mirror of
https://github.com/vladmandic/automatic
synced 2026-09-20 01:31:13 +02:00
OpenVINO fix caching and recompile when using Lora
This commit is contained in:
Disty0
+48
-41
@@ -123,8 +123,8 @@ class CompiledModelState:
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self.width = 512
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self.batch_size = 1
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self.partition_id = 0
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self.cn_model = "None"
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self.lora_model = "None"
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self.cn_model = []
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self.lora_model = []
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class NoWatermark:
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@@ -657,6 +657,50 @@ def detect_pipeline(f: str, op: str = 'model'):
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pipeline = None, None
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return pipeline, guess
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def compile_diffusers(sd_model):
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try:
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if shared.opts.ipex_optimize:
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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sd_model.unet.training = False
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sd_model.unet = ipex.optimize(sd_model.unet, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'vae'):
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sd_model.vae.training = False
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sd_model.vae = ipex.optimize(sd_model.vae, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'movq'):
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sd_model.movq.training = False
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sd_model.movq = ipex.optimize(sd_model.movq, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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shared.log.info("Applied IPEX Optimize.")
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except Exception as err:
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shared.log.warning(f"IPEX Optimize not supported: {err}")
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try:
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if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
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shared.log.info(f"Compiling pipeline={sd_model.__class__.__name__} shape={8 * sd_model.unet.config.sample_size} mode={shared.opts.cuda_compile_backend}")
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import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
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if shared.opts.cuda_compile_backend == "openvino_fx":
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torch._dynamo.reset() # pylint: disable=protected-access
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from modules.intel.openvino import openvino_fx, openvino_clear_caches # pylint: disable=unused-import
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openvino_clear_caches()
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torch._dynamo.eval_frame.check_if_dynamo_supported = lambda: True # pylint: disable=protected-access
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if shared.compiled_model_state is None:
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shared.compiled_model_state = CompiledModelState()
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shared.compiled_model_state.first_pass = True if not shared.opts.cuda_compile_precompile else False
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log_level = logging.WARNING if shared.opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
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if hasattr(torch, '_logging'):
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torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
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torch._dynamo.config.verbose = shared.opts.cuda_compile_verbose # pylint: disable=protected-access
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torch._dynamo.config.suppress_errors = shared.opts.cuda_compile_errors # pylint: disable=protected-access
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sd_model.unet = torch.compile(sd_model.unet, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'vae'):
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sd_model.vae.decode = torch.compile(sd_model.vae.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'movq'):
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sd_model.movq.decode = torch.compile(sd_model.movq.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph) # pylint: disable=attribute-defined-outside-init
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if shared.opts.cuda_compile_precompile:
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sd_model("dummy prompt")
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shared.log.info("Complilation done.")
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return sd_model
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except Exception as err:
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shared.log.warning(f"Model compile not supported: {err}")
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def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=None, op='model'): # pylint: disable=unused-argument
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import torch # pylint: disable=reimported,redefined-outer-name
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@@ -869,45 +913,8 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
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base_sent_to_cpu=True
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elif not sd_model.has_accelerate:
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sd_model.to(devices.device)
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try:
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if shared.opts.ipex_optimize:
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sd_model.unet.training = False
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sd_model.unet = torch.xpu.optimize(sd_model.unet, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'vae'):
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sd_model.vae.training = False
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sd_model.vae = torch.xpu.optimize(sd_model.vae, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'movq'):
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sd_model.movq.training = False
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sd_model.movq = torch.xpu.optimize(sd_model.movq, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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shared.log.info("Applied IPEX Optimize.")
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except Exception as err:
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shared.log.warning(f"IPEX Optimize not supported: {err}")
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try:
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if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
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shared.log.info(f"Compiling pipeline={sd_model.__class__.__name__} shape={8 * sd_model.unet.config.sample_size} mode={shared.opts.cuda_compile_backend}")
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import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
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if shared.opts.cuda_compile_backend == "openvino_fx":
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torch._dynamo.reset() # pylint: disable=protected-access
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from modules.intel.openvino import openvino_fx, openvino_clear_caches # pylint: disable=unused-import
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openvino_clear_caches()
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torch._dynamo.eval_frame.check_if_dynamo_supported = lambda: True # pylint: disable=protected-access
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shared.compiled_model_state = CompiledModelState()
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shared.compiled_model_state.first_pass = True if not shared.opts.cuda_compile_precompile else False
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log_level = logging.WARNING if shared.opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
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if hasattr(torch, '_logging'):
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torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
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torch._dynamo.config.verbose = shared.opts.cuda_compile_verbose # pylint: disable=protected-access
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torch._dynamo.config.suppress_errors = shared.opts.cuda_compile_errors # pylint: disable=protected-access
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sd_model.unet = torch.compile(sd_model.unet, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'vae'):
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sd_model.vae.decode = torch.compile(sd_model.vae.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'movq'):
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sd_model.movq.decode = torch.compile(sd_model.movq.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph) # pylint: disable=attribute-defined-outside-init
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if shared.opts.cuda_compile_precompile:
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sd_model("dummy prompt")
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shared.log.info("Complilation done.")
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except Exception as err:
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shared.log.warning(f"Model compile not supported: {err}")
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sd_model = compile_diffusers(sd_model)
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if sd_model is None:
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shared.log.error('Diffuser model not loaded')
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