add inference

app.py

@@ -30,6 +30,7 @@ from itertools import islice
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_repo_id = "Lykon/dreamshaper-xl-1-0"  # Replace to the model you would like to use
 from sampler import UniPCSampler
+from customed_unipc_scheduler import CustomedUniPCMultistepScheduler
 
 precision_scope = autocast
 
@@ -164,7 +165,7 @@ vae.to('cuda')
 pipe = StableDiffusionXLPipeline.from_pretrained("John6666/nova-anime-xl-il-v120-sdxl",torch_dtype=torch_dtype,vae=vae)
     # pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",torch_dtype=torch.float16,vae=vae)
  
-pipe.to('cuda')
+
     
 
 
@@ -175,46 +176,62 @@ accelerator = accelerate.Accelerator()
 
 def generate_image_with_steps(prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps):
     """Helper function to generate image with specific number of steps"""
+    scheduler = CustomedUniPCMultistepScheduler.from_config(pipe.scheduler.config
+                                                            , solver_order = 2 if num_inference_steps==8 else 1
+                                                            ,denoise_to_zero = False)
+    pipe.scheduler = scheduler
+    pipe.to('cuda')
     with torch.no_grad():
         with precision_scope("cuda"):
             prompts =  [prompt]
-            if num_inference_steps > 8:
-                sampler = UniPCSampler(pipe,model_closure=model_closure
-                                       , steps=num_inference_steps
-                                       , guidance_scale=guidance_scale
-                                       ,skip_type='time_uniform'
-                                       ,force_not_use_afs=True)
-            else:
-                sampler = UniPCSampler(pipe,model_closure=model_closure
-                           , steps=num_inference_steps
-                           , guidance_scale=guidance_scale)
-
-            c = prompts
-            uc = ['(worst quality:2), (low quality:2), (normal quality:2), bad anatomy, bad proportions, poorly drawn face, poorly drawn hands, missing fingers, extra limbs, blurry, pixelated, distorted, lowres, jpeg artifacts, watermark, signature, text, (deformed:1.5), (bad hands:1.3), overexposed, underexposed, censored, mutated, extra fingers, cloned face, bad eyes'] * len(c) if guidance_scale != 1.0 else None
-            shape = [4, width // 8, height // 8]
-                    # if opt.method == "dpm_solver_v3":
-                            # batch_size, shape, conditioning, x_T, unconditional_conditioning
-            samples, _ = sampler.sample(
-                conditioning=c,
-                batch_size=1,
-                shape=shape,
-                unconditional_conditioning=uc,
-                x_T=None,
-                start_free_u_step=6 if num_inference_steps == 8 else 4 if num_inference_steps < 8 else None,
-                xl_preprocess_closure = prepare_sdxl_pipeline_step_parameter,
-                        # npnet = npn_net,
-                use_corrector=True,
+            
+            latents = torch.randn(
+                (1, pipe.unet.config.in_channels, height // 8, width // 8),
+                device=device,
             )
-
-            x_samples = pipe.vae.decode(samples / pipe.vae.config.scaling_factor).sample
-            x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-            x_samples = x_samples.cpu().permute(0, 2, 3, 1).numpy()
-
-            x_image_torch = torch.from_numpy(x_samples).permute(0, 3, 1, 2) # need to pay attention
-
-            for x_sample in x_image_torch:
-                x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
-                img = Image.fromarray(x_sample.astype(np.uint8))
+            latents = latents * pipe.scheduler.init_noise_sigma
+                    
+            pipe.scheduler.set_timesteps(num_inference_steps)
+            idx = 0
+            register_free_upblock2d(pipe, b1=1.0, b2=1.0, s1=1.0, s2=1.0)
+            register_free_crossattn_upblock2d(pipe, b1=1.0, b2=1.0, s1=1.0, s2=1.0)
+            for t in tqdm(pipe.scheduler.timesteps):
+                        # Still not enough. I will tell you, what is the best implementation.  Although not via the following code.
