IOPaint/lama_cleaner/tests/test_sd_model.py

import os
from pathlib import Path

import cv2
import pytest
import torch

from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config, HDStrategy, LDMSampler, SDSampler
from lama_cleaner.tests.test_model import get_config, assert_equal

current_dir = Path(__file__).parent.absolute().resolve()
save_dir = current_dir / 'result'
save_dir.mkdir(exist_ok=True, parents=True)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = torch.device(device)


@pytest.mark.parametrize("sd_device", ['cpu', 'cuda'])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.ddim])
@pytest.mark.parametrize("cpu_textencoder", [True, False])
@pytest.mark.parametrize("disable_nsfw", [True, False])
def test_runway_sd_1_5_ddim(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
    def callback(i, t, latents):
        print(f"sd_step_{i}")

    if sd_device == 'cuda' and not torch.cuda.is_available():
        return

    sd_steps = 50 if sd_device == 'cuda' else 1
    model = ModelManager(name="sd1.5",
                         device=torch.device(sd_device),
                         hf_access_token="",
                         sd_run_local=True,
                         sd_disable_nsfw=disable_nsfw,
                         sd_cpu_textencoder=cpu_textencoder,
                         callback=callback)
    cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps)
    cfg.sd_sampler = sampler

    name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"

    assert_equal(
        model,
        cfg,
        f"runway_sd_{strategy.capitalize()}_{name}.png",
        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
        fx=1.3
    )


@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.pndm, SDSampler.k_lms, SDSampler.k_euler, SDSampler.k_euler_a])
@pytest.mark.parametrize("cpu_textencoder", [False])
@pytest.mark.parametrize("disable_nsfw", [True])
def test_runway_sd_1_5(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
    def callback(i, t, latents):
        print(f"sd_step_{i}")

    if sd_device == 'cuda' and not torch.cuda.is_available():
        return

    sd_steps = 50 if sd_device == 'cuda' else 1
    model = ModelManager(name="sd1.5",
                         device=torch.device(sd_device),
                         hf_access_token="",
                         sd_run_local=True,
                         sd_disable_nsfw=disable_nsfw,
                         sd_cpu_textencoder=cpu_textencoder,
                         callback=callback)
    cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps)
    cfg.sd_sampler = sampler

    name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"

    assert_equal(
        model,
        cfg,
        f"runway_sd_{strategy.capitalize()}_{name}.png",
        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
        fx=1.3
    )


@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.ddim])
def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
    def callback(i, t, latents):
        pass

    if sd_device == 'cuda' and not torch.cuda.is_available():
        return

    sd_steps = 50
    model = ModelManager(name="sd1.5",
                         device=torch.device(sd_device),
                         hf_access_token="",
                         sd_run_local=True,
                         sd_disable_nsfw=True,
                         sd_cpu_textencoder=True,
                         callback=callback)
    cfg = get_config(
        strategy,
        sd_steps=sd_steps,
        prompt='Face of a fox, high resolution, sitting on a park bench',
        negative_prompt='orange, yellow, small',
        sd_sampler=sampler
    )

    name = f"{sampler}_negative_prompt"

    assert_equal(
        model,
        cfg,
        f"runway_sd_{strategy.capitalize()}_{name}.png",
        img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
        mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
        fx=1
    )