Add NewbiePipeline and NextDiT_3B_GQA_patch2_Adaln_Refiner_WHIT_CLIP transformer

src/diffusers/__init__.py

@@ -280,6 +280,7 @@
             "WanVACETransformer3DModel",
             "ZImageTransformer2DModel",
             "attention_backend",
+            "NextDiT_3B_GQA_patch2_Adaln_Refiner_WHIT_CLIP",
         ]
     )
     _import_structure["modular_pipelines"].extend(
@@ -663,6 +664,7 @@
             "WuerstchenDecoderPipeline",
             "WuerstchenPriorPipeline",
             "ZImagePipeline",
+            "NewbiePipeline",
         ]
     )
 
@@ -1009,6 +1011,7 @@
             WanVACETransformer3DModel,
             ZImageTransformer2DModel,
             attention_backend,
+            NextDiT_3B_GQA_patch2_Adaln_Refiner_WHIT_CLIP,
         )
         from .modular_pipelines import ComponentsManager, ComponentSpec, ModularPipeline, ModularPipelineBlocks
         from .optimization import (
@@ -1361,6 +1364,7 @@
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
             ZImagePipeline,
+            NewbiePipeline,
         )
 
     try:

src/diffusers/models/__init__.py

@@ -126,6 +126,7 @@
     _import_structure["unets.unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
     _import_structure["unets.unet_stable_cascade"] = ["StableCascadeUNet"]
     _import_structure["unets.uvit_2d"] = ["UVit2DModel"]
+    _import_structure["transformers.transformer_newbie"] = ["NextDiT_3B_GQA_patch2_Adaln_Refiner_WHIT_CLIP"]
 
 if is_flax_available():
     _import_structure["controlnets.controlnet_flax"] = ["FlaxControlNetModel"]
@@ -230,6 +231,7 @@
             WanTransformer3DModel,
             WanVACETransformer3DModel,
             ZImageTransformer2DModel,
+            NextDiT_3B_GQA_patch2_Adaln_Refiner_WHIT_CLIP,
         )
         from .unets import (
             I2VGenXLUNet,

src/diffusers/models/components.py

@@ -0,0 +1,54 @@
+import warnings
+
+import torch
+import torch.nn as nn
+
+try:
+    from apex.normalization import FusedRMSNorm as RMSNorm
+except ImportError:
+    warnings.warn("Cannot import apex RMSNorm, switch to vanilla implementation")
+
+    class RMSNorm(torch.nn.Module):
+        def __init__(self, dim: int, eps: float = 1e-6):
+            """
+            Initialize the RMSNorm normalization layer.
+
+            Args:
+                dim (int): The dimension of the input tensor.
+                eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+            Attributes:
+                eps (float): A small value added to the denominator for numerical stability.
+                weight (nn.Parameter): Learnable scaling parameter.
+
+            """
+            super().__init__()
+            self.eps = eps
+            self.weight = nn.Parameter(torch.ones(dim))
+
+        def _norm(self, x):
+            """
+            Apply the RMSNorm normalization to the input tensor.
+
+            Args:
+                x (torch.Tensor): The input tensor.
+
+            Returns:
+                torch.Tensor: The normalized tensor.
+
+            """
+            return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+        def forward(self, x):
+            """
+            Forward pass through the RMSNorm layer.
+
+            Args:
+                x (torch.Tensor): The input tensor.
+
+            Returns:
+                torch.Tensor: The output tensor after applying RMSNorm.
+
+            """
+            output = self._norm(x.float()).type_as(x)
+            return output * self.weight

src/diffusers/models/transformers/__init__.py

@@ -48,3 +48,4 @@
     from .transformer_wan_animate import WanAnimateTransformer3DModel
     from .transformer_wan_vace import WanVACETransformer3DModel
     from .transformer_z_image import ZImageTransformer2DModel
+    from .transformer_newbie import NextDiT_3B_GQA_patch2_Adaln_Refiner_WHIT_CLIP