+                        
+                        # if idx == len(pipe.scheduler.timesteps) - 1: 
+                        #     break
+                if idx == -1:#(6 if num_inference_steps == 8 else 4):
+                    register_free_upblock2d(pipe, b1=1.2, b2=1.2, s1=0.9, s2=0.9)
+                    register_free_crossattn_upblock2d(pipe, b1=1.2, b2=1.2, s1=0.9, s2=0.9)
+                latent_model_input  = torch.cat([latents] * 2)
+                        
+                latent_model_input  = pipe.scheduler.scale_model_input(latent_model_input , timestep=t)
+                negative_prompts = '(worst quality:2), (low quality:2), (normal quality:2), bad anatomy, bad proportions, poorly drawn face, poorly drawn hands, missing fingers, extra limbs, blurry, pixelated, distorted, lowres, jpeg artifacts, watermark, signature, text, (deformed:1.5), (bad hands:1.3), overexposed, underexposed, censored, mutated, extra fingers, cloned face, bad eyes'
+                negative_prompts = 1 * [negative_prompts]
+                        
+                prompt_embeds, cond_kwargs = prepare_sdxl_pipeline_step_parameter(pipe
+                                                                                      , prompts
+                                                                                      , need_cfg=True
+                                                                                      , device=pipe.device
+                                                                                      , negative_prompt=negative_prompts
+                                                                                      , W=width
+                                                                                      , H=height)
+                noise_pred  = pipe.unet(latent_model_input 
+                                        , t
+                                        , encoder_hidden_states=prompt_embeds.to(device=latents.device, dtype=latents.dtype)
+                                        , added_cond_kwargs=cond_kwargs).sample
+                uncond, cond = noise_pred.chunk(2)
+                noise_pred  = uncond + (cond - uncond) * guidance_scale
+                latents = pipe.scheduler.step(noise_pred, t, latents).prev_sample
+                idx += 1
+                        
+            x_samples_ddim = pipe.vae.decode(latents / pipe.vae.config.scaling_factor).sample
+            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+            if True:
+                for x_sample in x_samples_ddim:
+                                # x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                    x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                    img = Image.fromarray(x_sample.astype(np.uint8))
     return img
 
 @spaces.GPU #[uncomment to use ZeroGPU]

customed_unipc_scheduler.py

@@ -0,0 +1,986 @@
+# Copyright 2025 TSAIL Team 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.
+
+# DISCLAIMER: check https://huggingface.co/papers/2302.04867 and https://github.com/wl-zhao/UniPC for more info
+# The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import copy
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.utils import deprecate, is_scipy_available
+from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
+
+if is_scipy_available():
+    import scipy.stats
+
+
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(
+    num_diffusion_timesteps,
+    max_beta=0.999,
+    alpha_transform_type="cosine",
+):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
+
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+
+
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
+                     Choose from `cosine` or `exp`
+
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+    if alpha_transform_type == "cosine":
+
+        def alpha_bar_fn(t):
+            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    elif alpha_transform_type == "exp":
+
+        def alpha_bar_fn(t):
+            return math.exp(t * -12.0)
+
+    else:
+        raise ValueError(f"Unsupported alpha_transform_type: {alpha_transform_type}")
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
+
+# Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
+def rescale_zero_terminal_snr(betas):
+    """
+    Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
+
+
+    Args:
+        betas (`torch.Tensor`):
+            the betas that the scheduler is being initialized with.
+
+    Returns:
+        `torch.Tensor`: rescaled betas with zero terminal SNR
+    """
+    # Convert betas to alphas_bar_sqrt
+    alphas = 1.0 - betas
+    alphas_cumprod = torch.cumprod(alphas, dim=0)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= alphas_bar_sqrt_T
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
+    alphas = torch.cat([alphas_bar[0:1], alphas])
+    betas = 1 - alphas
+
+    return betas
+
+
+class CustomedUniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
+    """
+    `UniPCMultistepScheduler` is a training-free framework designed for the fast sampling of diffusion models.
+
+    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.
+        beta_start (`float`, defaults to 0.0001):
+            The starting `beta` value of inference.
+        beta_end (`float`, defaults to 0.02):
+            The final `beta` value.
+        beta_schedule (`str`, defaults to `"linear"`):
+            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
+            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
+        trained_betas (`np.ndarray`, *optional*):
+            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
+        solver_order (`int`, default `2`):
+            The UniPC order which can be any positive integer. The effective order of accuracy is `solver_order + 1`
+            due to the UniC. It is recommended to use `solver_order=2` for guided sampling, and `solver_order=3` for
+            unconditional sampling.
