mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 01:04:32 +02:00
Vladimir Mandic
64 lines
3.0 KiB
Python
64 lines
3.0 KiB
Python
import diffusers
|
|
import transformers
|
|
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_krea2(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, _ = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
|
|
log.debug(f'Load model: type=Krea2 repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
|
|
|
|
from pipelines.krea2 import KREA2_SPEC
|
|
|
|
if 'nunchaku-lite' in repo_id.lower():
|
|
pass # nunchaku-lite works only with diffusers pipeline/transformer, but diffusers do not support img2img/inpaint
|
|
else:
|
|
from pipelines.krea2.transformer_krea2 import Krea2Transformer2DModel
|
|
from pipelines.krea2.pipeline_krea2 import Krea2Pipeline, Krea2Img2ImgPipeline
|
|
from pipelines.krea2.pipeline_krea2_inpaint import Krea2InpaintPipeline
|
|
diffusers.Krea2Transformer2DModel = Krea2Transformer2DModel
|
|
diffusers.Krea2Pipeline = Krea2Pipeline
|
|
diffusers.Krea2Img2ImgPipeline = Krea2Img2ImgPipeline
|
|
diffusers.Krea2InpaintPipeline = Krea2InpaintPipeline
|
|
from diffusers.pipelines import auto_pipeline
|
|
auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING['krea2'] = diffusers.Krea2Pipeline
|
|
auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING['krea2'] = Krea2Img2ImgPipeline
|
|
auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING['krea2'] = Krea2InpaintPipeline
|
|
|
|
generic.set_pipeline('Krea2', diffusers.Krea2Pipeline)
|
|
|
|
if repo_id is None or repo_id.lower() == 'none':
|
|
return None
|
|
|
|
# Keep small/sensitive layers in compute dtype. `first` (in=64) and `txtfusion.projector`
|
|
# (in=12) are below the int8 GEMM's minimum K; `last` is the output projection; `tmlp`/`tproj`
|
|
# produce the global per-block modulation, too int8-sensitive to quantize (its error compounds
|
|
# across blocks and steps). All are tiny next to the 28 blocks, so the memory cost is small.
|
|
transformer = generic.load_transformer(repo_id, cls_name=diffusers.Krea2Transformer2DModel, load_config=diffusers_load_config, native_spec=KREA2_SPEC, modules_to_not_convert=['first', 'last', 'projector', 'tmlp', 'tproj'])
|
|
text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen3VLModel, load_config=diffusers_load_config)
|
|
|
|
pipe = diffusers.Krea2Pipeline.from_pretrained(
|
|
repo_id,
|
|
cache_dir=shared.opts.diffusers_dir,
|
|
transformer=transformer,
|
|
text_encoder=text_encoder,
|
|
**load_args,
|
|
)
|
|
pipe.task_args = {
|
|
'dense_mask': shared.opts.model_krea2_dense,
|
|
}
|
|
|
|
generic.load_vae_override(pipe, diffusers_load_config)
|
|
|
|
del transformer
|
|
del text_encoder
|
|
sd_hijack_te.init_hijack(pipe)
|
|
sd_hijack_vae.init_hijack(pipe)
|
|
devices.torch_gc(force=True, reason='load')
|
|
return pipe
|