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https://github.com/vladmandic/automatic
synced 2026-09-18 16:54:33 +02:00
simplify backend select/lookups
This commit is contained in:
Vladimir Mandic
@@ -69,10 +69,6 @@ class Shared(sys.modules[__name__].__class__):
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import modules.sd_models # pylint: disable=W0621
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modules.sd_models.model_data.set_sd_refiner(value)
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@property
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def backend(self):
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return shared.Backend.ORIGINAL if not shared.cmd_opts.use_openvino and shared.opts.data['sd_backend'] == 'original' else shared.Backend.DIFFUSERS
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@property
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def sd_model_type(self):
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try:
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@@ -440,7 +440,7 @@ def load_model_weights(model: torch.nn.Module, checkpoint_info: CheckpointInfo,
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return False
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del state_dict
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timer.record("apply")
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if shared.opts.sd_checkpoint_cache > 0:
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if shared.opts.sd_checkpoint_cache > 0 and shared.backend == shared.Backend.ORIGINAL:
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# cache newly loaded model
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checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
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if shared.opts.opt_channelslast:
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+95
-94
@@ -83,11 +83,96 @@ class Backend(Enum):
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state = shared_state.State()
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if not hasattr(cmd_opts, "use_openvino"):
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cmd_opts.use_openvino = False
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if cmd_opts.use_openvino:
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def readfile(filename, silent=False, lock=False):
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data = {}
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lock_file = None
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locked = False
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try:
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# if not os.path.exists(filename):
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# return {}
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t0 = time.time()
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if lock:
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lock_file = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
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locked = lock_file.acquire_read_lock(blocking=True, timeout=3)
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with open(filename, "rb") as file:
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b = file.read()
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data = orjson.loads(b) # pylint: disable=no-member
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# if type(data) is str:
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# data = json.loads(data)
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t1 = time.time()
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if not silent:
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log.debug(f'Read: file="{filename}" json={len(data)} bytes={os.path.getsize(filename)} time={t1-t0:.3f}')
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except Exception as e:
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if not silent:
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log.error(f'Reading failed: {filename} {e}')
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finally:
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if lock_file is not None:
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lock_file.release_read_lock()
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if locked and os.path.exists(f"{filename}.lock"):
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os.remove(f"{filename}.lock")
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return data
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def writefile(data, filename, mode='w', silent=False, atomic=False):
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lock = None
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locked = False
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import tempfile
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def default(obj):
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log.error(f"Saving: {filename} not a valid object: {obj}")
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return str(obj)
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try:
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t0 = time.time()
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# skipkeys=True, ensure_ascii=True, check_circular=True, allow_nan=True
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if type(data) == dict:
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output = json.dumps(data, indent=2, default=default)
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elif type(data) == list:
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output = json.dumps(data, indent=2, default=default)
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elif isinstance(data, object):
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simple = {}
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for k in data.__dict__:
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if data.__dict__[k] is not None:
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simple[k] = data.__dict__[k]
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output = json.dumps(simple, indent=2, default=default)
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else:
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raise ValueError('not a valid object')
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lock = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
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locked = lock.acquire_write_lock(blocking=True, timeout=3)
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if atomic:
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with tempfile.NamedTemporaryFile(mode=mode, encoding="utf8", delete=False, dir=os.path.dirname(filename)) as f:
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f.write(output)
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f.flush()
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os.fsync(f.fileno())
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os.replace(f.name, filename)
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else:
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with open(filename, mode=mode, encoding="utf8") as file:
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file.write(output)
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t1 = time.time()
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if not silent:
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log.debug(f'Save: file="{filename}" json={len(data)} bytes={len(output)} time={t1-t0:.3f}')
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except Exception as e:
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log.error(f'Saving failed: {filename} {e}')
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errors.display(e, 'Saving failed')
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finally:
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if lock is not None:
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lock.release_read_lock()
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if locked and os.path.exists(f"{filename}.lock"):
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os.remove(f"{filename}.lock")
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# early select backend
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default_backend = 'original'
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early_opts = readfile(cmd_opts.config, silent=True)
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early_backend = early_opts.get('sd_backend', default_backend)
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backend = Backend.DIFFUSERS if early_backend.lower() == 'diffusers' else Backend.ORIGINAL
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if cmd_opts.backend is not None: # override with args
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backend = Backend.DIFFUSERS if cmd_opts.backend.lower() == 'diffusers' else Backend.ORIGINAL
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if cmd_opts.use_openvino: # override for openvino
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backend = Backend.DIFFUSERS
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cmd_opts.backend = 'diffusers'
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else:
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backend = Backend.DIFFUSERS if (cmd_opts.backend is not None) and (cmd_opts.backend.lower() == 'diffusers') else Backend.ORIGINAL # initial since we don't have opts loaded yet
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class OptionInfo:
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@@ -191,85 +276,6 @@ def temp_disable_extensions():
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return disabled
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def readfile(filename, silent=False, lock=False):
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data = {}
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lock_file = None
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locked = False
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try:
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# if not os.path.exists(filename):
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# return {}
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t0 = time.time()
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if lock:
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lock_file = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
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locked = lock_file.acquire_read_lock(blocking=True, timeout=3)
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with open(filename, "rb") as file:
