mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-05 12:00:15 +00:00
95a8fa31a9
while the previous version did produce much better blending it also makes images that lack detail for some reason. tests: Added more tests to help catch this sort of thing earlies fix: found that median blur is really slow, so I made sure we only do it on downsampled masks. Was taking like 3 minutes to run on the large pearl girl picture on M1 - docs: update examples
269 lines
9.0 KiB
Python
269 lines
9.0 KiB
Python
import os.path
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import pytest
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from imaginairy import LazyLoadingImage
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from imaginairy.api import imagine, imagine_image_files, prompt_normalized
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from imaginairy.img_utils import pillow_fit_image_within
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from imaginairy.schema import ImaginePrompt
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from imaginairy.utils import get_device
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from . import TESTS_FOLDER
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device_sampler_type_test_cases = {
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"mps:0": [
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("plms", "78539ae3a3097dc8232da6d630551ab3"),
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("ddim", "828fc143cd40586347b2f8403c288c9b"),
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("k_lms", "53d25e59add39c8447537be30e4eff4b"),
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("k_dpm_2", "5108bceb58a38d88a585f37b2ba1b072"),
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("k_dpm_2_a", "20396daa6c920d1cfd6db90e73558c01"),
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("k_euler", "9ab4666ebe6c3aa68673912bb17fb2b1"),
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("k_euler_a", "c4b03829cc93422801f3243a46bad4bc"),
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("k_heun", "0d3aad6800d4a9a43f0b0514af9d23b5"),
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],
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"cuda": [
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("plms", "b98e1248ad1f144d34122d8809b39fb8"),
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("ddim", "a645ca24575ed3f18bf48f11354233bb"),
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("k_lms", "3ddbdef45e3f38768730961771d01727"),
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("k_dpm_2", "b6e88e16ec2c43e6382b1adec828479d"),
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("k_dpm_2_a", "b0791770d48cb22d308ad76c72fb660f"),
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("k_euler", "bcf375769d64d9ca224864d35565ac1d"),
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("k_euler_a", "38b970ff6a67428efbf00df66a9e48f7"),
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("k_heun", "ccbd0804c7ce2bb637c682951bd8b693"),
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],
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"cpu": [],
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}
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sampler_type_test_cases = device_sampler_type_test_cases[get_device()]
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@pytest.mark.parametrize("sampler_type,expected_md5", sampler_type_test_cases)
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def test_imagine(sampler_type, expected_md5, filename_base_for_outputs):
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prompt_text = "a scenic landscape"
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prompt = ImaginePrompt(
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prompt_text, width=512, height=256, steps=20, seed=1, sampler_type=sampler_type
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)
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result = next(imagine(prompt))
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result.img.save(f"{filename_base_for_outputs}.jpg")
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assert result.md5() == expected_md5
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device_sampler_type_test_cases_img_2_img = {
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"mps:0": {
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("plms", "0d9c40c348cdac7bdc8d5a472f378f42"),
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("ddim", "0d9c40c348cdac7bdc8d5a472f378f42"),
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},
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"cuda": {
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("plms", "841723966344dd8678aee1ce5f9cbb3d"),
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("ddim", "1f0d72370fabcf2ff716e4068d5b2360"),
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},
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}
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sampler_type_test_cases_img_2_img = device_sampler_type_test_cases_img_2_img[
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get_device()
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]
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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@pytest.mark.parametrize("sampler_type,expected_md5", sampler_type_test_cases_img_2_img)
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def test_img2img_beach_to_sunset(sampler_type, expected_md5, filename_base_for_outputs):
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img = LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/beach_at_sainte_adresse.jpg")
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prompt = ImaginePrompt(
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"a painting of beautiful cloudy sunset at the beach",
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init_image=img,
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init_image_strength=0.5,
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prompt_strength=15,
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mask_prompt="(sky|clouds) AND !(buildings|trees)",
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mask_mode="replace",
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width=512,
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height=512,
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steps=40 * 2,
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seed=1,
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sampler_type=sampler_type,
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)
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result = next(imagine(prompt))
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img = pillow_fit_image_within(img)
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img.save(f"{filename_base_for_outputs}__orig.jpg")
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result.img.save(f"{filename_base_for_outputs}.jpg")
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device_sampler_type_test_cases_img_2_img = {
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"mps:0": {
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("plms", "e9bb714771f7984e61debabc4bb3cd22"),
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("ddim", "62bacc4ae391e6775a3723c88738ec61"),
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},
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"cuda": {
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("plms", "b8c7b52da977c1531a9a61c0a082404c"),
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("ddim", "d6784710dd78e4cb628aba28322b04cf"),
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},
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}
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sampler_type_test_cases_img_2_img = device_sampler_type_test_cases_img_2_img[
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get_device()
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]
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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@pytest.mark.parametrize("sampler_type,expected_md5", sampler_type_test_cases_img_2_img)
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def test_img_to_img_from_url_cats(
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sampler_type, expected_md5, filename_base_for_outputs
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):
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img = LazyLoadingImage(url="http://images.cocodataset.org/val2017/000000039769.jpg")
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prompt = ImaginePrompt(
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"dogs lying on a hot pink couch",
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init_image=img,
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init_image_strength=0.5,
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width=512,
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height=512,
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steps=50,
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seed=1,
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sampler_type=sampler_type,
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)
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result = next(imagine(prompt))
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img = pillow_fit_image_within(img)
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img.save(f"{filename_base_for_outputs}__orig.jpg")
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result.img.save(f"{filename_base_for_outputs}.jpg")
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assert result.md5() == expected_md5
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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@pytest.mark.parametrize("sampler_type", ["ddim", "plms"])
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@pytest.mark.parametrize("init_strength", [0, 0.05, 0.2, 1])
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def test_img_to_img_fruit_2_gold(
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filename_base_for_outputs, sampler_type, init_strength
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):
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img = LazyLoadingImage(
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url="https://raw.githubusercontent.com/brycedrennan/imaginAIry/master/assets/000056_293284644_PLMS40_PS7.5_photo_of_a_bowl_of_fruit.jpg"
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)
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prompt = ImaginePrompt(
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"a white bowl filled with gold coins",
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prompt_strength=12,
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init_image=img,
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init_image_strength=init_strength,
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mask_prompt="(fruit OR stem{*5} OR fruit stem)",
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mask_mode="replace",
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steps=80,
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seed=1,
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sampler_type=sampler_type,
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)
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result = next(imagine(prompt))
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img = pillow_fit_image_within(img)
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img.save(f"{filename_base_for_outputs}__orig.jpg")
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result.img.save(f"{filename_base_for_outputs}.jpg")
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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def test_img_to_img_fruit_2_gold_repeat():
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"""Run this test manually to"""
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img = LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/bowl_of_fruit.jpg")
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outdir = f"{TESTS_FOLDER}/test_output/"
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run_count = 1
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def _record_step(img, description, step_count, prompt):
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steps_path = os.path.join(
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outdir,
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f"steps_fruit_2_gold_repeat_{get_device()}_S{prompt.seed}_run_{run_count:02}",
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)
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os.makedirs(steps_path, exist_ok=True)
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filename = f"fruit_2_gold_repeat_{get_device()}_S{prompt.seed}_step{step_count:04}_{prompt_normalized(description)[:40]}.jpg"
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destination = os.path.join(steps_path, filename)
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img.save(destination)
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kwargs = dict(
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prompt="a white bowl filled with gold coins. sharp focus",
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prompt_strength=12,
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init_image=img,
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init_image_strength=0.2,
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mask_prompt="(fruit OR stem{*5} OR fruit stem)",
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mask_mode="replace",
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steps=20,
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seed=946188797,
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sampler_type="plms",
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)
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prompts = [
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ImaginePrompt(**kwargs),
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ImaginePrompt(**kwargs),
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]
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for result in imagine(prompts, img_callback=None):
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img = pillow_fit_image_within(img)
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img.save(f"{TESTS_FOLDER}/test_output/img2img_fruit_2_gold__orig.jpg")
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result.img.save(
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f"{TESTS_FOLDER}/test_output/img2img_fruit_2_gold_plms_{get_device()}_run-{run_count:02}.jpg"
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)
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run_count += 1
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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def test_img_to_file():
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prompt = ImaginePrompt(
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"an old growth forest, diffuse light poking through the canopy. high-resolution, nature photography, nat geo photo",
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width=512 + 64,
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height=512 - 64,
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steps=20,
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seed=2,
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sampler_type="PLMS",
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upscale=True,
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)
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out_folder = f"{TESTS_FOLDER}/test_output"
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imagine_image_files(prompt, outdir=out_folder)
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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def test_inpainting_bench(filename_base_for_outputs):
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img = LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/bench2.png")
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prompt = ImaginePrompt(
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"a wise old man",
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init_image=img,
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init_image_strength=0.4,
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mask_image=LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/bench2_mask.png"),
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width=512,
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height=512,
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steps=40,
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seed=1,
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sampler_type="plms",
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)
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result = next(imagine(prompt))
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img = pillow_fit_image_within(img)
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img.save(f"{filename_base_for_outputs}__orig.jpg")
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result.img.save(f"{filename_base_for_outputs}.jpg")
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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def test_cliptext_inpainting_pearl_doctor(filename_base_for_outputs):
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img = LazyLoadingImage(
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filepath=f"{TESTS_FOLDER}/data/girl_with_a_pearl_earring.jpg"
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)
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prompt = ImaginePrompt(
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"a female doctor in the hospital",
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prompt_strength=12,
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init_image=img,
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init_image_strength=0.2,
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mask_prompt="face AND NOT (bandana OR hair OR blue fabric){*6}",
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mask_mode=ImaginePrompt.MaskMode.KEEP,
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width=512,
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height=512,
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steps=40,
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sampler_type="plms",
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seed=181509347,
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)
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result = next(imagine(prompt))
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img = pillow_fit_image_within(img)
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img.save(f"{filename_base_for_outputs}__orig.jpg")
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result.img.save(f"{filename_base_for_outputs}_{prompt.seed}.jpg")
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found_match = result.md5() in set(
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[
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"84868e7477a7375f7089160ac6adc064",
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"c5c0166185c284fc849901123e78d608",
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"6ef63037f5a1bd8bce6aec1c7ad46880",
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] # mps
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)
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assert found_match
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