import pytest import torch import numpy as np from copy import copy from dataclasses import dataclass from internal_controlnet.args import ControlNetUnit H = W = 128 img1 = np.ones(shape=[H, W, 3], dtype=np.uint8) img2 = np.ones(shape=[H, W, 3], dtype=np.uint8) * 2 mask_diff = np.ones(shape=[H - 1, W - 1, 3], dtype=np.uint8) * 2 mask_2d = np.ones(shape=[H, W], dtype=np.uint8) img_bad_channel = np.ones(shape=[H, W, 2], dtype=np.uint8) * 2 img_bad_dim = np.ones(shape=[1, H, W, 3], dtype=np.uint8) * 2 ui_img_diff = np.ones(shape=[H - 1, W - 1, 4], dtype=np.uint8) * 2 ui_img = np.ones(shape=[H, W, 4], dtype=np.uint8) tensor1 = torch.zeros(size=[1, 1], dtype=torch.float16) @pytest.fixture(scope="module") def set_cls_funcs(): ControlNetUnit.cls_match_model = lambda s: s in { "None", "model1", "model2", "control_v11p_sd15_inpaint [ebff9138]", } ControlNetUnit.cls_match_module = lambda s: s in { "none", "module1", "inpaint_only+lama", } ControlNetUnit.cls_decode_base64 = lambda s: { "b64img1": img1, "b64img2": img2, "b64mask_diff": mask_diff, }[s] ControlNetUnit.cls_torch_load_base64 = lambda s: { "b64tensor1": tensor1, }[s] ControlNetUnit.cls_get_preprocessor = lambda s: { "module1": MockPreprocessor(), "none": MockPreprocessor(), "inpaint_only+lama": MockPreprocessor(), }[s] def test_module_invalid(set_cls_funcs): with pytest.raises(ValueError) as excinfo: ControlNetUnit(module="foo") assert "module(foo) not found in supported modules." in str(excinfo.value) def test_module_valid(set_cls_funcs): ControlNetUnit(module="module1") def test_model_invalid(set_cls_funcs): with pytest.raises(ValueError) as excinfo: ControlNetUnit(model="foo") assert "model(foo) not found in supported models." in str(excinfo.value) def test_model_valid(set_cls_funcs): ControlNetUnit(model="model1") @pytest.mark.parametrize( "d", [ # API dict(image={"image": "b64img1"}), dict(image={"image": "b64img1", "mask": "b64img2"}), dict(image=["b64img1", "b64img2"]), dict(image=("b64img1", "b64img2")), dict(image=[{"image": "b64img1", "mask": "b64img2"}]), dict(image=[{"image": "b64img1"}]), dict(image=[{"image": "b64img1", "mask": None}]), dict( image=[ {"image": "b64img1", "mask": "b64img2"}, {"image": "b64img1", "mask": "b64img2"}, ] ), dict( image=[ {"image": "b64img1", "mask": None}, {"image": "b64img1", "mask": "b64img2"}, ] ), dict( image=[ {"image": "b64img1"}, {"image": "b64img1", "mask": "b64img2"}, ] ), dict(image="b64img1", mask="b64img2"), dict(image="b64img1"), dict(image="b64img1", mask_image="b64img2"), dict(image=None), # UI dict(image=dict(image=img1)), dict(image=dict(image=img1, mask=img2)), # Grey-scale mask/image should be accepted. dict(image=dict(image=img1, mask=mask_2d)), dict(image=img1, mask=mask_2d), dict(image=np.zeros(shape=[H, W, 1], dtype=np.uint8)), # RGBA image input should be accepted. dict(image=np.zeros(shape=[H, W, 4], dtype=np.uint8)), ], ) def test_valid_image_formats(set_cls_funcs, d): ControlNetUnit(**d) unit = ControlNetUnit.from_dict(d) unit.get_input_images_rgba() @pytest.mark.parametrize( "d", [ dict(image={"mask": "b64img1"}), dict(image={"foo": "b64img1", "bar": "b64img2"}), dict(image=["b64img1"]), dict(image=("b64img1", "b64img2", "b64img1")), dict(image=[]), dict(image=[{"mask": "b64img1"}]), dict(image=None, mask="b64img2"), # image & mask have different H x W dict(image="b64img1", mask="b64mask_diff"), ], ) def test_invalid_image_formats(set_cls_funcs, d): # Setting field will be fine. ControlNetUnit(**d) unit = ControlNetUnit.from_dict(d) # Error on eval. with pytest.raises((ValueError, AssertionError)): unit.get_input_images_rgba() def test_mask_alias_conflict(): with pytest.raises((ValueError, AssertionError)): ControlNetUnit.from_dict( dict( image="b64img1", mask="b64img1", mask_image="b64img1", ) ), def test_resize_mode(): ControlNetUnit(resize_mode="Just Resize") # Alias should also work. For deforum # See https://github.com/deforum-art/sd-webui-deforum/blob/322426851408ebca2cd49492bfeb1ec86e1dc869/scripts/deforum_helpers/deforum_controlnet.py#L150 ControlNetUnit(resize_mode="Inner Fit (Scale to Fit)") def test_weight(): ControlNetUnit(weight=0.5) ControlNetUnit(weight=0.0) with pytest.raises(ValueError): ControlNetUnit(weight=-1) with pytest.raises(ValueError): ControlNetUnit(weight=100) def test_start_end(): ControlNetUnit(guidance_start=0.0, guidance_end=1.0) ControlNetUnit(guidance_start=0.5, guidance_end=1.0) ControlNetUnit(guidance_start=0.5, guidance_end=0.5) with pytest.raises(ValueError): ControlNetUnit(guidance_start=1.0, guidance_end=0.0) with pytest.raises(ValueError): ControlNetUnit(guidance_start=11) with pytest.raises(ValueError): ControlNetUnit(guidance_end=11) def test_effective_region_mask(): ControlNetUnit(effective_region_mask="b64img1") ControlNetUnit(effective_region_mask=None) ControlNetUnit(effective_region_mask=img1) with pytest.raises(ValueError): ControlNetUnit(effective_region_mask=124) def test_ipadapter_input(): ControlNetUnit(ipadapter_input=["b64tensor1"]) ControlNetUnit(ipadapter_input="b64tensor1") ControlNetUnit(ipadapter_input=None) with pytest.raises(ValueError): ControlNetUnit(ipadapter_input=[]) @dataclass class MockSlider: value: float = 1 minimum: float = 0 maximum: float = 2 @dataclass class MockPreprocessor: slider_resolution = MockSlider() slider_1 = MockSlider() slider_2 = MockSlider() def test_preprocessor_sliders(): unit = ControlNetUnit(enabled=True, module="none") assert unit.processor_res == 1 assert unit.threshold_a == 1 assert unit.threshold_b == 1 def test_preprocessor_sliders_disabled(): unit = ControlNetUnit(enabled=False, module="none") assert unit.processor_res == -1 assert unit.threshold_a == -1 assert unit.threshold_b == -1 def test_infotext_parsing(): infotext = ( "Module: inpaint_only+lama, Model: control_v11p_sd15_inpaint [ebff9138], Weight: 1, " "Resize Mode: Resize and Fill, Low Vram: False, Guidance Start: 0, Guidance End: 1, " "Pixel Perfect: True, Control Mode: Balanced" ) assert ControlNetUnit( enabled=True, module="inpaint_only+lama", model="control_v11p_sd15_inpaint [ebff9138]", weight=1, resize_mode="Resize and Fill", low_vram=False, guidance_start=0, guidance_end=1, pixel_perfect=True, control_mode="Balanced", ) == ControlNetUnit.parse(infotext) def test_alias(): ControlNetUnit.from_dict({"lowvram": True}) def test_copy(): unit1 = ControlNetUnit(enabled=True, module="none") unit2 = copy(unit1) unit2.enabled = False assert unit1.enabled assert not unit2.enabled