[Scheduler] fix: EDM schedulers when using the exp sigma schedule. (#8385)

* fix: euledm when using the exp sigma schedule.

* fix-copies

* remove print.

* reduce friction

* yiyi's suggestioms

Author: sayakpaul

src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py

@@ -243,13 +243,13 @@ 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)
+        ramp = torch.linspace(0, 1, self.num_inference_steps)
         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)
+        sigmas = sigmas.to(dtype=torch.float32, device=device)
         self.timesteps = self.precondition_noise(sigmas)
 
         if self.config.final_sigmas_type == "sigma_min":
@@ -283,7 +283,6 @@ def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.
         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

src/diffusers/schedulers/scheduling_edm_euler.py

@@ -16,7 +16,6 @@
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
-import numpy as np
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
@@ -210,13 +209,13 @@ 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)
+        ramp = torch.linspace(0, 1, self.num_inference_steps)
         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)
+        sigmas = sigmas.to(dtype=torch.float32, device=device)
         self.timesteps = self.precondition_noise(sigmas)
 
         self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
@@ -234,7 +233,6 @@ def _compute_karras_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.
         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.Tensor: