You can do the same with pruna by following this minimal example:

import torch
from f_lite import FLitePipeline

# Trick required because it is not a native diffusers model
from diffusers.pipelines.pipeline_loading_utils import LOADABLE_CLASSES, ALL_IMPORTABLE_CLASSES
LOADABLE_CLASSES["f_lite"] = LOADABLE_CLASSES["f_lite.model"] = {"DiT": ["save_pretrained", "from_pretrained"]}
ALL_IMPORTABLE_CLASSES["DiT"] = ["save_pretrained", "from_pretrained"]

pipeline = FLitePipeline.from_pretrained("Freepik/F-Lite", torch_dtype=torch.bfloat16).to("cuda")
pipeline.dit_model = torch.compile(pipeline.dit_model)

from pruna_pro import SmashConfig, smash

# Initialize the SmashConfig
smash_config = SmashConfig()
smash_config["cacher"] = "auto"
# smash_config["auto_cache_mode"] = "taylor"
smash_config["auto_speed_factor"] = 0.8  # Lower is faster, but reduces quality
smash_config["auto_custom_model"] = True

smashed_pipe = smash(
    model=pipeline,
    smash_config=smash_config,
    experimental=True,
)

smashed_pipe.cache_helper.configure(
    pipe=pipeline,
    pipe_call_method="__call__",
    step_argument="num_inference_steps",
    backbone=pipeline.dit_model,
    backbone_call_method="forward",
)

smashed_pipe(
    prompt="A cake with 'pruna' written on it",
    height=1024,
    width=1024,
    num_inference_steps=30,
    guidance_scale=3.0,
    negative_prompt=None,
    generator=torch.Generator(device="cuda").manual_seed(0),
).images[0]