Integrated WIP UniPC scheduler

6 files changed, 655 insertions, 14 deletions

diff --git a/infer.py b/infer.py
index aa75ee5..329c60b 100644
--- a/infer.py
+++ b/infer.py
@@ -29,6 +29,7 @@ from data.keywords import prompt_to_keywords, keywords_to_prompt
 from models.clip.embeddings import patch_managed_embeddings
 from models.clip.tokenizer import MultiCLIPTokenizer
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
+from schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
 from util import load_config, load_embeddings_from_dir
 
 
@@ -61,6 +62,7 @@ default_cmds = {
     "batch_num": 1,
     "steps": 30,
     "guidance_scale": 7.0,
+    "sag_scale": 0.75,
     "lora_scale": 0.5,
     "seed": None,
     "config": None,
@@ -122,7 +124,7 @@ def create_cmd_parser():
     parser.add_argument(
         "--scheduler",
         type=str,
-        choices=["plms", "ddim", "klms", "dpmsm", "dpmss", "euler_a", "kdpm2", "kdpm2_a"],
+        choices=["plms", "ddim", "klms", "dpmsm", "dpmss", "euler_a", "kdpm2", "kdpm2_a", "unipc"],
     )
     parser.add_argument(
         "--template",
@@ -175,6 +177,10 @@ def create_cmd_parser():
         type=float,
     )
     parser.add_argument(
+        "--sag_scale",
+        type=float,
+    )
+    parser.add_argument(
         "--lora_scale",
         type=float,
     )
@@ -304,6 +310,8 @@ def generate(output_dir: Path, pipeline, args):
         pipeline.scheduler = KDPM2DiscreteScheduler.from_config(pipeline.scheduler.config)
     elif args.scheduler == "kdpm2_a":
         pipeline.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
+    elif args.scheduler == "unipc":
+        pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
 
     for i in range(args.batch_num):
         pipeline.set_progress_bar_config(
@@ -322,10 +330,11 @@ def generate(output_dir: Path, pipeline, args):
             num_images_per_prompt=args.batch_size,
             num_inference_steps=args.steps,
             guidance_scale=args.guidance_scale,
+            sag_scale=args.sag_scale,
             generator=generator,
             image=init_image,
             strength=args.image_noise,
-            cross_attention_kwargs={"scale": args.lora_scale},
+            # cross_attention_kwargs={"scale": args.lora_scale},
         ).images
 
         for j, image in enumerate(images):
@@ -408,7 +417,7 @@ def main():
     pipeline = create_pipeline(args.model, dtype)
 
     load_embeddings(pipeline, args.ti_embeddings_dir)
-    pipeline.unet.load_attn_procs(args.lora_embeddings_dir)
+    # pipeline.unet.load_attn_procs(args.lora_embeddings_dir)
 
