diff --git a/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py
index 26a41d7335c5..dfc7978a2ee2 100644
--- a/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py
+++ b/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py
@@ -14,6 +14,7 @@
 
 # DISCLAIMER: This file is strongly influenced by https://github.com/LuChengTHU/dpm-solver and https://github.com/NVlabs/edm
 
+import math
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
@@ -44,6 +45,10 @@ class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             range is [0.2, 80.0].
         sigma_data (`float`, *optional*, defaults to 0.5):
             The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].
+        sigma_schedule (`str`, *optional*, defaults to `karras`):
+            Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper
+            (https://arxiv.org/abs/2206.00364). Other acceptable value is "exponential". The exponential schedule was
+            incorporated in this model: https://huggingface.co/stabilityai/cosxl.
         num_train_timesteps (`int`, defaults to 1000):
             The number of diffusion steps to train the model.
         solver_order (`int`, defaults to 2):
@@ -89,6 +94,7 @@ def __init__(
         sigma_min: float = 0.002,
         sigma_max: float = 80.0,
         sigma_data: float = 0.5,
+        sigma_schedule: str = "karras",
         num_train_timesteps: int = 1000,
         prediction_type: str = "epsilon",
         rho: float = 7.0,
@@ -121,7 +127,11 @@ def __init__(
             )
 
         ramp = torch.linspace(0, 1, num_train_timesteps)
-        sigmas = self._compute_sigmas(ramp)
+        if sigma_schedule == "karras":
+            sigmas = self._compute_karras_sigmas(ramp)
+        elif sigma_schedule == "exponential":
+            sigmas = self._compute_exponential_sigmas(ramp)
+
         self.timesteps = self.precondition_noise(sigmas)
 
         self.sigmas = self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
@@ -236,7 +246,10 @@ def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torc
         self.num_inference_steps = num_inference_steps
 
         ramp = np.linspace(0, 1, self.num_inference_steps)
-        sigmas = self._compute_sigmas(ramp)
+        if self.config.sigma_schedule == "karras":
+            sigmas = self._compute_karras_sigmas(ramp)
+        elif self.config.sigma_schedule == "exponential":
+            sigmas = self._compute_exponential_sigmas(ramp)
 
         sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
         self.timesteps = self.precondition_noise(sigmas)
@@ -262,10 +275,9 @@ def set_timesteps(self, num_inference_steps: int = None, device: Union[str, torc
         self._begin_index = None
         self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
 
-    # Taken from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L17
-    def _compute_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTensor:
+    # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._compute_karras_sigmas
+    def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTensor:
         """Constructs the noise schedule of Karras et al. (2022)."""
-
         sigma_min = sigma_min or self.config.sigma_min
         sigma_max = sigma_max or self.config.sigma_max
 
@@ -273,6 +285,18 @@ def _compute_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTe
         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
+
+        return sigmas
+
+    # Copied from diffusers.schedulers.scheduling_edm_euler.EDMEulerScheduler._compute_exponential_sigmas
+    def _compute_exponential_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTensor:
+        """Implementation closely follows k-diffusion.
+
+        https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26
+        """
+        sigma_min = sigma_min or self.config.sigma_min
+        sigma_max = sigma_max or self.config.sigma_max
+        sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), len(ramp)).exp().flip(0)
         return sigmas
 
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
diff --git a/src/diffusers/schedulers/scheduling_edm_euler.py b/src/diffusers/schedulers/scheduling_edm_euler.py
index f6a09ca1ee16..0ef9263c9e30 100644
--- a/src/diffusers/schedulers/scheduling_edm_euler.py
+++ b/src/diffusers/schedulers/scheduling_edm_euler.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
@@ -65,6 +66,10 @@ class EDMEulerScheduler(SchedulerMixin, ConfigMixin):
             range is [0.2, 80.0].
         sigma_data (`float`, *optional*, defaults to 0.5):
             The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].
+        sigma_schedule (`str`, *optional*, defaults to `karras`):
+            Sigma schedule to compute the `sigmas`. By default, we the schedule introduced in the EDM paper
+            (https://arxiv.org/abs/2206.00364). Other acceptable value is "exponential". The exponential schedule was
+            incorporated in this model: https://huggingface.co/stabilityai/cosxl.
         num_train_timesteps (`int`, defaults to 1000):
             The number of diffusion steps to train the model.
         prediction_type (`str`, defaults to `epsilon`, *optional*):
@@ -84,15 +89,23 @@ def __init__(
         sigma_min: float = 0.002,
         sigma_max: float = 80.0,
         sigma_data: float = 0.5,
+        sigma_schedule: str = "karras",
         num_train_timesteps: int = 1000,
         prediction_type: str = "epsilon",
         rho: float = 7.0,
     ):
+        if sigma_schedule not in ["karras", "exponential"]:
+            raise ValueError(f"Wrong value for provided for `{sigma_schedule=}`.`")
+
         # setable values
         self.num_inference_steps = None
 
         ramp = torch.linspace(0, 1, num_train_timesteps)
-        sigmas = self._compute_sigmas(ramp)
+        if sigma_schedule == "karras":
+            sigmas = self._compute_karras_sigmas(ramp)
+        elif sigma_schedule == "exponential":
+            sigmas = self._compute_exponential_sigmas(ramp)
+
         self.timesteps = self.precondition_noise(sigmas)
 
         self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
@@ -200,7 +213,10 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
         self.num_inference_steps = num_inference_steps
 
         ramp = np.linspace(0, 1, self.num_inference_steps)
-        sigmas = self._compute_sigmas(ramp)
+        if self.config.sigma_schedule == "karras":
+            sigmas = self._compute_karras_sigmas(ramp)
+        elif self.config.sigma_schedule == "exponential":
+            sigmas = self._compute_exponential_sigmas(ramp)
 
         sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
         self.timesteps = self.precondition_noise(sigmas)
@@ -211,9 +227,8 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
         self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
 
     # Taken from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L17
-    def _compute_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTensor:
+    def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTensor:
         """Constructs the noise schedule of Karras et al. (2022)."""
-
         sigma_min = sigma_min or self.config.sigma_min
         sigma_max = sigma_max or self.config.sigma_max
 
@@ -221,6 +236,17 @@ def _compute_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTe
         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
+
+        return sigmas
+
+    def _compute_exponential_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTensor:
+        """Implementation closely follows k-diffusion.
+
+        https://github.com/crowsonkb/k-diffusion/blob/6ab5146d4a5ef63901326489f31f1d8e7dd36b48/k_diffusion/sampling.py#L26
+        """
+        sigma_min = sigma_min or self.config.sigma_min
+        sigma_max = sigma_max or self.config.sigma_max
+        sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), len(ramp)).exp().flip(0)
         return sigmas
 
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep