{"record":{"id":"cb79461f13a7ae4f","repo":"lllyasviel/ControlNet","slug":"invalid-beta-parameter-at-index-1","errorCode":null,"errorMessage":"Invalid beta parameter at index 1: {}","messagePattern":"Invalid beta parameter at index 1: (.+?)","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"ldm/util.py","lineNumber":103,"sourceCode":"        module_imp = importlib.import_module(module)\n        importlib.reload(module_imp)\n    return getattr(importlib.import_module(module, package=None), cls)\n\n\nclass AdamWwithEMAandWings(optim.Optimizer):\n    # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298\n    def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8,  # TODO: check hyperparameters before using\n                 weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999,   # ema decay to match previous code\n                 ema_power=1., param_names=()):\n        \"\"\"AdamW that saves EMA versions of the parameters.\"\"\"\n        if not 0.0 <= lr:\n            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n        if not 0.0 <= eps:\n            raise ValueError(\"Invalid epsilon value: {}\".format(eps))\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\n        if not 0.0 <= weight_decay:\n            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n        if not 0.0 <= ema_decay <= 1.0:\n            raise ValueError(\"Invalid ema_decay value: {}\".format(ema_decay))\n        defaults = dict(lr=lr, betas=betas, eps=eps,\n                        weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,\n                        ema_power=ema_power, param_names=param_names)\n        super().__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super().__setstate__(state)\n        for group in self.param_groups:\n            group.setdefault('amsgrad', False)\n\n    @torch.no_grad()\n    def step(self, closure=None):\n        \"\"\"Performs a single optimization step.\n        Args:","sourceCodeStart":85,"sourceCodeEnd":121,"githubUrl":"https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/ldm/util.py#L85-L121","documentation":"Raised by the EMA-tracking AdamW optimizer when betas[1] (beta2, the decay rate for the second-moment/squared-gradient moving average) is outside [0.0, 1.0). beta2 must be strictly below 1; values like 1.0 or negatives abort construction.","triggerScenarios":"Constructing AdamW(params, betas=(0.9, 1.0)) or betas=(0.9, -0.999); commonly beta2=1.0 from configs tuned for other optimizers or sweep bounds off by one.","commonSituations":"Config files specifying beta2 as 1 to 'disable' second-moment decay, sweep scripts generating inclusive upper bounds of 1.0, or transposed betas tuples.","solutions":["Set beta2 to a valid value in [0, 1), typically 0.999 (or 0.99 / 0.9999 variants)","Check the tuple isn't transposed or malformed in the config","Validate betas programmatically before constructing the optimizer"],"exampleFix":"# before\nopt = AdamW(params, betas=(0.9, 1.0))\n# after\nopt = AdamW(params, betas=(0.9, 0.999))","handlingStrategy":"validation","validationCode":"assert 0.0 <= betas[1] < 1.0, f'bad beta2: {betas[1]}'","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Use standard beta2 values (0.999, 0.9999)","Never set beta2 to 1.0 to 'disable' decay","Check betas tuple order when porting configs"],"tags":["optimizer","adamw","betas","diffusion-training","validation"],"backgroundTag":"invalid-optimizer-hyperparameter","analyzedSha":"ed85cd1e25a5ed592f7d8178495b4483de0331bf","analyzedAt":"2026-08-27T12:58:54.167Z","schemaVersion":2},"datasetVersion":"2026-08-27T13:17:12.746Z"}