     cmd_parser = create_cmd_parser()
     cmd_prompt = CmdParse(output_dir, args.ti_embeddings_dir, args.lora_embeddings_dir, pipeline, cmd_parser)
diff --git a/schedulers/scheduling_unipc_multistep.py b/schedulers/scheduling_unipc_multistep.py
new file mode 100644
index 0000000..ff5db24
--- /dev/null
+++ b/schedulers/scheduling_unipc_multistep.py
@@ -0,0 +1,615 @@
+# Copyright 2022 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: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver
+
+import math
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
+    """
+    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.
+
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+
+    def alpha_bar(time_step):
+        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    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(t2) / alpha_bar(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
+class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
+    """
+    UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of 
+    a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.
+    UniPC is by desinged model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional 
+    sampling. It can also be applied to both noise prediction model and data prediction model. The corrector
+    UniC can be also applied after any off-the-shelf solvers to increase the order of accuracy.
+
+    For more details, see the original paper: https://arxiv.org/abs/2302.04867
+
+    Currently, we support the multistep UniPC for both noise prediction models and data prediction models. We
+    recommend to use `solver_order=2` for guided sampling, and `solver_order=3` for unconditional sampling.
+
+    We also support the "dynamic thresholding" method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space
+    diffusion models, you can set both `algorithm_type="dpmsolver++"` and `thresholding=True` to use the dynamic
+    thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as
+    stable-diffusion).
+
+    [`~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`.
+    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
+    [`~SchedulerMixin.from_pretrained`] functions.
+
+    Args:
+        num_train_timesteps (`int`): number of diffusion steps used to train the model.
+        beta_start (`float`): the starting `beta` value of inference.
+        beta_end (`float`): the final `beta` value.
+        beta_schedule (`str`):
+            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):
+            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
+        solver_order (`int`, default `2`):
+            the order of UniPC, also the p in UniPC-p; can be any positive integer. Note that the effective order of
+            accuracy is `solver_order + 1` due to the UniC. We recommend to use `solver_order=2` for guided
+            sampling, and `solver_order=3` for unconditional sampling.
+        prediction_type (`str`, default `epsilon`, optional):
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
+        thresholding (`bool`, default `False`):
+            whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
+            For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to
+            use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion
+            models (such as stable-diffusion).
+        dynamic_thresholding_ratio (`float`, default `0.995`):
+            the ratio for the dynamic thresholding method. Default is `0.995`, the same as Imagen
+            (https://arxiv.org/abs/2205.11487).
+        sample_max_value (`float`, default `1.0`):
+            the threshold value for dynamic thresholding. Valid only when `thresholding=True` and
+            `predict_x0=True`.
+        predict_x0 (`bool`, default `True`):
+            whether to use the updating algrithm on the predicted x0. See https://arxiv.org/abs/2211.01095 for details
+        solver_type (`str`, default `bh1`):
+            the solver type of UniPC. We recommend use `bh1` for unconditional sampling when steps < 10, and use
+            `bh2` otherwise.
+        lower_order_final (`bool`, default `True`):
+            whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically
+            find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10.
+        disable_corrector (`list`, default `[]`):
+            decide which step to disable the corrector. For large guidance scale, the misalignment between the
+            `epsilon_theta(x_t, c)`and `epsilon_theta(x_t^c, c)` might influence the convergence. This can be 
+            mitigated by disable the corrector at the first few steps (e.g., disable_corrector=[0])
+        solver_p (`SchedulerMixin`):
+            can be any other scheduler. If specified, the algorithm will become solver_p + UniC.
+    """
+
+    _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 = "bh1",
+        lower_order_final: bool = True,
+        disable_corrector: List[int] = [],
+        solver_p: SchedulerMixin = None,
+    ):
+        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} does is not implemented for {self.__class__}")
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+        # 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)
+
+        # standard deviation of the initial noise distribution
+        self.init_noise_sigma = 1.0
+
+        if solver_type not in ["bh1", "bh2"]:
+            raise NotImplementedError(f"{solver_type} does 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.lower_order_nums = 0
+        self.disable_corrector = disable_corrector
+        self.solver_p = solver_p
+
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+        """
+        Sets the 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.
+            device (`str` or `torch.device`, optional):
+                the device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        """
+        self.num_inference_steps = num_inference_steps
+        timesteps = (
+            np.linspace(0, self.num_train_timesteps - 1, num_inference_steps + 1)
+            .round()[::-1][:-1]
+            .copy()
+            .astype(np.int64)
+        )
+        self.timesteps = torch.from_numpy(timesteps).to(device)
+        self.model_outputs = [
+            None,
+        ] * self.config.solver_order
+        self.lower_order_nums = 0
+        if self.solver_p:
+            self.solver_p.set_timesteps(num_inference_steps, device=device)
+
+    def convert_model_output(
+        self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor
+    ):
+        r"""
+        Convert the model output to the corresponding type that the algorithm PC needs.
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+            timestep (`int`): current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                current instance of sample being created by diffusion process.
+
+        Returns:
+            `torch.FloatTensor`: the converted model output.
+        """
+        if self.predict_x0:
+            if self.config.prediction_type == "epsilon":
+                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
+                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":
+                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
+                x0_pred = alpha_t * sample - sigma_t * model_output
+            else:
+                raise ValueError(
+                    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
+                    " `v_prediction` for the DPMSolverMultistepScheduler."
+                )
+
+            if self.config.thresholding:
+                # Dynamic thresholding in https://arxiv.org/abs/2205.11487
+                orig_dtype = x0_pred.dtype
+                if orig_dtype not in [torch.float, torch.double]:
+                    x0_pred = x0_pred.float()
+                dynamic_max_val = torch.quantile(
+                    torch.abs(x0_pred).reshape((x0_pred.shape[0], -1)), self.config.dynamic_thresholding_ratio, dim=1
+                )
+                dynamic_max_val = torch.maximum(
+                    dynamic_max_val,
+                    self.config.sample_max_value * torch.ones_like(dynamic_max_val).to(dynamic_max_val.device),
+                )[(...,) + (None,) * (x0_pred.ndim - 1)]
+                x0_pred = torch.clamp(x0_pred, -dynamic_max_val, dynamic_max_val) / dynamic_max_val
+                x0_pred = x0_pred.type(orig_dtype)
+            return x0_pred
+        else:
+            if self.config.prediction_type == "epsilon":
+                return model_output
+            elif self.config.prediction_type == "sample":
