Traceback (most recent call last):
  File "F:\stable-diffusion\modules\call_queue.py", line 56, in f
    res = list(func(*args, **kwargs))
  File "F:\stable-diffusion\modules\call_queue.py", line 37, in f
    res = func(*args, **kwargs)
  File "F:\stable-diffusion\modules\img2img.py", line 148, in img2img
    processed = process_images(p)
  File "F:\stable-diffusion\modules\processing.py", line 475, in process_images
    res = process_images_inner(p)
  File "F:\stable-diffusion\modules\processing.py", line 610, in process_images_inner
    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
  File "F:\stable-diffusion\modules\processing.py", line 1018, in sample
    samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
  File "F:\stable-diffusion\modules\sd_samplers.py", line 518, in sample_img2img
    samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
  File "F:\stable-diffusion\modules\sd_samplers.py", line 447, in launch_sampling
    return func()
  File "F:\stable-diffusion\modules\sd_samplers.py", line 518, in <lambda>
    samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
  File "F:\stable-diffusion\venv\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "F:\stable-diffusion\repositories\k-diffusion\k_diffusion\sampling.py", line 145, in sample_euler_ancestral
    denoised = model(x, sigmas[i] * s_in, **extra_args)
  File "F:\stable-diffusion\venv\lib\site-packages\torch\nn\modules\module.py", line 1488, in _call_impl
    return forward_call(*args, **kwargs)
  File "F:\stable-diffusion\modules\sd_samplers.py", line 354, in forward
    devices.test_for_nans(x_out, "unet")
  File "F:\stable-diffusion\modules\devices.py", line 136, in test_for_nans
    raise NansException(message)
modules.devices.NansException: A tensor with all NaNs was produced in Unet. This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try using --no-half commandline argument to fix this.