Fix unipc use_karras_sigmas exception - fixes #4580 (#4581)

* Fix unipc karras sigmas exception - fixes #4580

* Add unipc scheduler tests for karras sigmas

Author: reimager

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -278,6 +278,44 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
 
         return sample
 
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
+    def _sigma_to_t(self, sigma, log_sigmas):
+        # get log sigma
+        log_sigma = np.log(sigma)
+
+        # get distribution
+        dists = log_sigma - log_sigmas[:, np.newaxis]
+
+        # get sigmas range
+        low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
+        high_idx = low_idx + 1
+
+        low = log_sigmas[low_idx]
+        high = log_sigmas[high_idx]
+
+        # interpolate sigmas
+        w = (low - log_sigma) / (low - high)
+        w = np.clip(w, 0, 1)
+
+        # transform interpolation to time range
+        t = (1 - w) * low_idx + w * high_idx
+        t = t.reshape(sigma.shape)
+        return t
+
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
+    def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
+        """Constructs the noise schedule of Karras et al. (2022)."""
+
+        sigma_min: float = in_sigmas[-1].item()
+        sigma_max: float = in_sigmas[0].item()
+
+        rho = 7.0  # 7.0 is the value used in the paper
+        ramp = np.linspace(0, 1, num_inference_steps)
+        min_inv_rho = sigma_min ** (1 / rho)
+        max_inv_rho = sigma_max ** (1 / rho)
+        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+        return sigmas
+
     def convert_model_output(
         self, model_output: torch.FloatTensor, timestep: int, sample: torch.FloatTensor
     ) -> torch.FloatTensor:

tests/schedulers/test_scheduler_unipc.py

@@ -208,12 +208,24 @@ def test_full_loop_no_noise(self):
 
         assert abs(result_mean.item() - 0.2464) < 1e-3
 
+    def test_full_loop_with_karras(self):
+        sample = self.full_loop(use_karras_sigmas=True)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.2925) < 1e-3
+
     def test_full_loop_with_v_prediction(self):
         sample = self.full_loop(prediction_type="v_prediction")
         result_mean = torch.mean(torch.abs(sample))
 
         assert abs(result_mean.item() - 0.1014) < 1e-3
 
+    def test_full_loop_with_karras_and_v_prediction(self):
+        sample = self.full_loop(prediction_type="v_prediction", use_karras_sigmas=True)
+        result_mean = torch.mean(torch.abs(sample))
+
+        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)