adding properties, formatting

src/diffusers/models/unet_rl.py

@@ -5,7 +5,6 @@ import math
 import torch
 import torch.nn as nn
 
-
 try:
     import einops
     from einops.layers.torch import Rearrange
@@ -13,7 +12,6 @@ except:
     print("Einops is not installed")
     pass
 
-
 from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 
@@ -107,14 +105,21 @@ class ResidualTemporalBlock(nn.Module):
 class TemporalUNet(ModelMixin, ConfigMixin):  # (nn.Module):
     def __init__(
             self,
-        horizon,
+            training_horizon,
             transition_dim,
             cond_dim,
+            predict_epsilon=False,
+            clip_denoised=True,
             dim=32,
             dim_mults=(1, 2, 4, 8),
     ):
         super().__init__()
 
+        self.transition_dim = transition_dim
+        self.cond_dim = cond_dim
+        self.predict_epsilon = predict_epsilon
+        self.clip_denoised = clip_denoised
+
         dims = [transition_dim, *map(lambda m: dim * m, dim_mults)]
         in_out = list(zip(dims[:-1], dims[1:]))
         # print(f'[ models/temporal ] Channel dimensions: {in_out}')
@@ -138,19 +143,19 @@ class TemporalUNet(ModelMixin, ConfigMixin):  # (nn.Module):
             self.downs.append(
                 nn.ModuleList(
                     [
-                        ResidualTemporalBlock(dim_in, dim_out, embed_dim=time_dim, horizon=horizon),
-                        ResidualTemporalBlock(dim_out, dim_out, embed_dim=time_dim, horizon=horizon),
+                        ResidualTemporalBlock(dim_in, dim_out, embed_dim=time_dim, horizon=training_horizon),
+                        ResidualTemporalBlock(dim_out, dim_out, embed_dim=time_dim, horizon=training_horizon),
                         Downsample1d(dim_out) if not is_last else nn.Identity(),
                     ]
                 )
             )
 
             if not is_last:
-                horizon = horizon // 2
+                training_horizon = training_horizon // 2
 
         mid_dim = dims[-1]
-        self.mid_block1 = ResidualTemporalBlock(mid_dim, mid_dim, embed_dim=time_dim, horizon=horizon)
-        self.mid_block2 = ResidualTemporalBlock(mid_dim, mid_dim, embed_dim=time_dim, horizon=horizon)
+        self.mid_block1 = ResidualTemporalBlock(mid_dim, mid_dim, embed_dim=time_dim, horizon=training_horizon)
+        self.mid_block2 = ResidualTemporalBlock(mid_dim, mid_dim, embed_dim=time_dim, horizon=training_horizon)
 
         for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
             is_last = ind >= (num_resolutions - 1)
@@ -158,15 +163,15 @@ class TemporalUNet(ModelMixin, ConfigMixin):  # (nn.Module):
             self.ups.append(
                 nn.ModuleList(
                     [
-                        ResidualTemporalBlock(dim_out * 2, dim_in, embed_dim=time_dim, horizon=horizon),
-                        ResidualTemporalBlock(dim_in, dim_in, embed_dim=time_dim, horizon=horizon),
+                        ResidualTemporalBlock(dim_out * 2, dim_in, embed_dim=time_dim, horizon=training_horizon),
+                        ResidualTemporalBlock(dim_in, dim_in, embed_dim=time_dim, horizon=training_horizon),
                         Upsample1d(dim_in) if not is_last else nn.Identity(),
                     ]
                 )
             )
 
             if not is_last:
-                horizon = horizon * 2
+                training_horizon = training_horizon * 2
 
         self.final_conv = nn.Sequential(
             Conv1dBlock(dim, dim, kernel_size=5),
@@ -232,7 +237,6 @@ class TemporalValue(nn.Module):
 
         print(in_out)
         for dim_in, dim_out in in_out:
-
             self.blocks.append(
                 nn.ModuleList(
                     [