Merge remote-tracking branch 'origin/main'

# Conflicts:
#	src/diffusers/__init__.py
#	src/diffusers/pipelines/__init__.py
#	src/diffusers/schedulers/scheduling_ddim.py

src/diffusers/__init__.py

@@ -9,6 +9,6 @@ from .models.unet import UNetModel
 from .models.unet_glide import GLIDESuperResUNetModel, GLIDETextToImageUNetModel
 from .models.unet_ldm import UNetLDMModel
 from .pipeline_utils import DiffusionPipeline
-from .pipelines import DDIM, DDPM, GLIDE, BDDMPipeline, LatentDiffusion
+from .pipelines import DDIM, DDPM, GLIDE, LatentDiffusion, BDDM
 from .schedulers import DDIMScheduler, DDPMScheduler, SchedulerMixin
 from .schedulers.classifier_free_guidance import ClassifierFreeGuidanceScheduler

src/diffusers/pipelines/__init__.py

-from .pipeline_bddm import BDDMPipeline
 from .pipeline_ddim import DDIM
 from .pipeline_ddpm import DDPM
 from .pipeline_glide import GLIDE
 from .pipeline_latent_diffusion import LatentDiffusion
+from .pipeline_bddm import BDDM

src/diffusers/pipelines/pipeline_bddm.py

@@ -271,20 +271,21 @@ class DiffWave(ModelMixin, ConfigMixin):
         return self.final_conv(x)
 
 
-class BDDMPipeline(DiffusionPipeline):
+class BDDM(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):
+    def __call__(self, mel_spectrogram, generator, torch_device=None):
         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
+        
+        mel_spectrogram = mel_spectrogram.to(torch_device)
+        audio_length = mel_spectrogram.size(-1) * 256
         audio_size = (1, 1, audio_length)
 
         # Sample gaussian noise to begin loop
@@ -294,9 +295,8 @@ class BDDMPipeline(DiffusionPipeline):
         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)
+            ts = (torch.tensor(timestep_values[t]) * torch.ones((1, 1))).to(torch_device)
+            residual = self.diffwave((audio, mel_spectrogram, ts))
 
             # 2. predict previous mean of audio x_t-1
             pred_prev_audio = self.noise_scheduler.step(residual, audio, t)

src/diffusers/schedulers/scheduling_ddim.py

@@ -42,9 +42,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         self.timestep_values = timestep_values  # save the fixed timestep values for BDDM
         self.clip_image = clip_predicted_image
 
-        if trained_betas is not None:
-            self.betas = np.asarray(trained_betas)
-        elif beta_schedule == "linear":
+        if 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

src/diffusers/schedulers/scheduling_ddpm.py

@@ -26,6 +26,8 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         beta_start=0.0001,
         beta_end=0.02,
         beta_schedule="linear",
+        trained_betas=None,
+        timestep_values=None,
         variance_type="fixed_small",
         clip_predicted_image=True,
         tensor_format="np",
@@ -36,14 +38,19 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             beta_start=beta_start,
             beta_end=beta_end,
             beta_schedule=beta_schedule,
+            trained_betas=trained_betas,
+            timestep_values=timestep_values,
             variance_type=variance_type,
             clip_predicted_image=clip_predicted_image,
         )
         self.timesteps = int(timesteps)
+        self.timestep_values = timestep_values # save the fixed timestep values for BDDM
         self.clip_image = clip_predicted_image
         self.variance_type = variance_type
 
-        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