+        prediction_type (`str`, defaults to `epsilon`, *optional*):
+            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
+            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
+            Video](https://imagen.research.google/video/paper.pdf) paper).
+        thresholding (`bool`, defaults to `False`):
+            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
+            as Stable Diffusion.
+        dynamic_thresholding_ratio (`float`, defaults to 0.995):
+            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
+        sample_max_value (`float`, defaults to 1.0):
+            The threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`.
+        predict_x0 (`bool`, defaults to `True`):
+            Whether to use the updating algorithm on the predicted x0.
+        solver_type (`str`, default `bh2`):
+            Solver type for UniPC. It is recommended to use `bh1` for unconditional sampling when steps < 10, and `bh2`
+            otherwise.
+        lower_order_final (`bool`, default `True`):
+            Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
+            stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
+        disable_corrector (`list`, default `[]`):
+            Decides which step to disable the corrector to mitigate the misalignment between `epsilon_theta(x_t, c)`
+            and `epsilon_theta(x_t^c, c)` which can influence convergence for a large guidance scale. Corrector is
+            usually disabled during the first few steps.
+        solver_p (`SchedulerMixin`, default `None`):
+            Any other scheduler that if specified, the algorithm becomes `solver_p + UniC`.
+        use_karras_sigmas (`bool`, *optional*, defaults to `False`):
+            Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
+            the sigmas are determined according to a sequence of noise levels {σi}.
+        use_exponential_sigmas (`bool`, *optional*, defaults to `False`):
+            Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
+        use_beta_sigmas (`bool`, *optional*, defaults to `False`):
+            Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta
+            Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information.
+        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.
+        steps_offset (`int`, defaults to 0):
+            An offset added to the inference steps, as required by some model families.
+        final_sigmas_type (`str`, defaults to `"zero"`):
+            The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final
+            sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
+        rescale_betas_zero_snr (`bool`, defaults to `False`):
+            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
+            dark samples instead of limiting it to samples with medium brightness. Loosely related to
+            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
+    """
+
+    _compatibles = [e.name for e in KarrasDiffusionSchedulers]
+    order = 1
+
+    @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[Union[np.ndarray, List[float]]] = None,
+        solver_order: int = 2,
+        prediction_type: str = "epsilon",
+        thresholding: bool = False,
+        dynamic_thresholding_ratio: float = 0.995,
+        sample_max_value: float = 1.0,
+        predict_x0: bool = True,
+        solver_type: str = "bh2",
+        lower_order_final: bool = True,
+        disable_corrector: List[int] = [],
+        solver_p: SchedulerMixin = None,
+        use_karras_sigmas: Optional[bool] = False,
+        use_exponential_sigmas: Optional[bool] = False,
+        use_beta_sigmas: Optional[bool] = False,
+        use_flow_sigmas: Optional[bool] = False,
+        flow_shift: Optional[float] = 1.0,
+        timestep_spacing: str = "linspace",
+        steps_offset: int = 0,
+        final_sigmas_type: Optional[str] = "zero",  # "zero", "sigma_min"
+        skip_type: str = "customed_time_karras",
+        denoise_to_zero: bool = False,
+        rescale_betas_zero_snr: bool = False,
+    ):
+        
+        if self.config.use_beta_sigmas and not is_scipy_available():
+            raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
+        if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1:
+            raise ValueError(
+                "Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used."
+            )
+        if trained_betas is not None:
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
+        elif 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} is not implemented for {self.__class__}")
+
+        self.skip_type = skip_type
+        self.denoise_to_zero = denoise_to_zero
+        if rescale_betas_zero_snr:
+            self.betas = rescale_zero_terminal_snr(self.betas)
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        if rescale_betas_zero_snr:
+            # Close to 0 without being 0 so first sigma is not inf
+            # FP16 smallest positive subnormal works well here
+            self.alphas_cumprod[-1] = 2**-24
+
+        # Currently we only support VP-type noise schedule
+        self.alpha_t = torch.sqrt(self.alphas_cumprod)
+        self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
+        self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
+        self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
+
+        # standard deviation of the initial noise distribution
+        self.init_noise_sigma = 1.0
+
+        if solver_type not in ["bh1", "bh2"]:
+            if solver_type in ["midpoint", "heun", "logrho"]:
+                self.register_to_config(solver_type="bh2")
+            else:
+                raise NotImplementedError(f"{solver_type} is not implemented for {self.__class__}")
+
+        self.predict_x0 = predict_x0
+        # setable values
+        self.num_inference_steps = None
+        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
+        self.timesteps = torch.from_numpy(timesteps)
+        self.model_outputs = [None] * solver_order
+        self.timestep_list = [None] * solver_order
+        self.solver_order = solver_order
+        self.lower_order_nums = 0
+        self.disable_corrector = disable_corrector
+        self.solver_p = solver_p
+        self.last_sample = None
+        self._step_index = None
+        self._begin_index = None
+        self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    @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 set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = 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.
+        """
+        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://huggingface.co/papers/2305.08891
+
+        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+        if self.skip_type == "customed_time_karras":
+            sigma_T = sigmas[-1]
+            sigma_0 = sigmas[0]
+            N = num_inference_steps
+            if N == 9:
+                log_sigmas = np.log(sigmas)
+                sigmas = self.get_sigmas_karras(12, sigma_0, sigma_T, rho=7.0)
+                ct_start = self._sigma_to_t(sigmas[0], log_sigmas)
+                ct_end = self._sigma_to_t(sigmas[9], log_sigmas)
+                if self.denoise_to_zero:
+                    ct_real_end = self._sigma_to_t(sigmas[-1], log_sigmas)
+                timesteps = self.get_sigmas_karras(9, ct_end, ct_start,rho=1.2, customed_final_sigma= ct_real_end if self.denoise_to_zero else None)
+            elif N == 5:
+                log_sigmas = np.log(sigmas)
+                sigmas = self.get_sigmas_karras(8, sigma_0, sigma_T, rho=5.0)
+                ct_start = self._sigma_to_t(sigmas[0], log_sigmas)
+                ct_end = self._sigma_to_t(sigmas[6], log_sigmas)
+                if self.denoise_to_zero:
+                    ct_real_end = self._sigma_to_t(sigmas[-1], log_sigmas)
+                timesteps = self.get_sigmas_karras(5, ct_end, ct_start,rho=1.2, customed_final_sigma= ct_real_end if self.denoise_to_zero else None)
+            elif N == 6:
+                log_sigmas = np.log(sigmas)
+                sigmas = self.get_sigmas_karras(8, sigma_0, sigma_T, rho=5.0)
+                ct_start = self._sigma_to_t(sigmas[0], log_sigmas)
+                ct_end = self._sigma_to_t(sigmas[6], log_sigmas)
+                if self.denoise_to_zero:
+                    ct_real_end = self._sigma_to_t(sigmas[-1], log_sigmas)
+                timesteps = self.get_sigmas_karras(6, ct_end, ct_start,rho=1.2, customed_final_sigma= ct_real_end if self.denoise_to_zero else None)
+            elif N == 7:
+                log_sigmas = np.log(sigmas)
+                sigmas = self.get_sigmas_karras(8, sigma_0, sigma_T, rho=5.0)
+                ct_start = self._sigma_to_t(sigmas[0], log_sigmas)
+                ct_end = self._sigma_to_t(sigmas[6], log_sigmas)
+                if self.denoise_to_zero:
+                    ct_real_end = self._sigma_to_t(sigmas[-1], log_sigmas)
+                timesteps = self.get_sigmas_karras(7, ct_end, ct_start,rho=1.2, customed_final_sigma= ct_real_end if self.denoise_to_zero else None)
+            elif N == 8:
+                log_sigmas = np.log(sigmas).copy()
+                sigmas = self.get_sigmas_karras(8, sigma_0, sigma_T, rho=5.0)
+                ct_start = self._sigma_to_t(sigmas[0], log_sigmas)
+                ct_end = self._sigma_to_t(sigmas[6], log_sigmas)
+                if self.denoise_to_zero:
+                    ct_real_end = self._sigma_to_t(sigmas[-1], log_sigmas)
+                timesteps = self.get_sigmas_karras(8, ct_end, ct_start,rho=1.2, customed_final_sigma= ct_real_end if self.denoise_to_zero else None)
+                timesteps_tmp = copy.deepcopy(timesteps)
+                timesteps_tmp = np.append(timesteps_tmp, self._sigma_to_t(sigmas[-1], log_sigmas))
+                sigmas = np.array([self._t_to_sigma(t, log_sigmas) for t in timesteps_tmp])
+
+        self.sigmas = torch.from_numpy(sigmas)
+        self.timesteps = torch.from_numpy(timesteps).to(device=device) 
+
+        self.num_inference_steps = len(timesteps)
+
+        self.model_outputs = [
+            None,
+        ] * self.solver_order
+        self.lower_order_nums = 0
+        self.last_sample = None
+        if self.solver_p:
+            self.solver_p.set_timesteps(self.num_inference_steps, device=device)
+
+        # add an index counter for schedulers that allow duplicated timesteps
+        self._step_index = None
+        self._begin_index = None
+        self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
+    def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
+        """
+        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
+        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
+        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
+        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
+        photorealism as well as better image-text alignment, especially when using very large guidance weights."
