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IPEX rework

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Disty0
2023-07-14 17:33:24 +03:00
parent 42cba64ac8
commit 2a9133bfec
10 changed files with 47 additions and 113 deletions
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modules/hypernetworks/hypernetwork.py
+3 -12
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@@ -589,10 +589,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
print("Cannot resume from saved optimizer!")
print(e)
if devices.backend == 'ipex':
scaler = torch.xpu.amp.GradScaler()
else:
scaler = torch.cuda.amp.GradScaler()
scaler = torch.cuda.amp.GradScaler()
batch_size = ds.batch_size
gradient_step = ds.gradient_step
@@ -706,10 +703,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
hypernetwork.eval()
rng_state = torch.get_rng_state()
cuda_rng_state = None
if devices.backend == 'ipex':
cuda_rng_state = torch.xpu.get_rng_state_all()
elif torch.cuda.is_available():
cuda_rng_state = torch.cuda.get_rng_state_all()
cuda_rng_state = torch.cuda.get_rng_state_all()
shared.sd_model.cond_stage_model.to(devices.device)
shared.sd_model.first_stage_model.to(devices.device)
@@ -745,10 +739,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
shared.sd_model.cond_stage_model.to(devices.cpu)
shared.sd_model.first_stage_model.to(devices.cpu)
torch.set_rng_state(rng_state)
if devices.backend == 'ipex':
torch.xpu.set_rng_state_all(cuda_rng_state)
elif torch.cuda.is_available():
torch.cuda.set_rng_state_all(cuda_rng_state)
torch.cuda.set_rng_state_all(cuda_rng_state)
hypernetwork.train()
if image is not None:
shared.state.assign_current_image(image)