+                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
+                epsilon = (sample - alpha_t * model_output) / sigma_t
+                return epsilon
+            elif self.config.prediction_type == "v_prediction":
+                alpha_t, sigma_t = self.alpha_t[timestep], self.sigma_t[timestep]
+                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 DPMSolverMultistepScheduler."
+                )
+
+    def multistep_uni_p_bh_update(
+        self,
+        model_output: torch.FloatTensor,
+        prev_timestep: int,
+        sample: torch.FloatTensor,
+        order: int,
+    ):
+        """
+        One step for the UniP (B(h) version). Alternatively, `self.solver_p` is used if is specified.
+
+        Args:
+            model_output (`torch.FloatTensor`):
+                direct outputs from learned diffusion model at the current timestep.
+            prev_timestep (`int`): previous discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                current instance of sample being created by diffusion process.
+            order (`int`): the order of UniP at this step, also the p in UniPC-p.
+
+        Returns:
+            `torch.FloatTensor`: the sample tensor at the previous timestep.
+        """
+        timestep_list = self.timestep_list
+        model_output_list = self.model_outputs
+
+        s0, t = self.timestep_list[-1], prev_timestep
+        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
+
+        lambda_t, lambda_s0 = self.lambda_t[t], self.lambda_t[s0]
+        alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0]
+        sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0]
+
+        h = lambda_t - lambda_s0
+        device = sample.device
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            si = timestep_list[-(i + 1)]
+            mi = model_output_list[-(i + 1)]
+            lambda_si = self.lambda_t[si]
+            rk = ((lambda_si - lambda_s0) / h)
+            rks.append(rk)
+            D1s.append((mi - m0) / rk)
+
+        rks.append(1.)
+        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])
+        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,bkchw->bchw', 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,bkchw->bchw', 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.FloatTensor,
+        this_timestep: int,
+        last_sample: torch.FloatTensor,
+        this_sample: torch.FloatTensor,
+        order: int,
+    ):
+        """
+        One step for the UniC (B(h) version). 
+
+        Args:
+            this_model_output (`torch.FloatTensor`): the model outputs at `x_t`
+            this_timestep (`int`): the current timestep `t`
+            last_sample (`torch.FloatTensor`): the generated sample before the last predictor: `x_{t-1}`
+            this_sample (`torch.FloatTensor`): the generated sample after the last predictor: `x_{t}`
+            order (`int`): the `p` of UniC-p at this step. Note that the effective order of accuracy 
+                should be order + 1
+
+        Returns:
+            `torch.FloatTensor`: the corrected sample tensor at the current timestep.
+        """
+        timestep_list = self.timestep_list
+        model_output_list = self.model_outputs
+
+        s0, t = timestep_list[-1], this_timestep
+        m0 = model_output_list[-1]
+        x = last_sample
+        x_t = this_sample
+        model_t = this_model_output
+
+        lambda_t, lambda_s0 = self.lambda_t[t], self.lambda_t[s0]
+        alpha_t, alpha_s0 = self.alpha_t[t], self.alpha_t[s0]
+        sigma_t, sigma_s0 = self.sigma_t[t], self.sigma_t[s0]
+
+        h = lambda_t - lambda_s0
+        device = this_sample.device
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            si = timestep_list[-(i + 1)]
+            mi = model_output_list[-(i + 1)]
+            lambda_si = self.lambda_t[si]
+            rk = ((lambda_si - lambda_s0) / h)
+            rks.append(rk)
+            D1s.append((mi - m0) / rk)
+
+        rks.append(1.)
+        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)
+
+        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,bkchw->bchw', 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,bkchw->bchw', 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
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: int,
+        sample: torch.FloatTensor,
+        return_dict: bool = True,
+    ):
+        # -> Union[SchedulerOutput, Tuple]:
+        """
+        Step function propagating the sample with the multistep UniPC.
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+            timestep (`int`): current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                current instance of sample being created by diffusion process.
+            return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
+
+        Returns:
+            [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
+            True, otherwise a `tuple`. When returning a tuple, 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 isinstance(timestep, torch.Tensor):
+            timestep = timestep.to(self.timesteps.device)
+        step_index = (self.timesteps == timestep).nonzero()
+        if len(step_index) == 0:
+            step_index = len(self.timesteps) - 1
+        else:
+            step_index = step_index.item()
+
+        use_corrector = step_index > 0 and step_index - 1 not in self.disable_corrector  # step_index not in self.disable_corrector
+
+        model_output_convert = self.convert_model_output(model_output, timestep, sample)
+        if use_corrector:
+            sample = self.multistep_uni_c_bh_update(
+                this_model_output=model_output_convert,
+                this_timestep=timestep,
+                last_sample=self.last_sample,
+                this_sample=sample,
+                order=self.this_order,
+            )
+
+        # now prepare to run the predictor
+        prev_timestep = 0 if step_index == len(self.timesteps) - 1 else self.timesteps[step_index + 1]
+
+        for i in range(self.config.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.config.solver_order, len(self.timesteps) - step_index)
+        else:
+            this_order = self.config.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
+            prev_timestep=prev_timestep,
+            sample=sample,
+            order=self.this_order,
+        )
+
+        if self.lower_order_nums < self.config.solver_order:
+            self.lower_order_nums += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return SchedulerOutput(prev_sample=prev_sample)
+
+    def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs):  # -> torch.FloatTensor:
+        """
+        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
+        current timestep.
+
+        Args:
+            sample (`torch.FloatTensor`): input sample
+
+        Returns:
+            `torch.FloatTensor`: scaled input sample
+        """
+        return sample
+
+    def add_noise(
+        self,
+        original_samples: torch.FloatTensor,
+        noise: torch.FloatTensor,
+        timesteps: torch.IntTensor,
+    ):
+        # -> torch.FloatTensor:
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        self.alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
+        timesteps = timesteps.to(original_samples.device)
+
+        sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
+        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
+        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
+            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
+
+        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
+        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
+        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
+            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
+
+        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
+        return noisy_samples
+
+    def __len__(self):
+        return self.config.num_train_timesteps
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 4c1ec31..5a7911c 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -375,7 +375,7 @@ def parse_args():
     parser.add_argument(
         "--sample_steps",
         type=int,
-        default=20,
+        default=10,
         help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
     )
     parser.add_argument(
@@ -511,6 +511,7 @@ def main():
         dtype=weight_dtype,
         with_prior_preservation=args.num_class_images != 0,
         prior_loss_weight=args.prior_loss_weight,
+        no_val=args.valid_set_size == 0,
     )
 
