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diff --git a/schedulers/scheduling_euler_a.py b/schedulers/scheduling_euler_a.py
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+
+
+import math
+import warnings
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
+
+
+'''
+helper functions:   append_zero(),
+                    t_to_sigma(),
+                    get_sigmas(),
+                    append_dims(),
+                    CFGDenoiserForward(),
+                    get_scalings(),
+                    DSsigma_to_t(),
+                    DiscreteEpsDDPMDenoiserForward(),
+                    to_d(),
+                    get_ancestral_step()
+need cleaning 
+'''
+
+
+def append_zero(x):
+    return torch.cat([x, x.new_zeros([1])])
+
+
+def t_to_sigma(t, sigmas):
+    t = t.float()
+    low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
+    return (1 - w) * sigmas[low_idx] + w * sigmas[high_idx]
+
+
+def get_sigmas(sigmas, n=None):
+    if n is None:
+        return append_zero(sigmas.flip(0))
+    t_max = len(sigmas) - 1  # = 999
+    t = torch.linspace(t_max, 0, n, device=sigmas.device)
+    # t = torch.linspace(t_max, 0, n, device=sigmas.device)
+    return append_zero(t_to_sigma(t, sigmas))
+
+# from k_samplers utils.py
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+    return x[(...,) + (None,) * dims_to_append]
+
+
+def CFGDenoiserForward(Unet, x_in, sigma_in, cond_in, cond_scale, DSsigmas=None):
+    # x_in = torch.cat([x] * 2)#A# concat the latent
+    # sigma_in = torch.cat([sigma] * 2) #A# concat sigma
+    # cond_in = torch.cat([uncond, cond])
+    # uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
+    # uncond, cond = DiscreteEpsDDPMDenoiserForward(Unet,x_in, sigma_in,DSsigmas=DSsigmas, cond=cond_in).chunk(2)
+    # return uncond + (cond - uncond) * cond_scale
+    noise_pred = DiscreteEpsDDPMDenoiserForward(Unet, x_in, sigma_in, DSsigmas=DSsigmas, cond=cond_in)
+    return noise_pred
+
+# from k_samplers sampling.py
+
+
+def to_d(x, sigma, denoised):
+    """Converts a denoiser output to a Karras ODE derivative."""
+    return (x - denoised) / append_dims(sigma.to(denoised.device), x.ndim)
+
+
+def get_scalings(sigma):
+    sigma_data = 1.
+    c_out = -sigma
+    c_in = 1 / (sigma ** 2 + sigma_data ** 2) ** 0.5
+    return c_out, c_in
+
+# DiscreteSchedule DS
+
+
+def DSsigma_to_t(sigma, quantize=None, DSsigmas=None):
+    # quantize = self.quantize if quantize is None else quantize
+    quantize = False
+    dists = torch.abs(sigma - DSsigmas[:, None])
+    if quantize:
+        return torch.argmin(dists, dim=0).view(sigma.shape)
+    low_idx, high_idx = torch.sort(torch.topk(dists, dim=0, k=2, largest=False).indices, dim=0)[0]
+    low, high = DSsigmas[low_idx], DSsigmas[high_idx]
+    w = (low - sigma) / (low - high)
+    w = w.clamp(0, 1)
+    t = (1 - w) * low_idx + w * high_idx
+    return t.view(sigma.shape)
+
+
+def DiscreteEpsDDPMDenoiserForward(Unet, input, sigma, DSsigmas=None, **kwargs):
+    sigma = sigma.to(Unet.device)
+    DSsigmas = DSsigmas.to(Unet.device)
+    c_out, c_in = [append_dims(x, input.ndim) for x in get_scalings(sigma)]
+    # ??? what is eps?
+    # eps = CVDget_eps(Unet,input * c_in, DSsigma_to_t(sigma), **kwargs)
+    eps = Unet(input * c_in, DSsigma_to_t(sigma, DSsigmas=DSsigmas),
+               encoder_hidden_states=kwargs['cond']).sample
+    return input + eps * c_out
+
+
+# from k_samplers sampling.py
+def get_ancestral_step(sigma_from, sigma_to):
+    """Calculates the noise level (sigma_down) to step down to and the amount
+    of noise to add (sigma_up) when doing an ancestral sampling step."""
+    sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5
+    sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
+    return sigma_down, sigma_up
+
+
+'''
+Euler Ancestral Scheduler
+'''
+
+
+class EulerAScheduler(SchedulerMixin, ConfigMixin):
+    """
+    Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
+    the VE column of Table 1 from [1] for reference.
+
+    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
+    https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
+    differential equations." https://arxiv.org/abs/2011.13456
+
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functions.
+
+    For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of
+    Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the
+    optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper.
+
+    Args:
+        sigma_min (`float`): minimum noise magnitude
+        sigma_max (`float`): maximum noise magnitude
+        s_noise (`float`): the amount of additional noise to counteract loss of detail during sampling.
+            A reasonable range is [1.000, 1.011].
+        s_churn (`float`): the parameter controlling the overall amount of stochasticity.
+            A reasonable range is [0, 100].
+        s_min (`float`): the start value of the sigma range where we add noise (enable stochasticity).
+            A reasonable range is [0, 10].
+        s_max (`float`): the end value of the sigma range where we add noise.
