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from pathlib import Path
from typing import Any
from optimum.intel.openvino import OVDiffusionPipeline
from optimum.intel.openvino.modeling_diffusion import (
OVModelVae,
OVModelVaeDecoder,
OVModelVaeEncoder,
)
from backend.device import is_openvino_device
from backend.tiny_autoencoder import get_tiny_autoencoder_repo_id
from constants import DEVICE, LCM_DEFAULT_MODEL_OPENVINO
from paths import get_base_folder_name
if is_openvino_device():
from huggingface_hub import snapshot_download
from optimum.intel.openvino.modeling_diffusion import (
OVBaseModel,
OVStableDiffusionImg2ImgPipeline,
OVStableDiffusionPipeline,
OVStableDiffusionXLImg2ImgPipeline,
OVStableDiffusionXLPipeline,
)
def ov_load_tiny_autoencoder(
pipeline: Any,
use_local_model: bool = False,
):
taesd_dir = snapshot_download(
repo_id=get_tiny_autoencoder_repo_id(pipeline.__class__.__name__),
local_files_only=use_local_model,
)
vae_decoder = OVModelVaeDecoder(
model=OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"),
parent_pipeline=pipeline,
model_name="vae_decoder",
)
vae_encoder = OVModelVaeEncoder(
model=OVBaseModel.load_model(f"{taesd_dir}/vae_encoder/openvino_model.xml"),
parent_pipeline=pipeline,
model_name="vae_encoder",
)
pipeline.vae = OVModelVae(
decoder=vae_decoder,
encoder=vae_encoder,
)
pipeline.vae.config.scaling_factor = 1.0
def get_ov_text_to_image_pipeline(
model_id: str = LCM_DEFAULT_MODEL_OPENVINO,
use_local_model: bool = False,
) -> Any:
if "xl" in get_base_folder_name(model_id).lower():
pipeline = OVStableDiffusionXLPipeline.from_pretrained(
model_id,
local_files_only=use_local_model,
ov_config={"CACHE_DIR": ""},
device=DEVICE.upper(),
)
else:
pipeline = OVStableDiffusionPipeline.from_pretrained(
model_id,
local_files_only=use_local_model,
ov_config={"CACHE_DIR": ""},
device=DEVICE.upper(),
)
return pipeline
def get_ov_image_to_image_pipeline(
model_id: str = LCM_DEFAULT_MODEL_OPENVINO,
use_local_model: bool = False,
) -> Any:
if "xl" in get_base_folder_name(model_id).lower():
pipeline = OVStableDiffusionXLImg2ImgPipeline.from_pretrained(
model_id,
local_files_only=use_local_model,
ov_config={"CACHE_DIR": ""},
device=DEVICE.upper(),
)
else:
pipeline = OVStableDiffusionImg2ImgPipeline.from_pretrained(
model_id,
local_files_only=use_local_model,
ov_config={"CACHE_DIR": ""},
device=DEVICE.upper(),
)
return pipeline
def get_ov_diffusion_pipeline(
model_id: str,
use_local_model: bool = False,
) -> Any:
pipeline = OVDiffusionPipeline.from_pretrained(
model_id,
local_files_only=use_local_model,
ov_config={"CACHE_DIR": ""},
device=DEVICE.upper(),
)
return pipeline
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