[Examples] Test all examples on CPU

.github/workflows/pr_tests.yml

@@ -36,6 +36,11 @@ jobs:
             runner: docker-cpu
             image: diffusers/diffusers-onnxruntime-cpu
             report: onnx_cpu
+          - name: PyTorch Example CPU tests on Ubuntu
+            framework: pytorch_examples
+            runner: docker-cpu
+            image: diffusers/diffusers-pytorch-cpu
+            report: torch_cpu
 
     name: ${{ matrix.config.name }}
 
@@ -90,6 +95,13 @@ jobs:
           --make-reports=tests_${{ matrix.config.report }} \
           tests/
 
+    - name: Run example PyTorch CPU tests
+      if: ${{ matrix.config.framework == 'pytorch_examples' }}
+      run: |
+        python -m pytest -n 2 --max-worker-restart=0 --dist=loadfile \
+          --make-reports=tests_${{ matrix.config.report }} \
+          examples/test_examples.py 
+
     - name: Failure short reports
       if: ${{ failure() }}
       run: cat reports/tests_${{ matrix.config.report }}_failures_short.txt

examples/test_examples.py

@@ -25,8 +25,6 @@
 
 from accelerate.utils import write_basic_config
 
-from diffusers.utils import slow
-
 
 logging.basicConfig(level=logging.DEBUG)
 
@@ -74,51 +72,94 @@ def tearDownClass(cls):
         super().tearDownClass()
         shutil.rmtree(cls._tmpdir)
 
-    @slow
     def test_train_unconditional(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""
                 examples/unconditional_image_generation/train_unconditional.py
-                --dataset_name huggan/few-shot-aurora
+                --dataset_name hf-internal-testing/dummy_image_class_data
+                --model_config_name_or_path diffusers/ddpm_dummy
                 --resolution 64
                 --output_dir {tmpdir}
-                --train_batch_size 4
+                --train_batch_size 2
                 --num_epochs 1
                 --gradient_accumulation_steps 1
+                --ddpm_num_inference_steps 2
                 --learning_rate 1e-3
                 --lr_warmup_steps 5
-                --mixed_precision fp16
                 """.split()
 
             run_command(self._launch_args + test_args, return_stdout=True)
             # save_pretrained smoke test
             self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
             self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
-            # logging test
-            self.assertTrue(len(os.listdir(os.path.join(tmpdir, "logs", "train_unconditional"))) > 0)
 
-    @slow
     def test_textual_inversion(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""
                 examples/textual_inversion/textual_inversion.py
-                --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5
+                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
                 --train_data_dir docs/source/en/imgs
                 --learnable_property object
                 --placeholder_token <cat-toy>
-                --initializer_token toy
+                --initializer_token a
                 --resolution 64
                 --train_batch_size 1
-                --gradient_accumulation_steps 2
-                --max_train_steps 10
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
                 --learning_rate 5.0e-04
                 --scale_lr
                 --lr_scheduler constant
                 --lr_warmup_steps 0
                 --output_dir {tmpdir}
-                --mixed_precision fp16
                 """.split()
 
             run_command(self._launch_args + test_args)
             # save_pretrained smoke test
             self.assertTrue(os.path.isfile(os.path.join(tmpdir, "learned_embeds.bin")))
+
+    def test_dreambooth(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                examples/dreambooth/train_dreambooth.py
+                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
+                --instance_data_dir docs/source/en/imgs
+                --instance_prompt photo
+                --resolution 64
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+            # save_pretrained smoke test
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))
+
+    def test_text_to_image(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                examples/text_to_image/train_text_to_image.py
+                --pretrained_model_name_or_path hf-internal-testing/tiny-stable-diffusion-pipe
+                --dataset_name hf-internal-testing/dummy_image_text_data
+                --resolution 64
+                --center_crop
+                --random_flip
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+            # save_pretrained smoke test
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "unet", "diffusion_pytorch_model.bin")))
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "scheduler", "scheduler_config.json")))

examples/unconditional_image_generation/train_unconditional.py

@@ -22,7 +22,7 @@
 from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version
+from diffusers.utils import check_min_version, is_tensorboard_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
@@ -67,6 +67,12 @@ def parse_args():
         default=None,
         help="The config of the Dataset, leave as None if there's only one config.",
     )
+    parser.add_argument(
+        "--model_config_name_or_path",
+        type=str,
+        default=None,
+        help="The config of the UNet model to train, leave as None to use standard DDPM configuration.",
+    )
     parser.add_argument(
         "--train_data_dir",
         type=str,
@@ -222,6 +228,7 @@ def parse_args():
         help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
     )
     parser.add_argument("--ddpm_num_steps", type=int, default=1000)
+    parser.add_argument("--ddpm_num_inference_steps", type=int, default=1000)
     parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
     parser.add_argument(
         "--checkpointing_steps",
@@ -340,29 +347,33 @@ def load_model_hook(models, input_dir):
             os.makedirs(args.output_dir, exist_ok=True)
 
