backend/pipelines/lcm_lora.py

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