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sdnext/pipelines/model_sd3.py
2025-10-30 03:11:50 +03:00

40 lines
1.8 KiB
Python

import diffusers
import transformers
from modules import shared, devices, sd_models, model_quant, sd_hijack_te
from pipelines import generic
def load_sd3(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=SD3 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
transformer = generic.load_transformer(repo_id, cls_name=diffusers.SD3Transformer2DModel, load_config=diffusers_load_config)
# text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.CLIPTextModelWithProjection, load_config=diffusers_load_config, subfolder="text_encoder")
# text_encoder_2 = generic.load_text_encoder(repo_id, cls_name=transformers.CLIPTextModelWithProjection, load_config=diffusers_load_config, subfolder="text_encoder_2")
if shared.opts.model_sd3_disable_te5:
text_encoder_3 = None
else:
text_encoder_3 = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder_3")
pipe = diffusers.StableDiffusion3Pipeline.from_pretrained(
repo_id,
transformer=transformer,
# text_encoder=text_encoder,
# text_encoder_2=text_encoder_2,
text_encoder_3=text_encoder_3,
cache_dir=shared.opts.diffusers_dir,
**load_args,
)
del text_encoder_3
del transformer
sd_hijack_te.init_hijack(pipe)
devices.torch_gc(force=True, reason='load')
return pipe