diff --git a/src/diffusers/pipelines/pipeline_bddm.py b/src/diffusers/pipelines/pipeline_bddm.py
index dd2753cbec..85dddff33e 100644
--- a/src/diffusers/pipelines/pipeline_bddm.py
+++ b/src/diffusers/pipelines/pipeline_bddm.py
@@ -17,6 +17,9 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import tqdm
+
+from ..pipeline_utils import DiffusionPipeline
 
 
 def calc_diffusion_step_embedding(diffusion_steps, diffusion_step_embed_dim_in):
@@ -234,3 +237,45 @@ class DiffWave(nn.Module):
         x = self.init_conv(x).clone()
         x = self.residual_layer((x, mel_spectrogram, diffusion_steps))
         return self.final_conv(x)
+
+
+class BDDMPipeline(DiffusionPipeline):
+    def __init__(self, diffwave, noise_scheduler):
+        super().__init__()
+        noise_scheduler = noise_scheduler.set_format("pt")
+        self.register_modules(diffwave=diffwave, noise_scheduler=noise_scheduler)
+    
+    @torch.no_grad()
+    def __call__(self, mel_spectrogram, generator):
+        if torch_device is None:
+            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+        self.diffwave.to(torch_device)
+
+        audio_length = mel_spectrogram.size(-1) * self.config.hop_len
+        audio_size = (1, 1, audio_length)
+
+        # Sample gaussian noise to begin loop
+        audio = torch.normal(0, 1, size=audio_size, generator=generator).to(torch_device)
+
+        timestep_values = self.noise_scheduler.timestep_values
+        num_prediction_steps = len(self.noise_scheduler)
+        for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
+            # 1. predict noise residual
+            with torch.no_grad():
+                t = (torch.tensor(timestep_values[t]) * torch.ones((1, 1))).to(torch_device)
+                residual = self.diffwave(audio, mel_spectrogram, t)
+
+            # 2. predict previous mean of audio x_t-1
+            pred_prev_audio = self.noise_scheduler.step(residual, audio, t)
+
+            # 3. optionally sample variance
+            variance = 0
+            if t > 0:
+                noise = torch.normal(0, 1, size=audio_size, generator=generator).to(torch_device)
+                variance = self.noise_scheduler.get_variance(t).sqrt() * noise
+
+            # 4. set current audio to prev_audio: x_t -> x_t-1
+            audio = pred_prev_audio + variance
+
+        return audio
\ No newline at end of file
diff --git a/src/diffusers/schedulers/scheduling_ddim.py b/src/diffusers/schedulers/scheduling_ddim.py
index 883a358d34..842348d106 100644
--- a/src/diffusers/schedulers/scheduling_ddim.py
+++ b/src/diffusers/schedulers/scheduling_ddim.py
@@ -26,6 +26,8 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         beta_start=0.0001,
         beta_end=0.02,
         beta_schedule="linear",
+        trained_betas=None,
+        timestep_values=None,
         clip_predicted_image=True,
         tensor_format="np",
     ):
@@ -37,9 +39,12 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
             beta_schedule=beta_schedule,
         )
         self.timesteps = int(timesteps)
+        self.timestep_values = timestep_values # save the fixed timestep values for BDDM
         self.clip_image = clip_predicted_image
 
-        if beta_schedule == "linear":
+        if trained_betas is not None:
+            self.betas = np.asarray(trained_betas)
+        elif beta_schedule == "linear":
             self.betas = linear_beta_schedule(timesteps, beta_start=beta_start, beta_end=beta_end)
         elif beta_schedule == "squaredcos_cap_v2":
             # GLIDE cosine schedule