[FlaxAutoencoderKL] rename weights to align with PT (#584)

* rename weights to align with PT

* DiagonalGaussianDistribution => FlaxDiagonalGaussianDistribution

* fix name

src/diffusers/models/vae_flax.py

@@ -34,15 +34,15 @@ class FlaxAutoencoderKLOutput(BaseOutput):
     Output of AutoencoderKL encoding method.
 
     Args:
-        latent_dist (`DiagonalGaussianDistribution`):
-            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
-            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
+        latent_dist (`FlaxDiagonalGaussianDistribution`):
+            Encoded outputs of `Encoder` represented as the mean and logvar of `FlaxDiagonalGaussianDistribution`.
+            `FlaxDiagonalGaussianDistribution` allows for sampling latents from the distribution.
     """
 
-    latent_dist: "DiagonalGaussianDistribution"
+    latent_dist: "FlaxDiagonalGaussianDistribution"
 
 
-class Upsample2D(nn.Module):
+class FlaxUpsample2D(nn.Module):
     in_channels: int
     dtype: jnp.dtype = jnp.float32
 
@@ -66,7 +66,7 @@ class Upsample2D(nn.Module):
         return hidden_states
 
 
-class Downsample2D(nn.Module):
+class FlaxDownsample2D(nn.Module):
     in_channels: int
     dtype: jnp.dtype = jnp.float32
 
@@ -86,7 +86,7 @@ class Downsample2D(nn.Module):
         return hidden_states
 
 
-class ResnetBlock2D(nn.Module):
+class FlaxResnetBlock2D(nn.Module):
     in_channels: int
     out_channels: int = None
     dropout_prob: float = 0.0
@@ -144,7 +144,7 @@ class ResnetBlock2D(nn.Module):
         return hidden_states + residual
 
 
-class AttentionBlock(nn.Module):
+class FlaxAttentionBlock(nn.Module):
     channels: int
     num_head_channels: int = None
     dtype: jnp.dtype = jnp.float32
@@ -201,7 +201,7 @@ class AttentionBlock(nn.Module):
         return hidden_states
 
 
-class DownEncoderBlock2D(nn.Module):
+class FlaxDownEncoderBlock2D(nn.Module):
     in_channels: int
     out_channels: int
     dropout: float = 0.0
@@ -214,7 +214,7 @@ class DownEncoderBlock2D(nn.Module):
         for i in range(self.num_layers):
             in_channels = self.in_channels if i == 0 else self.out_channels
 
-            res_block = ResnetBlock2D(
+            res_block = FlaxResnetBlock2D(
                 in_channels=in_channels,
                 out_channels=self.out_channels,
                 dropout_prob=self.dropout,
@@ -224,19 +224,19 @@ class DownEncoderBlock2D(nn.Module):
         self.resnets = resnets
 
         if self.add_downsample:
-            self.downsample = Downsample2D(self.out_channels, dtype=self.dtype)
+            self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
 
     def __call__(self, hidden_states, deterministic=True):
         for resnet in self.resnets:
             hidden_states = resnet(hidden_states, deterministic=deterministic)
 
         if self.add_downsample:
-            hidden_states = self.downsample(hidden_states)
+            hidden_states = self.downsamplers_0(hidden_states)
 
         return hidden_states
 
 
-class UpEncoderBlock2D(nn.Module):
+class FlaxUpEncoderBlock2D(nn.Module):
     in_channels: int
     out_channels: int
     dropout: float = 0.0
@@ -248,7 +248,7 @@ class UpEncoderBlock2D(nn.Module):
         resnets = []
         for i in range(self.num_layers):
             in_channels = self.in_channels if i == 0 else self.out_channels
-            res_block = ResnetBlock2D(
+            res_block = FlaxResnetBlock2D(
                 in_channels=in_channels,
                 out_channels=self.out_channels,
                 dropout_prob=self.dropout,
@@ -259,19 +259,19 @@ class UpEncoderBlock2D(nn.Module):
         self.resnets = resnets
 
         if self.add_upsample:
-            self.upsample = Upsample2D(self.out_channels, dtype=self.dtype)
+            self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
 
     def __call__(self, hidden_states, deterministic=True):
         for resnet in self.resnets:
             hidden_states = resnet(hidden_states, deterministic=deterministic)
 
         if self.add_upsample:
-            hidden_states = self.upsample(hidden_states)
+            hidden_states = self.upsamplers_0(hidden_states)
 
