Inpainting doesn't work with the Cosmos Predict2 text to image model.

I'm on the latest version of ComfyUI and normal text to image generation with the model works well. But inpainting doesn't work at all.

When trying to use the InpaintModelConditioning node:

I get this error at the KSampler node:
`!!! Exception during processing !!! Dimension out of range (expected to be in range of [-4, 3], but got 4)
Traceback (most recent call last):
File "/home/user/ComfyUI/execution.py", line 361, 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 "/home/user/ComfyUI/execution.py", line 236, 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 "/home/user/ComfyUI/execution.py", line 208, in _map_node_over_list
process_inputs(input_dict, i)
File "/home/user/ComfyUI/execution.py", line 197, in process_inputs
results.append(getattr(obj, func)(**inputs))
File "/home/user/ComfyUI/nodes.py", line 1516, in sample
return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
File "/home/user/ComfyUI/nodes.py", line 1483, in common_ksampler
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
File "/home/user/ComfyUI/comfy/sample.py", line 45, 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 "/home/user/ComfyUI/comfy/samplers.py", line 1139, 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 "/home/user/ComfyUI/comfy/samplers.py", line 1029, in sample
return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
File "/home/user/ComfyUI/comfy/samplers.py", line 1014, in sample
output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
File "/home/user/ComfyUI/comfy/patcher_extension.py", line 111, in execute
return self.original(*args, **kwargs)
File "/home/user/ComfyUI/comfy/samplers.py", line 982, in outer_sample
output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
File "/home/user/ComfyUI/comfy/samplers.py", line 965, in inner_sample
samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
File "/home/user/ComfyUI/comfy/patcher_extension.py", line 111, in execute
return self.original(*args, **kwargs)
File "/home/user/ComfyUI/comfy/samplers.py", line 744, in sample
samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
File "/home/user/ComfyUI/venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/home/user/ComfyUI/comfy/k_diffusion/sampling.py", line 189, in sample_euler
denoised = model(x, sigma_hat * s_in, **extra_args)
File "/home/user/ComfyUI/comfy/samplers.py", line 396, in __call__
out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
File "/home/user/ComfyUI/comfy/samplers.py", line 945, in __call__
return self.predict_noise(*args, **kwargs)
File "/home/user/ComfyUI/comfy/samplers.py", line 948, 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 "/home/user/ComfyUI/comfy/samplers.py", line 376, in sampling_function
out = calc_cond_batch(model, conds, x, timestep, model_options)
File "/home/user/ComfyUI/comfy/samplers.py", line 206, in calc_cond_batch
return executor.execute(model, conds, x_in, timestep, model_options)
File "/home/user/ComfyUI/comfy/patcher_extension.py", line 111, in execute
return self.original(*args, **kwargs)
File "/home/user/ComfyUI/comfy/samplers.py", line 325, in _calc_cond_batch
output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
File "/home/user/ComfyUI/comfy/model_base.py", line 151, in apply_model
return comfy.patcher_extension.WrapperExecutor.new_class_executor(
File "/home/user/ComfyUI/comfy/patcher_extension.py", line 111, in execute
return self.original(*args, **kwargs)
File "/home/user/ComfyUI/comfy/model_base.py", line 188, in _apply_model
t = self.process_timestep(t, x=x, **extra_conds)
File "/home/user/ComfyUI/comfy/model_base.py", line 1027, in process_timestep
condition_video_mask_B_1_T_1_1 = denoise_mask.mean(dim=[1, 3, 4], keepdim=True)
IndexError: Dimension out of range (expected to be in range of [-4, 3], but got 4)`

When trying to use the VAE Encode (for Inpainting) node:

I get this error at the VAE Encode (for Inpainting) node:
`!!! Exception during processing !!! unsupported operand type(s) for //: 'int' and 'tuple'
Traceback (most recent call last):
File "/home/user/ComfyUI/execution.py", line 361, 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 "/home/user/ComfyUI/execution.py", line 236, 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 "/home/user/ComfyUI/execution.py", line 208, in _map_node_over_list
process_inputs(input_dict, i)
File "/home/user/ComfyUI/execution.py", line 197, in process_inputs
results.append(getattr(obj, func)(**inputs))
File "/home/user/ComfyUI/nodes.py", line 369, in encode
x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio
TypeError: unsupported operand type(s) for //: 'int' and 'tuple'
`