Use top-level torch import for all torch stuff.

invokeai/backend/flux/controlnet/xlabs_controlnet_flux.py

@@ -4,7 +4,6 @@
 
 import torch
 from einops import rearrange
-from torch import Tensor, nn
 
 from invokeai.backend.flux.model import FluxParams
 from invokeai.backend.flux.modules.layers import DoubleStreamBlock, EmbedND, MLPEmbedder, timestep_embedding
@@ -13,11 +12,11 @@ from invokeai.backend.flux.modules.layers import DoubleStreamBlock, EmbedND, MLP
 def _zero_module(module: torch.nn.Module) -> torch.nn.Module:
     """Initialize the parameters of a module to zero."""
     for p in module.parameters():
-        nn.init.zeros_(p)
+        torch.nn.init.zeros_(p)
     return module
 
 
-class XLabsControlNetFlux(nn.Module):
+class XLabsControlNetFlux(torch.nn.Module):
     """A ControlNet model for FLUX.
 
     The architecture is very similar to the base FLUX model, with the following differences:
@@ -40,15 +39,15 @@ class XLabsControlNetFlux(nn.Module):
         self.hidden_size = params.hidden_size
         self.num_heads = params.num_heads
         self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.img_in = torch.nn.Linear(self.in_channels, self.hidden_size, bias=True)
         self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
         self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
         self.guidance_in = (
-            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else torch.nn.Identity()
         )
-        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+        self.txt_in = torch.nn.Linear(params.context_in_dim, self.hidden_size)
 
-        self.double_blocks = nn.ModuleList(
+        self.double_blocks = torch.nn.ModuleList(
             [
                 DoubleStreamBlock(
                     self.hidden_size,
@@ -61,41 +60,41 @@ class XLabsControlNetFlux(nn.Module):
         )
 
         # Add ControlNet blocks.
-        self.controlnet_blocks = nn.ModuleList([])
+        self.controlnet_blocks = torch.nn.ModuleList([])
         for _ in range(controlnet_depth):
-            controlnet_block = nn.Linear(self.hidden_size, self.hidden_size)
+            controlnet_block = torch.nn.Linear(self.hidden_size, self.hidden_size)
             controlnet_block = _zero_module(controlnet_block)
             self.controlnet_blocks.append(controlnet_block)
-        self.pos_embed_input = nn.Linear(self.in_channels, self.hidden_size, bias=True)
-        self.input_hint_block = nn.Sequential(
-            nn.Conv2d(3, 16, 3, padding=1),
-            nn.SiLU(),
-            nn.Conv2d(16, 16, 3, padding=1),
-            nn.SiLU(),
-            nn.Conv2d(16, 16, 3, padding=1, stride=2),
-            nn.SiLU(),
-            nn.Conv2d(16, 16, 3, padding=1),
-            nn.SiLU(),
-            nn.Conv2d(16, 16, 3, padding=1, stride=2),
-            nn.SiLU(),
-            nn.Conv2d(16, 16, 3, padding=1),
-            nn.SiLU(),
-            nn.Conv2d(16, 16, 3, padding=1, stride=2),
-            nn.SiLU(),
-            _zero_module(nn.Conv2d(16, 16, 3, padding=1)),
+        self.pos_embed_input = torch.nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.input_hint_block = torch.nn.Sequential(
+            torch.nn.Conv2d(3, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1),
+            torch.nn.SiLU(),
+            torch.nn.Conv2d(16, 16, 3, padding=1, stride=2),
+            torch.nn.SiLU(),
+            _zero_module(torch.nn.Conv2d(16, 16, 3, padding=1)),
         )
 
     def forward(
         self,
-        img: Tensor,
-        img_ids: Tensor,
-        controlnet_cond: Tensor,
-        txt: Tensor,
-        txt_ids: Tensor,
-        timesteps: Tensor,
-        y: Tensor,
-        guidance: Tensor | None = None,
-    ) -> list[Tensor]:
+        img: torch.Tensor,
+        img_ids: torch.Tensor,
+        controlnet_cond: torch.Tensor,
+        txt: torch.Tensor,
+        txt_ids: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: torch.Tensor,
+        guidance: torch.Tensor | None = None,
+    ) -> list[torch.Tensor]:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")