Change fp16 error to warning (#764)

* Swap fp16 error to warning

Also remove the associated test

* Formatting

* warn -> warning

* Update src/diffusers/pipeline_utils.py

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

* make style

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

src/diffusers/pipeline_utils.py

@@ -169,10 +169,12 @@ class DiffusionPipeline(ConfigMixin):
             module = getattr(self, name)
             if isinstance(module, torch.nn.Module):
                 if module.dtype == torch.float16 and str(torch_device) in ["cpu", "mps"]:
-                    raise ValueError(
-                        "Pipelines loaded with `torch_dtype=torch.float16` cannot be moved to `cpu` or `mps` "
-                        "due to the lack of support for `float16` operations on those devices in PyTorch. "
-                        "Please remove the `torch_dtype=torch.float16` argument, or use a `cuda` device."
+                    logger.warning(
+                        "Pipelines loaded with `torch_dtype=torch.float16` cannot run with `cpu` or `mps` device. It"
+                        " is not recommended to move them to `cpu` or `mps` as running them will fail. Please make"
+                        " sure to use a `cuda` device to run the pipeline in inference. due to the lack of support for"
+                        " `float16` operations on those devices in PyTorch. Please remove the"
+                        " `torch_dtype=torch.float16` argument, or use a `cuda` device to run inference."
                     )
                 module.to(torch_device)
         return self

tests/test_pipelines.py

@@ -247,17 +247,6 @@ class PipelineFastTests(unittest.TestCase):
 
         return extract
 
-    def test_pipeline_fp16_cpu_error(self):
-        model = self.dummy_uncond_unet
-        scheduler = DDPMScheduler(num_train_timesteps=10)
-        pipe = DDIMPipeline(model.half(), scheduler)
-
-        if str(torch_device) in ["cpu", "mps"]:
-            self.assertRaises(ValueError, pipe.to, torch_device)
-        else:
-            # moving the pipeline to GPU should work
-            pipe.to(torch_device)
-
     def test_ddim(self):
         unet = self.dummy_uncond_unet
         scheduler = DDIMScheduler()