:
            model_output (`torch.FloatTensor`):
                The direct output from learned diffusion model.
            timestep (`float`):
                The current discrete timestep in the diffusion chain.
            sample (`torch.FloatTensor`):
                A current instance of a sample created by the diffusion process.
            s_churn (`float`):
            s_tmin  (`float`):
            s_tmax  (`float`):
            s_noise (`float`, defaults to 1.0):
                Scaling factor for noise added to the sample.
            generator (`torch.Generator`, *optional*):
                A random number generator.
            return_dict (`bool`):
                Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or
                tuple.

        Returns:
            [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
                If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
                returned, otherwise a tuple is returned where the first element is the sample tensor.
        """

        def logistic_function(x, L=0.9, U=1.1, x_0=0.0, k=1):
            # L = Lower bound
            # U = Upper bound
            # x_0 = Midpoint (x corresponding to y = 1.0)
            # k = Steepness, can adjust based on preference

            if isinstance(x, torch.Tensor):
                device_ = x.device
                x = x.to(torch.float).cpu().numpy()

            new_x = L + (U - L) / (1 + np.exp(-k * (x - x_0)))

            if isinstance(new_x, np.ndarray):
                new_x = torch.from_numpy(new_x).to(device_)
            return new_x

        self.omega_bef_rescale = omega
        omega = logistic_function(omega, k=0.1)
        self.omega_aft_rescale = omega

        if (
            isinstance(timestep, int)
            or isinstance(timestep, torch.IntTensor)
            or isinstance(timestep, torch.LongTensor)
        ):
            raise ValueError(
                (
                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                    " one of the `scheduler.timesteps` as a timestep."
                ),
            )

        if self.step_index is None:
            self._init_step_index(timestep)

        # Upcast to avoid precision issues when computing prev_sample
        sample = sample.to(torch.float32)

        sigma = self.sigmas[self.step_index]
        sigma_next = self.sigmas[self.step_index + 1]

        ## --
        ## mean shift 1
        dx = (sigma_next - sigma) * model_output
        m = dx.mean()
        # print(dx.shape) # torch.Size([1, 16, 128, 128])
        # print(f'm: {m}') # m: -0.0014209747314453125
        # raise NotImplementedError
        dx_ = (dx - m) * omega + m
        prev_sample = sample + dx_

        # ## --
        # ## mean shift 2
        # m = model_output.mean()
        # model_output_ = (model_output - m) * omega + m
        # prev_sample = sample + (sigma_next - sigma) * model_output_

        # ## --
        # ## original
        # prev_sample = sample + (sigma_next - sigma) * model_output * omega

        # ## --
        # ## spatial mean 1
        # dx = (sigma_next - sigma) * model_output
        # m = dx.mean(dim=(0, 1), keepdim=True)
        # # print(dx.shape) # torch.Size([1, 16, 128, 128])
        # # print(m.shape) # torch.Size([1, 1, 128, 128])
        # # raise NotImplementedError
        # dx_ = (dx - m) * omega + m
        # prev_sample = sample + dx_

        # ## --
        # ## spatial mean 2
        # m = model_output.mean(dim=(0, 1), keepdim=True)
        # model_output_ = (model_output - m) * omega + m
        # prev_sample = sample + (sigma_next - sigma) * model_output_

        # ## --
        # ## channel mean 1
        # m = model_output.mean(dim=(2, 3), keepdim=True)
        # # print(m.shape) # torch.Size([1, 16, 1, 1])
        # model_output_ = (model_output - m) * omega + m
        # prev_sample = sample + (sigma_next - sigma) * model_output_

        # ## --
        # ## channel mean 2
        # dx = (sigma_next - sigma) * model_output 
        # m = dx.mean(dim=(2, 3), keepdim=True)
        # # print(m.shape) # torch.Size([1, 16, 1, 1])
        # dx_ = (dx - m) * omega + m
        # prev_sample = sample + dx_

        # ## --
        # ## keep sample mean
        # m_tgt = sample.mean()
        # prev_sample_ = sample + (sigma_next - sigma) * model_output * omega
        # m_src = prev_sample_.mean()
        # prev_sample = prev_sample_ - m_src + m_tgt

        # ## --
        # ## test
        # # print(sample.mean())
        # prev_sample = sample + (sigma_next - sigma) * model_output * omega
        # # raise NotImplementedError

        # Cast sample back to model compatible dtype
        prev_sample = prev_sample.to(model_output.dtype)

        # upon completion increase step index by one
        self._step_index += 1

        if not return_dict:
            return (prev_sample,)

        return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample)

    def __len__(self):
        return self.config.num_train_timesteps