Add v_prediction and update docstrings (#165)

Signed-off-by: Walter Hugo Lopez Pinaya <ianonimato@hotmail.com>

Author: Warvito

generative/networks/schedulers/ddim.py

@@ -55,8 +55,9 @@ class DDIMScheduler(nn.Module):
         steps_offset: an offset added to the inference steps. You can use a combination of `steps_offset=1` and
             `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
             stable diffusion.
-        prediction_type: prediction type of the scheduler function, one of `epsilon` (predicting the noise of the
-            diffusion process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+        prediction_type: {``"epsilon"``, ``"sample"``, ``"v_prediction"``}
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
             https://imagen.research.google/video/paper.pdf)
     """
 

generative/networks/schedulers/ddpm.py

@@ -51,6 +51,10 @@ class DDPMScheduler(nn.Module):
         variance_type: {``"fixed_small"``, ``"fixed_large"``, ``"learned"``, ``"learned_range"``}
             options to clip the variance used when adding noise to the denoised sample.
         clip_sample: option to clip predicted sample between -1 and 1 for numerical stability.
+        prediction_type: {``"epsilon"``, ``"sample"``, ``"v_prediction"``}
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
     """
 
     def __init__(

generative/networks/schedulers/pndm.py

@@ -55,6 +55,10 @@ class PNDMScheduler(nn.Module):
             each diffusion step uses the value of alphas product at that step and at the previous one. For the final
             step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
             otherwise it uses the value of alpha at step 0.
+        prediction_type: {``"epsilon"``, ``"v_prediction"``}
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
         steps_offset:
             an offset added to the inference steps. You can use a combination of `offset=1` and
             `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
@@ -69,6 +73,7 @@ def __init__(
         beta_schedule: str = "linear",
         skip_prk_steps: bool = False,
         set_alpha_to_one: bool = False,
+        prediction_type: str = "epsilon",
         steps_offset: int = 0,
     ) -> None:
         super().__init__()
@@ -83,6 +88,10 @@ def __init__(
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
+        if prediction_type.lower() not in ["epsilon", "v_prediction"]:
+            raise ValueError(f"prediction_type given as {prediction_type} must be one of `epsilon` or `v_prediction`")
+
+        self.prediction_type = prediction_type
         self.num_train_timesteps = num_train_timesteps
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
@@ -301,6 +310,9 @@ def _get_prev_sample(self, sample: torch.Tensor, timestep: int, prev_timestep: i
         beta_prod_t = 1 - alpha_prod_t
         beta_prod_t_prev = 1 - alpha_prod_t_prev
 
+        if self.prediction_type == "v_prediction":
+            model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
+
         # corresponds to (α_(t−δ) - α_t) divided by
         # denominator of x_t in formula (9) and plus 1
         # Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =