Rename LTX blocks and docs title

docs/source/en/_toctree.yml

@@ -429,7 +429,7 @@
     - local: api/pipelines/ledits_pp
       title: LEDITS++
     - local: api/pipelines/ltx_video
-      title: LTX
+      title: LTXVideo
     - local: api/pipelines/lumina
       title: Lumina-T2X
     - local: api/pipelines/marigold

docs/source/en/api/models/autoencoderkl_ltx_video.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AutoencoderKLLTXVideo
 
-vae = AutoencoderKLLTXVideo.from_pretrained("TODO/TODO", subfolder="vae", torch_dtype=torch.float32).to("cuda")
+vae = AutoencoderKLLTXVideo.from_pretrained("Lightricks/LTX-Video", subfolder="vae", torch_dtype=torch.float32).to("cuda")
 ```
 
 ## AutoencoderKLLTXVideo

docs/source/en/api/models/ltx_video_transformer3d.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import LTXVideoTransformer3DModel
 
-transformer = LTXVideoTransformer3DModel.from_pretrained("TODO/TODO", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+transformer = LTXVideoTransformer3DModel.from_pretrained("Lightricks/LTX-Video", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
 ```
 
 ## LTXVideoTransformer3DModel

src/diffusers/models/autoencoders/autoencoder_kl_ltx.py

@@ -28,7 +28,7 @@
 from .vae import DecoderOutput, DiagonalGaussianDistribution
 
 
-class LTXCausalConv3d(nn.Module):
+class LTXVideoCausalConv3d(nn.Module):
     def __init__(
         self,
         in_channels: int,
@@ -79,9 +79,9 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-class LTXResnetBlock3d(nn.Module):
+class LTXVideoResnetBlock3d(nn.Module):
     r"""
-    A 3D ResNet block used in the LTX model.
+    A 3D ResNet block used in the LTXVideo model.
 
     Args:
         in_channels (`int`):
@@ -117,21 +117,21 @@ def __init__(
         self.nonlinearity = get_activation(non_linearity)
 
         self.norm1 = RMSNorm(in_channels, eps=1e-8, elementwise_affine=elementwise_affine)
-        self.conv1 = LTXCausalConv3d(
+        self.conv1 = LTXVideoCausalConv3d(
             in_channels=in_channels, out_channels=out_channels, kernel_size=3, is_causal=is_causal
         )
 
         self.norm2 = RMSNorm(out_channels, eps=1e-8, elementwise_affine=elementwise_affine)
         self.dropout = nn.Dropout(dropout)
-        self.conv2 = LTXCausalConv3d(
+        self.conv2 = LTXVideoCausalConv3d(
             in_channels=out_channels, out_channels=out_channels, kernel_size=3, is_causal=is_causal
         )
 
         self.norm3 = None
         self.conv_shortcut = None
         if in_channels != out_channels:
             self.norm3 = nn.LayerNorm(in_channels, eps=eps, elementwise_affine=True, bias=True)
-            self.conv_shortcut = LTXCausalConv3d(
+            self.conv_shortcut = LTXVideoCausalConv3d(
                 in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, is_causal=is_causal
             )
 
@@ -157,7 +157,7 @@ def forward(self, inputs: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-class LTXUpsampler3d(nn.Module):
+class LTXVideoUpsampler3d(nn.Module):
     def __init__(
         self,
         in_channels: int,
@@ -170,7 +170,7 @@ def __init__(
 
         out_channels = in_channels * stride[0] * stride[1] * stride[2]
 
-        self.conv = LTXCausalConv3d(
+        self.conv = LTXVideoCausalConv3d(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=3,
@@ -191,9 +191,9 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-class LTXDownBlock3D(nn.Module):
+class LTXVideoDownBlock3D(nn.Module):
     r"""
-    Down block used in the LTX model.
+    Down block used in the LTXVideo model.
 
     Args:
         in_channels (`int`):
@@ -235,7 +235,7 @@ def __init__(
         resnets = []
         for _ in range(num_layers):
             resnets.append(
-                LTXResnetBlock3d(
+                LTXVideoResnetBlock3d(
                     in_channels=in_channels,
                     out_channels=in_channels,
                     dropout=dropout,
@@ -250,7 +250,7 @@ def __init__(
         if spatio_temporal_scale:
             self.downsamplers = nn.ModuleList(
                 [
-                    LTXCausalConv3d(
+                    LTXVideoCausalConv3d(
                         in_channels=in_channels,
                         out_channels=in_channels,
                         kernel_size=3,
@@ -262,7 +262,7 @@ def __init__(
 
         self.conv_out = None
         if in_channels != out_channels:
-            self.conv_out = LTXResnetBlock3d(
+            self.conv_out = LTXVideoResnetBlock3d(
                 in_channels=in_channels,
                 out_channels=out_channels,
                 dropout=dropout,
@@ -300,9 +300,9 @@ def create_forward(*inputs):
 
 
 # Adapted from diffusers.models.autoencoders.autoencoder_kl_cogvideox.CogVideoMidBlock3d
-class LTXMidBlock3d(nn.Module):
+class LTXVideoMidBlock3d(nn.Module):
     r"""
-    A middle block used in the LTX model.
+    A middle block used in the LTXVideo model.
 
     Args:
         in_channels (`int`):
@@ -335,7 +335,7 @@ def __init__(
         resnets = []
         for _ in range(num_layers):
             resnets.append(
-                LTXResnetBlock3d(
+                LTXVideoResnetBlock3d(
                     in_channels=in_channels,
                     out_channels=in_channels,
                     dropout=dropout,
@@ -367,9 +367,9 @@ def create_forward(*inputs):
         return hidden_states
 
 
-class LTXUpBlock3d(nn.Module):
+class LTXVideoUpBlock3d(nn.Module):
     r"""
-    Up block used in the LTX model.
+    Up block used in the LTXVideo model.
 
     Args:
         in_channels (`int`):
@@ -410,7 +410,7 @@ def __init__(
 
         self.conv_in = None
         if in_channels != out_channels:
-            self.conv_in = LTXResnetBlock3d(
+            self.conv_in = LTXVideoResnetBlock3d(
                 in_channels=in_channels,
                 out_channels=out_channels,
                 dropout=dropout,
@@ -421,12 +421,12 @@ def __init__(
 
         self.upsamplers = None
         if spatio_temporal_scale:
-            self.upsamplers = nn.ModuleList([LTXUpsampler3d(out_channels, stride=(2, 2, 2), is_causal=is_causal)])
+            self.upsamplers = nn.ModuleList([LTXVideoUpsampler3d(out_channels, stride=(2, 2, 2), is_causal=is_causal)])
 
         resnets = []
         for _ in range(num_layers):
             resnets.append(
-                LTXResnetBlock3d(
+                LTXVideoResnetBlock3d(
                     in_channels=out_channels,
                     out_channels=out_channels,
                     dropout=dropout,
@@ -463,9 +463,9 @@ def create_forward(*inputs):
         return hidden_states
 
 
-class LTXEncoder3d(nn.Module):
+class LTXVideoEncoder3d(nn.Module):
     r"""
-    The `LTXEncoder3D` layer of a variational autoencoder that encodes input video samples to its latent
+    The `LTXVideoEncoder3d` layer of a variational autoencoder that encodes input video samples to its latent
     representation.
 
     Args:
@@ -509,7 +509,7 @@ def __init__(
 
         output_channel = block_out_channels[0]
 
-        self.conv_in = LTXCausalConv3d(
+        self.conv_in = LTXVideoCausalConv3d(
             in_channels=self.in_channels,
             out_channels=output_channel,
             kernel_size=3,
@@ -524,7 +524,7 @@ def __init__(
             input_channel = output_channel
             output_channel = block_out_channels[i + 1] if i + 1 < num_block_out_channels else block_out_channels[i]
 
-            down_block = LTXDownBlock3D(
+            down_block = LTXVideoDownBlock3D(
                 in_channels=input_channel,
                 out_channels=output_channel,
                 num_layers=layers_per_block[i],
@@ -536,7 +536,7 @@ def __init__(
             self.down_blocks.append(down_block)
 
         # mid block
-        self.mid_block = LTXMidBlock3d(
+        self.mid_block = LTXVideoMidBlock3d(
             in_channels=output_channel,
             num_layers=layers_per_block[-1],
             resnet_eps=resnet_norm_eps,
@@ -546,14 +546,14 @@ def __init__(
         # out
         self.norm_out = RMSNorm(out_channels, eps=1e-8, elementwise_affine=False)
         self.conv_act = nn.SiLU()
-        self.conv_out = LTXCausalConv3d(
+        self.conv_out = LTXVideoCausalConv3d(
             in_channels=output_channel, out_channels=out_channels + 1, kernel_size=3, stride=1, is_causal=is_causal
         )
 
         self.gradient_checkpointing = False
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        r"""The forward method of the `LTXEncoder3D` class."""
+        r"""The forward method of the `LTXVideoEncoder3d` class."""
 
         p = self.patch_size
         p_t = self.patch_size_t
@@ -599,9 +599,10 @@ def create_forward(*inputs):
         return hidden_states
 
 
-class LTXDecoder3d(nn.Module):
+class LTXVideoDecoder3d(nn.Module):
     r"""
-    The `LTXDecoder3d` layer of a variational autoencoder that decodes its latent representation into an output sample.
+    The `LTXVideoDecoder3d` layer of a variational autoencoder that decodes its latent representation into an output
+    sample.
 
