Source code for dgenerate.invoker

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import typing

import dgenerate.arguments as _arguments
import dgenerate.mediainput as _mediainput
import dgenerate.messages as _messages
import dgenerate.pipelinewrapper as _pipelinewrapper
import dgenerate.preprocessors as _preprocessors
import dgenerate.renderloop as _renderloop


[docs] def invoke_dgenerate( args: typing.Sequence[str], render_loop: typing.Optional[_renderloop.RenderLoop] = None, throw: bool = False): """ Invoke dgenerate using its command line arguments and return a return code. dgenerate is invoked in the current process, this method does not spawn a subprocess. :param args: dgenerate command line arguments in the form of a list, see: shlex module, or sys.argv :param render_loop: :py:class:`dgenerate.renderloop.RenderLoop` instance, if None is provided one will be created. :param throw: Whether to throw exceptions or handle them. :raises dgenerate.arguments.DgenerateUsageError: :raises dgenerate.mediainput.ImageSeedError: :raises dgenerate.mediainput.UnknownMimetypeError: :raises dgenerate.preprocessors.ImagePreprocessorArgumentError: :raises dgenerate.preprocessors.ImagePreprocessorNotFoundError: :raises dgenerate.pipelinewrapper.InvalidModelUriError: :raises dgenerate.pipelinewrapper.InvalidSchedulerName: :raises dgenerate.pipelinewrapper.OutOfMemoryError: :raises dgenerate.pipelinewrapper.ModelNotFoundError: :raises NotImplementedError: :raises EnvironmentError: :return: integer return-code, anything other than 0 is failure """ if render_loop is None: render_loop = _renderloop.RenderLoop() if '--image-preprocessor-help' in args: try: return _preprocessors.image_preprocessor_help(args, throw=throw) except _preprocessors.PreprocessorHelpUsageError as e: raise _arguments.DgenerateUsageError(e) if '--templates-help' in args: _messages.log(render_loop.generate_template_variables_help(show_values=False) + '\n', underline=True) return 0 arguments = None constraint_lists = [] try: arguments = _arguments.parse_args(args) except _arguments.DgenerateUsageError as e: if throw: raise e return 1 try: if arguments.cache_memory_constraints: constraint_lists.append(_pipelinewrapper.CACHE_MEMORY_CONSTRAINTS) _pipelinewrapper.CACHE_MEMORY_CONSTRAINTS = arguments.cache_memory_constraints if arguments.pipeline_cache_memory_constraints: constraint_lists.append(_pipelinewrapper.PIPELINE_CACHE_MEMORY_CONSTRAINTS) _pipelinewrapper.PIPELINE_CACHE_MEMORY_CONSTRAINTS = arguments.pipeline_cache_memory_constraints if arguments.vae_cache_memory_constraints: constraint_lists.append(_pipelinewrapper.VAE_CACHE_MEMORY_CONSTRAINTS) _pipelinewrapper.VAE_CACHE_MEMORY_CONSTRAINTS = arguments.vae_cache_memory_constraints if arguments.control_net_cache_memory_constraints: constraint_lists.append(_pipelinewrapper.CONTROL_NET_CACHE_MEMORY_CONSTRAINTS) _pipelinewrapper.CONTROL_NET_CACHE_MEMORY_CONSTRAINTS = arguments.control_net_cache_memory_constraints render_loop.config = arguments render_loop.preprocessor_loader. \ load_plugin_modules(arguments.plugin_module_paths) if arguments.verbose: _messages.LEVEL = _messages.DEBUG else: # enable setting and unsetting in batch processing _messages.LEVEL = _messages.INFO render_loop.run() except (_mediainput.ImageSeedError, _mediainput.UnknownMimetypeError, _pipelinewrapper.ModelNotFoundError, _pipelinewrapper.InvalidModelUriError, _pipelinewrapper.InvalidSchedulerName, _pipelinewrapper.OutOfMemoryError, _preprocessors.ImagePreprocessorArgumentError, _preprocessors.ImagePreprocessorNotFoundError, NotImplementedError, EnvironmentError) as e: _messages.log(f'Error: {e}', level=_messages.ERROR) if throw: raise e return 1 finally: if arguments is not None: if arguments.control_net_cache_memory_constraints: _pipelinewrapper.CONTROL_NET_CACHE_MEMORY_CONSTRAINTS = constraint_lists.pop() if arguments.vae_cache_memory_constraints: _pipelinewrapper.VAE_CACHE_MEMORY_CONSTRAINTS = constraint_lists.pop() if arguments.pipeline_cache_memory_constraints: _pipelinewrapper.PIPELINE_CACHE_MEMORY_CONSTRAINTS = constraint_lists.pop() if arguments.cache_memory_constraints: _pipelinewrapper.CACHE_MEMORY_CONSTRAINTS = constraint_lists.pop() # Return the template environment for pipelining return 0