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b = file.read()
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data = orjson.loads(b) # pylint: disable=no-member
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# if type(data) is str:
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# data = json.loads(data)
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t1 = time.time()
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if not silent:
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log.debug(f'Read: file="{filename}" json={len(data)} bytes={os.path.getsize(filename)} time={t1-t0:.3f}')
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except Exception as e:
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if not silent:
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log.error(f'Reading failed: {filename} {e}')
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finally:
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if lock_file is not None:
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lock_file.release_read_lock()
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if locked and os.path.exists(f"{filename}.lock"):
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os.remove(f"{filename}.lock")
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return data
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def writefile(data, filename, mode='w', silent=False, atomic=False):
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lock = None
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locked = False
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import tempfile
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def default(obj):
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log.error(f"Saving: {filename} not a valid object: {obj}")
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return str(obj)
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try:
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t0 = time.time()
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# skipkeys=True, ensure_ascii=True, check_circular=True, allow_nan=True
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if type(data) == dict:
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output = json.dumps(data, indent=2, default=default)
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elif type(data) == list:
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output = json.dumps(data, indent=2, default=default)
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elif isinstance(data, object):
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simple = {}
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for k in data.__dict__:
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if data.__dict__[k] is not None:
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simple[k] = data.__dict__[k]
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output = json.dumps(simple, indent=2, default=default)
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else:
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raise ValueError('not a valid object')
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lock = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
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locked = lock.acquire_write_lock(blocking=True, timeout=3)
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if atomic:
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with tempfile.NamedTemporaryFile(mode=mode, encoding="utf8", delete=False, dir=os.path.dirname(filename)) as f:
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f.write(output)
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f.flush()
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os.fsync(f.fileno())
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os.replace(f.name, filename)
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else:
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with open(filename, mode=mode, encoding="utf8") as file:
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file.write(output)
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t1 = time.time()
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if not silent:
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log.debug(f'Save: file="{filename}" json={len(data)} bytes={len(output)} time={t1-t0:.3f}')
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except Exception as e:
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log.error(f'Saving failed: {filename} {e}')
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errors.display(e, 'Saving failed')
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finally:
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if lock is not None:
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lock.release_read_lock()
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if locked and os.path.exists(f"{filename}.lock"):
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os.remove(f"{filename}.lock")
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if devices.backend == "cpu":
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cross_attention_optimization_default = "Doggettx's"
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elif devices.backend == "mps":
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@@ -285,18 +291,18 @@ else: # cuda
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options_templates.update(options_section(('sd', "Execution & Models"), {
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"sd_backend": OptionInfo("original" if not cmd_opts.use_openvino else "diffusers", "Execution backend", gr.Radio, {"choices": ["original", "diffusers"] }),
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"sd_backend": OptionInfo('diffusers' if backend == Backend.DIFFUSERS else 'original', "Execution backend", gr.Radio, {"choices": ["original", "diffusers"] }),
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"sd_checkpoint_autoload": OptionInfo(True, "Model autoload on server start"),
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"sd_model_checkpoint": OptionInfo(default_checkpoint, "Base model", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
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"sd_model_dict": OptionInfo('None', "Use baseline data from a different model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
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"stream_load": OptionInfo(False, "Load models using stream loading method"),
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"stream_load": OptionInfo(False, "Load models using stream loading method", gr.Checkbox, {"visible": backend == Backend.ORIGINAL }),
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"model_reuse_dict": OptionInfo(False, "When loading models attempt to reuse previous model dictionary", gr.Checkbox, {"visible": False}),
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"prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, {"choices": ["Full parser", "Compel parser", "A1111 parser", "Fixed attention"] }),
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"prompt_mean_norm": OptionInfo(True, "Prompt attention mean normalization"),
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"comma_padding_backtrack": OptionInfo(20, "Prompt padding for long prompts", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
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"sd_checkpoint_cache": OptionInfo(0, "Number of cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
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"prompt_mean_norm": OptionInfo(True, "Prompt attention mean normalization", gr.Checkbox, {"visible": backend == Backend.ORIGINAL }),
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"comma_padding_backtrack": OptionInfo(20, "Prompt padding for long prompts", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1, "visible": backend == Backend.ORIGINAL }),
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"sd_checkpoint_cache": OptionInfo(0, "Number of cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": backend == Backend.ORIGINAL }),
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"sd_vae_checkpoint_cache": OptionInfo(0, "Number of cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
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"sd_disable_ckpt": OptionInfo(False, "Disallow usage of models in ckpt format"),
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}))
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@@ -823,16 +829,11 @@ opts = Options()
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config_filename = cmd_opts.config
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opts.load(config_filename)
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cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
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if cmd_opts.backend is None:
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backend = Backend.DIFFUSERS if cmd_opts.use_openvino or opts.data.get('sd_backend', 'original') == 'diffusers' else Backend.ORIGINAL
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else:
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backend = Backend.DIFFUSERS if cmd_opts.use_openvino or cmd_opts.backend.lower() == 'diffusers' else Backend.ORIGINAL
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opts.data['sd_backend'] = 'diffusers' if backend == Backend.DIFFUSERS else 'original'
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if cmd_opts.use_xformers:
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opts.data['cross_attention_optimization'] = 'xFormers'
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opts.data['uni_pc_lower_order_final'] = opts.schedulers_use_loworder # compatibility
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opts.data['uni_pc_order'] = opts.schedulers_solver_order # compatibility
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log.info(f'Engine: backend={backend} compute={devices.backend} mode={devices.inference_context.__name__} device={devices.get_optimal_device_name()} cross-optimization="{opts.cross_attention_optimization}"')
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log.info(f'Engine: backend={backend} compute={devices.backend} device={devices.get_optimal_device_name()} attention="{opts.cross_attention_optimization}" mode={devices.inference_context.__name__}')
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log.info(f'Device: {print_dict(devices.get_gpu_info())}')
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prompt_styles = modules.styles.StyleDatabase(opts)
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