     checkpoint_output_dir = output_dir / "model"
diff --git a/train_lora.py b/train_lora.py
index a8c1cf6..330bcd6 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -335,7 +335,7 @@ def parse_args():
     parser.add_argument(
         "--sample_steps",
         type=int,
-        default=20,
+        default=10,
         help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
     )
     parser.add_argument(
@@ -487,6 +487,7 @@ def main():
         dtype=weight_dtype,
         with_prior_preservation=args.num_class_images != 0,
         prior_loss_weight=args.prior_loss_weight,
+        no_val=args.valid_set_size == 0,
     )
 
     checkpoint_output_dir = output_dir / "model"
diff --git a/train_ti.py b/train_ti.py
index f78c7d2..d1defb3 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -392,7 +392,7 @@ def parse_args():
     parser.add_argument(
         "--sample_steps",
         type=int,
-        default=20,
+        default=10,
         help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
     )
     parser.add_argument(
@@ -586,6 +586,7 @@ def main():
         seed=args.seed,
         with_prior_preservation=args.num_class_images != 0,
         prior_loss_weight=args.prior_loss_weight,
+        no_val=args.valid_set_size == 0,
         low_freq_noise=0,
         strategy=textual_inversion_strategy,
         num_train_epochs=args.num_train_epochs,
diff --git a/training/functional.py b/training/functional.py
index e1035ce..b7ea90d 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -12,7 +12,7 @@ from torch.utils.data import DataLoader
 
 from accelerate import Accelerator
 from transformers import CLIPTextModel
-from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, DPMSolverMultistepScheduler
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
 
 from tqdm.auto import tqdm
 from PIL import Image
@@ -22,6 +22,7 @@ from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embeddings
 from models.clip.util import get_extended_embeddings
 from models.clip.tokenizer import MultiCLIPTokenizer
+from schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
 from training.util import AverageMeter
 
