[Tests] Speed up slow tests

tests/pipelines/dance_diffusion/test_dance_diffusion.py

@@ -86,7 +86,7 @@ def tearDown(self):
     def test_dance_diffusion(self):
         device = torch_device
 
-        pipe = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k")
+        pipe = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k", device_map="auto")
         pipe = pipe.to(device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -103,7 +103,9 @@ def test_dance_diffusion(self):
     def test_dance_diffusion_fp16(self):
         device = torch_device
 
-        pipe = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k", torch_dtype=torch.float16)
+        pipe = DanceDiffusionPipeline.from_pretrained(
+            "harmonai/maestro-150k", torch_dtype=torch.float16, device_map="auto"
+        )
         pipe = pipe.to(device)
         pipe.set_progress_bar_config(disable=None)
 

tests/pipelines/ddim/test_ddim.py

@@ -78,7 +78,7 @@ class DDIMPipelineIntegrationTests(unittest.TestCase):
     def test_inference_ema_bedroom(self):
         model_id = "google/ddpm-ema-bedroom-256"
 
-        unet = UNet2DModel.from_pretrained(model_id)
+        unet = UNet2DModel.from_pretrained(model_id, device_map="auto")
         scheduler = DDIMScheduler.from_config(model_id)
 
         ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
@@ -97,7 +97,7 @@ def test_inference_ema_bedroom(self):
     def test_inference_cifar10(self):
         model_id = "google/ddpm-cifar10-32"
 
-        unet = UNet2DModel.from_pretrained(model_id)
+        unet = UNet2DModel.from_pretrained(model_id, device_map="auto")
         scheduler = DDIMScheduler()
 
         ddim = DDIMPipeline(unet=unet, scheduler=scheduler)

tests/pipelines/ddpm/test_ddpm.py

@@ -38,7 +38,7 @@ class DDPMPipelineIntegrationTests(unittest.TestCase):
     def test_inference_cifar10(self):
         model_id = "google/ddpm-cifar10-32"
 
-        unet = UNet2DModel.from_pretrained(model_id)
+        unet = UNet2DModel.from_pretrained(model_id, device_map="auto")
         scheduler = DDPMScheduler.from_config(model_id)
 
         ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)

tests/pipelines/karras_ve/test_karras_ve.py

@@ -70,7 +70,7 @@ def test_inference(self):
 class KarrasVePipelineIntegrationTests(unittest.TestCase):
     def test_inference(self):
         model_id = "google/ncsnpp-celebahq-256"
-        model = UNet2DModel.from_pretrained(model_id)
+        model = UNet2DModel.from_pretrained(model_id, device_map="auto")
         scheduler = KarrasVeScheduler()
 
         pipe = KarrasVePipeline(unet=model, scheduler=scheduler)

tests/pipelines/latent_diffusion/test_latent_diffusion.py

@@ -121,7 +121,7 @@ def test_inference_text2img(self):
 @require_torch
 class LDMTextToImagePipelineIntegrationTests(unittest.TestCase):
     def test_inference_text2img(self):
-        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
+        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256", device_map="auto")
         ldm.to(torch_device)
         ldm.set_progress_bar_config(disable=None)
 
@@ -138,7 +138,7 @@ def test_inference_text2img(self):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
     def test_inference_text2img_fast(self):
-        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
+        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256", device_map="auto")
         ldm.to(torch_device)
         ldm.set_progress_bar_config(disable=None)
 

tests/pipelines/pndm/test_pndm.py

@@ -71,7 +71,7 @@ class PNDMPipelineIntegrationTests(unittest.TestCase):
     def test_inference_cifar10(self):
         model_id = "google/ddpm-cifar10-32"
 
-        unet = UNet2DModel.from_pretrained(model_id)
+        unet = UNet2DModel.from_pretrained(model_id, device_map="auto")
         scheduler = PNDMScheduler()
 
         pndm = PNDMPipeline(unet=unet, scheduler=scheduler)

tests/pipelines/score_sde_ve/test_score_sde_ve.py

@@ -72,7 +72,7 @@ def test_inference(self):
 class ScoreSdeVePipelineIntegrationTests(unittest.TestCase):
     def test_inference(self):
         model_id = "google/ncsnpp-church-256"
-        model = UNet2DModel.from_pretrained(model_id)
+        model = UNet2DModel.from_pretrained(model_id, device_map="auto")
 
         scheduler = ScoreSdeVeScheduler.from_config(model_id)
 

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -528,7 +528,7 @@ def tearDown(self):
 
     def test_stable_diffusion(self):
         # make sure here that pndm scheduler skips prk
-        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1")
+        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", device_map="auto")
         sd_pipe = sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
 
