mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
test and fix edge cases
This commit is contained in:
Lincoln Stein
@ -442,46 +442,46 @@ to allow InvokeAI to download restricted styles & subjects from the "Concept Lib
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begin_entry_at=4,
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scroll_exit=True,
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)
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self.nextrely += 1
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self.add_widget_intelligent(
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npyscreen.FixedText,
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value="Directories containing textual inversion, controlnet and LoRA models (<tab> autocompletes, ctrl-N advances):",
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editable=False,
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color="CONTROL",
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)
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self.embedding_dir = self.add_widget_intelligent(
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npyscreen.TitleFilename,
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name=" Textual Inversion Embeddings:",
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value=str(default_embedding_dir()),
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select_dir=True,
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must_exist=False,
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use_two_lines=False,
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labelColor="GOOD",
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begin_entry_at=32,
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scroll_exit=True,
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)
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self.lora_dir = self.add_widget_intelligent(
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npyscreen.TitleFilename,
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name=" LoRA and LyCORIS:",
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value=str(default_lora_dir()),
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select_dir=True,
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must_exist=False,
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use_two_lines=False,
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labelColor="GOOD",
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begin_entry_at=32,
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scroll_exit=True,
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)
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self.controlnet_dir = self.add_widget_intelligent(
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npyscreen.TitleFilename,
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name=" ControlNets:",
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value=str(default_controlnet_dir()),
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select_dir=True,
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must_exist=False,
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use_two_lines=False,
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labelColor="GOOD",
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begin_entry_at=32,
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scroll_exit=True,
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)
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# self.nextrely += 1
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# self.add_widget_intelligent(
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# npyscreen.FixedText,
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# value="Directories containing textual inversion, controlnet and LoRA models (<tab> autocompletes, ctrl-N advances):",
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# editable=False,
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# color="CONTROL",
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# )
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# self.embedding_dir = self.add_widget_intelligent(
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# npyscreen.TitleFilename,
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# name=" Textual Inversion Embeddings:",
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# value=str(default_embedding_dir()),
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# select_dir=True,
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# must_exist=False,
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# use_two_lines=False,
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# labelColor="GOOD",
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# begin_entry_at=32,
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# scroll_exit=True,
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# )
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# self.lora_dir = self.add_widget_intelligent(
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# npyscreen.TitleFilename,
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# name=" LoRA and LyCORIS:",
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# value=str(default_lora_dir()),
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# select_dir=True,
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# must_exist=False,
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# use_two_lines=False,
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# labelColor="GOOD",
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# begin_entry_at=32,
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# scroll_exit=True,
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# )
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# self.controlnet_dir = self.add_widget_intelligent(
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# npyscreen.TitleFilename,
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# name=" ControlNets:",
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# value=str(default_controlnet_dir()),
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# select_dir=True,
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# must_exist=False,
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# use_two_lines=False,
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# labelColor="GOOD",
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# begin_entry_at=32,
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# scroll_exit=True,
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# )
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self.nextrely += 1
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self.add_widget_intelligent(
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npyscreen.TitleFixedText,
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@ -546,10 +546,10 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
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bad_fields.append(
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f"The output directory does not seem to be valid. Please check that {str(Path(opt.outdir).parent)} is an existing directory."
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)
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if not Path(opt.embedding_dir).parent.exists():
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bad_fields.append(
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f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_dir).parent)} is an existing directory."
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)
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# if not Path(opt.embedding_dir).parent.exists():
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# bad_fields.append(
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# f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_dir).parent)} is an existing directory."
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# )
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if len(bad_fields) > 0:
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message = "The following problems were detected and must be corrected:\n"
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for problem in bad_fields:
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@ -569,9 +569,9 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
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"max_loaded_models",
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"xformers_enabled",
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"always_use_cpu",
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"embedding_dir",
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"lora_dir",
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"controlnet_dir",
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# "embedding_dir",
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# "lora_dir",
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# "controlnet_dir",
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]:
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setattr(new_opts, attr, getattr(self, attr).value)
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@ -591,6 +591,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
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self.program_opts = program_opts
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self.invokeai_opts = invokeai_opts
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self.user_cancelled = False
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self.autoload_pending = True
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self.install_selections = default_user_selections(program_opts)
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def onStart(self):
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@ -719,17 +720,17 @@ def write_opts(opts: Namespace, init_file: Path):
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def default_output_dir() -> Path:
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return config.root_path / "outputs"
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# -------------------------------------
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def default_embedding_dir() -> Path:
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return config.root_path / "embeddings"
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# # -------------------------------------
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# def default_embedding_dir() -> Path:
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# return config.root_path / "embeddings"
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# -------------------------------------
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def default_lora_dir() -> Path:
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return config.root_path / "loras"
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# # -------------------------------------
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# def default_lora_dir() -> Path:
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# return config.root_path / "loras"
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# -------------------------------------
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def default_controlnet_dir() -> Path:
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return config.root_path / "controlnets"
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# # -------------------------------------
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# def default_controlnet_dir() -> Path:
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# return config.root_path / "controlnets"
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# -------------------------------------
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def write_default_options(program_opts: Namespace, initfile: Path):
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@ -755,7 +756,7 @@ def migrate_init_file(legacy_format:Path):
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new.nsfw_checker = old.safety_checker
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new.xformers_enabled = old.xformers
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new.conf_path = old.conf
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new.embedding_dir = old.embedding_path
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# new.embedding_dir = old.embedding_path
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invokeai_yaml = legacy_format.parent / 'invokeai.yaml'
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with open(invokeai_yaml,"w", encoding="utf-8") as outfile:
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@ -227,10 +227,14 @@ class ModelInstall(object):
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# the model from being probed twice in the event that it has already been probed.
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def _install_path(self, path: Path, info: ModelProbeInfo=None):
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try:
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logger.info(f'Probing {path}')
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info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
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if info.model_type == ModelType.Pipeline:
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model_name = path.stem if info.format=='checkpoint' else path.name
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if self.mgr.model_exists(model_name, info.base_type, info.model_type):
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raise Exception(f'A model named "{model_name}" is already installed.')
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attributes = self._make_attributes(path,info)
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self.mgr.add_model(model_name = path.stem if info.format=='checkpoint' else path.name,
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self.mgr.add_model(model_name = model_name,
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base_model = info.base_type,
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model_type = info.model_type,
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model_attributes = attributes
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@ -322,7 +326,7 @@ class ModelInstall(object):
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)
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if info.format=="checkpoint":
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try:
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legacy_conf = LEGACY_CONFIGS[info.base_type][info.variant_type][info.prediction_type] if BaseModelType.StableDiffusion2 \
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legacy_conf = LEGACY_CONFIGS[info.base_type][info.variant_type][info.prediction_type] if info.base_type == BaseModelType.StableDiffusion2 \
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else LEGACY_CONFIGS[info.base_type][info.variant_type]
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except KeyError:
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legacy_conf = 'v1-inference.yaml' # best guess
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