feat: optimize performance lora filtering in metadata (#3048)
* feat: add remove_performance_lora method * feat: use class PerformanceLoRA instead of strings in config * refactor: cleanup flags, use __member__ to check if enums contains key * feat: only filter lora of selected performance instead of all performance LoRAs * fix: disable intermediate results for all restricted performances too fast for Gradio, which becomes a bottleneck * refactor: rename parse_json to to_json, rename parse_string to to_string * feat: use speed steps as default instead of hardcoded 30 * feat: add method to_steps to Performance * refactor: remove method ordinal_suffix, not needed anymore * feat: only filter lora of selected performance instead of all performance LoRAs both metadata and history log * feat: do not filter LoRAs in metadata parser but rather in metadata load action
This commit is contained in:
Manuel Schmid
GitHub
parent
3ef663c5b7
commit
4e658bb63a
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@ -462,8 +462,10 @@ def worker():
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progressbar(async_task, 2, 'Loading models ...')
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loras, prompt = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number)
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lora_filenames = modules.util.remove_performance_lora(modules.config.lora_filenames, performance_selection)
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loras, prompt = parse_lora_references_from_prompt(prompt, loras, modules.config.default_max_lora_number, lora_filenames=lora_filenames)
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loras += performance_loras
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pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
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loras=loras, base_model_additional_loras=base_model_additional_loras,
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use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
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@ -548,25 +548,9 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
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model_filenames = []
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lora_filenames = []
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lora_filenames_no_special = []
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vae_filenames = []
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wildcard_filenames = []
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sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
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sdxl_lightning_lora = 'sdxl_lightning_4step_lora.safetensors'
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sdxl_hyper_sd_lora = 'sdxl_hyper_sd_4step_lora.safetensors'
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loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora, sdxl_hyper_sd_lora]
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def remove_special_loras(lora_filenames):
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global loras_metadata_remove
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loras_no_special = lora_filenames.copy()
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for lora_to_remove in loras_metadata_remove:
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if lora_to_remove in loras_no_special:
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loras_no_special.remove(lora_to_remove)
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return loras_no_special
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def get_model_filenames(folder_paths, extensions=None, name_filter=None):
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if extensions is None:
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@ -582,10 +566,9 @@ def get_model_filenames(folder_paths, extensions=None, name_filter=None):
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def update_files():
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global model_filenames, lora_filenames, lora_filenames_no_special, vae_filenames, wildcard_filenames, available_presets
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global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets
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model_filenames = get_model_filenames(paths_checkpoints)
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lora_filenames = get_model_filenames(paths_loras)
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lora_filenames_no_special = remove_special_loras(lora_filenames)
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vae_filenames = get_model_filenames(path_vae)
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wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
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available_presets = get_presets()
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@ -634,26 +617,27 @@ def downloading_sdxl_lcm_lora():
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load_file_from_url(
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url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors',
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model_dir=paths_loras[0],
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file_name=sdxl_lcm_lora
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file_name=modules.flags.PerformanceLoRA.EXTREME_SPEED.value
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)
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return sdxl_lcm_lora
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return modules.flags.PerformanceLoRA.EXTREME_SPEED.value
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def downloading_sdxl_lightning_lora():
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load_file_from_url(
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url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_lightning_4step_lora.safetensors',
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model_dir=paths_loras[0],
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file_name=sdxl_lightning_lora
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file_name=modules.flags.PerformanceLoRA.LIGHTNING.value
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)
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return sdxl_lightning_lora
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return modules.flags.PerformanceLoRA.LIGHTNING.value
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def downloading_sdxl_hyper_sd_lora():
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load_file_from_url(
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url='https://huggingface.co/mashb1t/misc/resolve/main/sdxl_hyper_sd_4step_lora.safetensors',
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model_dir=paths_loras[0],
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file_name=sdxl_hyper_sd_lora
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file_name=modules.flags.PerformanceLoRA.HYPER_SD.value
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)
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return sdxl_hyper_sd_lora
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return modules.flags.PerformanceLoRA.HYPER_SD.value
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def downloading_controlnet_canny():
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@ -48,7 +48,8 @@ SAMPLERS = KSAMPLER | SAMPLER_EXTRA
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KSAMPLER_NAMES = list(KSAMPLER.keys())
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SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo", "align_your_steps", "tcd"]
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SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo",
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"align_your_steps", "tcd"]
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SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
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sampler_list = SAMPLER_NAMES
