UniPC Multistep fix tensor dtype/device on order=3 (huggingface#7532)

* UniPC UTs iterate solvers on FP16

It wasn't catching errs on order==3. Might be excessive?

* UniPC Multistep fix tensor dtype/device on order=3

* UniPC UTs Add v_pred to fp16 test iter

For completions sake. Probably overkill?

Author: Beinsezii

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -576,7 +576,7 @@ def multistep_uni_p_bh_update(
             if order == 2:
                 rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
             else:
-                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
+                rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)
         else:
             D1s = None
 
@@ -714,7 +714,7 @@ def multistep_uni_c_bh_update(
         if order == 1:
             rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
         else:
-            rhos_c = torch.linalg.solve(R, b)
+            rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)
 
         if self.predict_x0:
             x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0

tests/schedulers/test_scheduler_unipc.py

@@ -229,20 +229,29 @@ def test_full_loop_with_karras_and_v_prediction(self):
         assert abs(result_mean.item() - 0.1966) < 1e-3
 
     def test_fp16_support(self):
-        scheduler_class = self.scheduler_classes[0]
-        scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)
-        scheduler = scheduler_class(**scheduler_config)
+        for order in [1, 2, 3]:
+            for solver_type in ["bh1", "bh2"]:
+                for prediction_type in ["epsilon", "sample", "v_prediction"]:
+                    scheduler_class = self.scheduler_classes[0]
+                    scheduler_config = self.get_scheduler_config(
+                        thresholding=True,
+                        dynamic_thresholding_ratio=0,
+                        prediction_type=prediction_type,
+                        solver_order=order,
+                        solver_type=solver_type,
+                    )
+                    scheduler = scheduler_class(**scheduler_config)
 
-        num_inference_steps = 10
-        model = self.dummy_model()
-        sample = self.dummy_sample_deter.half()
-        scheduler.set_timesteps(num_inference_steps)
+                    num_inference_steps = 10
+                    model = self.dummy_model()
+                    sample = self.dummy_sample_deter.half()
+                    scheduler.set_timesteps(num_inference_steps)
 
-        for i, t in enumerate(scheduler.timesteps):
-            residual = model(sample, t)
-            sample = scheduler.step(residual, t, sample).prev_sample
+                    for i, t in enumerate(scheduler.timesteps):
+                        residual = model(sample, t)
+                        sample = scheduler.step(residual, t, sample).prev_sample
 
-        assert sample.dtype == torch.float16
+                    assert sample.dtype == torch.float16
 
     def test_full_loop_with_noise(self):
         scheduler_class = self.scheduler_classes[0]