diff --git a/installer.py b/installer.py
index 40d2b2d76..2abb8b33d 100644
--- a/installer.py
+++ b/installer.py
@@ -434,7 +434,7 @@ def check_onnx():
 
 
 def install_rocm_zluda(torch_command):
-    check_python(supported_minors=[10,11], reason='RocM or Zluda backends require Python 3.10 or 3.11')
+    check_python(supported_minors=[10, 11], reason='ROCm or ZLUDA backends require Python 3.10 or 3.11')
     is_windows = platform.system() == 'Windows'
     log.info('AMD ROCm toolkit detected')
     os.environ.setdefault('PYTORCH_HIP_ALLOC_CONF', 'garbage_collection_threshold:0.8,max_split_size_mb:512')
diff --git a/modules/dml/backend.py b/modules/dml/backend.py
index 5f34b4542..7947dc81b 100644
--- a/modules/dml/backend.py
+++ b/modules/dml/backend.py
@@ -78,6 +78,3 @@ class DirectML:
 
     def reset_peak_memory_stats(device: Optional[rDevice]=None):
         return
-
-    def synchronize_tensor(tensor: torch.Tensor) -> None:
-        tensor.__str__()
diff --git a/modules/dml/hijack/__init__.py b/modules/dml/hijack/__init__.py
index 4f27b500c..46aecf4bd 100644
--- a/modules/dml/hijack/__init__.py
+++ b/modules/dml/hijack/__init__.py
@@ -1,8 +1,4 @@
-import modules.dml.hijack.kdiffusion
-import modules.dml.hijack.stablediffusion
 import modules.dml.hijack.torch
 import modules.dml.hijack.realesrgan_model
-import modules.dml.hijack.plms
-import modules.dml.hijack.diffusers
 import modules.dml.hijack.transformers
 import modules.dml.hijack.tomesd
diff --git a/modules/dml/hijack/diffusers.py b/modules/dml/hijack/diffusers.py
deleted file mode 100644
index 56b7d85cb..000000000
--- a/modules/dml/hijack/diffusers.py
+++ /dev/null
@@ -1,227 +0,0 @@
-from typing import Optional, Union, Tuple
-import torch
-import diffusers
-import diffusers.utils.torch_utils
-
-
-# copied from diffusers.PNDMScheduler._get_prev_sample
-def PNDMScheduler__get_prev_sample(self, sample: torch.FloatTensor, timestep, prev_timestep, model_output):
-    torch.dml.synchronize_tensor(sample) # DML synchronize
-    alpha_prod_t = self.alphas_cumprod[timestep]
-    alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-    beta_prod_t = 1 - alpha_prod_t
-    beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-    if self.config.prediction_type == "v_prediction":
-        model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
-    elif self.config.prediction_type != "epsilon":
-        raise ValueError(
-            f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `v_prediction`"
-        )
-
-    sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)
-
-    model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + (
-        alpha_prod_t * beta_prod_t * alpha_prod_t_prev
-    ) ** (0.5)
-
-    # full formula (9)
-    prev_sample = (
-        sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff
-    )
-
-    return prev_sample
-
-
-diffusers.PNDMScheduler._get_prev_sample = PNDMScheduler__get_prev_sample # pylint: disable=protected-access
-
-
-# copied from diffusers.UniPCMultistepScheduler.multistep_uni_p_bh_update
-def UniPCMultistepScheduler_multistep_uni_p_bh_update(
-    self: diffusers.UniPCMultistepScheduler,
-    model_output: torch.FloatTensor,
-    *args,
-    sample: torch.FloatTensor = None,
-    order: int = None,
-    **_,
-) -> torch.FloatTensor:
-    if sample is None:
-        if len(args) > 1:
-            sample = args[1]
-        else:
-            raise ValueError(" missing `sample` as a required keyward argument")
-    if order is None:
-        if len(args) > 2:
-            order = args[2]
-        else:
-            raise ValueError(" missing `order` as a required keyward argument")
-    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
-
-    torch.dml.synchronize_tensor(sample) # DML synchronize
-    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)
-
-    rhos_p = None
-    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,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
-
-
-diffusers.UniPCMultistepScheduler.multistep_uni_p_bh_update = UniPCMultistepScheduler_multistep_uni_p_bh_update
-
-
-# copied from diffusers.LCMScheduler.step
-def LCMScheduler_step(
-        self: diffusers.LCMScheduler,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        sample: torch.FloatTensor,
-        generator: Optional[torch.Generator] = None,
-        return_dict: bool = True,
-    ) -> Union[diffusers.schedulers.scheduling_lcm.LCMSchedulerOutput, Tuple]:
-    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)
-
-    # 1. get previous step value
-    prev_step_index = self.step_index + 1
-    if prev_step_index < len(self.timesteps):
-        prev_timestep = self.timesteps[prev_step_index]
-    else:
-        prev_timestep = timestep
-
-    # 2. compute alphas, betas
-    torch.dml.synchronize_tensor(sample) # DML synchronize
