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Speed up SDXL on 16xx series with fp16 weights and manual cast.

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comfyanonymous 2024-02-04 13:23:43 -05:00
parent 98b80ad1f5
commit 24129d78e6
1 changed files with 3 additions and 3 deletions
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6
comfy/model_management.py
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@ -496,7 +496,7 @@ def unet_dtype(device=None, model_params=0):
return torch.float8_e4m3fn return torch.float8_e4m3fn
if args.fp8_e5m2_unet: if args.fp8_e5m2_unet:
return torch.float8_e5m2 return torch.float8_e5m2
if should_use_fp16(device=device, model_params=model_params): if should_use_fp16(device=device, model_params=model_params, manual_cast=True):
return torch.float16 return torch.float16
return torch.float32 return torch.float32
@ -696,7 +696,7 @@ def is_device_mps(device):
return True return True
return False return False
def should_use_fp16(device=None, model_params=0, prioritize_performance=True): def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
global directml_enabled global directml_enabled
if device is not None: if device is not None:
@ -738,7 +738,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
if x in props.name.lower(): if x in props.name.lower():
fp16_works = True fp16_works = True
if fp16_works: if fp16_works or manual_cast:
free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory()) free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
if (not prioritize_performance) or model_params * 4 > free_model_memory: if (not prioritize_performance) or model_params * 4 > free_model_memory:
return True return True