+
+        https://huggingface.co/papers/2205.11487
+        """
+        dtype = sample.dtype
+        batch_size, channels, *remaining_dims = sample.shape
+
+        if dtype not in (torch.float32, torch.float64):
+            sample = sample.float()  # upcast for quantile calculation, and clamp not implemented for cpu half
+
+        # Flatten sample for doing quantile calculation along each image
+        sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
+
+        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
+
+        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
+        s = torch.clamp(
+            s, min=1, max=self.config.sample_max_value
+        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
+        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
+        sample = torch.clamp(sample, -s, s) / s  # "we threshold xt0 to the range [-s, s] and then divide by s"
+
+        sample = sample.reshape(batch_size, channels, *remaining_dims)
+        sample = sample.to(dtype)
+
+        return sample
+
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
+    def _sigma_to_t(self, sigma, log_sigmas):
+        # get log sigma
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
+
+        # get distribution
+        dists = log_sigma - log_sigmas[:, np.newaxis]
+
+        # get sigmas range
+        low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
+        high_idx = low_idx + 1
+
+        low = log_sigmas[low_idx]
+        high = log_sigmas[high_idx]
+
+        # interpolate sigmas
+        w = (low - log_sigma) / (low - high)
+        w = np.clip(w, 0, 1)
+
+        # transform interpolation to time range
+        t = (1 - w) * low_idx + w * high_idx
+        t = t.reshape(sigma.shape)
+        return t
+    
+    def _t_to_sigma(self, t, log_sigmas):
+        # t = t
+        low_idx, high_idx, w = np.int64(np.floor(t)), np.int64(np.ceil(t)), t - np.floor(t)
+        log_sigma = (1 - w) * log_sigmas[low_idx] + w * log_sigmas[high_idx]
+        return np.exp(log_sigma)
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
+    def _sigma_to_alpha_sigma_t(self, sigma):
+        if self.config.use_flow_sigmas:
+            alpha_t = 1 - sigma
+            sigma_t = sigma
+        else:
+            alpha_t = 1 / ((sigma**2 + 1) ** 0.5)
+            sigma_t = sigma * alpha_t
+
+        return alpha_t, sigma_t
+
+    def get_sigmas_karras(self, n, in_sigma_min: torch.Tensor, in_sigma_max: torch.Tensor, rho=7., customed_final_sigma = None) -> torch.Tensor:
+        """Constructs the noise schedule of Karras et al. (2022)."""
+        if hasattr(self.config, "sigma_min"):
+            sigma_min = self.config.sigma_min
+        else:
+            sigma_min = in_sigma_min.item()
+
+        if hasattr(self.config, "sigma_max"):
+            sigma_max = self.config.sigma_max
+        else:
+            sigma_max = in_sigma_max.item()
+
+        ramp = np.linspace(0, 1, n)
+        min_inv_rho = sigma_min ** (1 / rho)
+        max_inv_rho = sigma_max ** (1 / rho)
+        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+        if customed_final_sigma is not None :
+            sigmas[-1] = customed_final_sigma
+        return sigmas
+
+    def convert_model_output(
+        self,
+        model_output: torch.Tensor,
+        *args,
+        sample: torch.Tensor = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Convert the model output to the corresponding type the UniPC algorithm needs.
+
+        Args:
+            model_output (`torch.Tensor`):
+                The direct output from the learned diffusion model.
