Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 17:24:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

Cleanup IPEX Optimize

Browse Source
This commit is contained in:
Disty0
2024-01-27 21:16:05 +03:00
parent d92f85474d
commit d6c4f6072e
2 changed files with 17 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/sd_hijack.py
+2 -1
View File
@@ -178,8 +178,9 @@ class StableDiffusionModelHijack:
if "Model" in shared.opts.ipex_optimize and shared.backend == shared.Backend.ORIGINAL:
try:
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
m.model.eval()
m.model.training = False
m.model = ipex.optimize(m.model, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
m.model = ipex.optimize(m.model, dtype=devices.dtype, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
shared.log.info("Applied IPEX Optimize.")
except Exception as err:
shared.log.warning(f"IPEX Optimize not supported: {err}")
modules/sd_models_compile.py
+15 -19
View File
@@ -26,38 +26,33 @@ class CompiledModelState:
def ipex_optimize(sd_model):
try:
t0 = time.time()
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
def ipex_optimize_model(model):
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
model.eval()
model.training = False
model = ipex.optimize(model, dtype=devices.dtype, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
return model
if "Model" in shared.opts.ipex_optimize:
if hasattr(sd_model, 'unet'):
sd_model.unet.eval()
sd_model.unet.training = False
sd_model.unet = ipex.optimize(sd_model.unet, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
sd_model.unet = ipex_optimize_model(sd_model.unet)
elif hasattr(sd_model, 'transformer'):
sd_model.transformer.eval()
sd_model.transformer.training = False
sd_model.transformer = ipex.optimize(sd_model.transformer, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
sd_model.transformer = ipex_optimize_model(sd_model.transformer)
else:
shared.log.warning('IPEX Optimize enabled but model has no Unet or Transformer')
if "VAE" in shared.opts.ipex_optimize:
if hasattr(sd_model, 'vae'):
sd_model.vae.eval()
sd_model.vae.training = False
sd_model.vae = ipex.optimize(sd_model.vae, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
sd_model.vae = ipex_optimize_model(sd_model.vae)
elif hasattr(sd_model, 'movq'):
sd_model.movq.eval()
sd_model.movq.training = False
sd_model.movq = ipex.optimize(sd_model.movq, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
sd_model.movq = ipex_optimize_model(sd_model.movq)
else:
shared.log.warning('Compress VAE Weights enabled but model has no VAE')
if "Text Encoder" in shared.opts.ipex_optimize:
if hasattr(sd_model, 'text_encoder'):
sd_model.text_encoder.eval()
sd_model.text_encoder.training = False
sd_model.text_encoder = ipex.optimize(sd_model.text_encoder, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
sd_model.text_encoder = ipex_optimize_model(sd_model.text_encoder)
if hasattr(sd_model, 'text_encoder_2'):
sd_model.text_encoder_2.eval()
sd_model.text_encoder_2.training = False
sd_model.text_encoder_2 = ipex.optimize(sd_model.text_encoder_2, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
sd_model.text_encoder_2 = ipex_optimize_model(sd_model.text_encoder_2)
else:
shared.log.warning('IPEX Optimize Text Encoder Weights enabled but model has no Text Encoder')
t1 = time.time()
@@ -66,6 +61,7 @@ def ipex_optimize(sd_model):
except Exception as e:
shared.log.warning(f"IPEX Optimize: error: {e}")
def nncf_compress_weights(sd_model):
try:
t0 = time.time()