-    alpha_prod_t = self.alphas_cumprod[timestep]
-    alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-
-    beta_prod_t = 1 - alpha_prod_t
-    beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-    # 3. Get scalings for boundary conditions
-    c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)
-
-    # 4. Compute the predicted original sample x_0 based on the model parameterization
-    if self.config.prediction_type == "epsilon":  # noise-prediction
-        predicted_original_sample = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()
-    elif self.config.prediction_type == "sample":  # x-prediction
-        predicted_original_sample = model_output
-    elif self.config.prediction_type == "v_prediction":  # v-prediction
-        predicted_original_sample = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output
-    else:
-        raise ValueError(
-            f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` or"
-            " `v_prediction` for `LCMScheduler`."
-        )
-
-    # 5. Clip or threshold "predicted x_0"
-    if self.config.thresholding:
-        predicted_original_sample = self._threshold_sample(predicted_original_sample)
-    elif self.config.clip_sample:
-        predicted_original_sample = predicted_original_sample.clamp(
-            -self.config.clip_sample_range, self.config.clip_sample_range
-        )
-
-    # 6. Denoise model output using boundary conditions
-    denoised = c_out * predicted_original_sample + c_skip * sample
-
-    # 7. Sample and inject noise z ~ N(0, I) for MultiStep Inference
-    # Noise is not used for one-step sampling.
-    if len(self.timesteps) > 1:
-        noise = diffusers.utils.torch_utils.randn_tensor(model_output.shape, generator=generator, device=model_output.device)
-        prev_sample = alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise
-    else:
-        prev_sample = denoised
-
-    # upon completion increase step index by one
-    self._step_index += 1
-
-    if not return_dict:
-        return (prev_sample, denoised)
-
-    return diffusers.schedulers.scheduling_lcm.LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)
-
-
-diffusers.LCMScheduler.step = LCMScheduler_step
diff --git a/modules/dml/hijack/kdiffusion.py b/modules/dml/hijack/kdiffusion.py
deleted file mode 100644
index d772dc88f..000000000
--- a/modules/dml/hijack/kdiffusion.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import torch
-from tqdm.auto import tqdm
-from k_diffusion import sampling
-import modules.devices as devices
-
-
-def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
-    noise_sampler = sampling.default_noise_sampler(x) if noise_sampler is None else noise_sampler
-    if order not in {2, 3}:
-        raise ValueError('order should be 2 or 3')
-    forward = t_end > t_start
-    if not forward and eta:
-        raise ValueError('eta must be 0 for reverse sampling')
-    h_init = abs(h_init) * (1 if forward else -1)
-    atol = torch.tensor(atol, device=devices.device)
-    rtol = torch.tensor(rtol, device=devices.device)
-    s = t_start
-    x_prev = x
-    accept = True
-    pid = sampling.PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)
-    info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}
-
-    while s < t_end - 1e-5 if forward else s > t_end + 1e-5:
-        eps_cache = {}
-        t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)
-        if eta:
-            sd, su = sampling.get_ancestral_step(self.sigma(s), self.sigma(t), eta)
-            t_ = torch.minimum(t_end, self.t(sd))
-            su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5
-        else:
-            t_, su = t, 0.
-
-        eps, eps_cache = self.eps(eps_cache, 'eps', x, s)
-        denoised = x - self.sigma(s) * eps
-
-        if order == 2:
-            x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)
-            x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)
-        else:
-            x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)
-            x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)
-        delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))
-        error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5
-        accept = pid.propose_step(error)
-        if accept:
-            x_prev = x_low
-            x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))
-            s = t
-            info['n_accept'] += 1
-        else:
-            info['n_reject'] += 1
-        info['nfe'] += order
-        info['steps'] += 1
-
-        if self.info_callback is not None:
-            self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})
-
-    return x, info
-
-
-@devices.inference_context()
-def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None):
-    """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927."""
