LTXV lowvram fixes.

comfy/ldm/lightricks/model.py

@@ -304,7 +304,7 @@ class BasicTransformerBlock(nn.Module):
         self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
 
     def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None] + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
 
         x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
 
@@ -479,7 +479,7 @@ class LTXVModel(torch.nn.Module):
 
         # 3. Output
         scale_shift_values = (
-            self.scale_shift_table[None, None] + embedded_timestep[:, :, None]
+            self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]
         )
         shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
         x = self.norm_out(x)

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -2,6 +2,8 @@ from typing import Tuple, Union
 
 import torch
 import torch.nn as nn
+import comfy.ops
+ops = comfy.ops.disable_weight_init
 
 
 class CausalConv3d(nn.Module):
@@ -29,7 +31,7 @@ class CausalConv3d(nn.Module):
         width_pad = kernel_size[2] // 2
         padding = (0, height_pad, width_pad)
 
-        self.conv = nn.Conv3d(
+        self.conv = ops.Conv3d(
             in_channels,
             out_channels,
             kernel_size,

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -628,10 +628,10 @@ class processor(nn.Module):
         self.register_buffer("channel", torch.empty(128))
 
     def un_normalize(self, x):
-        return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1)
+        return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)
 
     def normalize(self, x):
-        return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1)
+        return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)
 
 class VideoVAE(nn.Module):
     def __init__(self):

comfy/ldm/lightricks/vae/conv_nd_factory.py

@@ -4,7 +4,8 @@ import torch
 
 from .dual_conv3d import DualConv3d
 from .causal_conv3d import CausalConv3d
-
+import comfy.ops
+ops = comfy.ops.disable_weight_init
 
 def make_conv_nd(
     dims: Union[int, Tuple[int, int]],
@@ -19,7 +20,7 @@ def make_conv_nd(
     causal=False,
 ):
     if dims == 2:
-        return torch.nn.Conv2d(
+        return ops.Conv2d(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=kernel_size,
@@ -41,7 +42,7 @@ def make_conv_nd(
                 groups=groups,
                 bias=bias,
             )
-        return torch.nn.Conv3d(
+        return ops.Conv3d(
             in_channels=in_channels,
             out_channels=out_channels,
             kernel_size=kernel_size,
@@ -71,11 +72,11 @@ def make_linear_nd(
     bias=True,
 ):
     if dims == 2:
-        return torch.nn.Conv2d(
+        return ops.Conv2d(
             in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
         )
     elif dims == 3 or dims == (2, 1):
-        return torch.nn.Conv3d(
+        return ops.Conv3d(
             in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
         )
     else: