Delete tests

tests/inference/test_inference.py

@@ -1,111 +0,0 @@
-import numpy
-from PIL import Image
-import pytest
-from pytest import fixture
-import torch
-from typing import Tuple
-
-from sgm.inference.api import (
-    model_specs,
-    SamplingParams,
-    SamplingPipeline,
-    Sampler,
-    ModelArchitecture,
-)
-import sgm.inference.helpers as helpers
-
-
-@pytest.mark.inference
-class TestInference:
-    @fixture(scope="class", params=model_specs.keys())
-    def pipeline(self, request) -> SamplingPipeline:
-        pipeline = SamplingPipeline(request.param)
-        yield pipeline
-        del pipeline
-        torch.cuda.empty_cache()
-
-    @fixture(
-        scope="class",
-        params=[
-            [ModelArchitecture.SDXL_V1_BASE, ModelArchitecture.SDXL_V1_REFINER],
-            [ModelArchitecture.SDXL_V0_9_BASE, ModelArchitecture.SDXL_V0_9_REFINER],
-        ],
-        ids=["SDXL_V1", "SDXL_V0_9"],
-    )
-    def sdxl_pipelines(self, request) -> Tuple[SamplingPipeline, SamplingPipeline]:
-        base_pipeline = SamplingPipeline(request.param[0])
-        refiner_pipeline = SamplingPipeline(request.param[1])
-        yield base_pipeline, refiner_pipeline
-        del base_pipeline
-        del refiner_pipeline
-        torch.cuda.empty_cache()
-
-    def create_init_image(self, h, w):
-        image_array = numpy.random.rand(h, w, 3) * 255
-        image = Image.fromarray(image_array.astype("uint8")).convert("RGB")
-        return helpers.get_input_image_tensor(image)
-
-    @pytest.mark.parametrize("sampler_enum", Sampler)
-    def test_txt2img(self, pipeline: SamplingPipeline, sampler_enum):
-        output = pipeline.text_to_image(
-            params=SamplingParams(sampler=sampler_enum.value, steps=10),
-            prompt="A professional photograph of an astronaut riding a pig",
-            negative_prompt="",
-            samples=1,
-        )
-
-        assert output is not None
-
-    @pytest.mark.parametrize("sampler_enum", Sampler)
-    def test_img2img(self, pipeline: SamplingPipeline, sampler_enum):
-        output = pipeline.image_to_image(
-            params=SamplingParams(sampler=sampler_enum.value, steps=10),
-            image=self.create_init_image(pipeline.specs.height, pipeline.specs.width),
-            prompt="A professional photograph of an astronaut riding a pig",
-            negative_prompt="",
-            samples=1,
-        )
-        assert output is not None
-
-    @pytest.mark.parametrize("sampler_enum", Sampler)
-    @pytest.mark.parametrize(
-        "use_init_image", [True, False], ids=["img2img", "txt2img"]
-    )
-    def test_sdxl_with_refiner(
-        self,
-        sdxl_pipelines: Tuple[SamplingPipeline, SamplingPipeline],
-        sampler_enum,
-        use_init_image,
-    ):
-        base_pipeline, refiner_pipeline = sdxl_pipelines
-        if use_init_image:
-            output = base_pipeline.image_to_image(
-                params=SamplingParams(sampler=sampler_enum.value, steps=10),
-                image=self.create_init_image(
-                    base_pipeline.specs.height, base_pipeline.specs.width
-                ),
-                prompt="A professional photograph of an astronaut riding a pig",
-                negative_prompt="",
-                samples=1,
-                return_latents=True,
-            )
-        else:
-            output = base_pipeline.text_to_image(
-                params=SamplingParams(sampler=sampler_enum.value, steps=10),
-                prompt="A professional photograph of an astronaut riding a pig",
-                negative_prompt="",
-                samples=1,
-                return_latents=True,
-            )
-
-        assert isinstance(output, (tuple, list))
-        samples, samples_z = output
-        assert samples is not None
-        assert samples_z is not None
-        refiner_pipeline.refiner(
-            params=SamplingParams(sampler=sampler_enum.value, steps=10),
-            image=samples_z,
-            prompt="A professional photograph of an astronaut riding a pig",
-            negative_prompt="",
-            samples=1,
-        )