pixel_unshuffle expects width to be divisible by downscale_factor, but input.size(-1)=1000 is not divisible by 16

hello, with SDXL CN I keep getting pixel_unshuffle expects width to be divisible by downscale_factor, but input.size(-1)=1000 is not divisible by 16
Unless I changed the width and height to be around the same, so for example if I put the width 1000 and height 1200 I get this error but it's fine if I make both 1000
Getting this with canny, depth, scripple and lineart SDXL CN models, probably others too but I haven't tested them all yet.
Is there something I am doing wrong?

In the above screenshot I used 1000x1010 resolution and like I said, if I use the exact same width and height it works fine (e.g. 800x800, 1000x1000 etc.).

Here's the full console error:

*** Error completing request
*** Arguments: ('task(astt86172t72b46)', 0, 'ant drawing for kids', '', [], <PIL.Image.Image image mode=RGBA size=485x857 at 0x1673111B250>, None, None, None, None, None, None, 20, 'DPM++ 2S a Karras', 4, 0, 1, 1, 1, 7, 1.5, 0.75, 0, 1100, 1000, 1, 0, 0, 32, 0, '', '', '', [], False, [], '', <gradio.routes.Request object at 0x000001673111B430>, 0, False, '', 0.8, -1, False, -1, 0, 0, 0, False, False, False, False, 'base', False, {'ad_model': 'face_yolov8n.pt', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_k_largest': 0, 'ad_mask_min_ratio': 0, 'ad_mask_max_ratio': 1, 'ad_x_offset': 0, 'ad_y_offset': 0, 'ad_dilate_erode': 4, 'ad_mask_merge_invert': 'None', 'ad_mask_blur': 4, 'ad_denoising_strength': 0.4, 'ad_inpaint_only_masked': True, 'ad_inpaint_only_masked_padding': 32, 'ad_use_inpaint_width_height': False, 'ad_inpaint_width': 512, 'ad_inpaint_height': 512, 'ad_use_steps': False, 'ad_steps': 28, 'ad_use_cfg_scale': False, 'ad_cfg_scale': 7, 'ad_use_checkpoint': False, 'ad_checkpoint': 'Use same checkpoint', 'ad_use_vae': False, 'ad_vae': 'Use same VAE', 'ad_use_sampler': False, 'ad_sampler': 'Euler a', 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_use_clip_skip': False, 'ad_clip_skip': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'inpaint_global_harmonious', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': ()}, {'ad_model': 'None', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_k_largest': 0, 'ad_mask_min_ratio': 0, 'ad_mask_max_ratio': 1, 'ad_x_offset': 0, 'ad_y_offset': 0, 'ad_dilate_erode': 4, 'ad_mask_merge_invert': 'None', 'ad_mask_blur': 4, 'ad_denoising_strength': 0.4, 'ad_inpaint_only_masked': True, 'ad_inpaint_only_masked_padding': 32, 'ad_use_inpaint_width_height': False, 'ad_inpaint_width': 512, 'ad_inpaint_height': 512, 'ad_use_steps': False, 'ad_steps': 28, 'ad_use_cfg_scale': False, 'ad_cfg_scale': 7, 'ad_use_checkpoint': False, 'ad_checkpoint': 'Use same checkpoint', 'ad_use_vae': False, 'ad_vae': 'Use same VAE', 'ad_use_sampler': False, 'ad_sampler': 'Euler a', 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_use_clip_skip': False, 'ad_clip_skip': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'inpaint_global_harmonious', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': ()}, False, 'MultiDiffusion', False, True, 1024, 1024, 96, 96, 48, 4, 'None', 2, False, 10, 1, 1, 64, False, False, False, False, False, 0.4, 0.4, 0.2, 0.2, '', '', 'Background', 0.2, -1.0, False, 0.4, 0.4, 0.2, 0.2, '', '', 'Background', 0.2, -1.0, False, 0.4, 0.4, 0.2, 0.2, '', '', 'Background', 0.2, -1.0, False, 0.4, 0.4, 0.2, 0.2, '', '', 'Background', 0.2, -1.0, False, 0.4, 0.4, 0.2, 0.2, '', '', 'Background', 0.2, -1.0, False, 0.4, 0.4, 0.2, 0.2, '', '', 'Background', 0.2, -1.0, False, 0.4, 0.4, 0.2, 0.2, '', '', 'Background', 0.2, -1.0, False, 0.4, 0.4, 0.2, 0.2, '', '', 'Background', 0.2, -1.0, False, 3072, 192, True, True, True, False, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x00000167218112D0>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x0000016721811B10>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x0000016721811EA0>, None, False, '0', '0', 'inswapper_128.onnx', 'CodeFormer', 1, True, '', 1, 1, False, True, 1, 0, 0, False, False, False, 0, None, [], 0, False, [], [], False, 0, 1, False, False, 0, None, [], -2, False, [], False, 0, None, None, '* CFG Scale should be 2 or lower.', True, True, '', '', True, 50, True, 1, 0, False, 4, 0.5, 'Linear', 'None', '

Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8

', 128, 8, ['left', 'right', 'up', 'down'], 1, 0.05, 128, 4, 0, ['left', 'right', 'up', 'down'], False, False, 'positive', 'comma', 0, False, False, '', '

Will upscale the image by the selected scale factor; use width and height sliders to set tile size

', 64, 0, 2, 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, False, None, None, False, None, None, False, None, None, False, 50, '

Will upscale the image depending on the selected target size type

', 512, 0, 8, 32, 64, 0.35, 32, 0, True, 0, False, 8, 0, 0, 2048, 2048, 2) {}
Traceback (most recent call last):
File "G:\stable-diffusion-webui\modules\call_queue.py", line 57, in f
res = list(func(*args, **kwargs))
File "G:\stable-diffusion-webui\modules\call_queue.py", line 36, in f
res = func(*args, **kwargs)
File "G:\stable-diffusion-webui\modules\img2img.py", line 208, in img2img
processed = process_images(p)
File "G:\stable-diffusion-webui\modules\processing.py", line 732, in process_images
res = process_images_inner(p)
File "G:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 42, in processing_process_images_hijack
return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
File "G:\stable-diffusion-webui\modules\processing.py", line 867, in process_images_inner
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
File "G:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 451, in process_sample
return process.sample_before_CN_hack(*args, **kwargs)
File "G:\stable-diffusion-webui\modules\processing.py", line 1528, in sample
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
File "G:\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 188, in sample_img2img
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 "G:\stable-diffusion-webui\modules\sd_samplers_common.py", line 261, in launch_sampling
return func()
File "G:\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 188, in
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 "G:\stable-diffusion-webui\venv\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "G:\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 518, in sample_dpmpp_2s_ancestral
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "G:\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stable-diffusion-webui\modules\sd_samplers_cfg_denoiser.py", line 169, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
File "G:\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 112, in forward
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
File "G:\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "G:\stable-diffusion-webui\modules\sd_models_xl.py", line 37, in apply_model
return self.model(x, t, cond)
File "G:\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stable-diffusion-webui\modules\sd_hijack_utils.py", line 17, in
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "G:\stable-diffusion-webui\modules\sd_hijack_utils.py", line 28, in call
return self.__orig_func(*args, **kwargs)
File "G:\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\wrappers.py", line 28, in forward
return self.diffusion_model(
File "G:\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 858, in forward_webui
raise e
File "G:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 855, in forward_webui
return forward(*args, **kwargs)
File "G:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\hook.py", line 592, in forward
control = param.control_model(x=x_in, hint=hint, timesteps=timesteps, context=context, y=y)
File "G:\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\adapter.py", line 70, in forward
self.control = self.control_model(hint_in)
File "G:\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\adapter.py", line 270, in forward
x = self.unshuffle(x)
File "G:\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\pixelshuffle.py", line 104, in forward
return F.pixel_unshuffle(input, self.downscale_factor)
RuntimeError: pixel_unshuffle expects height to be divisible by downscale_factor, but input.size(-2)=1096 is not divisible by 16

Start with scripts/adapter.py, especially the adapter forward path at the unshuffle call, and reproduce the failure using the reported non-square SDXL ControlNet dimensions. Trace how the input height and width reach torch.nn.PixelUnshuffle and compare the working square case with the failing 1000x1200 or 1000x1010 cases. Done means the reported valid use case no longer raises the divisibility error, with regression coverage for the relevant dimensions.