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365 lines
11 KiB
Python
365 lines
11 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
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from imaginairy.img_processors.control_modes import CONTROL_MODES
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from imaginairy.img_utils import pillow_fit_image_within
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from imaginairy.schema import ControlNetInput, ImaginePrompt
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from imaginairy.utils import get_device
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from . import TESTS_FOLDER
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from .utils import assert_image_similar_to_expectation
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def test_imagine(sampler_type, filename_base_for_outputs):
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prompt_text = "a scenic old-growth forest with diffuse light poking through the canopy. high resolution nature photography"
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prompt = ImaginePrompt(
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prompt_text, width=512, height=512, steps=20, seed=1, sampler_type=sampler_type
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)
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result = next(imagine(prompt))
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threshold_lookup = {"k_dpm_2_a": 26000}
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threshold = threshold_lookup.get(sampler_type, 10000)
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(
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result.img, img_path=img_path, threshold=threshold
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)
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compare_prompts = [
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"a photo of a bowl of fruit",
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"a headshot photo of a happy couple smiling at the camera",
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"a painting of a beautiful cloudy sunset at the beach",
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"a photo of a dog",
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"a photo of a handshake",
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"a photo of an astronaut riding a horse on the moon. the earth visible in the background",
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]
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@pytest.mark.skipif(get_device() != "cuda", reason="Too slow to run on CPU or MPS")
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@pytest.mark.parametrize(
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"model_version", ["SD-1.4", "SD-1.5", "SD-2.0", "SD-2.0-v", "SD-2.1", "SD-2.1-v"]
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)
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def test_model_versions(filename_base_for_orig_outputs, model_version):
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"""Test that we can switch between model versions."""
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prompts = []
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for prompt_text in compare_prompts:
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prompts.append(
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ImaginePrompt(
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prompt_text,
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seed=1,
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model=model_version,
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)
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)
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threshold = 35000
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for i, result in enumerate(imagine(prompts)):
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img_path = f"{filename_base_for_orig_outputs}_{result.prompt.prompt_text}_{result.prompt.model}.png"
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result.img.save(img_path)
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for i, result in enumerate(imagine(prompts)):
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img_path = f"{filename_base_for_orig_outputs}_{result.prompt.prompt_text}_{result.prompt.model}.png"
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assert_image_similar_to_expectation(
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result.img, img_path=img_path, threshold=threshold
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)
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def test_img2img_beach_to_sunset(
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sampler_type, filename_base_for_outputs, filename_base_for_orig_outputs
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):
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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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pillow_fit_image_within(img).save(f"{filename_base_for_orig_outputs}__orig.jpg")
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(result.img, img_path=img_path, threshold=2900)
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def test_img_to_img_from_url_cats(
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sampler_type,
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filename_base_for_outputs,
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mocked_responses,
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filename_base_for_orig_outputs,
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):
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with open(
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os.path.join(TESTS_FOLDER, "data", "val2017-000000039769-cococats.jpg"), "rb"
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) as f:
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img_data = f.read()
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mocked_responses.get(
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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body=img_data,
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status=200,
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content_type="image/jpeg",
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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_orig_outputs}__orig.jpg")
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(result.img, img_path=img_path, threshold=17000)
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def test_img2img_low_noise(
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filename_base_for_outputs,
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sampler_type,
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):
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fruit_path = os.path.join(TESTS_FOLDER, "data", "bowl_of_fruit.jpg")
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img = LazyLoadingImage(filepath=fruit_path)
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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=0.5,
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mask_prompt="(fruit{*2} OR stem{*10} OR fruit stem{*3})",
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mask_mode="replace",
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# steps=40,
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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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threshold_lookup = {
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"k_dpm_2_a": 26000,
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"k_euler_a": 18000,
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"k_dpm_adaptive": 13000,
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}
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threshold = threshold_lookup.get(sampler_type, 14000)
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(
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result.img, img_path=img_path, threshold=threshold
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)
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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,
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sampler_type,
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init_strength,
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filename_base_for_orig_outputs,
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):
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img = LazyLoadingImage(
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filepath=os.path.join(TESTS_FOLDER, "data", "bowl_of_fruit.jpg")
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)
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target_steps = 25
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needed_steps = 25 if init_strength >= 1 else int(target_steps / (1 - init_strength))
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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{*2} OR stem{*10} OR fruit stem{*3})",
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mask_mode="replace",
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steps=needed_steps,
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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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threshold_lookup = {
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"k_dpm_2_a": 32000,
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"k_euler_a": 18000,
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"k_dpm_adaptive": 13000,
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"k_dpmpp_2s": 16000,
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}
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threshold = threshold_lookup.get(sampler_type, 16000)
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pillow_fit_image_within(img).save(f"{filename_base_for_orig_outputs}__orig.jpg")
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(
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result.img, img_path=img_path, threshold=threshold
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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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def test_img_to_img_fruit_2_gold_repeat():
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img = LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/bowl_of_fruit.jpg")
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run_count = 1
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kwargs = {
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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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"fix_faces": True,
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"upscale": True,
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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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ImaginePrompt(**kwargs),
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]
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for result in imagine(prompts, debug_img_callback=None):
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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, filename_base_for_orig_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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pillow_fit_image_within(img).save(f"{filename_base_for_orig_outputs}_orig.jpg")
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(result.img, img_path=img_path, threshold=2800)
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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(
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filename_base_for_outputs, filename_base_for_orig_outputs
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):
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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){*5}",
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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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pillow_fit_image_within(img).save(f"{filename_base_for_orig_outputs}_orig.jpg")
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(result.img, img_path=img_path, threshold=32000)
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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def test_tile_mode(filename_base_for_outputs):
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prompt_text = "gold coins"
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prompt = ImaginePrompt(
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prompt_text,
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width=400,
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height=400,
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steps=15,
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seed=1,
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tile_mode="xy",
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)
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result = next(imagine(prompt))
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(result.img, img_path=img_path, threshold=25000)
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control_modes = list(CONTROL_MODES.keys())
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@pytest.mark.parametrize("control_mode", control_modes)
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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def test_controlnet(filename_base_for_outputs, control_mode):
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prompt_text = "a photo of a woman sitting on a bench"
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control_input = ControlNetInput(
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mode=control_mode,
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image=LazyLoadingImage(filepath=f"{TESTS_FOLDER}/data/bench2.png"),
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)
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prompt = ImaginePrompt(
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prompt_text,
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width=512,
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height=512,
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steps=15,
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seed=0,
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control_inputs=[control_input],
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fix_faces=True,
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)
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prompt.steps = 1
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prompt.width = 256
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prompt.height = 256
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result = next(imagine(prompt))
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prompt.steps = 15
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prompt.width = 512
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prompt.height = 512
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result = next(imagine(prompt))
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(result.img, img_path=img_path, threshold=24000)
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@pytest.mark.skipif(get_device() == "cpu", reason="Too slow to run on CPU")
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def test_large_image(filename_base_for_outputs):
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prompt_text = "a stormy ocean. oil painting"
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prompt = ImaginePrompt(
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prompt_text,
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width=1920,
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height=1080,
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steps=15,
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seed=0,
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)
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result = next(imagine(prompt))
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img_path = f"{filename_base_for_outputs}.png"
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assert_image_similar_to_expectation(result.img, img_path=img_path, threshold=35000)
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