-    if sigma_min <= 0 or sigma_max <= 0:
-        raise ValueError('sigma_min and sigma_max must not be 0')
-    with tqdm(total=n, disable=disable) as pbar:
-        dpm_solver = sampling.DPMSolver(model, extra_args, eps_callback=pbar.update)
-        if callback is not None:
-            dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
-        return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max, device=devices.device)), dpm_solver.t(torch.tensor(sigma_min, device=devices.device)), n, eta, s_noise, noise_sampler)
-
-
-@devices.inference_context()
-def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False):
-    """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927."""
-    if sigma_min <= 0 or sigma_max <= 0:
-        raise ValueError('sigma_min and sigma_max must not be 0')
-    with tqdm(disable=disable) as pbar:
-        dpm_solver = sampling.DPMSolver(model, extra_args, eps_callback=pbar.update)
-        if callback is not None:
-            dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
-        x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max, device=devices.device)), dpm_solver.t(torch.tensor(sigma_min, device=devices.device)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler)
-    if return_info:
-        return x, info
-    return x
-
-sampling.DPMSolver.dpm_solver_adaptive = dpm_solver_adaptive
-sampling.sample_dpm_fast = sample_dpm_fast
-sampling.sample_dpm_adaptive = sample_dpm_adaptive
diff --git a/modules/dml/hijack/plms.py b/modules/dml/hijack/plms.py
deleted file mode 100644
index d19c46629..000000000
--- a/modules/dml/hijack/plms.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import torch
-from ldm.models.diffusion.ddim import noise_like
-import modules.sd_hijack_inpainting as plms_hijack
-import modules.devices as devices
-
-
-@devices.inference_context()
-def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
-    b, *_, device = *x.shape, x.device
-
-    def get_model_output(x, t):
-        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-            e_t = self.model.apply_model(x, t, c)
-        else:
-            x_in = torch.cat([x] * 2)
-            t_in = torch.cat([t] * 2)
-
-            if isinstance(c, dict):
-                assert isinstance(unconditional_conditioning, dict)
-                c_in = {}
-                for k in c:
-                    if isinstance(c[k], list):
-                        c_in[k] = [
-                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
-                            for i in range(len(c[k]))
-                        ]
-                    else:
-                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
-            else:
-                c_in = torch.cat([unconditional_conditioning, c])
-
-            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-        if score_corrector is not None:
-            assert self.model.parameterization == "eps"
-            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-        return e_t
-
-    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-
-    def get_x_prev_and_pred_x0(e_t, index):
-        # select parameters corresponding to the currently considered timestep
-        torch.dml.synchronize_tensor(alphas[index]) # DML synchronize
-        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        if quantize_denoised:
-            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-        if dynamic_threshold is not None:
-            from ldm.models.diffusion.sampling_util import norm_thresholding
-            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-        if noise_dropout > 0.:
-            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-        return x_prev, pred_x0
-
-    e_t = get_model_output(x, t)
-    if len(old_eps) == 0:
-        # Pseudo Improved Euler (2nd order)
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-        e_t_next = get_model_output(x_prev, t_next)
-        e_t_prime = (e_t + e_t_next) / 2
-    elif len(old_eps) == 1:
-        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (3 * e_t - old_eps[-1]) / 2
-    elif len(old_eps) == 2:
-        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-    elif len(old_eps) >= 3:
-        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-    return x_prev, pred_x0, e_t
-plms_hijack.p_sample_plms = p_sample_plms
diff --git a/modules/dml/hijack/stablediffusion.py b/modules/dml/hijack/stablediffusion.py
deleted file mode 100644
index fc2518aa7..000000000
--- a/modules/dml/hijack/stablediffusion.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import torch
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.modules.diffusionmodules.util import noise_like
-import modules.devices as devices
-
-
-@devices.inference_context()
-def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                    unconditional_guidance_scale=1., unconditional_conditioning=None,