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@ -91,6 +92,7 @@ sdxl_aspect_ratios = [
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'1664*576', '1728*576'
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]
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class MetadataScheme(Enum):
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FOOOCUS = 'fooocus'
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A1111 = 'a1111'
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@ -115,6 +117,14 @@ class OutputFormat(Enum):
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return list(map(lambda c: c.value, cls))
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class PerformanceLoRA(Enum):
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QUALITY = None
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SPEED = None
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EXTREME_SPEED = 'sdxl_lcm_lora.safetensors'
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LIGHTNING = 'sdxl_lightning_4step_lora.safetensors'
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HYPER_SD = 'sdxl_hyper_sd_4step_lora.safetensors'
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class Steps(IntEnum):
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QUALITY = 60
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SPEED = 30
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@ -142,6 +152,10 @@ class Performance(Enum):
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def list(cls) -> list:
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return list(map(lambda c: c.value, cls))
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@classmethod
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def by_steps(cls, steps: int | str):
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return cls[Steps(int(steps)).name]
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@classmethod
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def has_restricted_features(cls, x) -> bool:
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if isinstance(x, Performance):
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@ -149,7 +163,10 @@ class Performance(Enum):
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return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value, cls.HYPER_SD.value]
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def steps(self) -> int | None:
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return Steps[self.name].value if Steps[self.name] else None
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return Steps[self.name].value if self.name in Steps.__members__ else None
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def steps_uov(self) -> int | None:
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return StepsUOV[self.name].value if Steps[self.name] else None
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return StepsUOV[self.name].value if self.name in StepsUOV.__members__ else None
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def lora_filename(self) -> str | None:
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return PerformanceLoRA[self.name].value if self.name in PerformanceLoRA.__members__ else None
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@ -32,7 +32,7 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
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get_str('prompt', 'Prompt', loaded_parameter_dict, results)
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get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
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get_list('styles', 'Styles', loaded_parameter_dict, results)
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get_str('performance', 'Performance', loaded_parameter_dict, results)
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performance = get_str('performance', 'Performance', loaded_parameter_dict, results)
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get_steps('steps', 'Steps', loaded_parameter_dict, results)
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get_number('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results)
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get_resolution('resolution', 'Resolution', loaded_parameter_dict, results)
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@ -59,19 +59,27 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
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get_freeu('freeu', 'FreeU', loaded_parameter_dict, results)
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# prevent performance LoRAs to be added twice, by performance and by lora
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performance_filename = None
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if performance is not None and performance in Performance.list():
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performance = Performance(performance)
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performance_filename = performance.lora_filename()
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for i in range(modules.config.default_max_lora_number):
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get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results)
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get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results, performance_filename)
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return results
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def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None) -> str | None:
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try:
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h = source_dict.get(key, source_dict.get(fallback, default))
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assert isinstance(h, str)
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results.append(h)
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return h
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except:
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results.append(gr.update())
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return None
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def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
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@ -181,7 +189,7 @@ def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list,
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results.append(gr.update())
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def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
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def get_lora(key: str, fallback: str | None, source_dict: dict, results: list, performance_filename: str | None):
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try:
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split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ')
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enabled = True
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@ -193,6 +201,9 @@ def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
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name = split_data[1]
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weight = split_data[2]
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if name == performance_filename:
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raise Exception
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weight = float(weight)
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results.append(enabled)
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results.append(name)
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@ -248,7 +259,7 @@ class MetadataParser(ABC):
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self.full_prompt: str = ''
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self.raw_negative_prompt: str = ''
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self.full_negative_prompt: str = ''
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self.steps: int = 30
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self.steps: int = Steps.SPEED.value
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self.base_model_name: str = ''
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self.base_model_hash: str = ''
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self.refiner_model_name: str = ''
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@ -261,11 +272,11 @@ class MetadataParser(ABC):
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raise NotImplementedError
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@abstractmethod
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def parse_json(self, metadata: dict | str) -> dict:
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def to_json(self, metadata: dict | str) -> dict:
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raise NotImplementedError
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@abstractmethod
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def parse_string(self, metadata: dict) -> str:
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def to_string(self, metadata: dict) -> str:
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raise NotImplementedError
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def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
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@ -328,7 +339,7 @@ class A1111MetadataParser(MetadataParser):
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'version': 'Version'
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}
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def parse_json(self, metadata: str) -> dict:
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def to_json(self, metadata: str) -> dict:
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metadata_prompt = ''
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metadata_negative_prompt = ''
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@ -382,9 +393,9 @@ class A1111MetadataParser(MetadataParser):
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data['styles'] = str(found_styles)
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# try to load performance based on steps, fallback for direct A1111 imports
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if 'steps' in data and 'performance' not in data:
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if 'steps' in data and 'performance' in data is None:
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try:
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data['performance'] = Performance[Steps(int(data['steps'])).name].value
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data['performance'] = Performance.by_steps(data['steps']).value
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except ValueError | KeyError:
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pass
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@ -414,7 +425,7 @@ class A1111MetadataParser(MetadataParser):
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lora_split = lora.split(': ')
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lora_name = lora_split[0]
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lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1]
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for filename in modules.config.lora_filenames_no_special:
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for filename in modules.config.lora_filenames:
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path = Path(filename)
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if lora_name == path.stem:
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data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}'
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@ -422,7 +433,7 @@ class A1111MetadataParser(MetadataParser):
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return data
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def parse_string(self, metadata: dict) -> str:
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def to_string(self, metadata: dict) -> str:
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data = {k: v for _, k, v in metadata}
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width, height = eval(data['resolution'])
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@ -502,14 +513,14 @@ class FooocusMetadataParser(MetadataParser):
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def get_scheme(self) -> MetadataScheme:
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return MetadataScheme.FOOOCUS
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def parse_json(self, metadata: dict) -> dict:
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def to_json(self, metadata: dict) -> dict:
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for key, value in metadata.items():
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if value in ['', 'None']:
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continue
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if key in ['base_model', 'refiner_model']:
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metadata[key] = self.replace_value_with_filename(key, value, modules.config.model_filenames)
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elif key.startswith('lora_combined_'):
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metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames_no_special)
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metadata[key] = self.replace_value_with_filename(key, value, modules.config.lora_filenames)
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elif key == 'vae':
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metadata[key] = self.replace_value_with_filename(key, value, modules.config.vae_filenames)
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else:
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@ -517,7 +528,7 @@ class FooocusMetadataParser(MetadataParser):
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return metadata
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def parse_string(self, metadata: list) -> str:
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def to_string(self, metadata: list) -> str:
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for li, (label, key, value) in enumerate(metadata):
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# remove model folder paths from metadata
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if key.startswith('lora_combined_'):
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@ -557,6 +568,8 @@ class FooocusMetadataParser(MetadataParser):
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elif value == path.stem:
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return filename
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return None
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def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser:
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match metadata_scheme:
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@ -27,7 +27,7 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
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date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
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os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
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parsed_parameters = metadata_parser.parse_string(metadata.copy()) if metadata_parser is not None else ''
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parsed_parameters = metadata_parser.to_string(metadata.copy()) if metadata_parser is not None else ''
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image = Image.fromarray(img)
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if output_format == OutputFormat.PNG.value:
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@ -16,6 +16,7 @@ from PIL import Image
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import modules.config
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import modules.sdxl_styles
