Files
automatic/pipelines/model_step1x_edit.py
Vladimir Mandic e7e317191a automated pipeline registrations and tests
Signed-off-by: Vladimir Mandic <mandic00@live.com>
2026-06-04 10:18:09 +02:00

51 lines
2.1 KiB
Python

import transformers
import diffusers
from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
from modules.logger import log
from pipelines import generic
def load_step1x_edit(checkpoint_info, diffusers_load_config=None):
from pipelines.step1x.pipeline_step1x_edit import Step1XEditPipeline
from pipelines.step1x.transformer_step1x_edit import Step1XEditTransformer2DModel
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)
log.debug(f'Load model: type=Step1XEdit repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
diffusers.Step1XEditPipeline = Step1XEditPipeline
diffusers.Step1XEditTransformer2DModel = Step1XEditTransformer2DModel
generic.set_pipeline('Step1XEdit', Step1XEditPipeline)
text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)
from pipelines.step1x import STEP1X_SPEC
transformer = generic.load_transformer(repo_id, cls_name=Step1XEditTransformer2DModel, load_config=diffusers_load_config, native_spec=STEP1X_SPEC)
if repo_id is None or repo_id.lower() == 'none':
return None
processor = transformers.Qwen2_5_VLProcessor.from_pretrained(repo_id, cache_dir=shared.opts.hfcache_dir, subfolder='processor')
pipe = Step1XEditPipeline.from_pretrained(
repo_id,
cache_dir=shared.opts.diffusers_dir,
transformer=transformer,
text_encoder=text_encoder,
processor=processor,
**load_args,
)
pipe.task_args = {
'output_type': 'pil', # step1x is buggy with np
}
del text_encoder
del processor
del transformer
sd_hijack_te.init_hijack(pipe)
sd_hijack_vae.init_hijack(pipe)
devices.torch_gc(force=True, reason='load')
return pipe