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Add some command line arguments to store text encoder weights in fp8.

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Pytorch supports two variants of fp8:
--fp8_e4m3fn-text-enc (the one that seems to give better results)
--fp8_e5m2-text-enc
This commit is contained in:
comfyanonymous 2023-11-17 02:56:59 -05:00
parent 107e78b1cb
commit 0cf4e86939
3 changed files with 23 additions and 4 deletions
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7
comfy/cli_args.py
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@ -62,6 +62,13 @@ fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in
fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.")
fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in bf16.")
fpte_group = parser.add_mutually_exclusive_group()
fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Store text encoder weights in fp8 (e4m3fn variant).")
fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).")
fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.")

15
comfy/model_management.py
View File

@ -482,6 +482,21 @@ def text_encoder_device():
else:
return torch.device("cpu")
def text_encoder_dtype(device=None):
if args.fp8_e4m3fn_text_enc:
return torch.float8_e4m3fn
elif args.fp8_e5m2_text_enc:
return torch.float8_e5m2
elif args.fp16_text_enc:
return torch.float16
elif args.fp32_text_enc:
return torch.float32
if should_use_fp16(device, prioritize_performance=False):
return torch.float16
else:
return torch.float32
def vae_device():
return get_torch_device()

5
comfy/sd.py
View File

@ -95,10 +95,7 @@ class CLIP:
load_device = model_management.text_encoder_device()
offload_device = model_management.text_encoder_offload_device()
params['device'] = offload_device
if model_management.should_use_fp16(load_device, prioritize_performance=False):
params['dtype'] = torch.float16
else:
params['dtype'] = torch.float32
params['dtype'] = model_management.text_encoder_dtype(load_device)
self.cond_stage_model = clip(**(params))