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ComfyUI
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Add option to inference the diffusion model in fp32 and fp64.

This commit is contained in:
comfyanonymous 2024-11-25 05:00:23 -05:00
parent b4526d3fc3
commit 61196d8857
2 changed files with 9 additions and 3 deletions
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6
comfy/cli_args.py
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@ -60,8 +60,10 @@ fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If
fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
fpunet_group = parser.add_mutually_exclusive_group()
fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
fpunet_group.add_argument("--fp16-unet", action="store_true", help="Store unet weights in fp16.")
fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.")
fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64.")
fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16.")
fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16")
fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.")
fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.")

6
comfy/model_management.py
View File

@ -628,6 +628,10 @@ def maximum_vram_for_weights(device=None):
def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
if model_params < 0:
model_params = 1000000000000000000000
if args.fp32_unet:
return torch.float32
if args.fp64_unet:
return torch.float64
if args.bf16_unet:
return torch.bfloat16
if args.fp16_unet:
@ -674,7 +678,7 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
# None means no manual cast
def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
if weight_dtype == torch.float32:
if weight_dtype == torch.float32 or weight_dtype == torch.float64:
return None
fp16_supported = should_use_fp16(inference_device, prioritize_performance=False)