[Flux] add lora integration tests. (#9353)

* add lora integration tests.

* internal note

* add a skip marker.

tests/lora/test_lora_layers_flux.py

@@ -12,6 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import gc
 import os
 import sys
 import tempfile
@@ -23,7 +24,14 @@ import torch
 from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, T5EncoderModel
 
 from diffusers import FlowMatchEulerDiscreteScheduler, FluxPipeline, FluxTransformer2DModel
-from diffusers.utils.testing_utils import floats_tensor, is_peft_available, require_peft_backend, torch_device
+from diffusers.utils.testing_utils import (
+    floats_tensor,
+    is_peft_available,
+    require_peft_backend,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
 
 
 if is_peft_available():
@@ -145,3 +153,89 @@ class FluxLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
             "Loading from saved checkpoints should give same results.",
         )
         self.assertFalse(np.allclose(images_lora_with_alpha, images_lora, atol=1e-3, rtol=1e-3))
+
+
+@slow
+@require_torch_gpu
+@require_peft_backend
+@unittest.skip("We cannot run inference on this model with the current CI hardware")
+# TODO (DN6, sayakpaul): move these tests to a beefier GPU
+class FluxLoRAIntegrationTests(unittest.TestCase):
+    """internal note: The integration slices were obtained on audace."""
+
+    num_inference_steps = 10
+    seed = 0
+
+    def setUp(self):
+        super().setUp()
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        self.pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+
+    def tearDown(self):
+        super().tearDown()
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_flux_the_last_ben(self):
+        self.pipeline.load_lora_weights("TheLastBen/Jon_Snow_Flux_LoRA", weight_name="jon_snow.safetensors")
+        self.pipeline.fuse_lora()
+        self.pipeline.unload_lora_weights()
+        self.pipeline.enable_model_cpu_offload()
+
+        prompt = "jon snow eating pizza with ketchup"
+
+        out = self.pipeline(
+            prompt,
+            num_inference_steps=self.num_inference_steps,
+            guidance_scale=4.0,
+            output_type="np",
+            generator=torch.manual_seed(self.seed),
+        ).images
+        out_slice = out[0, -3:, -3:, -1].flatten()
+        expected_slice = np.array([0.1719, 0.1719, 0.1699, 0.1719, 0.1719, 0.1738, 0.1641, 0.1621, 0.2090])
+
+        assert np.allclose(out_slice, expected_slice, atol=1e-4, rtol=1e-4)
+
+    def test_flux_kohya(self):
+        self.pipeline.load_lora_weights("Norod78/brain-slug-flux")
+        self.pipeline.fuse_lora()
+        self.pipeline.unload_lora_weights()
+        self.pipeline.enable_model_cpu_offload()
+
+        prompt = "The cat with a brain slug earring"
+        out = self.pipeline(
+            prompt,
+            num_inference_steps=self.num_inference_steps,
+            guidance_scale=4.5,
+            output_type="np",
+            generator=torch.manual_seed(self.seed),
+        ).images
+
+        out_slice = out[0, -3:, -3:, -1].flatten()
+        expected_slice = np.array([0.6367, 0.6367, 0.6328, 0.6367, 0.6328, 0.6289, 0.6367, 0.6328, 0.6484])
+
+        assert np.allclose(out_slice, expected_slice, atol=1e-4, rtol=1e-4)
+
+    def test_flux_xlabs(self):
+        self.pipeline.load_lora_weights("XLabs-AI/flux-lora-collection", weight_name="disney_lora.safetensors")
+        self.pipeline.fuse_lora()
+        self.pipeline.unload_lora_weights()
+        self.pipeline.enable_model_cpu_offload()
+
+        prompt = "A blue jay standing on a large basket of rainbow macarons, disney style"
+
+        out = self.pipeline(
+            prompt,
+            num_inference_steps=self.num_inference_steps,
+            guidance_scale=3.5,
+            output_type="np",
+            generator=torch.manual_seed(self.seed),
+        ).images
+        out_slice = out[0, -3:, -3:, -1].flatten()
+        expected_slice = np.array([0.3984, 0.4199, 0.4453, 0.4102, 0.4375, 0.4590, 0.4141, 0.4355, 0.4980])
+
+        assert np.allclose(out_slice, expected_slice, atol=1e-4, rtol=1e-4)