     Args:
         in_channels (`int`, defaults to 128):
@@ -647,11 +648,11 @@ def __init__(
         layers_per_block = tuple(reversed(layers_per_block))
         output_channel = block_out_channels[0]
 
-        self.conv_in = LTXCausalConv3d(
+        self.conv_in = LTXVideoCausalConv3d(
             in_channels=in_channels, out_channels=output_channel, kernel_size=3, stride=1, is_causal=is_causal
         )
 
-        self.mid_block = LTXMidBlock3d(
+        self.mid_block = LTXVideoMidBlock3d(
             in_channels=output_channel, num_layers=layers_per_block[0], resnet_eps=resnet_norm_eps, is_causal=is_causal
         )
 
@@ -662,7 +663,7 @@ def __init__(
             input_channel = output_channel
             output_channel = block_out_channels[i]
 
-            up_block = LTXUpBlock3d(
+            up_block = LTXVideoUpBlock3d(
                 in_channels=input_channel,
                 out_channels=output_channel,
                 num_layers=layers_per_block[i + 1],
@@ -676,7 +677,7 @@ def __init__(
         # out
         self.norm_out = RMSNorm(out_channels, eps=1e-8, elementwise_affine=False)
         self.conv_act = nn.SiLU()
-        self.conv_out = LTXCausalConv3d(
+        self.conv_out = LTXVideoCausalConv3d(
             in_channels=output_channel, out_channels=self.out_channels, kernel_size=3, stride=1, is_causal=is_causal
         )
 
@@ -777,7 +778,7 @@ def __init__(
     ) -> None:
         super().__init__()
 
-        self.encoder = LTXEncoder3d(
+        self.encoder = LTXVideoEncoder3d(
             in_channels=in_channels,
             out_channels=latent_channels,
             block_out_channels=block_out_channels,
@@ -788,7 +789,7 @@ def __init__(
             resnet_norm_eps=resnet_norm_eps,
             is_causal=encoder_causal,
         )
-        self.decoder = LTXDecoder3d(
+        self.decoder = LTXVideoDecoder3d(
             in_channels=latent_channels,
             out_channels=out_channels,
             block_out_channels=block_out_channels,
@@ -837,7 +838,7 @@ def __init__(
         self.tile_sample_stride_width = 448
 
     def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (LTXEncoder3d, LTXDecoder3d)):
+        if isinstance(module, (LTXVideoEncoder3d, LTXVideoDecoder3d)):
             module.gradient_checkpointing = value
 
     def enable_tiling(

src/diffusers/models/transformers/transformer_ltx.py

@@ -35,7 +35,7 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class LTXAttentionProcessor2_0:
+class LTXVideoAttentionProcessor2_0:
     r"""
     Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is
     used in the LTX model. It applies a normalization layer and rotary embedding on the query and key vector.
@@ -44,7 +44,7 @@ class LTXAttentionProcessor2_0:
     def __init__(self):
         if not hasattr(F, "scaled_dot_product_attention"):
             raise ImportError(
-                "LTXAttentionProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+                "LTXVideoAttentionProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
             )
 
     def __call__(
@@ -92,7 +92,7 @@ def __call__(
         return hidden_states
 
 
-class LTXRotaryPosEmbed(nn.Module):
+class LTXVideoRotaryPosEmbed(nn.Module):
     def __init__(
         self,
         dim: int,
@@ -164,7 +164,7 @@ def forward(
 
 
 @maybe_allow_in_graph
-class LTXTransformerBlock(nn.Module):
+class LTXVideoTransformerBlock(nn.Module):
     r"""
     Transformer block used in [LTX](https://huggingface.co/Lightricks/LTX-Video).
 
@@ -208,7 +208,7 @@ def __init__(
             cross_attention_dim=None,
             out_bias=attention_out_bias,
             qk_norm=qk_norm,
-            processor=LTXAttentionProcessor2_0(),
+            processor=LTXVideoAttentionProcessor2_0(),
         )
 
         self.norm2 = RMSNorm(dim, eps=eps, elementwise_affine=elementwise_affine)
@@ -221,7 +221,7 @@ def __init__(
             bias=attention_bias,
             out_bias=attention_out_bias,
             qk_norm=qk_norm,
-            processor=LTXAttentionProcessor2_0(),
+            processor=LTXVideoAttentionProcessor2_0(),
         )
 
         self.ff = FeedForward(dim, activation_fn=activation_fn)
@@ -327,7 +327,7 @@ def __init__(
 
         self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim)
 
-        self.rope = LTXRotaryPosEmbed(
+        self.rope = LTXVideoRotaryPosEmbed(
             dim=inner_dim,
             base_num_frames=20,
             base_height=2048,
@@ -339,7 +339,7 @@ def __init__(
 
         self.transformer_blocks = nn.ModuleList(
             [
-                LTXTransformerBlock(
+                LTXVideoTransformerBlock(
                     dim=inner_dim,
                     num_attention_heads=num_attention_heads,
                     attention_head_dim=attention_head_dim,