@@ -548,7 +548,7 @@ def test_stable_diffusion(self):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
     def test_stable_diffusion_fast_ddim(self):
-        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1")
+        sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1", device_map="auto")
         sd_pipe = sd_pipe.to(torch_device)
         sd_pipe.set_progress_bar_config(disable=None)
 
@@ -576,7 +576,7 @@ def test_stable_diffusion_fast_ddim(self):
 
     def test_lms_stable_diffusion_pipeline(self):
         model_id = "CompVis/stable-diffusion-v1-1"
-        pipe = StableDiffusionPipeline.from_pretrained(model_id).to(torch_device)
+        pipe = StableDiffusionPipeline.from_pretrained(model_id, device_map="auto").to(torch_device)
         pipe.set_progress_bar_config(disable=None)
         scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler")
         pipe.scheduler = scheduler
@@ -595,9 +595,10 @@ def test_lms_stable_diffusion_pipeline(self):
     def test_stable_diffusion_memory_chunking(self):
         torch.cuda.reset_peak_memory_stats()
         model_id = "CompVis/stable-diffusion-v1-4"
-        pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16).to(
-            torch_device
+        pipe = StableDiffusionPipeline.from_pretrained(
+            model_id, revision="fp16", torch_dtype=torch.float16, device_map="auto"
         )
+        pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
         prompt = "a photograph of an astronaut riding a horse"
@@ -633,9 +634,10 @@ def test_stable_diffusion_memory_chunking(self):
     def test_stable_diffusion_text2img_pipeline_fp16(self):
         torch.cuda.reset_peak_memory_stats()
         model_id = "CompVis/stable-diffusion-v1-4"
-        pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16).to(
-            torch_device
+        pipe = StableDiffusionPipeline.from_pretrained(
+            model_id, revision="fp16", device_map="auto", torch_dtype=torch.float16
         )
+        pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
         prompt = "a photograph of an astronaut riding a horse"
@@ -670,6 +672,7 @@ def test_stable_diffusion_text2img_pipeline(self):
         pipe = StableDiffusionPipeline.from_pretrained(
             model_id,
             safety_checker=None,
+            device_map="auto",
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -711,7 +714,7 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
         test_callback_fn.has_been_called = False
 
         pipe = StableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, device_map="auto"
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -737,7 +740,7 @@ def test_stable_diffusion_accelerate_auto_device(self):
 
         start_time = time.time()
         pipeline_normal_load = StableDiffusionPipeline.from_pretrained(
-            pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True
+            pipeline_id, revision="fp16", torch_dtype=torch.float16, device_map="auto"
         )
         pipeline_normal_load.to(torch_device)
         normal_load_time = time.time() - start_time
@@ -758,7 +761,9 @@ def test_stable_diffusion_pipeline_with_unet_on_gpu_only(self):
         pipeline_id = "CompVis/stable-diffusion-v1-4"
         prompt = "Andromeda galaxy in a bottle"
 
-        pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, revision="fp16", torch_dtype=torch.float16)
+        pipeline = StableDiffusionPipeline.from_pretrained(
+            pipeline_id, revision="fp16", torch_dtype=torch.float16, device_map="auto"
+        )
         pipeline.enable_attention_slicing(1)
         pipeline.enable_sequential_cpu_offload()
 

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -488,6 +488,7 @@ def test_stable_diffusion_img2img_pipeline(self):
         pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
             model_id,
             safety_checker=None,
+            device_map="auto",
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -529,6 +530,7 @@ def test_stable_diffusion_img2img_pipeline_k_lms(self):
             model_id,
             scheduler=lms,
             safety_checker=None,
+            device_map="auto",
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -580,7 +582,7 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
         init_image = init_image.resize((768, 512))
 
         pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, device_map="auto"
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -288,6 +288,7 @@ def test_stable_diffusion_inpaint_pipeline(self):
         pipe = StableDiffusionInpaintPipeline.from_pretrained(
             model_id,
             safety_checker=None,
+            device_map="auto",
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -329,6 +330,7 @@ def test_stable_diffusion_inpaint_pipeline_fp16(self):
             revision="fp16",
             torch_dtype=torch.float16,
             safety_checker=None,
+            device_map="auto",
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -366,7 +368,9 @@ def test_stable_diffusion_inpaint_pipeline_pndm(self):
 
         pndm = PNDMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True)
         model_id = "runwayml/stable-diffusion-inpainting"
-        pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id, safety_checker=None, scheduler=pndm)
+        pipe = StableDiffusionInpaintPipeline.from_pretrained(
+            model_id, safety_checker=None, scheduler=pndm, device_map="auto"
+        )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
         pipe.enable_attention_slicing()