+            A reasonable range is [0.2, 80].
+
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.0001,
+        beta_end: float = 0.02,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[np.ndarray] = None,
+        clip_sample: bool = True,
+        set_alpha_to_one: bool = True,
+        steps_offset: int = 0,
+    ):
+        if trained_betas is not None:
+            self.betas = torch.from_numpy(trained_betas)
+        if beta_schedule == "linear":
+            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+        elif beta_schedule == "scaled_linear":
+            # this schedule is very specific to the latent diffusion model.
+            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
+        else:
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        # At every step in ddim, we are looking into the previous alphas_cumprod
+        # For the final step, there is no previous alphas_cumprod because we are already at 0
+        # `set_alpha_to_one` decides whether we set this parameter simply to one or
+        # whether we use the final alpha of the "non-previous" one.
+        self.final_alpha_cumprod = torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
+
+        # setable values
+        self.num_inference_steps = None
+        self.timesteps = np.arange(0, num_train_timesteps)[::-1]
+
+    # A# take number of steps as input
+    # A# store 1) number of steps 2) timesteps 3) schedule
+
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, **kwargs):
+        """
+        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
+
+        Args:
+            num_inference_steps (`int`):
+                the number of diffusion steps used when generating samples with a pre-trained model.
+        """
+
+        # offset = self.config.steps_offset
+
+        # if "offset" in kwargs:
+        #     warnings.warn(
+        #         "`offset` is deprecated as an input argument to `set_timesteps` and will be removed in v0.4.0."
+        #         " Please pass `steps_offset` to `__init__` instead.",
+        #         DeprecationWarning,
+        #     )
+
+        #     offset = kwargs["offset"]
+
+        self.num_inference_steps = num_inference_steps
+        self.DSsigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
+        self.sigmas = get_sigmas(self.DSsigmas, self.num_inference_steps).to(device=device)
+        self.timesteps = self.sigmas
+
+    def add_noise_to_input(
+        self, sample: torch.FloatTensor, sigma: float, generator: Optional[torch.Generator] = None
+    ) -> Tuple[torch.FloatTensor, float]:
+        """
+        Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a
+        higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.
+
+        TODO Args:
+        """
+        if self.config.s_min <= sigma <= self.config.s_max:
+            gamma = min(self.config.s_churn / self.num_inference_steps, 2**0.5 - 1)
+        else:
+            gamma = 0
+
+        # sample eps ~ N(0, S_noise^2 * I)
+        eps = self.config.s_noise * torch.randn(sample.shape, generator=generator).to(sample.device)
+        sigma_hat = sigma + gamma * sigma
+        sample_hat = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
+
+        return sample_hat, sigma_hat
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: torch.IntTensor,
+        timestep_prev: torch.IntTensor,
+        sample: torch.FloatTensor,
+        generator: None,
+        # ,sigma_hat: float,
+        #  sigma_prev: float,
+        # sample_hat: torch.FloatTensor,
+        return_dict: bool = True,
+    ) -> Union[SchedulerOutput, Tuple]:
+        """
+        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+            sigma_hat (`float`): TODO
+            sigma_prev (`float`): TODO
+            sample_hat (`torch.FloatTensor`): TODO
+            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+
+            EulerAOutput: updated sample in the diffusion chain and derivative (TODO double check).
+        Returns:
+            [`~schedulers.scheduling_karras_ve.EulerAOutput`] or `tuple`:
+            [`~schedulers.scheduling_karras_ve.EulerAOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+
+        """
+        latents = sample
+        sigma_down, sigma_up = get_ancestral_step(timestep, timestep_prev)
+
+        # if callback is not None:
+        #     callback({'x': latents, 'i': i, 'sigma': timestep, 'sigma_hat': timestep, 'denoised': model_output})
+        d = to_d(latents, timestep, model_output)
+        # Euler method
+        dt = sigma_down - timestep
+        latents = latents + d * dt
+        latents = latents + torch.randn(latents.shape, layout=latents.layout, device=latents.device,
+                                        generator=generator) * sigma_up
+        return SchedulerOutput(prev_sample=latents)
+
+    def step_correct(
+        self,
+        model_output: torch.FloatTensor,
+        sigma_hat: float,
+        sigma_prev: float,
+        sample_hat: torch.FloatTensor,
+        sample_prev: torch.FloatTensor,
+        derivative: torch.FloatTensor,
+        generator: None,
+        return_dict: bool = True,
+    ) -> Union[SchedulerOutput, Tuple]:
+        """
+        Correct the predicted sample based on the output model_output of the network. TODO complete description
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+            sigma_hat (`float`): TODO
+            sigma_prev (`float`): TODO
+            sample_hat (`torch.FloatTensor`): TODO
+            sample_prev (`torch.FloatTensor`): TODO
+            derivative (`torch.FloatTensor`): TODO
+            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+
+        Returns:
+            prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO
+
+        """
+        pred_original_sample = sample_prev + sigma_prev * model_output
+        derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
+        sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
+
+        if not return_dict:
+            return (sample_prev, derivative)
+
+        return SchedulerOutput(prev_sample=sample_prev)
+
+    def add_noise(self, original_samples, noise, timesteps):
+        raise NotImplementedError()