diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index 0aa550b1..5d5310f9 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -22,6 +22,7 @@ samplers_k_diffusion = [
     ("LMS", "sample_lms", ["k_lms"], {}),
     ("Heun", "sample_heun", ["k_heun"], {"second_order": True}),
     ("DPM2", "sample_dpm_2", ["k_dpm_2"], {"scheduler": "karras", "discard_next_to_last_sigma": True, "second_order": True}),
+    ("Res Multistep", "res_multistep", ["res_multistep"], {}),
     ("Restart", sd_samplers_extra.restart_sampler, ["restart"], {"scheduler": "karras", "second_order": True}),
     ("UniPC", sd_samplers_extra.sample_unipc, ["unipc"], {"discard_next_to_last_sigma": True}),
 ]
diff --git a/modules/sd_schedulers.py b/modules/sd_schedulers.py
index b090d61b..cfe71286 100644
--- a/modules/sd_schedulers.py
+++ b/modules/sd_schedulers.py
@@ -95,6 +95,23 @@ def get_align_your_steps_sigmas(n, sigma_min, sigma_max, device):
     return torch.FloatTensor(sigmas).to(device)
 
 
+def linear_quadratic(n, sigma_min, sigma_max, device, *, threshold_noise=0.025):
+    if n == 1:
+        sigma_schedule = [1.0, 0.0]
+    else:
+        linear_steps = n // 2
+        linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)]
+        threshold_noise_step_diff = linear_steps - threshold_noise * n
+        quadratic_steps = n - linear_steps
+        quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps**2)
+        linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps**2)
+        const = quadratic_coef * (linear_steps**2)
+        quadratic_sigma_schedule = [quadratic_coef * (i**2) + linear_coef * i + const for i in range(linear_steps, n)]
+        sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0]
+        sigma_schedule = [1.0 - x for x in sigma_schedule]
+    return torch.FloatTensor(sigma_schedule).to(device) * sigma_max
+
+
 def kl_optimal(n, sigma_min, sigma_max, device):
     alpha_min = torch.arctan(torch.tensor(sigma_min, device=device))
     alpha_max = torch.arctan(torch.tensor(sigma_max, device=device))
@@ -192,6 +209,7 @@ schedulers = [
     Scheduler("simple", "Simple", simple_scheduler, need_inner_model=True),
     Scheduler("uniform", "Uniform", uniform, need_inner_model=True),
     Scheduler("sgm_uniform", "SGM Uniform", sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
+    Scheduler("linear_quadratic", "Linear Quadratic", linear_quadratic),
     Scheduler("kl_optimal", "KL Optimal", kl_optimal),
     Scheduler("ddim", "DDIM", ddim_scheduler, need_inner_model=True),
     Scheduler("align_your_steps", "Align Your Steps", get_align_your_steps_sigmas),
diff --git a/modules_forge/packages/k_diffusion/sampling.py b/modules_forge/packages/k_diffusion/sampling.py
index ea53da4e..bedd18ae 100644
--- a/modules_forge/packages/k_diffusion/sampling.py
+++ b/modules_forge/packages/k_diffusion/sampling.py
@@ -794,6 +794,46 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     return x
 
 
+@torch.no_grad()
+def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None):
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    sigma_fn = lambda t: t.neg().exp()
+    t_fn = lambda sigma: sigma.log().neg()
+    phi1_fn = lambda t: torch.expm1(t) / t
+    phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t
+
+    old_sigma_down = None
+    old_denoised = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=0.0)
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+        if sigma_down == 0 or old_denoised is None:
+            d = to_d(x, sigmas[i], denoised)
+            dt = sigma_down - sigmas[i]
+            x = x + d * dt
+        else:
+            t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1])
+            h = t_next - t
+            c2 = (t_prev - t_old) / h
+
+            phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
+            b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
+            b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
+            x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
+
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+
+        old_denoised = denoised
+        old_sigma_down = sigma_down
+    return x
+
+
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0):
     # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/