1
0
mirror of https://github.com/vladmandic/sdnext.git synced 2026-01-27 15:02:48 +03:00
Files
sdnext/pipelines/model_flite.py
2025-10-30 03:11:50 +03:00

40 lines
1.5 KiB
Python

import sys
import diffusers
import transformers
from modules import shared, devices, sd_models, model_quant, sd_hijack_te
from pipelines import generic
def load_flite(checkpoint_info, diffusers_load_config=None):
if diffusers_load_config is None:
diffusers_load_config = {}
repo_id = sd_models.path_to_repo(checkpoint_info)
sd_models.hf_auth_check(checkpoint_info)
load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
shared.log.debug(f'Load model: type=FLite repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
from pipelines import f_lite
diffusers.FLitePipeline = f_lite.FLitePipeline
sys.modules['f_lite'] = f_lite
dit_model = generic.load_transformer(repo_id, cls_name=f_lite.DiT, load_config=diffusers_load_config, subfolder="dit_model")
text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder")
pipe = f_lite.FLitePipeline.from_pretrained(
"Freepik/F-Lite", # pr only exists on main repo
revision="refs/pr/8",
dit_model=dit_model,
text_encoder=text_encoder,
trust_remote_code=True,
cache_dir=shared.opts.diffusers_dir,
**load_args,
)
del text_encoder
del dit_model
sd_hijack_te.init_hijack(pipe)
devices.torch_gc()
return pipe