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https://github.com/Comfy-Org/ComfyUI.git
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a9ab2b62da
* Stop adding an opaque alpha channel to API node images bytesio_to_image_tensor converted every downloaded image to RGBA, so nodes whose API returns no transparency still emitted a 4 channel IMAGE. Keep the alpha when the decoded image has one, stay RGB when it does not. --------- Signed-off-by: bigcat88 <bigcat88@icloud.com> Co-authored-by: bigcat88 <bigcat88@icloud.com>
81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
from io import BytesIO
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import pytest
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import torch
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from PIL import Image
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from comfy.cli_args import args
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if not torch.cuda.is_available():
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args.cpu = True
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from comfy_api_nodes.util.conversions import bytesio_to_image_tensor, pad_images_to_common_channels # noqa: E402
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def encode(image: Image.Image, image_format: str = "PNG") -> BytesIO:
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buffer = BytesIO()
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image.save(buffer, format=image_format)
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buffer.seek(0)
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return buffer
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def test_rgb_png_stays_three_channels():
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tensor = bytesio_to_image_tensor(encode(Image.new("RGB", (4, 4), (10, 20, 30))))
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assert tensor.shape == (1, 4, 4, 3)
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def test_jpeg_stays_three_channels():
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tensor = bytesio_to_image_tensor(encode(Image.new("RGB", (4, 4), (10, 20, 30)), "JPEG"))
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assert tensor.shape == (1, 4, 4, 3)
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def test_grayscale_is_expanded_to_rgb():
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tensor = bytesio_to_image_tensor(encode(Image.new("L", (4, 4), 128)))
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assert tensor.shape == (1, 4, 4, 3)
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def test_rgba_png_keeps_its_alpha():
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tensor = bytesio_to_image_tensor(encode(Image.new("RGBA", (4, 4), (10, 20, 30, 0))))
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assert tensor.shape == (1, 4, 4, 4)
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assert tensor[..., 3].max() == 0.0
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def test_palette_png_with_transparency_keeps_its_alpha():
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image = Image.new("P", (4, 4), 1)
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image.putpalette([0, 0, 0, 255, 255, 255])
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image.info["transparency"] = 0
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image.putpixel((0, 0), 0)
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tensor = bytesio_to_image_tensor(encode(image))
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assert tensor.shape == (1, 4, 4, 4)
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assert tensor[0, 0, 0, 3] == 0.0
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assert tensor[0, 1, 1, 3] == 1.0
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@pytest.mark.parametrize("mode,channels", [("RGB", 3), ("RGBA", 4)])
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def test_explicit_mode_is_respected(mode, channels):
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tensor = bytesio_to_image_tensor(encode(Image.new("RGBA", (4, 4), (10, 20, 30, 128))), mode=mode)
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assert tensor.shape == (1, 4, 4, channels)
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def test_pad_mixed_channels_concatenates():
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rgb = torch.rand(1, 4, 4, 3)
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rgba = torch.rand(2, 4, 4, 4)
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padded = pad_images_to_common_channels([rgb, rgba])
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result = torch.cat(padded, dim=0)
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assert result.shape == (3, 4, 4, 4)
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def test_pad_adds_opaque_alpha_and_keeps_rgb_values():
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rgb = torch.rand(1, 4, 4, 3)
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rgba = torch.rand(1, 4, 4, 4)
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padded_rgb, padded_rgba = pad_images_to_common_channels([rgb, rgba])
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assert torch.equal(padded_rgb[..., :3], rgb)
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assert padded_rgb[..., 3].min() == 1.0
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assert padded_rgba is rgba
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def test_pad_leaves_homogeneous_channels_unchanged():
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images = [torch.rand(1, 4, 4, 3), torch.rand(2, 4, 4, 3)]
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padded = pad_images_to_common_channels(images)
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assert all(p is i for p, i in zip(padded, images))
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