fast-stable-diffusion: NansException when trying to generate larger images.

Here’s the full error message

 0% 0/20 [00:03<?, ?it/s]
Error completing request
Arguments: ('task(681nzcxdv4qmpin)', '', '', [], 20, 0, False, False, 1, 1, 7, -1.0, -1.0, 0, 0, 0, False, 1800, 1200, False, 0.7, 2, 'Latent', 0, 0, 0, [], 0, True, False, 1, False, False, False, 1.1, 1.5, 100, 0.7, False, False, True, False, False, 0, 'Gustavosta/MagicPrompt-Stable-Diffusion', '', <scripts.external_code.ControlNetUnit object at 0x7f9625db67f0>, <scripts.external_code.ControlNetUnit object at 0x7f9625db6520>, <scripts.external_code.ControlNetUnit object at 0x7f9625db6880>, <scripts.external_code.ControlNetUnit object at 0x7f9625db60d0>, <scripts.external_code.ControlNetUnit object at 0x7f9625db6910>, <scripts.external_code.ControlNetUnit object at 0x7f9625db61f0>, <scripts.external_code.ControlNetUnit object at 0x7f9625db6550>, <scripts.external_code.ControlNetUnit object at 0x7f9625db6b20>, <scripts.external_code.ControlNetUnit object at 0x7f9625dbb9a0>, <scripts.external_code.ControlNetUnit object at 0x7f9625dbb910>, False, False, 'Horizontal', '1,1', '0.2', False, False, False, 'Attention', False, False, 3, 0, False, False, 0, False, False, '1:1,1:2,1:2', '0:0,0:0,0:1', '0.2,0.8,0.8', 20, False, False, 'positive', 'comma', 0, False, False, '', 1, '', 0, '', 0, '', True, False, False, False, 0, None, False, None, False, None, False, None, False, None, False, None, False, None, False, None, False, None, False, None, False, 50) {}
Traceback (most recent call last):
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/call_queue.py", line 56, in f
    res = list(func(*args, **kwargs))
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/call_queue.py", line 37, in f
    res = func(*args, **kwargs)
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/txt2img.py", line 56, in txt2img
    processed = process_images(p)
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/processing.py", line 503, in process_images
    res = process_images_inner(p)
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/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 "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/processing.py", line 869, in sample
    samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 358, in sample
    samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 234, in launch_sampling
    return func()
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 358, in <lambda>
    samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
  File "/usr/local/lib/python3.9/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/content/gdrive/MyDrive/sd/stablediffusion/src/k-diffusion/k_diffusion/sampling.py", line 145, in sample_euler_ancestral
    denoised = model(x, sigmas[i] * s_in, **extra_args)
  File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 152, in forward
    devices.test_for_nans(x_out, "unet")
  File "/content/gdrive/.shortcut-targets-by-id/1S33CUH0-cqG4EkR8r0vGFnH9aECswn4r/sd/stable-diffusion-webui/modules/devices.py", line 152, 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 setting the "Upcast cross attention layer to float32" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this. Use --disable-nan-check commandline argument to disable this check.

This all started a few days ago. There are no issues when I try to generate smaller images. 1280x1280 is fine, but if I were to go 1200x1800 or 1536x1536, that error is thrown. Basically, I can’t hires fix moderately sized images anymore. I’m using the exact same model, loras, settings, prompts and everything else as I always have.

I’ve tried:

• running it without connecting my gdrive, ensuring it use a freshly downloaded SD with nothing extra
• using the Use_Latest_Working_Commit checkbox
• reverting back to even older versions
• switch to different google accounts Nothing has worked so far.

Maybe something was changed on colab’s side? Any help would be appreciated.