LoRA test fixes (#6163)

* update

* update

* update

* update

---------

Co-authored-by: Sayak Paul <[email protected]>

Author: DN6

tests/lora/test_lora_layers_old_backend.py

@@ -343,6 +343,21 @@ def test_stable_diffusion_attn_processors(self):
         image = sd_pipe(**inputs).images
         assert image.shape == (1, 64, 64, 3)
 
+    @unittest.skipIf(not torch.cuda.is_available() or not is_xformers_available(), reason="xformers requires cuda")
+    def test_stable_diffusion_set_xformers_attn_processors(self):
+        # disable_full_determinism()
+        device = "cuda"  # ensure determinism for the device-dependent torch.Generator
+        components, _ = self.get_dummy_components()
+        sd_pipe = StableDiffusionPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        _, _, inputs = self.get_dummy_inputs()
+
+        # run normal sd pipe
+        image = sd_pipe(**inputs).images
+        assert image.shape == (1, 64, 64, 3)
+
         # run lora xformers attention
         attn_processors, _ = create_unet_lora_layers(sd_pipe.unet)
         attn_processors = {
@@ -607,7 +622,7 @@ def test_unload_lora_sd(self):
             orig_image_slice, orig_image_slice_two, atol=1e-3
         ), "Unloading LoRA parameters should lead to results similar to what was obtained with the pipeline without any LoRA parameters."
 
-    @unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU")
+    @unittest.skipIf(torch_device != "cuda" or not is_xformers_available(), "This test is supposed to run on GPU")
     def test_lora_unet_attn_processors_with_xformers(self):
         with tempfile.TemporaryDirectory() as tmpdirname:
             self.create_lora_weight_file(tmpdirname)
@@ -644,7 +659,7 @@ def test_lora_unet_attn_processors_with_xformers(self):
                 if isinstance(module, Attention):
                     self.assertIsInstance(module.processor, XFormersAttnProcessor)
 
-    @unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU")
+    @unittest.skipIf(torch_device != "cuda" or not is_xformers_available(), "This test is supposed to run on GPU")
     def test_lora_save_load_with_xformers(self):
         pipeline_components, lora_components = self.get_dummy_components()
         sd_pipe = StableDiffusionPipeline(**pipeline_components)
@@ -2270,8 +2285,8 @@ def test_sdxl_1_0_lora_fusion_efficiency(self):
         lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
         lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
 
-        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
-        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.float16)
         pipe.enable_model_cpu_offload()
 
         start_time = time.time()
@@ -2284,13 +2299,13 @@ def test_sdxl_1_0_lora_fusion_efficiency(self):
 
         del pipe
 
-        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
-        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.float16)
         pipe.fuse_lora()
         pipe.enable_model_cpu_offload()
 
-        start_time = time.time()
         generator = torch.Generator().manual_seed(0)
+        start_time = time.time()
         for _ in range(3):
             pipe(
                 "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2

tests/lora/test_lora_layers_peft.py

@@ -46,6 +46,7 @@
     floats_tensor,
     load_image,
     nightly,
+    numpy_cosine_similarity_distance,
     require_peft_backend,
     require_torch_gpu,
     slow,
@@ -1713,7 +1714,7 @@ def test_sdxl_0_9_lora_three(self):
         release_memory(pipe)
 
     def test_sdxl_1_0_lora(self):
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator("cpu").manual_seed(0)
 
         pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
         pipe.enable_model_cpu_offload()
@@ -1736,7 +1737,7 @@ def test_sdxl_lcm_lora(self):
         pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
         pipe.enable_model_cpu_offload()
 
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator("cpu").manual_seed(0)
 
         lora_model_id = "latent-consistency/lcm-lora-sdxl"
 
@@ -1753,7 +1754,8 @@ def test_sdxl_lcm_lora(self):
         image_np = pipe.image_processor.pil_to_numpy(image)
         expected_image_np = pipe.image_processor.pil_to_numpy(expected_image)
 
-        self.assertTrue(np.allclose(image_np, expected_image_np, atol=1e-2))
+        max_diff = numpy_cosine_similarity_distance(image_np.flatten(), expected_image_np.flatten())
+        assert max_diff < 1e-4
 
         pipe.unload_lora_weights()
 
