mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 09:14:35 +02:00
+3
-3
@@ -429,14 +429,14 @@ def set_cudnn_params():
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torch.use_deterministic_algorithms(opts.cudnn_deterministic)
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if opts.cudnn_deterministic:
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os.environ.setdefault('CUBLAS_WORKSPACE_CONFIG', ':4096:8')
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log.debug('Torch cuDNN: deterministic=True')
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log.debug(f'Torch cuDNN: deterministic={opts.cudnn_deterministic}')
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torch.backends.cudnn.benchmark = opts.cudnn_benchmark
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if opts.cudnn_benchmark:
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log.debug('Torch cuDNN: benchmark=True')
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log.debug(f'Torch cuDNN: benchmark={opts.cudnn_benchmark}')
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torch.backends.cudnn.benchmark_limit = opts.cudnn_benchmark_limit
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torch.backends.cudnn.allow_tf32 = True
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except Exception as e:
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log.warning(f'Torch cudnn: {e}')
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log.warning(f'Torch cuDNN: {e}')
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def override_ipex_math():
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@@ -252,7 +252,7 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
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# hires runs on original pipeline
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if hasattr(shared.sd_model, 'restore_pipeline') and (shared.sd_model.restore_pipeline is not None) and (not shared.opts.control_hires):
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shared.sd_model.restore_pipeline()
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if (getattr(shared.sd_model, 'controlnet', None) is not None) and ((isinstance(shared.sd_model.controlnet, list) and len(shared.sd_model.controlnet) > 1)) or ('Multi' in type(shared.sd_model.controlnet).__name__):
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if (getattr(shared.sd_model, 'controlnet', None) is not None) and (((isinstance(shared.sd_model.controlnet, list) and len(shared.sd_model.controlnet) > 1)) or ('Multi' in type(shared.sd_model.controlnet).__name__)):
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shared.log.warning(f'Process: control={type(shared.sd_model.controlnet)} not supported in hires')
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return output
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