add test for loading model from pipeline module

tests/test_modeling_utils.py

@@ -19,9 +19,10 @@ import unittest
 
 import torch
 
-from diffusers import DDIM, DDPM, DDIMScheduler, DDPMScheduler, LatentDiffusion, UNetModel, PNDM, PNDMScheduler
+from diffusers import DDIM, DDPM, BDDM, DDIMScheduler, DDPMScheduler, LatentDiffusion, UNetModel, PNDM, PNDMScheduler
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.pipeline_bddm import DiffWave
 from diffusers.testing_utils import floats_tensor, slow, torch_device
 
 
@@ -212,3 +213,19 @@ class PipelineTesterMixin(unittest.TestCase):
         assert image.shape == (1, 3, 256, 256)
         expected_slice = torch.tensor([0.7295, 0.7358, 0.7256, 0.7435, 0.7095, 0.6884, 0.7325, 0.6921, 0.6458])
         assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
+
+    def test_module_from_pipeline(self):
+        model = DiffWave(num_res_layers=4)
+        noise_scheduler = DDPMScheduler(timesteps=12)
+
+        bddm = BDDM(model, noise_scheduler)
+
+        # check if the library name for the diffwave moduel is set to pipeline module
+        self.assertTrue(bddm.config["diffwave"][0] == "pipeline_bddm")
+
+        # check if we can save and load the pipeline
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            bddm.save_pretrained(tmpdirname)
+            _ = BDDM.from_pretrained(tmpdirname)
+            # check if the same works using the DifusionPipeline class
+            _ = DiffusionPipeline.from_pretrained(tmpdirname)
\ No newline at end of file