@@ -1764,7 +1766,7 @@ def test_sdv1_5_lcm_lora(self):
         pipe.to("cuda")
         pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
 
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator("cpu").manual_seed(0)
 
         lora_model_id = "latent-consistency/lcm-lora-sdv1-5"
         pipe.load_lora_weights(lora_model_id)
@@ -1780,7 +1782,8 @@ def test_sdv1_5_lcm_lora(self):
         image_np = pipe.image_processor.pil_to_numpy(image)
         expected_image_np = pipe.image_processor.pil_to_numpy(expected_image)
 
-        self.assertTrue(np.allclose(image_np, expected_image_np, atol=1e-2))
+        max_diff = numpy_cosine_similarity_distance(image_np.flatten(), expected_image_np.flatten())
+        assert max_diff < 1e-4
 
         pipe.unload_lora_weights()
 
@@ -1795,7 +1798,7 @@ def test_sdv1_5_lcm_lora_img2img(self):
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape.png"
         )
 
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator("cpu").manual_seed(0)
 
         lora_model_id = "latent-consistency/lcm-lora-sdv1-5"
         pipe.load_lora_weights(lora_model_id)
@@ -1816,7 +1819,8 @@ def test_sdv1_5_lcm_lora_img2img(self):
         image_np = pipe.image_processor.pil_to_numpy(image)
         expected_image_np = pipe.image_processor.pil_to_numpy(expected_image)
 
-        self.assertTrue(np.allclose(image_np, expected_image_np, atol=1e-2))
+        max_diff = numpy_cosine_similarity_distance(image_np.flatten(), expected_image_np.flatten())
+        assert max_diff < 1e-4
 
         pipe.unload_lora_weights()
 
@@ -1849,7 +1853,7 @@ def test_sdxl_1_0_lora_fusion(self):
         release_memory(pipe)
 
     def test_sdxl_1_0_lora_unfusion(self):
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator("cpu").manual_seed(0)
 
         pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
         lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
@@ -1860,16 +1864,16 @@ def test_sdxl_1_0_lora_unfusion(self):
         pipe.enable_model_cpu_offload()
 
         images = pipe(
-            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=3
         ).images
-        images_with_fusion = images[0, -3:, -3:, -1].flatten()
+        images_with_fusion = images.flatten()
 
         pipe.unfuse_lora()
-        generator = torch.Generator().manual_seed(0)
+        generator = torch.Generator("cpu").manual_seed(0)
         images = pipe(
-            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2
+            "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=3
         ).images
-        images_without_fusion = images[0, -3:, -3:, -1].flatten()
+        images_without_fusion = images.flatten()
 
         self.assertTrue(np.allclose(images_with_fusion, images_without_fusion, atol=1e-3))
         release_memory(pipe)
@@ -1913,10 +1917,8 @@ def test_sdxl_1_0_lora_fusion_efficiency(self):
         lora_model_id = "hf-internal-testing/sdxl-1.0-lora"
         lora_filename = "sd_xl_offset_example-lora_1.0.safetensors"
 
-        pipe = DiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16
-        )
-        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.bfloat16)
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.float16)
         pipe.enable_model_cpu_offload()
 
         start_time = time.time()
@@ -1929,19 +1931,17 @@ def test_sdxl_1_0_lora_fusion_efficiency(self):
 
         del pipe
 
-        pipe = DiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16
-        )
-        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.bfloat16)
+        pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename, torch_dtype=torch.float16)
         pipe.fuse_lora()
+
         # We need to unload the lora weights since in the previous API `fuse_lora` led to lora weights being
         # silently deleted - otherwise this will CPU OOM
         pipe.unload_lora_weights()
-
         pipe.enable_model_cpu_offload()
 
-        start_time = time.time()
         generator = torch.Generator().manual_seed(0)
+        start_time = time.time()
         for _ in range(3):
             pipe(
                 "masterpiece, best quality, mountain", output_type="np", generator=generator, num_inference_steps=2