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from modules.flags import Performance
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LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
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@ -381,9 +382,6 @@ def get_file_from_folder_list(name, folders):
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return os.path.abspath(os.path.realpath(os.path.join(folders[0], name)))
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def ordinal_suffix(number: int) -> str:
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return 'th' if 10 <= number % 100 <= 20 else {1: 'st', 2: 'nd', 3: 'rd'}.get(number % 10, 'th')
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def makedirs_with_log(path):
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try:
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@ -397,10 +395,15 @@ def get_enabled_loras(loras: list, remove_none=True) -> list:
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def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, float]], loras_limit: int = 5,
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skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True) -> tuple[List[Tuple[AnyStr, float]], str]:
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skip_file_check=False, prompt_cleanup=True, deduplicate_loras=True,
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lora_filenames=None) -> tuple[List[Tuple[AnyStr, float]], str]:
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if lora_filenames is None:
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lora_filenames = []
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found_loras = []
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prompt_without_loras = ''
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cleaned_prompt = ''
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for token in prompt.split(','):
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matches = LORAS_PROMPT_PATTERN.findall(token)
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@ -410,7 +413,7 @@ def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, flo
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for match in matches:
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lora_name = match[1] + '.safetensors'
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if not skip_file_check:
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lora_name = get_filname_by_stem(match[1], modules.config.lora_filenames_no_special)
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lora_name = get_filname_by_stem(match[1], lora_filenames)
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if lora_name is not None:
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found_loras.append((lora_name, float(match[2])))
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token = token.replace(match[0], '')
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@ -440,6 +443,22 @@ def parse_lora_references_from_prompt(prompt: str, loras: List[Tuple[AnyStr, flo
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return updated_loras[:loras_limit], cleaned_prompt
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def remove_performance_lora(filenames: list, performance: Performance | None):
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loras_without_performance = filenames.copy()
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if performance is None:
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return loras_without_performance
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performance_lora = performance.lora_filename()
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for filename in filenames:
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path = Path(filename)
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if performance_lora == path.name:
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loras_without_performance.remove(filename)
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return loras_without_performance
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def cleanup_prompt(prompt):
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prompt = re.sub(' +', ' ', prompt)
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prompt = re.sub(',+', ',', prompt)
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|
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|
|||
|
|
@ -1,5 +1,7 @@
|
|||
import os
|
||||
import unittest
|
||||
|
||||
import modules.flags
|
||||
from modules import util
|
||||
|
||||
|
||||
|
|
@ -77,5 +79,59 @@ class TestUtils(unittest.TestCase):
|
|||
for test in test_cases:
|
||||
prompt, loras, loras_limit, skip_file_check = test["input"]
|
||||
expected = test["output"]
|
||||
actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, skip_file_check=skip_file_check)
|
||||
actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit,
|
||||
skip_file_check=skip_file_check)
|
||||
self.assertEqual(expected, actual)
|
||||
|
||||
def test_can_parse_tokens_and_strip_performance_lora(self):
|
||||
lora_filenames = [
|
||||
'hey-lora.safetensors',
|
||||
modules.flags.PerformanceLoRA.EXTREME_SPEED.value,
|
||||
modules.flags.PerformanceLoRA.LIGHTNING.value,
|
||||
os.path.join('subfolder', modules.flags.PerformanceLoRA.HYPER_SD.value)
|
||||
]
|
||||
|
||||
test_cases = [
|
||||
{
|
||||
"input": ("some prompt, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.QUALITY),
|
||||
"output": (
|
||||
[('hey-lora.safetensors', 0.4)],
|
||||
'some prompt'
|
||||
),
|
||||
},
|
||||
{
|
||||
"input": ("some prompt, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.SPEED),
|
||||
"output": (
|
||||
[('hey-lora.safetensors', 0.4)],
|
||||
'some prompt'
|
||||
),
|
||||
},
|
||||
{
|
||||
"input": ("some prompt, <lora:sdxl_lcm_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.EXTREME_SPEED),
|
||||
"output": (
|
||||
[('hey-lora.safetensors', 0.4)],
|
||||
'some prompt'
|
||||
),
|
||||
},
|
||||
{
|
||||
"input": ("some prompt, <lora:sdxl_lightning_4step_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.LIGHTNING),
|
||||
"output": (
|
||||
[('hey-lora.safetensors', 0.4)],
|
||||
'some prompt'
|
||||
),
|
||||
},
|
||||
{
|
||||
"input": ("some prompt, <lora:sdxl_hyper_sd_4step_lora:1>, <lora:hey-lora:0.4>", [], 5, True, modules.flags.Performance.HYPER_SD),
|
||||
"output": (
|
||||
[('hey-lora.safetensors', 0.4)],
|
||||
'some prompt'
|
||||
),
|
||||
}
|
||||
]
|
||||
|
||||
for test in test_cases:
|
||||
prompt, loras, loras_limit, skip_file_check, performance = test["input"]
|
||||
lora_filenames = modules.util.remove_performance_lora(lora_filenames, performance)
|
||||
expected = test["output"]
|
||||
actual = util.parse_lora_references_from_prompt(prompt, loras, loras_limit=loras_limit, lora_filenames=lora_filenames)
|
||||
self.assertEqual(expected, actual)
|
||||
|
|
|
|||
6
webui.py
6
webui.py
|
|
@ -461,8 +461,8 @@ with shared.gradio_root:
|
|||
interactive=not modules.config.default_black_out_nsfw,
|
||||
info='Disable preview during generation.')
|
||||
disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results',
|
||||
value=modules.config.default_performance == flags.Performance.EXTREME_SPEED.value,
|
||||
interactive=modules.config.default_performance != flags.Performance.EXTREME_SPEED.value,
|
||||
value=flags.Performance.has_restricted_features(modules.config.default_performance),
|
||||
interactive=not flags.Performance.has_restricted_features(modules.config.default_performance),
|
||||
info='Disable intermediate results during generation, only show final gallery.')
|
||||
disable_seed_increment = gr.Checkbox(label='Disable seed increment',
|
||||
info='Disable automatic seed increment when image number is > 1.',
|
||||
|
|
@ -713,7 +713,7 @@ with shared.gradio_root:
|
|||
parsed_parameters = {}
|
||||
else:
|
||||
metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
|
||||
parsed_parameters = metadata_parser.parse_json(parameters)
|
||||
parsed_parameters = metadata_parser.to_json(parameters)
|
||||
|
||||
return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue