jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
"""
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Fixtures for base images.
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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 setup_utils import execute
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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("base_images")
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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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# 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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@pytest.fixture(scope="session")
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def base_image(loaded_checkpoint, seed, test_dirs: DirectoryConfig, 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(EmptyLatentImage, width=512, height=512, batch_size=2)
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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[0:1], base_img1_path)
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save_image(image[1:2], 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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