+            timestep (`int`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.Tensor`):
+                A current instance of a sample created by the diffusion process.
+
+        Returns:
+            `torch.Tensor`:
+                The converted model output.
+        """
+        timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
+        if sample is None:
+            if len(args) > 1:
+                sample = args[1]
+            else:
+                raise ValueError("missing `sample` as a required keyword argument")
+        if timestep is not None:
+            deprecate(
+                "timesteps",
+                "1.0.0",
+                "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+            )
+
+        sigma = self.sigmas[self.step_index]
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
+
+        if self.predict_x0:
+            if self.config.prediction_type == "epsilon":
+                x0_pred = (sample - sigma_t * model_output) / alpha_t
+            elif self.config.prediction_type == "sample":
+                x0_pred = model_output
+            elif self.config.prediction_type == "v_prediction":
+                x0_pred = alpha_t * sample - sigma_t * model_output
+            elif self.config.prediction_type == "flow_prediction":
+                sigma_t = self.sigmas[self.step_index]
+                x0_pred = sample - sigma_t * model_output
+            else:
+                raise ValueError(
+                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, "
+                    "`v_prediction`, or `flow_prediction` for the UniPCMultistepScheduler."
+                )
+
+            if self.config.thresholding:
+                x0_pred = self._threshold_sample(x0_pred)
+
+            return x0_pred
+        else:
+            if self.config.prediction_type == "epsilon":
+                return model_output
+            elif self.config.prediction_type == "sample":
+                epsilon = (sample - alpha_t * model_output) / sigma_t
+                return epsilon
+            elif self.config.prediction_type == "v_prediction":
+                epsilon = alpha_t * model_output + sigma_t * sample
+                return epsilon
+            else:
+                raise ValueError(
+                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
+                    " `v_prediction` for the UniPCMultistepScheduler."
+                )
+
+    def multistep_uni_p_bh_update(
+        self,
+        model_output: torch.Tensor,
+        *args,
+        sample: torch.Tensor = None,
+        order: int = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified.
+
+        Args:
+            model_output (`torch.Tensor`):
+                The direct output from the learned diffusion model at the current timestep.
+            prev_timestep (`int`):
+                The previous discrete timestep in the diffusion chain.
+            sample (`torch.Tensor`):
+                A current instance of a sample created by the diffusion process.
+            order (`int`):
+                The order of UniP at this timestep (corresponds to the *p* in UniPC-p).
+
+        Returns:
+            `torch.Tensor`:
+                The sample tensor at the previous timestep.
+        """
+        prev_timestep = args[0] if len(args) > 0 else kwargs.pop("prev_timestep", None)
+        if sample is None:
+            if len(args) > 1:
+                sample = args[1]
+            else:
+                raise ValueError("missing `sample` as a required keyword argument")
+        if order is None:
+            if len(args) > 2:
+                order = args[2]
+            else:
+                raise ValueError("missing `order` as a required keyword argument")
+        if prev_timestep is not None:
+            deprecate(
+                "prev_timestep",
+                "1.0.0",
+                "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+            )
+        model_output_list = self.model_outputs
+
+        s0 = self.timestep_list[-1]
+        m0 = model_output_list[-1]
+        x = sample
+
+        if self.solver_p:
+            x_t = self.solver_p.step(model_output, s0, x).prev_sample
+            return x_t
+
+        sigma_t, sigma_s0 = self.sigmas[self.step_index + 1], self.sigmas[self.step_index]
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
+        alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
+
+        lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
+        lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
+
+        h = lambda_t - lambda_s0
+        device = sample.device
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            si = self.step_index - i
+            mi = model_output_list[-(i + 1)]
+            alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
+            lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
+            rk = (lambda_si - lambda_s0) / h
+            rks.append(rk)
+            D1s.append((mi - m0) / rk)
+
+        rks.append(1.0)
+        rks = torch.tensor(rks, device=device)
+
+        R = []
+        b = []
+
+        hh = -h if self.predict_x0 else h
+        h_phi_1 = torch.expm1(hh)  # h\phi_1(h) = e^h - 1
+        h_phi_k = h_phi_1 / hh - 1
+
+        factorial_i = 1
+
+        if self.config.solver_type == "bh1":
+            B_h = hh
+        elif self.config.solver_type == "bh2":
+            B_h = torch.expm1(hh)
+        else:
+            raise NotImplementedError()
+
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= i + 1
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
+
+        R = torch.stack(R)
+        b = torch.tensor(b, device=device)
+
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1)  # (B, K)
+            # for order 2, we use a simplified version
+            if order == 2:
+                rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
+            else:
+                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)
+        else:
+            D1s = None
+
+        if self.predict_x0:
+            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
+            if D1s is not None:
+                pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
+            else:
+                pred_res = 0
+            x_t = x_t_ - alpha_t * B_h * pred_res
+        else:
+            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
+            if D1s is not None:
+                pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
+            else:
+                pred_res = 0
+            x_t = x_t_ - sigma_t * B_h * pred_res
+
+        x_t = x_t.to(x.dtype)
+        return x_t
+
+    def multistep_uni_c_bh_update(
+        self,
+        this_model_output: torch.Tensor,
+        *args,
+        last_sample: torch.Tensor = None,
+        this_sample: torch.Tensor = None,
+        order: int = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        One step for the UniC (B(h) version).
+
+        Args:
+            this_model_output (`torch.Tensor`):
+                The model outputs at `x_t`.
+            this_timestep (`int`):
+                The current timestep `t`.
+            last_sample (`torch.Tensor`):
+                The generated sample before the last predictor `x_{t-1}`.
+            this_sample (`torch.Tensor`):
+                The generated sample after the last predictor `x_{t}`.
+            order (`int`):
+                The `p` of UniC-p at this step. The effective order of accuracy should be `order + 1`.
+
+        Returns:
+            `torch.Tensor`:
+                The corrected sample tensor at the current timestep.
+        """
+        this_timestep = args[0] if len(args) > 0 else kwargs.pop("this_timestep", None)
+        if last_sample is None:
+            if len(args) > 1:
+                last_sample = args[1]
+            else:
+                raise ValueError("missing `last_sample` as a required keyword argument")
+        if this_sample is None:
+            if len(args) > 2:
+                this_sample = args[2]
+            else:
+                raise ValueError("missing `this_sample` as a required keyword argument")
+        if order is None:
+            if len(args) > 3:
+                order = args[3]
+            else:
+                raise ValueError("missing `order` as a required keyword argument")
+        if this_timestep is not None:
+            deprecate(
+                "this_timestep",
+                "1.0.0",
+                "Passing `this_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+            )
+
+        model_output_list = self.model_outputs
+
+        m0 = model_output_list[-1]
+        x = last_sample
+        x_t = this_sample
+        model_t = this_model_output
+
+        sigma_t, sigma_s0 = self.sigmas[self.step_index], self.sigmas[self.step_index - 1]
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
+        alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
+
+        lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
+        lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
+
+        h = lambda_t - lambda_s0
+        device = this_sample.device
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            si = self.step_index - (i + 1)
+            mi = model_output_list[-(i + 1)]
+            alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
+            lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
+            rk = (lambda_si - lambda_s0) / h
+            rks.append(rk)
+            D1s.append((mi - m0) / rk)
+
+        rks.append(1.0)
+        rks = torch.tensor(rks, device=device)
+
+        R = []
+        b = []
+
+        hh = -h if self.predict_x0 else h
+        h_phi_1 = torch.expm1(hh)  # h\phi_1(h) = e^h - 1
+        h_phi_k = h_phi_1 / hh - 1
+
+        factorial_i = 1
+
+        if self.config.solver_type == "bh1":
+            B_h = hh
+        elif self.config.solver_type == "bh2":
+            B_h = torch.expm1(hh)
+        else:
+            raise NotImplementedError()
+
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= i + 1
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
+
+        R = torch.stack(R)
+        b = torch.tensor(b, device=device)
+
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1)
+        else:
+            D1s = None
+
+        # for order 1, we use a simplified version
+        if order == 1:
+            rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
+        else:
+            rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)
+
+        if self.predict_x0:
+            x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
+            if D1s is not None:
+                corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
+            else:
+                corr_res = 0
+            D1_t = model_t - m0
+            x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
+        else:
+            x_t_ = alpha_t / alpha_s0 * x - sigma_t * h_phi_1 * m0
+            if D1s is not None:
+                corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
+            else:
+                corr_res = 0
+            D1_t = model_t - m0
+            x_t = x_t_ - sigma_t * B_h * (corr_res + rhos_c[-1] * D1_t)
+        x_t = x_t.to(x.dtype)
+        return x_t
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        index_candidates = (schedule_timesteps == timestep).nonzero()
+
+        if len(index_candidates) == 0:
+            step_index = len(self.timesteps) - 1
+        # 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)
+        elif len(index_candidates) > 1:
+            step_index = index_candidates[1].item()
+        else:
+            step_index = index_candidates[0].item()
+
+        return step_index
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._init_step_index
+    def _init_step_index(self, timestep):
+        """
+        Initialize the step_index counter for the scheduler.