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint_legacy.py

@@ -368,6 +368,7 @@ def test_stable_diffusion_inpaint_legacy_pipeline(self):
         pipe = StableDiffusionInpaintPipeline.from_pretrained(
             model_id,
             safety_checker=None,
+            device_map="auto",
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -413,6 +414,7 @@ def test_stable_diffusion_inpaint_legacy_pipeline_k_lms(self):
             model_id,
             scheduler=lms,
             safety_checker=None,
+            device_map="auto",
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -469,7 +471,7 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
         )
 
         pipe = StableDiffusionInpaintPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
+            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, device_map="auto"
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

tests/test_pipelines.py

@@ -108,8 +108,8 @@ def test_local_custom_pipeline(self):
     def test_load_pipeline_from_git(self):
         clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
 
-        feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
-        clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)
+        feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id, device_map="auto")
+        clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16, device_map="auto")
 
         pipeline = DiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4",
@@ -118,6 +118,7 @@ def test_load_pipeline_from_git(self):
             feature_extractor=feature_extractor,
             torch_dtype=torch.float16,
             revision="fp16",
+            device_map="auto",
         )
         pipeline.enable_attention_slicing()
         pipeline = pipeline.to(torch_device)
@@ -312,7 +313,9 @@ def tearDown(self):
     def test_smart_download(self):
         model_id = "hf-internal-testing/unet-pipeline-dummy"
         with tempfile.TemporaryDirectory() as tmpdirname:
-            _ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True)
+            _ = DiffusionPipeline.from_pretrained(
+                model_id, cache_dir=tmpdirname, force_download=True, device_map="auto"
+            )
             local_repo_name = "--".join(["models"] + model_id.split("/"))
             snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots")
             snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])
@@ -335,7 +338,9 @@ def test_warning_unused_kwargs(self):
         logger = logging.get_logger("diffusers.pipeline_utils")
         with tempfile.TemporaryDirectory() as tmpdirname:
             with CaptureLogger(logger) as cap_logger:
-                DiffusionPipeline.from_pretrained(model_id, not_used=True, cache_dir=tmpdirname, force_download=True)
+                DiffusionPipeline.from_pretrained(
+                    model_id, not_used=True, cache_dir=tmpdirname, force_download=True, device_map="auto"
+                )
 
         assert cap_logger.out == "Keyword arguments {'not_used': True} not recognized.\n"
 
@@ -358,7 +363,7 @@ def test_from_pretrained_save_pretrained(self):
 
         with tempfile.TemporaryDirectory() as tmpdirname:
             ddpm.save_pretrained(tmpdirname)
-            new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
+            new_ddpm = DDPMPipeline.from_pretrained(tmpdirname, device_map="auto")
             new_ddpm.to(torch_device)
 
         generator = torch.manual_seed(0)
@@ -374,10 +379,10 @@ def test_from_pretrained_hub(self):
 
         scheduler = DDPMScheduler(num_train_timesteps=10)
 
-        ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler)
+        ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler, device_map="auto")
         ddpm.to(torch_device)
         ddpm.set_progress_bar_config(disable=None)
-        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
+        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, device_map="auto")
         ddpm_from_hub.to(torch_device)
         ddpm_from_hub.set_progress_bar_config(disable=None)
 
@@ -395,12 +400,14 @@ def test_from_pretrained_hub_pass_model(self):
         scheduler = DDPMScheduler(num_train_timesteps=10)
 
         # pass unet into DiffusionPipeline
-        unet = UNet2DModel.from_pretrained(model_path)
-        ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler)
+        unet = UNet2DModel.from_pretrained(model_path, device_map="auto")
+        ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(
+            model_path, unet=unet, scheduler=scheduler, device_map="auto"
+        )
         ddpm_from_hub_custom_model.to(torch_device)
         ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
 
-        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
+        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler, device_map="auto")
         ddpm_from_hub.to(torch_device)
         ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)
 
@@ -415,7 +422,7 @@ def test_from_pretrained_hub_pass_model(self):
     def test_output_format(self):
         model_path = "google/ddpm-cifar10-32"
 
-        pipe = DDIMPipeline.from_pretrained(model_path)
+        pipe = DDIMPipeline.from_pretrained(model_path, device_map="auto")
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -437,7 +444,7 @@ def test_output_format(self):
     def test_ddpm_ddim_equality(self):
         model_id = "google/ddpm-cifar10-32"
 
-        unet = UNet2DModel.from_pretrained(model_id)
+        unet = UNet2DModel.from_pretrained(model_id, device_map="auto")
         ddpm_scheduler = DDPMScheduler()
         ddim_scheduler = DDIMScheduler()
 
@@ -461,7 +468,7 @@ def test_ddpm_ddim_equality(self):
     def test_ddpm_ddim_equality_batched(self):
         model_id = "google/ddpm-cifar10-32"
 
-        unet = UNet2DModel.from_pretrained(model_id)
+        unet = UNet2DModel.from_pretrained(model_id, device_map="auto")
         ddpm_scheduler = DDPMScheduler()
         ddim_scheduler = DDIMScheduler()