mirror of
https://github.com/vladmandic/sdnext.git
synced 2026-01-27 15:02:48 +03:00
58 lines
2.4 KiB
Python
58 lines
2.4 KiB
Python
import transformers
|
|
import diffusers
|
|
from modules import shared, devices, sd_models, model_quant, sd_hijack_te
|
|
from pipelines import generic
|
|
|
|
|
|
def load_cogview3(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)
|
|
shared.log.debug(f'Load model: type=CogView3 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.CogView3PlusTransformer2DModel, load_config=diffusers_load_config, subfolder="transformer")
|
|
text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder")
|
|
|
|
pipe = diffusers.CogView3PlusPipeline.from_pretrained(
|
|
repo_id,
|
|
text_encoder=text_encoder,
|
|
transformer=transformer,
|
|
cache_dir=shared.opts.diffusers_dir,
|
|
**load_args,
|
|
)
|
|
sd_hijack_te.init_hijack(pipe)
|
|
del transformer
|
|
del text_encoder
|
|
devices.torch_gc()
|
|
return pipe
|
|
|
|
|
|
def load_cogview4(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)
|
|
shared.log.debug(f'Load model: type=CogView4 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.CogView4Transformer2DModel, load_config=diffusers_load_config, subfolder="transformer")
|
|
text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.GlmModel, load_config=diffusers_load_config, subfolder="text_encoder", allow_quant=True)
|
|
|
|
pipe = diffusers.CogView4Pipeline.from_pretrained(
|
|
repo_id,
|
|
text_encoder=text_encoder,
|
|
transformer=transformer,
|
|
cache_dir=shared.opts.diffusers_dir,
|
|
**load_args,
|
|
)
|
|
sd_hijack_te.init_hijack(pipe)
|
|
del transformer
|
|
del text_encoder
|
|
|
|
devices.torch_gc()
|
|
return pipe
|