#!/usr/bin/env python3
import torch
import gc
from diffusers import DiffusionPipeline

shape = (30_000, 30_000)

input = torch.randn(shape, device="cuda")


def clear_memory(model):
    model.to('cpu')
    gc.collect()
    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()
    torch.clear_autocast_cache()

for _ids in ["runwayml/stable-diffusion-v1-5", "CompVis/stable-diffusion-v1-4", "runwayml/stable-diffusion-v1-5", "CompVis/stable-diffusion-v1-4", "runwayml/stable-diffusion-v1-5"]:
    pipe = DiffusionPipeline.from_pretrained(_ids, use_safetensors=True).to("cuda")
    pipe("hey", num_inference_steps=1)
    print("finished...")

    clear_memory(pipe)