         return hidden_states
 
 
-class UNetMidBlock2D(nn.Module):
+class FlaxUNetMidBlock2D(nn.Module):
     in_channels: int
     dropout: float = 0.0
     num_layers: int = 1
@@ -281,7 +281,7 @@ class UNetMidBlock2D(nn.Module):
     def setup(self):
         # there is always at least one resnet
         resnets = [
-            ResnetBlock2D(
+            FlaxResnetBlock2D(
                 in_channels=self.in_channels,
                 out_channels=self.in_channels,
                 dropout_prob=self.dropout,
@@ -292,12 +292,12 @@ class UNetMidBlock2D(nn.Module):
         attentions = []
 
         for _ in range(self.num_layers):
-            attn_block = AttentionBlock(
+            attn_block = FlaxAttentionBlock(
                 channels=self.in_channels, num_head_channels=self.attn_num_head_channels, dtype=self.dtype
             )
             attentions.append(attn_block)
 
-            res_block = ResnetBlock2D(
+            res_block = FlaxResnetBlock2D(
                 in_channels=self.in_channels,
                 out_channels=self.in_channels,
                 dropout_prob=self.dropout,
@@ -317,7 +317,7 @@ class UNetMidBlock2D(nn.Module):
         return hidden_states
 
 
-class Encoder(nn.Module):
+class FlaxEncoder(nn.Module):
     in_channels: int = 3
     out_channels: int = 3
     down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
@@ -347,7 +347,7 @@ class Encoder(nn.Module):
             output_channel = block_out_channels[i]
             is_final_block = i == len(block_out_channels) - 1
 
-            down_block = DownEncoderBlock2D(
+            down_block = FlaxDownEncoderBlock2D(
                 in_channels=input_channel,
                 out_channels=output_channel,
                 num_layers=self.layers_per_block,
@@ -358,7 +358,7 @@ class Encoder(nn.Module):
         self.down_blocks = down_blocks
 
         # middle
-        self.mid_block = UNetMidBlock2D(
+        self.mid_block = FlaxUNetMidBlock2D(
             in_channels=block_out_channels[-1], attn_num_head_channels=None, dtype=self.dtype
         )
 
@@ -392,7 +392,7 @@ class Encoder(nn.Module):
         return sample
 
 
-class Decoder(nn.Module):
+class FlaxDecoder(nn.Module):
     dtype: jnp.dtype = jnp.float32
     in_channels: int = 3
     out_channels: int = 3
@@ -415,7 +415,7 @@ class Decoder(nn.Module):
         )
 
         # middle
-        self.mid_block = UNetMidBlock2D(
+        self.mid_block = FlaxUNetMidBlock2D(
             in_channels=block_out_channels[-1], attn_num_head_channels=None, dtype=self.dtype
         )
 
@@ -429,7 +429,7 @@ class Decoder(nn.Module):
 
             is_final_block = i == len(block_out_channels) - 1
 
-            up_block = UpEncoderBlock2D(
+            up_block = FlaxUpEncoderBlock2D(
                 in_channels=prev_output_channel,
                 out_channels=output_channel,
                 num_layers=self.layers_per_block + 1,
@@ -469,7 +469,7 @@ class Decoder(nn.Module):
         return sample
 
 
-class DiagonalGaussianDistribution(object):
+class FlaxDiagonalGaussianDistribution(object):
     def __init__(self, parameters, deterministic=False):
         # Last axis to account for channels-last
         self.mean, self.logvar = jnp.split(parameters, 2, axis=-1)
@@ -521,7 +521,7 @@ class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):
     dtype: jnp.dtype = jnp.float32
 
     def setup(self):
-        self.encoder = Encoder(
+        self.encoder = FlaxEncoder(
             in_channels=self.config.in_channels,
             out_channels=self.config.latent_channels,
             down_block_types=self.config.down_block_types,
@@ -532,7 +532,7 @@ class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):
             double_z=True,
             dtype=self.dtype,
         )
-        self.decoder = Decoder(
+        self.decoder = FlaxDecoder(
             in_channels=self.config.latent_channels,
             out_channels=self.config.out_channels,
             up_block_types=self.config.up_block_types,
@@ -572,7 +572,7 @@ class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):
 
         hidden_states = self.encoder(sample, deterministic=deterministic)
         moments = self.quant_conv(hidden_states)
-        posterior = DiagonalGaussianDistribution(moments)
+        posterior = FlaxDiagonalGaussianDistribution(moments)
 
         if not return_dict:
             return (posterior,)