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Workaround for nvidia issue where VAE uses 3x more memory on torch 2.9 (#10373)

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comfyanonymous 2025-10-16 15:16:03 -07:00 committed by GitHub
parent bc0ad9bb49
commit 19b466160c
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1 changed files with 19 additions and 0 deletions
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comfy/ops.py
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@ -52,6 +52,16 @@ try:
except (ModuleNotFoundError, TypeError):
logging.warning("Could not set sdpa backend priority.")
NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False
try:
if comfy.model_management.is_nvidia():
if torch.backends.cudnn.version() >= 91300 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10):
#TODO: change upper bound version once it's fixed'
NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True
logging.info("working around nvidia conv3d memory bug.")
except:
pass
cast_to = comfy.model_management.cast_to #TODO: remove once no more references
if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
@ -151,6 +161,15 @@ class disable_weight_init:
def reset_parameters(self):
return None
def _conv_forward(self, input, weight, bias, *args, **kwargs):
if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16):
out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True)
if bias is not None:
out += bias.reshape((1, -1) + (1,) * (out.ndim - 2))
return out
else:
return super()._conv_forward(input, weight, bias, *args, **kwargs)
def forward_comfy_cast_weights(self, input):
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)