[BUG] sd3.5 cannot work with CN upscale

[jasoncow007]

RuntimeError: Unexpected error accessing result["data"]["payload"]. Result: {'type': 'error', 'message': 'execution_error', 'details': None, 'extra_info': None, 'data_status': 'COMPLETED', 'data': {'prompt_id': '11381283-75ae-4c5e-b6fe-ad20f9ed1eae', 'node_id': '701', 'node_type': 'ControlNetApplySD3', 'executed': ['767', '3', '4', '61', '150', '152', '768', '118', '153', '483'], 'exception_message': "'NoneType' object has no attribute 'copy'", 'exception_type': 'AttributeError', 'traceback': [' File "/workspace/app/submodules/ComfyUI/execution.py", line 323, in execute\n 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)\n', ' File "/workspace/app/submodules/ComfyUI/execution.py", line 198, in get_output_data\n 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)\n', ' File "/workspace/app/submodules/ComfyUI/execution.py", line 169, in _map_node_over_list\n process_inputs(input_dict, i)\n', ' File "/workspace/app/submodules/ComfyUI/execution.py", line 158, in process_inputs\n results.append(getattr(obj, func)(**inputs))\n', ' File "/workspace/app/submodules/ComfyUI/nodes.py", line 860, in apply_controlnet\n c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae, extra_concat=extra_concat)\n'], 'current_inputs': {'control_net': [None], 'end_percent': [1.0], 'image': ['tensor([[[[0.6902, 0.6431, 0.6039],\n [0.6902, 0.6431, 0.6039],\n [0.6902, 0.6431, 0.6039],\n ...,\n [0.7098, 0.7608, 0.7804],\n [0.7098, 0.7608, 0.7804],\n [0.7176, 0.7647, 0.7843]],\n\n [[0.6902, 0.6431, 0.6039],\n [0.6902, 0.6431, 0.6039],\n [0.6902, 0.6431, 0.6039],\n ...,\n [0.7098, 0.7608, 0.7804],\n [0.7098, 0.7608, 0.7804],\n [0.7176, 0.7647, 0.7843]],\n\n [[0.6902, 0.6431, 0.6039],\n [0.6902, 0.6431, 0.6039],\n [0.6902, 0.6431, 0.6039],\n ...,\n [0.7098, 0.7608, 0.7804],\n [0.7098, 0.7608, 0.7804],\n [0.7176, 0.7647, 0.7843]],\n\n ...,\n\n [[0.6980, 0.6627, 0.6471],\n [0.6980, 0.6627, 0.6471],\n [0.6902, 0.6588, 0.6431],\n ...,\n [0.9490, 0.9608, 0.9373],\n [0.9490, 0.9608, 0.9373],\n [0.9490, 0.9608, 0.9373]],\n\n [[0.6980, 0.6627, 0.6471],\n [0.6980, 0.6627, 0.6471],\n [0.6902, 0.6588, 0.6431],\n ...,\n [0.9451, 0.9569, 0.9333],\n [0.9451, 0.9569, 0.9333],\n [0.9451, 0.9569, 0.9333]],\n\n [[0.6980, 0.6627, 0.6471],\n [0.6980, 0.6627, 0.6471],\n [0.6902, 0.6588, 0.6431],\n ...,\n [0.9373, 0.9529, 0.9294],\n [0.9373, 0.9529, 0.9294],\n [0.9373, 0.9529, 0.9294]]]])'], 'negative': ["[[tensor([[[0., 0., 0., ..., 0., 0., 0.],\n [0., 0., 0., ..., 0., 0., 0.],\n [0., 0., 0., ..., 0., 0., 0.],\n ...,\n [0., 0., 0., ..., 0., 0., 0.],\n [0., 0., 0., ..., 0., 0., 0.],\n [0., 0., 0., ..., 0., 0., 0.]]], device='cuda:0'), {'pooled_output': tensor([[0., 0., 0., ..., 0., 0., 0.]], device='cuda:0'), 'start_percent': 0.1, 'end_percent': 1.0}], [tensor([[[-3.8917e+00, -2.5113e+00, 4.7167e+00, ..., 0.0000e+00,\n 0.0000e+00, 0.0000e+00],\n [-3.7585e-01, -6.8345e-01, -4.7284e-01, ..., 0.0000e+00,\n 0.0000e+00, 0.0000e+00],\n [-4.8401e-01, -7.4149e-01, -4.3799e-01, ..., 0.0000e+00,\n 0.0000e+00, 0.0000e+00],\n ...,\n [ 5.8278e-02, -1.0670e-02, 4.4628e-02, ..., 1.2992e-03,\n -4.6298e-02, -1.7905e-02],\n [ 5.8345e-02, -8.9584e-03, 2.9449e-02, ..., -2.0960e-03,\n -3.9725e-02, -2.4705e-02],\n [ 5.2542e-04, 2.2571e-02, -1.1793e-02, ..., 6.6061e-03,\n -2.1331e-02, 1.3511e-02]]], device='cuda:0'), {'pooled_output': tensor([[-0.3714, -1.4495, -0.3403, ..., -0.6773, -0.3750, 0.6522]],\n device='cuda:0'), 'start_percent': 0.0, 'end_percent': 0.1}]]"], 'positive': ["[[tensor([[[-3.8917e+00, -2.5113e+00, 4.7167e+00, ..., 0.0000e+00,\n 0.0000e+00, 0.0000e+00],\n [ 5.0822e-01, 4.6841e-01, 7.2176e-02, ..., 0.0000e+00,\n 0.0000e+00, 0.0000e+00],\n [ 1.3224e+00, -6.9860e-01, 1.3204e-01, ..., 0.0000e+00,\n 0.0000e+00, 0.0000e+00],\n ...,\n [ 5.8658e-02, 1.2930e-02, -5.4710e-02, ..., -2.6366e-01,\n -2.1682e-02, 8.8016e-02],\n [ 5.0061e-02, -9.4078e-03, 4.4527e-02, ..., 1.2542e-03,\n -4.7462e-02, -1.9271e-02],\n [ 8.4994e-03, 1.7487e-02, -1.3537e-02, ..., -4.4523e-02,\n -7.7291e-03, -2.8472e-04]]], device='cuda:0'), {'pooled_output': tensor([[ 1.3180, -0.9755, 1.2002, ..., -1.6158, 0.2655, -0.2503]],\n device='cuda:0')}]]"], 'start_percent': [0.0], 'strength': [0.8], 'vae': ['<comfy.sd.VAE object at 0x7ff4843ee920>']}, 'current_outputs': ['3', '767', '4', '1', '61', '483', '692', '150', '152', '701', '768', '118', '153', '2'], 'timestamp': 1732694023834}, 'start_time': 1732693998.3401382, 'end_time': 1732694023.835185, 'task_id': '11381283-75ae-4c5e-b6fe-ad20f9ed1eae', 'upload_to_s3': None}

[doombeaker]

we cannot determine the problem without workflow

[jasoncow007]

we cannot determine the problem without workflow

just basic sd3.5 t2i + cn upscale