-                    dynamic_threshold=None):
-    b, *_, device = *x.shape, x.device
-
-    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-        model_output = self.model.apply_model(x, t, c)
-    else:
-        x_in = torch.cat([x] * 2)
-        t_in = torch.cat([t] * 2)
-        if isinstance(c, dict):
-            assert isinstance(unconditional_conditioning, dict)
-            c_in = dict()
-            for k in c:
-                if isinstance(c[k], list):
-                    c_in[k] = [torch.cat([
-                        unconditional_conditioning[k][i],
-                        c[k][i]]) for i in range(len(c[k]))]
-                else:
-                    c_in[k] = torch.cat([
-                            unconditional_conditioning[k],
-                            c[k]])
-        elif isinstance(c, list):
-            c_in = list()
-            assert isinstance(unconditional_conditioning, list)
-            for i in range(len(c)):
-                c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
-        else:
-            c_in = torch.cat([unconditional_conditioning, c])
-        model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-        model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
-
-    if self.model.parameterization == "v":
-        e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
-    else:
-        e_t = model_output
-
-    if score_corrector is not None:
-        assert self.model.parameterization == "eps", 'not implemented'
-        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-    # select parameters corresponding to the currently considered timestep
-    torch.dml.synchronize_tensor(alphas[index]) # DML synchronize
-    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-    # current prediction for x_0
-    if self.model.parameterization != "v":
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-    else:
-        pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
-
-    if quantize_denoised:
-        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-
-    if dynamic_threshold is not None:
-        raise NotImplementedError
-
-    # direction pointing to x_t
-    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-    if noise_dropout > 0.:
-        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-    return x_prev, pred_x0
-
-DDIMSampler.p_sample_ddim = p_sample_ddim
diff --git a/modules/dml/hijack/torch.py b/modules/dml/hijack/torch.py
index e4913a357..e216de46f 100644
--- a/modules/dml/hijack/torch.py
+++ b/modules/dml/hijack/torch.py
@@ -7,41 +7,6 @@ CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, d
 CondFunc('torch.Tensor.new', lambda orig, self, *args, **kwargs: orig(self.cpu(), *args, **kwargs).to(self.device), lambda orig, self, *args, **kwargs: torch.dml.is_directml_device(self.device))
 
 
-_lerp = torch.lerp
-def lerp(*args, **kwargs) -> torch.Tensor:
-    rep = None
-    for i in range(0, len(args)):
-        if torch.is_tensor(args[i]):
-            rep = args[i]
-            break
-    if rep is None:
-        for key in kwargs:
-            if torch.is_tensor(kwargs[key]):
-                rep = kwargs[key]
-                break
-    if torch.dml.is_directml_device(rep.device):
-        args = list(args)
-
-        if rep.dtype == torch.float16:
-            for i in range(len(args)):
-                if torch.is_tensor(args[i]):
-                    args[i] = args[i].float()
-        for i in range(len(args)):
-            if torch.is_tensor(args[i]):
-                args[i] = args[i].cpu()
-
-        if rep.dtype == torch.float16:
-            for kwarg in kwargs:
-                if torch.is_tensor(kwargs[kwarg]):
-                    kwargs[kwarg] = kwargs[kwarg].float()
-        for kwarg in kwargs:
-            if torch.is_tensor(kwargs[kwarg]):
-                kwargs[kwarg] = kwargs[kwarg].cpu()
-        return _lerp(*args, **kwargs).to(rep.device).type(rep.dtype)
-    return _lerp(*args, **kwargs)
-torch.lerp = lerp
-
-
 # https://github.com/lshqqytiger/stable-diffusion-webui-directml/issues/436
 _pow_ = torch.Tensor.pow_
 def pow_(self: torch.Tensor, *args, **kwargs):
diff --git a/modules/processing_callbacks.py b/modules/processing_callbacks.py
index d5b7d1922..824a79007 100644
--- a/modules/processing_callbacks.py
+++ b/modules/processing_callbacks.py
@@ -37,8 +37,6 @@ def diffusers_callback(pipe, step: int, timestep: int, kwargs: dict):
     if p is None:
         return kwargs
     latents = kwargs.get('latents', None)
-    if torch.is_tensor(latents) and latents.device.type == "privateuseone":
-        torch.dml.synchronize_tensor(latents) # DML synchronize
     debug_callback(f'Callback: step={step} timestep={timestep} latents={latents.shape if latents is not None else None} kwargs={list(kwargs)}')
     shared.state.sampling_step = step
     if shared.state.interrupted or shared.state.skipped: