Node threw an error during execution.

ComfyUI Error Report

Error Details

• Node ID: 3634:3721
• Node Type: DenoLTX23PresetLoader
• Exception Type: RuntimeError
• Exception Message: RuntimeError: Error(s) in loading state_dict for VideoVAE:
  size mismatch for decoder.conv_in.conv.weight: copying a param with shape torch.Size([1024, 128, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([2048, 128, 3, 3, 3]).
  size mismatch for decoder.conv_in.conv.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([2048]).
  size mismatch for decoder.up_blocks.0.res_blocks.0.conv1.conv.weight: copying a param with shape torch.Size([1024, 1024, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([2048, 2048, 3, 3, 3]).
  size mismatch for decoder.up_blocks.0.res_blocks.0.conv1.conv.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([2048]).
  size mismatch for decoder.up_blocks.0.res_blocks.0.conv2.conv.weight: copying a param with shape torch.Size([1024, 1024, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([2048, 2048, 3, 3, 3]).
  size mismatch for decoder.up_blocks.0.res_blocks.0.conv2.conv.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([2048]).
  size mismatch for decoder.up_blocks.0.res_blocks.1.conv1.conv.weight: copying a param with shape torch.Size([1024, 1024, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([2048, 2048, 3, 3, 3]).
  size mismatch for decoder.up_blocks.0.res_blocks.1.conv1.conv.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([2048]).
  size mismatch for decoder.up_blocks.0.res_blocks.1.conv2.conv.weight: copying a param with shape torch.Size([1024, 1024, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([2048, 2048, 3, 3, 3]).
  size mismatch for decoder.up_blocks.0.res_blocks.1.conv2.conv.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([2048]).
  size mismatch for decoder.up_blocks.1.conv.conv.weight: copying a param with shape torch.Size([4096, 1024, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([8192, 2048, 3, 3, 3]).
  size mismatch for decoder.up_blocks.1.conv.conv.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([8192]).
  size mismatch for decoder.up_blocks.2.res_blocks.0.conv1.conv.weight: copying a param with shape torch.Size([512, 512, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 3, 3, 3]).
  size mismatch for decoder.up_blocks.2.res_blocks.0.conv1.conv.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).
  size mismatch for decoder.up_blocks.2.res_blocks.0.conv2.conv.weight: copying a param with shape torch.Size([512, 512, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 3, 3, 3]).
  size mismatch for decoder.up_blocks.2.res_blocks.0.conv2.conv.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).
  size mismatch for decoder.up_blocks.2.res_blocks.1.conv1.conv.weight: copying a param with shape torch.Size([512, 512, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 3, 3, 3]).
  size mismatch for decoder.up_blocks.2.res_blocks.1.conv1.conv.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).
  size mismatch for decoder.up_blocks.2.res_blocks.1.conv2.conv.weight: copying a param with shape torch.Size([512, 512, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 3, 3, 3]).
  size mismatch for decoder.up_blocks.2.res_blocks.1.conv2.conv.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).
  size mismatch for decoder.up_blocks.3.conv1.conv.weight: copying a param with shape torch.Size([384, 512, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 3, 3, 3]).
  size mismatch for decoder.up_blocks.3.conv1.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.3.conv2.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.3.conv2.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.3.conv_shortcut.weight: copying a param with shape torch.Size([384, 512, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1, 1]).
  size mismatch for decoder.up_blocks.3.conv_shortcut.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.3.norm3.norm.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).
  size mismatch for decoder.up_blocks.3.norm3.norm.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).
  size mismatch for decoder.up_blocks.4.conv.conv.weight: copying a param with shape torch.Size([3072, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([4096, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.4.conv.conv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([4096]).
  size mismatch for decoder.up_blocks.5.res_blocks.0.conv1.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.5.res_blocks.0.conv1.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.5.res_blocks.0.conv2.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.5.res_blocks.0.conv2.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.5.res_blocks.1.conv1.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.5.res_blocks.1.conv1.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.5.res_blocks.1.conv2.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.5.res_blocks.1.conv2.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.5.res_blocks.2.conv1.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.5.res_blocks.2.conv1.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.5.res_blocks.2.conv2.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.5.res_blocks.2.conv2.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.5.res_blocks.3.conv1.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.5.res_blocks.3.conv1.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.5.res_blocks.3.conv2.conv.weight: copying a param with shape torch.Size([384, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.5.res_blocks.3.conv2.conv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.6.conv1.conv.weight: copying a param with shape torch.Size([256, 384, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 3, 3, 3]).
  size mismatch for decoder.up_blocks.6.conv_shortcut.weight: copying a param with shape torch.Size([256, 384, 1, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1, 1]).
  size mismatch for decoder.up_blocks.6.norm3.norm.weight: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).
  size mismatch for decoder.up_blocks.6.norm3.norm.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([512]).

Stack Trace

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\execution.py", line 543, in execute
    output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
                                                              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\execution.py", line 342, in get_output_data
    return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\custom_nodes\comfyui-lora-manager\py\metadata_collector\metadata_hook.py", line 177, in async_map_node_over_list_with_metadata
    results = await original_map_node_over_list(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ...<2 lines>...
    )
    ^

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\execution.py", line 316, in _async_map_node_over_list
    await process_inputs(input_dict, i)

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\execution.py", line 304, in process_inputs
    result = f(**inputs)

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\custom_nodes\deno-custom-nodes\deno_ltx23_preset_loader.py", line 755, in load_ltx_model
    model, clip, video_vae, audio_vae = self._load_kj_style(
                                        ~~~~~~~~~~~~~~~~~~~^
        diffusion_model_name=diffusion_model_name,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ...<5 lines>...
        weight_dtype=weight_dtype,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\custom_nodes\deno-custom-nodes\deno_ltx23_preset_loader.py", line 687, in _load_kj_style
    video_vae, audio_vae = self._load_kj_vaes(video_vae_name, audio_vae_name)
                           ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\custom_nodes\deno-custom-nodes\deno_ltx23_preset_loader.py", line 639, in _load_kj_vaes
    vae_loader.load_vae(video_vae_name, KJ_VAE_DEVICE, KJ_VAE_DTYPE),
    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\custom_nodes\comfyui-kjnodes\nodes\nodes.py", line 2425, in load_vae
    vae = VAE(sd=sd, device=device, dtype=dtype, metadata=metadata)

  File "E:\AIModels\ComfyUI_windows_portable\ComfyUI\comfy\sd.py", line 973, in __init__
    m, u = self.first_stage_model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

  File "E:\AIModels\ComfyUI_windows_portable\python_embeded\Lib\site-packages\torch\nn\modules\module.py", line 2638, in load_state_dict
    raise RuntimeError(
    ...<3 lines>...
    )

System Information

• ComfyUI Version: 0.29.2
• Arguments: ComfyUI\main.py --fast fp16_accumulation --use-sage-attention --lowvram
• OS: win32
• Python Version: 3.13.11 (tags/v3.13.11:6278944, Dec 5 2025, 16:26:58) [MSC v.1944 64 bit (AMD64)]
• Embedded Python: true
• PyTorch Version: 2.12.0+cu130

Devices

• Name: cuda:0 NVIDIA GeForce RTX 3060 Ti : cudaMallocAsync
  • Type: cuda
  • VRAM Total: 8589410304
  • VRAM Free: 7486832640
  • Torch VRAM Total: 0
  • Torch VRAM Free: 0