feat: add performance lightning with 4 step LoRA (#2415)

* feat: add performance sdxl lightning

based on https://huggingface.co/ByteDance/SDXL-Lightning/blob/main/sdxl_lightning_4step_lora.safetensors

* feat: add method for centralized restriction of features for specific performance modes

* feat: add lightning preset
This commit is contained in:
Manuel Schmid 2024-03-10 14:34:48 +01:00 committed by GitHub
parent b6e4bb86f4
commit 25650b4bc4
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5 changed files with 92 additions and 3 deletions

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@ -250,6 +250,25 @@ def worker():
adm_scaler_negative = 1.0
adm_scaler_end = 0.0
elif performance_selection == Performance.LIGHTNING:
print('Enter Lightning mode.')
progressbar(async_task, 1, 'Downloading Lightning components ...')
loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
if refiner_model_name != 'None':
print(f'Refiner disabled in Lightning mode.')
refiner_model_name = 'None'
sampler_name = 'euler'
scheduler_name = 'sgm_uniform'
sharpness = 0.0
guidance_scale = 1.0
adaptive_cfg = 1.0
refiner_switch = 1.0
adm_scaler_positive = 1.0
adm_scaler_negative = 1.0
adm_scaler_end = 0.0
print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
print(f'[Parameters] Sharpness = {sharpness}')
print(f'[Parameters] ControlNet Softness = {controlnet_softness}')

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@ -475,6 +475,7 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
model_filenames = []
lora_filenames = []
sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
sdxl_lightning_lora = 'sdxl_lightning_4step_lora.safetensors'
def get_model_filenames(folder_paths, name_filter=None):
@ -538,6 +539,14 @@ def downloading_sdxl_lcm_lora():
)
return sdxl_lcm_lora
def downloading_sdxl_lightning_lora():
load_file_from_url(
url='https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_4step_lora.safetensors',
model_dir=paths_loras[0],
file_name=sdxl_lightning_lora
)
return sdxl_lightning_lora
def downloading_controlnet_canny():
load_file_from_url(

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@ -106,23 +106,32 @@ class Steps(IntEnum):
QUALITY = 60
SPEED = 30
EXTREME_SPEED = 8
LIGHTNING = 4
class StepsUOV(IntEnum):
QUALITY = 36
SPEED = 18
EXTREME_SPEED = 8
LIGHTNING = 4
class Performance(Enum):
QUALITY = 'Quality'
SPEED = 'Speed'
EXTREME_SPEED = 'Extreme Speed'
LIGHTNING = 'Lightning'
@classmethod
def list(cls) -> list:
return list(map(lambda c: c.value, cls))
@classmethod
def has_restricted_features(cls, x) -> bool:
if isinstance(x, Performance):
x = x.value
return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value]
def steps(self) -> int | None:
return Steps[self.name].value if Steps[self.name] else None

52
presets/lightning.json Normal file
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@ -0,0 +1,52 @@
{
"default_model": "juggernautXL_v8Rundiffusion.safetensors",
"default_refiner": "None",
"default_refiner_switch": 0.5,
"default_loras": [
[
"None",
1.0
],
[
"None",
1.0
],
[
"None",
1.0
],
[
"None",
1.0
],
[
"None",
1.0
]
],
"default_cfg_scale": 4.0,
"default_sample_sharpness": 2.0,
"default_sampler": "dpmpp_2m_sde_gpu",
"default_scheduler": "karras",
"default_performance": "Lightning",
"default_prompt": "",
"default_prompt_negative": "",
"default_styles": [
"Fooocus V2",
"Fooocus Enhance",
"Fooocus Sharp"
],
"default_aspect_ratio": "1152*896",
"checkpoint_downloads": {
"juggernautXL_v8Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors"
},
"embeddings_downloads": {},
"lora_downloads": {},
"previous_default_models": [
"juggernautXL_version8Rundiffusion.safetensors",
"juggernautXL_version7Rundiffusion.safetensors",
"juggernautXL_v7Rundiffusion.safetensors",
"juggernautXL_version6Rundiffusion.safetensors",
"juggernautXL_v6Rundiffusion.safetensors"
]
}

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@ -526,9 +526,9 @@ with shared.gradio_root:
model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls,
queue=False, show_progress=False)
performance_selection.change(lambda x: [gr.update(interactive=x != flags.Performance.EXTREME_SPEED.value)] * 11 +
[gr.update(visible=x != flags.Performance.EXTREME_SPEED.value)] * 1 +
[gr.update(interactive=x != flags.Performance.EXTREME_SPEED.value, value=x == flags.Performance.EXTREME_SPEED.value, )] * 1,
performance_selection.change(lambda x: [gr.update(interactive=not flags.Performance.has_restricted_features(x))] * 11 +
[gr.update(visible=not flags.Performance.has_restricted_features(x))] * 1 +
[gr.update(interactive=not flags.Performance.has_restricted_features(x), value=flags.Performance.has_restricted_features(x))] * 1,
inputs=performance_selection,
outputs=[
guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,