StableDiffusionInstructPix2PixPipelineSingleFileSlowTests

Author: hlky

tests/single_file/test_stable_diffusion_single_file.py

@@ -4,7 +4,7 @@
 
 import torch
 
-from diffusers import EulerDiscreteScheduler, StableDiffusionPipeline
+from diffusers import EulerDiscreteScheduler, StableDiffusionInstructPix2PixPipeline, StableDiffusionPipeline
 from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name
 from diffusers.utils.testing_utils import (
     backend_empty_cache,
@@ -118,3 +118,39 @@ def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0
 
     def test_single_file_format_inference_is_same_as_pretrained(self):
         super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)
+
+
+@slow
+@require_torch_accelerator
+class StableDiffusionInstructPix2PixPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+    pipeline_class = StableDiffusionInstructPix2PixPipeline
+    ckpt_path = "https://huggingface.co/timbrooks/instruct-pix2pix/blob/main/instruct-pix2pix-00-22000.safetensors"
+    original_config = (
+        "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/refs/heads/main/configs/generate.yaml"
+    )
+    repo_id = "timbrooks/instruct-pix2pix"
+
+    def setUp(self):
+        super().setUp()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
+        generator = torch.Generator(device=generator_device).manual_seed(seed)
+        inputs = {
+            "prompt": "a fantasy landscape, concept art, high resolution",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "strength": 0.75,
+            "guidance_scale": 7.5,
+            "output_type": "np",
+        }
+        return inputs
+
+    def test_single_file_format_inference_is_same_as_pretrained(self):
+        super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)