     # Initialize the model
-    model = UNet2DModel(
-        sample_size=args.resolution,
-        in_channels=3,
-        out_channels=3,
-        layers_per_block=2,
-        block_out_channels=(128, 128, 256, 256, 512, 512),
-        down_block_types=(
-            "DownBlock2D",
-            "DownBlock2D",
-            "DownBlock2D",
-            "DownBlock2D",
-            "AttnDownBlock2D",
-            "DownBlock2D",
-        ),
-        up_block_types=(
-            "UpBlock2D",
-            "AttnUpBlock2D",
-            "UpBlock2D",
-            "UpBlock2D",
-            "UpBlock2D",
-            "UpBlock2D",
-        ),
-    )
+    if args.model_config_name_or_path is None:
+        model = UNet2DModel(
+            sample_size=args.resolution,
+            in_channels=3,
+            out_channels=3,
+            layers_per_block=2,
+            block_out_channels=(128, 128, 256, 256, 512, 512),
+            down_block_types=(
+                "DownBlock2D",
+                "DownBlock2D",
+                "DownBlock2D",
+                "DownBlock2D",
+                "AttnDownBlock2D",
+                "DownBlock2D",
+            ),
+            up_block_types=(
+                "UpBlock2D",
+                "AttnUpBlock2D",
+                "UpBlock2D",
+                "UpBlock2D",
+                "UpBlock2D",
+                "UpBlock2D",
+            ),
+        )
+    else:
+        config = UNet2DModel.load_config(args.model_config_name_or_path)
+        model = UNet2DModel.from_config(config)
 
     # Create EMA for the model.
     if args.use_ema:
@@ -586,13 +597,14 @@ def transform_images(examples):
                 images = pipeline(
                     generator=generator,
                     batch_size=args.eval_batch_size,
+                    num_inference_steps=args.ddpm_num_inference_steps,
                     output_type="numpy",
                 ).images
 
                 # denormalize the images and save to tensorboard
                 images_processed = (images * 255).round().astype("uint8")
 
-                if args.logger == "tensorboard":
+                if args.logger == "tensorboard" and is_tensorboard_available():
                     accelerator.get_tracker("tensorboard").add_images(
                         "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
                     )

src/diffusers/utils/__init__.py

@@ -52,6 +52,7 @@
     is_onnx_available,
     is_safetensors_available,
     is_scipy_available,
+    is_tensorboard_available,
     is_tf_available,
     is_torch_available,
     is_torch_version,

src/diffusers/utils/import_utils.py

@@ -224,6 +224,13 @@
 except importlib_metadata.PackageNotFoundError:
     _omegaconf_available = False
 
+_tensorboard_available = importlib.util.find_spec("tensorboard")
+try:
+    _tensorboard_version = importlib_metadata.version("tensorboard")
+    logger.debug(f"Successfully imported tensorboard version {_tensorboard_version}")
+except importlib_metadata.PackageNotFoundError:
+    _tensorboard_available = False
+
 
 def is_torch_available():
     return _torch_available
@@ -285,6 +292,10 @@ def is_omegaconf_available():
     return _omegaconf_available
 
 
+def is_tensorboard_available():
+    return _tensorboard_available
+
+
 # docstyle-ignore
 FLAX_IMPORT_ERROR = """
 {0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
@@ -351,6 +362,12 @@ def is_omegaconf_available():
 install omegaconf`
 """
 
+# docstyle-ignore
+TENSORBOARD_IMPORT_ERROR = """
+{0} requires the tensorboard library but it was not found in your environment. You can install it with pip: `pip
+install tensorboard`
+"""
+
 BACKENDS_MAPPING = OrderedDict(
     [
         ("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
@@ -364,6 +381,7 @@ def is_omegaconf_available():
         ("k_diffusion", (is_k_diffusion_available, K_DIFFUSION_IMPORT_ERROR)),
         ("wandb", (is_wandb_available, WANDB_IMPORT_ERROR)),
         ("omegaconf", (is_omegaconf_available, OMEGACONF_IMPORT_ERROR)),
+        ("tensorboard", (_tensorboard_available, TENSORBOARD_IMPORT_ERROR)),
     ]
 )