298977887/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-03-13 14:21:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

Rework IPEX check for future inclusion of XPU into Pytorch upstream and do a bit more optimization of ipex.optimize(). (#4562)

Browse Source
This commit is contained in:
Simon Lui 2024-08-23 00:59:57 -07:00 committed by GitHub
parent 9465b23432
commit 64ccb3c7e3
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 9 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
comfy/model_management.py
View File

@ -44,8 +44,10 @@ cpu_state = CPUState.GPU
total_vram = 0
torch_version = torch.version.__version__
lowvram_available = True
xpu_available = False
xpu_available = int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)
if args.deterministic:
logging.info("Using deterministic algorithms for pytorch")
@ -66,10 +68,10 @@ if args.directml is not None:
try:
import intel_extension_for_pytorch as ipex
if torch.xpu.is_available():
xpu_available = True
_ = torch.xpu.device_count()
xpu_available = torch.xpu.is_available()
except:
pass
xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available())
try:
if torch.backends.mps.is_available():
@ -189,7 +191,6 @@ VAE_DTYPES = [torch.float32]
try:
if is_nvidia():
torch_version = torch.version.__version__
if int(torch_version[0]) >= 2:
if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
ENABLE_PYTORCH_ATTENTION = True
@ -321,8 +322,9 @@ class LoadedModel:
self.model_unload()
raise e
if is_intel_xpu() and not args.disable_ipex_optimize:
self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True)
if is_intel_xpu() and not args.disable_ipex_optimize and self.real_model is not None:
with torch.no_grad():
self.real_model = ipex.optimize(self.real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)
self.weights_loaded = True
return self.real_model