QR code seems to output Bools

[TobTobXX]

I'm fairly new to stable diffusion et al., so I don't know if this is an issue:

When I pipe the output of the "Create QR Code" into the image input of a control net, I get an error.
doesnt-work.json

Python error message

Error occurred when executing KSampler:

"compute_indices_weights_nearest" not implemented for 'Bool'

  File "/app/execution.py", line 316, in execute
    output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
  File "/app/execution.py", line 191, in get_output_data
    return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
  File "/app/execution.py", line 168, in _map_node_over_list
    process_inputs(input_dict, i)
  File "/app/execution.py", line 157, in process_inputs
    results.append(getattr(obj, func)(**inputs))
  File "/app/nodes.py", line 1429, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
  File "/app/nodes.py", line 1396, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
  File "/app/comfy/sample.py", line 43, in sample
    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/app/comfy/samplers.py", line 829, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
  File "/app/comfy/samplers.py", line 729, in sample
    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/app/comfy/samplers.py", line 716, in sample
    output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
  File "/app/comfy/samplers.py", line 695, in inner_sample
    samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
  File "/app/comfy/samplers.py", line 600, in sample
    samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
  File "/usr/local/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/app/comfy/k_diffusion/sampling.py", line 144, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)
  File "/app/comfy/samplers.py", line 299, in __call__
    out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
  File "/app/comfy/samplers.py", line 682, in __call__
    return self.predict_noise(*args, **kwargs)
  File "/app/comfy/samplers.py", line 685, in predict_noise
    return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
  File "/app/comfy/samplers.py", line 279, in sampling_function
    out = calc_cond_batch(model, conds, x, timestep, model_options)
  File "/app/comfy/samplers.py", line 202, in calc_cond_batch
    c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
  File "/app/comfy/controlnet.py", line 217, in get_control
    self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
  File "/app/comfy/utils.py", line 711, in common_upscale
    return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
  File "/usr/local/lib/python3.10/site-packages/torch/nn/functional.py", line 4543, in interpolate
    return torch._C._nn._upsample_nearest_exact2d(input, output_size, scale_factors)

I have a workaround: If I pass it through a VAE Encode and a VAE Decode, it works afterwards.

Is this a bug? I have no idea. Help is appreciated. :)