 
@@ -79,7 +80,7 @@ def get_models(pretrained_model_name_or_path: str):
     vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder='vae')
     unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder='unet')
     noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder='scheduler')
-    sample_scheduler = DPMSolverMultistepScheduler.from_pretrained(
+    sample_scheduler = UniPCMultistepScheduler.from_pretrained(
         pretrained_model_name_or_path, subfolder='scheduler')
 
     embeddings = patch_managed_embeddings(text_encoder)
@@ -93,7 +94,7 @@ def save_samples(
     text_encoder: CLIPTextModel,
     tokenizer: MultiCLIPTokenizer,
     vae: AutoencoderKL,
-    sample_scheduler: DPMSolverMultistepScheduler,
+    sample_scheduler: UniPCMultistepScheduler,
     train_dataloader: DataLoader,
     val_dataloader: Optional[DataLoader],
     output_dir: Path,
@@ -180,7 +181,7 @@ def generate_class_images(
     vae: AutoencoderKL,
     unet: UNet2DConditionModel,
     tokenizer: MultiCLIPTokenizer,
-    sample_scheduler: DPMSolverMultistepScheduler,
+    sample_scheduler: UniPCMultistepScheduler,
     train_dataset: VlpnDataset,
     sample_batch_size: int,
     sample_image_size: int,
@@ -284,6 +285,7 @@ def loss_step(
             device=latents.device,
             generator=generator
         )
+
     bsz = latents.shape[0]
     # Sample a random timestep for each image
     timesteps = torch.randint(
@@ -351,6 +353,7 @@ def train_loop(
     train_dataloader: DataLoader,
     val_dataloader: Optional[DataLoader],
     loss_step: LossCallable,
+    no_val: bool = False,
     sample_frequency: int = 10,
     checkpoint_frequency: int = 50,
     global_step_offset: int = 0,
@@ -406,9 +409,15 @@ def train_loop(
         for epoch in range(num_epochs):
             if accelerator.is_main_process:
                 if epoch % sample_frequency == 0:
+                    local_progress_bar.clear()
+                    global_progress_bar.clear()
+
                     on_sample(global_step + global_step_offset)
 
                 if epoch % checkpoint_frequency == 0 and epoch != 0:
+                    local_progress_bar.clear()
+                    global_progress_bar.clear()
+
                     on_checkpoint(global_step + global_step_offset, "training")
 
             local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}")
@@ -463,7 +472,7 @@ def train_loop(
 
             on_after_epoch(lr_scheduler.get_last_lr()[0])
 
-            if val_dataloader is not None:
+            if val_dataloader is not None and not no_val:
                 model.eval()
 
                 cur_loss_val = AverageMeter()
@@ -498,11 +507,11 @@ def train_loop(
 
                 accelerator.log(logs, step=global_step)
 
-                local_progress_bar.clear()
-                global_progress_bar.clear()
-
                 if accelerator.is_main_process:
                     if avg_acc_val.avg.item() > best_acc_val:
+                        local_progress_bar.clear()
+                        global_progress_bar.clear()
+
                         accelerator.print(
                             f"Global step {global_step}: Validation accuracy reached new maximum: {best_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}")
                         on_checkpoint(global_step + global_step_offset, "milestone")
@@ -513,6 +522,9 @@ def train_loop(
             else:
                 if accelerator.is_main_process:
                     if avg_acc.avg.item() > best_acc:
+                        local_progress_bar.clear()
+                        global_progress_bar.clear()
+
                         accelerator.print(
                             f"Global step {global_step}: Training accuracy reached new maximum: {best_acc:.2e} -> {avg_acc.avg.item():.2e}")
                         on_checkpoint(global_step + global_step_offset, "milestone")
@@ -550,6 +562,7 @@ def train(
     optimizer: torch.optim.Optimizer,
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
     strategy: TrainingStrategy,
+    no_val: bool = False,
     num_train_epochs: int = 100,
     sample_frequency: int = 20,
     checkpoint_frequency: int = 50,
@@ -604,6 +617,7 @@ def train(
         lr_scheduler=lr_scheduler,
         train_dataloader=train_dataloader,
         val_dataloader=val_dataloader,
+        no_val=no_val,
         loss_step=loss_step_,
         sample_frequency=sample_frequency,
         checkpoint_frequency=checkpoint_frequency,