code: def is_using_v_parameterization_for_sd2(state_dict):
      """
      Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
      """
  
      import ldm.modules.diffusionmodules.openaimodel
  
      device = devices.cpu
  
      with sd_disable_initialization.DisableInitialization():
          unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
              use_checkpoint=True,
              use_fp16=False,
              image_size=32,
              in_channels=4,
              out_channels=4,
              model_channels=320,
              attention_resolutions=[4, 2, 1],
              num_res_blocks=2,
              channel_mult=[1, 2, 4, 4],
              num_head_channels=64,
              use_spatial_transformer=True,
              use_linear_in_transformer=True,
              transformer_depth=1,
              context_dim=1024,
              legacy=False,
          )
          unet.eval()
  
      with torch.no_grad():
          unet_sd = {
              k.replace("model.diffusion_model.", ""): v
              for k, v in state_dict.items()
              if "model.diffusion_model." in k
          }
          unet.load_state_dict(unet_sd, strict=True)
          unet.to(device=device, dtype=torch.float)
  
          test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
          x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
  
          out = (
              (
                  unet(
                      x_test,
                      torch.asarray([999], device=device),
                      context=test_cond,
                  )
                  - x_test
              )
              .mean()
              .item()
          )
  
      return out < -1
  
imports: import os
  import torch
  from modules import shared, paths, sd_disable_initialization, devices
label_desc: Use image node_id 1 for calling a global function with example usage: is_using_v_parameterization_for_sd2(state_dict) and returns: unknown