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import torch
from diffusers import DiffusionPipeline, LCMScheduler, AutoPipelineForText2Image
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={},
):
# pipeline = DiffusionPipeline.from_pretrained(
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,
)
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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