fix pipeline __setattr__ value == None (#3063)

* fix pipeline __setattr__

* add test

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/pipelines/pipeline_utils.py

@@ -510,7 +510,7 @@ class DiffusionPipeline(ConfigMixin):
         if hasattr(self, name) and hasattr(self.config, name):
             # We need to overwrite the config if name exists in config
             if isinstance(getattr(self.config, name), (tuple, list)):
-                if self.config[name][0] is not None:
+                if value is not None and self.config[name][0] is not None:
                     class_library_tuple = (value.__module__.split(".")[0], value.__class__.__name__)
                 else:
                     class_library_tuple = (None, None)

tests/test_pipelines.py

@@ -929,6 +929,46 @@ class PipelineFastTests(unittest.TestCase):
         sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config)
         assert isinstance(sd.scheduler, DPMSolverMultistepScheduler)
 
+    def test_set_component_to_none(self):
+        unet = self.dummy_cond_unet()
+        scheduler = PNDMScheduler(skip_prk_steps=True)
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        pipeline = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+
+        generator = torch.Generator(device="cpu").manual_seed(0)
+
+        prompt = "This is a flower"
+
+        out_image = pipeline(
+            prompt=prompt,
+            generator=generator,
+            num_inference_steps=1,
+            output_type="np",
+        ).images
+
+        pipeline.feature_extractor = None
+        generator = torch.Generator(device="cpu").manual_seed(0)
+        out_image_2 = pipeline(
+            prompt=prompt,
+            generator=generator,
+            num_inference_steps=1,
+            output_type="np",
+        ).images
+
+        assert out_image.shape == (1, 64, 64, 3)
+        assert np.abs(out_image - out_image_2).max() < 1e-3
+
     def test_set_scheduler_consistency(self):
         unet = self.dummy_cond_unet()
         pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")