mirror of https://github.com/vladmandic/automatic
25 lines
1.1 KiB
Python
25 lines
1.1 KiB
Python
import diffusers
|
|
|
|
|
|
def load_lumina(_checkpoint_info, diffusers_load_config={}):
|
|
from modules import shared, devices, modelloader
|
|
modelloader.hf_login()
|
|
# {'low_cpu_mem_usage': True, 'torch_dtype': torch.float16, 'load_connected_pipeline': True, 'safety_checker': None, 'requires_safety_checker': False}
|
|
if 'torch_dtype' not in diffusers_load_config:
|
|
diffusers_load_config['torch_dtype'] = 'torch.float16'
|
|
if 'low_cpu_mem_usage' in diffusers_load_config:
|
|
del diffusers_load_config['low_cpu_mem_usage']
|
|
if 'load_connected_pipeline' in diffusers_load_config:
|
|
del diffusers_load_config['load_connected_pipeline']
|
|
if 'safety_checker' in diffusers_load_config:
|
|
del diffusers_load_config['safety_checker']
|
|
if 'requires_safety_checker' in diffusers_load_config:
|
|
del diffusers_load_config['requires_safety_checker']
|
|
pipe = diffusers.LuminaText2ImgPipeline.from_pretrained(
|
|
'Alpha-VLLM/Lumina-Next-SFT-diffusers',
|
|
cache_dir = shared.opts.diffusers_dir,
|
|
**diffusers_load_config,
|
|
)
|
|
devices.torch_gc()
|
|
return pipe
|