203 lines
7.2 KiB
Python
203 lines
7.2 KiB
Python
"""
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Tests a common workflow for UltimateSDUpscale.
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"""
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import pathlib
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import pytest
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import torch
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from PIL import Image
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import usdu_utils
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from setup_utils import execute
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from tensor_utils import img_tensor_mae
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from io_utils import save_image, load_image
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from configs import DirectoryConfig
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# Image file names
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EXT = ".jpg"
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CATEGORY = pathlib.Path("main_workflow")
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BASE_IMAGE_1_NAME = "main1_sd15" + EXT
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BASE_IMAGE_2_NAME = "main2_sd15" + EXT
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UPSCALED_IMAGE_1_NAME = "main1_sd15_upscaled" + EXT
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UPSCALED_IMAGE_2_NAME = "main2_sd15_upscaled" + EXT
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# Prepend category path
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BASE_IMAGE_1 = CATEGORY / BASE_IMAGE_1_NAME
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BASE_IMAGE_2 = CATEGORY / BASE_IMAGE_2_NAME
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UPSCALED_IMAGE_1 = CATEGORY / UPSCALED_IMAGE_1_NAME
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UPSCALED_IMAGE_2 = CATEGORY / UPSCALED_IMAGE_2_NAME
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class TestMainWorkflow:
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"""Integration tests for the main upscaling workflow."""
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@pytest.fixture(scope="class")
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def base_image(self, loaded_checkpoint, seed, test_dirs, node_classes):
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"""Generate a base image for upscaling tests."""
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EmptyLatentImage = node_classes["EmptyLatentImage"]
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CLIPTextEncode = node_classes["CLIPTextEncode"]
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KSampler = node_classes["KSampler"]
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VAEDecode = node_classes["VAEDecode"]
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model, clip, vae = loaded_checkpoint
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with torch.inference_mode():
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(empty_latent,) = execute(
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EmptyLatentImage, width=512, height=512, batch_size=2
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)
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(positive,) = execute(
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CLIPTextEncode,
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text="beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
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clip=clip,
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)
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(negative,) = execute(CLIPTextEncode, text="text, watermark", clip=clip)
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(samples,) = execute(
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KSampler,
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model=model,
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positive=positive,
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negative=negative,
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latent_image=empty_latent,
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seed=seed,
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steps=10,
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cfg=8,
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sampler_name="dpmpp_2m",
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scheduler="karras",
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denoise=1.0,
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)
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(image,) = execute(VAEDecode, samples=samples, vae=vae)
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# Save base images
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sample_dir = test_dirs.sample_images
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base_img1_path = sample_dir / BASE_IMAGE_1
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base_img2_path = sample_dir / BASE_IMAGE_2
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save_image(image[0], base_img1_path)
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save_image(image[1], base_img2_path)
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# Load images back as tensors to account for compression
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image = torch.cat([load_image(base_img1_path), load_image(base_img2_path)])
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return image, positive, negative
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def test_base_image_matches_reference(self, base_image, test_dirs: DirectoryConfig):
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"""
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Verify generated base images match reference images.
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This is just to check if the checkpoint and generation pipeline are as expected for the tests dependent on their behavior.
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"""
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image, _, _ = base_image
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test_image_dir = test_dirs.test_images
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im1 = image[0]
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im2 = image[1]
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test_im1 = load_image(test_image_dir / BASE_IMAGE_1)
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test_im2 = load_image(test_image_dir / BASE_IMAGE_2)
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diff1 = img_tensor_mae(im1, test_im1)
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diff2 = img_tensor_mae(im2, test_im2)
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assert diff1 < 0.015, f"Image 1 does not match test image. Diff: {diff1}"
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assert diff2 < 0.015, f"Image 2 does not match test image. Diff: {diff2}"
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@pytest.fixture(scope="class")
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def upscaled_image(
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self,
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base_image,
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loaded_checkpoint,
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upscale_model,
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node_classes,
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seed,
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test_dirs,
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):
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"""Generate upscaled images using custom sampler."""
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image, positive, negative = base_image
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model, clip, vae = loaded_checkpoint
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with torch.inference_mode():
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# Setup custom scheduler and sampler
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custom_scheduler = node_classes["KarrasScheduler"]
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(sigmas,) = execute(custom_scheduler, 20, 14.614642, 0.0291675, 7.0)
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(_, sigmas) = execute(node_classes["SplitSigmasDenoise"], sigmas, 0.2)
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custom_sampler = node_classes["KSamplerSelect"]
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(sampler,) = execute(custom_sampler, "dpmpp_2m")
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# Run upscale
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usdu = node_classes["UltimateSDUpscaleCustomSample"]
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(upscaled,) = usdu().upscale(
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image=image,
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model=model,
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positive=positive,
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negative=negative,
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vae=vae,
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upscale_by=2.00000004, # Test small float difference doesn't add extra tiles
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seed=seed,
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steps=10,
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cfg=8,
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sampler_name="euler",
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scheduler="normal",
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denoise=0.2,
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upscale_model=upscale_model,
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mode_type="Chess",
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tile_width=512,
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tile_height=512,
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mask_blur=8,
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tile_padding=32,
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seam_fix_mode="None",
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seam_fix_denoise=1.0,
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seam_fix_width=64,
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seam_fix_mask_blur=8,
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seam_fix_padding=16,
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force_uniform_tiles=True,
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tiled_decode=False,
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custom_sampler=sampler,
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custom_sigmas=sigmas,
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)
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# Save images
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sample_dir = test_dirs.sample_images
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upscaled_img1_path = sample_dir / UPSCALED_IMAGE_1
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upscaled_img2_path = sample_dir / UPSCALED_IMAGE_2
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save_image(upscaled[0], upscaled_img1_path)
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save_image(upscaled[1], upscaled_img2_path)
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# Load
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upscaled = torch.cat(
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[load_image(upscaled_img1_path), load_image(upscaled_img2_path)]
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)
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# Save latent
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with torch.inference_mode():
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(latent, ) = execute(node_classes["VAEEncode"], vae=vae, pixels=upscaled[0:1])
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return upscaled
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def test_upscale_with_custom_sampler(
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self, upscaled_image, test_dirs: DirectoryConfig
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):
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"""Test upscaling with custom sampler and sigmas."""
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# Verify results
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test_image_dir = test_dirs.test_images
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im1_upscaled = upscaled_image[0]
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im2_upscaled = upscaled_image[1]
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test_im1_upscaled = load_image(test_image_dir / UPSCALED_IMAGE_1)
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test_im2_upscaled = load_image(test_image_dir / UPSCALED_IMAGE_2)
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diff1 = img_tensor_mae(im1_upscaled, test_im1_upscaled)
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diff2 = img_tensor_mae(im2_upscaled, test_im2_upscaled)
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# This tolerance is enough to handle both cpu and gpu as the device, as well as jpg compression differences.
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assert diff1 < 0.015, f"Upscaled Image 1 doesn't match. Diff: {diff1}"
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assert diff2 < 0.015, f"Upscaled Image 2 doesn't match. Diff: {diff2}"
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def test_save_sample_images(self, upscaled_image, test_dirs: DirectoryConfig):
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"""Save sample images for visual inspection (optional utility test)."""
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sample_dir = test_dirs.sample_images
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# Save upscaled images
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save_image(upscaled_image[0], sample_dir / UPSCALED_IMAGE_1)
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save_image(upscaled_image[1], sample_dir / UPSCALED_IMAGE_2)
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# Allow running directly for debugging
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if __name__ == "__main__":
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pytest.main([__file__, "-v", "-s"])
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