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  File "F:\Unstable Difusion\stable-diffusion-webui\modules\call_queue.py", line 56, in f
    res = list(func(*args, **kwargs))
  File "F:\Unstable Difusion\stable-diffusion-webui\modules\call_queue.py", line 37, in f
    res = func(*args, **kwargs)
  File "F:\Unstable Difusion\stable-diffusion-webui\modules\txt2img.py", line 53, in txt2img
    processed = modules.scripts.scripts_txt2img.run(p, *args)
  File "F:\Unstable Difusion\stable-diffusion-webui\modules\scripts.py", line 407, in run
    processed = script.run(p, *script_args)
  File "F:\Unstable Difusion\stable-diffusion-webui\scripts\xyz_grid.py", line 639, in run
    processed = draw_xyz_grid(
  File "F:\Unstable Difusion\stable-diffusion-webui\scripts\xyz_grid.py", line 307, in draw_xyz_grid
    process_cell(x, y, z, ix, iy, iz)
  File "F:\Unstable Difusion\stable-diffusion-webui\scripts\xyz_grid.py", line 250, in process_cell
    processed: Processed = cell(x, y, z, ix, iy, iz)
  File "F:\Unstable Difusion\stable-diffusion-webui\scripts\xyz_grid.py", line 602, in cell
    res = process_images(pc)
  File "F:\Unstable Difusion\stable-diffusion-webui\modules\processing.py", line 503, in process_images
    res = process_images_inner(p)
  File "F:\Unstable Difusion\stable-diffusion-webui\modules\processing.py", line 653, 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:\Unstable Difusion\stable-diffusion-webui\modules\processing.py", line 941, in sample
    samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
  File "F:\Unstable Difusion\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 319, in sample_img2img
    noise_sampler = self.create_noise_sampler(x, sigmas, p)
  File "F:\Unstable Difusion\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 291, in create_noise_sampler
    sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
RuntimeError: min(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument.