{"record":{"id":"fa08383d92e6fa96","repo":"lllyasviel/ControlNet","slug":"invalid-beta-parameter-at-index-0","errorCode":null,"errorMessage":"Invalid beta parameter at index 0: {}","messagePattern":"Invalid beta parameter at index 0: (.+?)","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"ldm/util.py","lineNumber":101,"sourceCode":"    module, cls = string.rsplit(\".\", 1)\n    if reload:\n        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):","sourceCodeStart":83,"sourceCodeEnd":119,"githubUrl":"https://github.com/lllyasviel/ControlNet/blob/ed85cd1e25a5ed592f7d8178495b4483de0331bf/ldm/util.py#L83-L119","documentation":"Raised by the EMA-tracking AdamW optimizer when betas[0] (beta1, the decay rate for the first-moment/gradient moving average) is outside the half-open range [0.0, 1.0). Adam requires beta1 strictly below 1 so the exponential average remains well-defined; 1.0 or negatives abort construction.","triggerScenarios":"Constructing AdamW(params, betas=(1.0, 0.999)) or betas=(-0.1, 0.999), or configs where beta1 was set to 1.0 intending 'no decay'.","commonSituations":"Hyperparameter sweeps writing beta1=1.0, YAML configs copying a different optimizer's semantics, or arithmetic that scales beta1 past 1.","solutions":["Set beta1 to a valid value in [0, 1), typically 0.9 or 0.99 for diffusion training","If a sweep produced it, constrain the sweep range to [0.5, 0.999]","Validate betas before constructing: all(0.0 <= b < 1.0 for b in betas)"],"exampleFix":"# before\nopt = AdamW(params, betas=(1.0, 0.999))\n# after\nopt = AdamW(params, betas=(0.9, 0.999))","handlingStrategy":"validation","validationCode":"assert 0.0 <= betas[0] < 1.0, f'bad beta1: {betas[0]}'","typeGuard":null,"tryCatchPattern":null,"preventionTips":["Use standard beta1 values (0.9, 0.99)","Bound sweep ranges to [0.5, 0.999] for beta1","Validate the full betas tuple before constructing the optimizer"],"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"}