+        """
+
+        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.Tensor,
+        timestep: Union[int, torch.Tensor],
+        sample: torch.Tensor,
+        return_dict: bool = True,
+    ) -> Union[SchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with
+        the multistep UniPC.
+
+        Args:
+            model_output (`torch.Tensor`):
+                The direct output from learned diffusion model.
+            timestep (`int`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.Tensor`):
+                A current instance of a sample created by the diffusion process.
+            return_dict (`bool`):
+                Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
+
+        Returns:
+            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
+                tuple is returned where the first element is the sample tensor.
+
+        """
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
+        if self.step_index is None:
+            self._init_step_index(timestep) # I remember is this part prevent us directly customed the discrete method
+
+        use_corrector = (
+            self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None
+        )
+
+        model_output_convert = self.convert_model_output(model_output, sample=sample)
+        if use_corrector:
+            sample = self.multistep_uni_c_bh_update(
+                this_model_output=model_output_convert,
+                last_sample=self.last_sample,
+                this_sample=sample,
+                order=self.this_order,
+            )
+
+        for i in range(self.solver_order - 1):
+            self.model_outputs[i] = self.model_outputs[i + 1]
+            self.timestep_list[i] = self.timestep_list[i + 1]
+
+        self.model_outputs[-1] = model_output_convert
+        self.timestep_list[-1] = timestep
+
+        if self.config.lower_order_final:
+            this_order = min(self.solver_order, len(self.timesteps) - self.step_index)
+        else:
+            this_order = self.solver_order
+
+        self.this_order = min(this_order, self.lower_order_nums + 1)  # warmup for multistep
+        assert self.this_order > 0
+
+        self.last_sample = sample
+        prev_sample = self.multistep_uni_p_bh_update(
+            model_output=model_output,  # pass the original non-converted model output, in case solver-p is used
+            sample=sample,
+            order=self.this_order,
+        )
+
+        if self.lower_order_nums < self.solver_order:
+            self.lower_order_nums += 1
+
+        # upon completion increase step index by one
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return SchedulerOutput(prev_sample=prev_sample)
+
+    def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        """
+        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
+        current timestep.
+
+        Args:
+            sample (`torch.Tensor`):
+                The input sample.
+
+        Returns:
+            `torch.Tensor`:
+                A scaled input sample.
+        """
+        return sample
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.add_noise
+    def add_noise(
+        self,
+        original_samples: torch.Tensor,
+        noise: torch.Tensor,
+        timesteps: torch.IntTensor,
+    ) -> torch.Tensor:
+        # Make sure sigmas and timesteps have the same device and dtype as original_samples
+        sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
+        if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
+            # mps does not support float64
+            schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
+            timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
+        else:
+            schedule_timesteps = self.timesteps.to(original_samples.device)
+            timesteps = timesteps.to(original_samples.device)
+
+        # begin_index is None when the 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 timesteps]
+        elif self.step_index is not None:
+            # add_noise is called after first denoising step (for inpainting)
+            step_indices = [self.step_index] * timesteps.shape[0]
+        else:
+            # add noise is called before first denoising step to create initial latent(img2img)
+            step_indices = [self.begin_index] * timesteps.shape[0]
+
+        sigma = sigmas[step_indices].flatten()
+        while len(sigma.shape) < len(original_samples.shape):
+            sigma = sigma.unsqueeze(-1)
+
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
+        noisy_samples = alpha_t * original_samples + sigma_t * noise
+        return noisy_samples
+
+    def __len__(self):
+        return self.config.num_train_timesteps