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import pathlib
from os import path
import torch
from diffusers import (
AutoPipelineForText2Image,
LCMScheduler,
StableDiffusionPipeline,
)
def load_lcm_weights(
pipeline,
use_local_model,
lcm_lora_id,
):
kwargs = {
"local_files_only": use_local_model,
"weight_name": "pytorch_lora_weights.safetensors",
}
pipeline.load_lora_weights(
lcm_lora_id,
**kwargs,
adapter_name="lcm",
)
def get_lcm_lora_pipeline(
base_model_id: str,
lcm_lora_id: str,
use_local_model: bool,
torch_data_type: torch.dtype,
pipeline_args={},
):
if pathlib.Path(base_model_id).suffix == ".safetensors":
# SD 1.5 models only
# When loading a .safetensors model, the pipeline has to be created
# with StableDiffusionPipeline() since it's the only class that
# defines the method from_single_file(); afterwards a new pipeline
# is created using AutoPipelineForText2Image() for ControlNet
# support, in case ControlNet is enabled
if not path.exists(base_model_id):
raise FileNotFoundError(
f"Model file not found,Please check your model path: {base_model_id}"
)
print("Using single file Safetensors model (Supported models - SD 1.5 models)")
dummy_pipeline = StableDiffusionPipeline.from_single_file(
base_model_id,
torch_dtype=torch_data_type,
safety_checker=None,
load_safety_checker=False,
local_files_only=use_local_model,
use_safetensors=True,
)
pipeline = AutoPipelineForText2Image.from_pipe(
dummy_pipeline,
**pipeline_args,
)
del dummy_pipeline
else:
pipeline = AutoPipelineForText2Image.from_pretrained(
base_model_id,
torch_dtype=torch_data_type,
local_files_only=use_local_model,
**pipeline_args,
)
load_lcm_weights(
pipeline,
use_local_model,
lcm_lora_id,
)
# Always fuse LCM-LoRA
pipeline.fuse_lora()
if "lcm" in lcm_lora_id.lower() or "hypersd" in lcm_lora_id.lower():
print("LCM LoRA model detected so using recommended LCMScheduler")
pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config)
# pipeline.unet.to(memory_format=torch.channels_last)
return pipeline
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