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UNET weights can now be stored in fp8.

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--fp8_e4m3fn-unet and --fp8_e5m2-unet are the two different formats
supported by pytorch.
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comfyanonymous
2023-12-04 11:10:00 -05:00
parent af365e4dd1
commit 31b0f6f3d8
6 changed files with 47 additions and 10 deletions
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5
comfy/cli_args.py
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@@ -55,7 +55,10 @@ fp_group = parser.add_mutually_exclusive_group()
fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
parser.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
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("--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.")
fpvae_group = parser.add_mutually_exclusive_group()
fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")