unsupported operand type(s) for %: 'int' and 'NoneType'

[fancy45daddy]

❓ Questions and Help

I follow the https://github.com/pytorch/xla/blob/master/contrib/kaggle/pytorch-xla-2-0-on-kaggle.ipynb

but the code in image = pipeline(prompt, callback=lambda *args: xm.mark_step(), generator=generator).images[0]
get

TypeError                                 Traceback (most recent call last)
Cell In[8], line 4
      1 generator = torch.Generator().manual_seed(0)
      2 # xm.mark_step compiles and executes the graph after each iteration.
      3 # The first few steps will be much slower than the rest.
----> 4 image = pipeline(prompt, callback=lambda *args: xm.mark_step(), generator=generator).images[0]
      5 image

File /usr/local/lib/python3.8/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
    112 @functools.wraps(func)
    113 def decorate_context(*args, **kwargs):
    114     with ctx_factory():
--> 115         return func(*args, **kwargs)

File /usr/local/lib/python3.8/site-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py:1035, in StableDiffusionPipeline.__call__(self, prompt, height, width, num_inference_steps, timesteps, sigmas, guidance_scale, negative_prompt, num_images_per_prompt, eta, generator, latents, prompt_embeds, negative_prompt_embeds, ip_adapter_image, ip_adapter_image_embeds, output_type, return_dict, cross_attention_kwargs, guidance_rescale, clip_skip, callback_on_step_end, callback_on_step_end_tensor_inputs, **kwargs)
   1033 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
   1034     progress_bar.update()
-> 1035     if callback is not None and i % callback_steps == 0:
   1036         step_idx = i // getattr(self.scheduler, "order", 1)
   1037         callback(step_idx, t, latents)

TypeError: unsupported operand type(s) for %: 'int' and 'NoneType'

how to fix the problem?