{"record":{"id":"0013c626d83b40b7","repo":"invoke-ai/InvokeAI","slug":"cannot-achieve-the-target-of-num-channels-num-cha","errorCode":null,"errorMessage":"Cannot achieve the target of num_channels={num_channels}.","messagePattern":"Cannot achieve the target of num_channels=(.+?)\\.","errorType":"exception","errorClass":null,"httpStatus":null,"severity":"error","filePath":"invokeai/app/util/controlnet_utils.py","lineNumber":426,"sourceCode":"        nimage = np.array(nimage).astype(np.float32) / 255.0\n        nimage = nimage.transpose(0, 3, 1, 2)\n        timage = torch.from_numpy(nimage)\n\n    # use fancy lvmin controlnet resizing\n    elif resize_mode == \"just_resize\" or resize_mode == \"crop_resize\" or resize_mode == \"fill_resize\":\n        nimage = np.array(image)\n        timage, nimage = np_img_resize(\n            np_img=nimage,\n            resize_mode=resize_mode,\n            h=height,\n            w=width,\n            device=torch.device(device),\n        )\n    else:\n        raise ValueError(f\"Unsupported resize_mode: '{resize_mode}'.\")\n\n    if timage.shape[1] < num_channels or num_channels <= 0:\n        raise ValueError(f\"Cannot achieve the target of num_channels={num_channels}.\")\n    timage = timage[:, :num_channels, :, :]\n\n    timage = timage.to(device=device, dtype=dtype)\n    cfg_injection = control_mode == \"more_control\" or control_mode == \"unbalanced\"\n    if do_classifier_free_guidance and not cfg_injection:\n        timage = torch.cat([timage] * 2)\n    return timage\n","sourceCodeStart":408,"sourceCodeEnd":434,"githubUrl":"https://github.com/invoke-ai/InvokeAI/blob/0b6a024f2ff6a86bfb953dcdb9cc504ef7397a06/invokeai/app/util/controlnet_utils.py#L408-L434","documentation":"prepare_control_image slices the resized control-image tensor down to exactly `num_channels`. If the tensor has fewer channels than requested (or num_channels is <= 0), the target channel count is unreachable and this ValueError is raised instead of silently producing a malformed control input.","triggerScenarios":"Calling prep_control_data / run_t2i_adapters / prepare_controlnet_cond with a control image whose effective channel count after resizing is less than the model's required channels (e.g. 1-channel or 4-channel image against 3 channels required is fine, but 3-channel against 4 required fails), or passing num_channels <= 0 via a misconfigured ControlNet/T2I-Adapter model field.","commonSituations":"Using a grayscale single-channel control image with a model expecting more channels; a ControlNet model record whose target channels value is 0 or negative due to a bad/legacy config; feeding an alpha-only PNG loaded with 2 channels.","solutions":["Check the control image's channel count and convert it to RGB (3 channels) before passing it in","Verify the ControlNet/T2I-Adapter model config has a correct positive channels field; re-import or fix the model record","Ensure the resize_mode branch you took actually produces a tensor with >= num_channels channels","Pass an explicit valid num_channels when calling prep_control_data instead of deriving it from a bad config"],"exampleFix":"// before\nimg = Image.open('mask.png')  # mode 'L', 1 channel\ncontrol_data = prep_control_data(..., control_image=img, ...)  # ValueError\n// after\nimg = Image.open('mask.png').convert('RGB')\ncontrol_data = prep_control_data(..., control_image=img, ...)","handlingStrategy":"validation","validationCode":"import numpy as np\nfrom PIL import Image\n\ndef ensure_channels_ok(image, num_channels):\n    if num_channels is None or num_channels <= 0:\n        raise ValueError(f\"num_channels must be positive, got {num_channels}\")\n    arr = np.asarray(Image.open(image) if isinstance(image, str) else image)\n    if arr.ndim == 2:\n        ch = 1\n    elif arr.shape[-1] in (1, 2, 3, 4):\n        ch = arr.shape[-1]\n    else:\n        ch = 3\n    if ch < num_channels:\n        raise ValueError(f\"image has {ch} channels; need {num_channels}\")","typeGuard":"def has_enough_channels(t, num_channels):\n    return num_channels > 0 and t.dim() >= 3 and t.shape[1] >= num_channels","tryCatchPattern":"try:\n    control_data = prep_control_data(..., control_image=img, ...)\nexcept ValueError as e:\n    if 'num_channels' in str(e):\n        img = img.convert('RGB')\n        control_data = prep_control_data(..., control_image=img, ...)\n    else:\n        raise","preventionTips":["Always .convert('RGB') control images before passing them in","Never hand-set num_channels; take it from the model's config","Validate model imports so channels fields cannot be 0/negative"],"tags":["python","valueerror","controlnet","image-processing"],"backgroundTag":"tensor-channel-count-mismatch","analyzedSha":"0b6a024f2ff6a86bfb953dcdb9cc504ef7397a06","analyzedAt":"2026-08-29T04:46:49.967Z","schemaVersion":2},"datasetVersion":"2026-08-29T07:17:48.351Z"}