Error Using v1.5 Model with ControlNet Tile Controlnet Mode Set and Multidiffusion with Tile Batch Size at the Same Time

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### Repro steps using integrated ControlNet and Multidiffusion:

1. load a v1.5 model in `sd` mode into img2img
2. enable ControlNet node 0 (tile, `control_v11f1e_sd15_tile [a371b31b]`, `My prompt is more important` or `ControlNet is more important`)
3. enable Multiduffusion (batch size > 1)

### Result
`RuntimeError: The size of tensor a (4) must match the size of tensor b (2) at non-singleton dimension 0`

```
2024-10-31 14:13:16,316 - ControlNet - INFO - ControlNet Input Mode: InputMode.SIMPLE
2024-10-31 14:13:16,426 - ControlNet - INFO - Using preprocessor: tile_resample
2024-10-31 14:13:16,426 - ControlNet - INFO - preprocessor resolution = 1536
2024-10-31 14:13:16,447 - ControlNet - INFO - Current ControlNet ControlNetPatcher: /home/rt/ai/models/stable-diffusion/ControlNet/v1.5/control_v11f1e_sd15_tile.pth
[Unload] Trying to free 14163.61 MB for cuda:0 with 1 models keep loaded ... Current free memory is 17865.67 MB ... Done.
2024-10-31 14:13:16,941 - ControlNet - INFO - ControlNet Method tile_resample patched.
[Unload] Trying to free 6009.88 MB for cuda:0 with 0 models keep loaded ... Current free memory is 17960.11 MB ... Done.
[Memory Management] Target: KModel, Free GPU: 17960.11 MB, Model Require: 0.00 MB, Previously Loaded: 1639.41 MB, Inference Require: 1021.00 MB, Remaining: 16939.11 MB, All loaded to GPU.
[Memory Management] Target: ControlNet, Free GPU: 17960.11 MB, Model Require: 0.00 MB, Previously Loaded: 689.09 MB, Inference Require: 1021.00 MB, Remaining: 16939.11 MB, All loaded to GPU.
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samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/home/rt/ai/repos/stable-diffusion-webui-forge/venv/lib64/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/k_diffusion/sampling.py", line 595, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/venv/lib64/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/venv/lib64/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/modules/sd_samplers_cfg_denoiser.py", line 199, in forward
denoised, cond_pred, uncond_pred = sampling_function(self, denoiser_params=denoiser_params, cond_scale=cond_scale, cond_composition=cond_composition)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/backend/sampling/sampling_function.py", line 362, in sampling_function
denoised, cond_pred, uncond_pred = sampling_function_inner(model, x, timestep, uncond, cond, cond_scale, model_options, seed, return_full=True)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/backend/sampling/sampling_function.py", line 303, in sampling_function_inner
cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/backend/sampling/sampling_function.py", line 271, in calc_cond_uncond_batch
output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/venv/lib64/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/extensions-builtin/sd_forge_multidiffusion/lib_multidiffusion/tiled_diffusion.py", line 457, in __call__
c_tile['control'] = c_in['control_model'].get_control(x_tile, ts_tile, c_tile, len(cond_or_uncond))
File "/home/rt/ai/repos/stable-diffusion-webui-forge/backend/patcher/controlnet.py", line 339, in get_control
return self.control_merge(None, control, control_prev, output_dtype)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/backend/patcher/controlnet.py", line 262, in control_merge
out = compute_controlnet_weighting(out, self)
File "/home/rt/ai/repos/stable-diffusion-webui-forge/backend/patcher/controlnet.py", line 148, in compute_controlnet_weighting
control[k][i] = control_signal * final_weight[:, None, None, None]
RuntimeError: The size of tensor a (4) must match the size of tensor b (2) at non-singleton dimension 0
The size of tensor a (4) must match the size of tensor b (2) at non-singleton dimension 0
```

Reproduce the v1.5 img2img case with ControlNet tile mode and Multidiffusion batch size greater than 1, then trace the call from extensions-builtin/sd_forge_multidiffusion/lib_multidiffusion/tiled_diffusion.py through backend/patcher/controlnet.py. Inspect compute_controlnet_weighting and get_control to understand the reported batch-size mismatch. Done means the same configuration completes without the RuntimeError.

Written by the indexing model from the issue text.