add test for ldm uncond

tests/test_modeling_utils.py

@@ -34,6 +34,7 @@
     GradTTSPipeline,
     GradTTSScheduler,
     LatentDiffusionPipeline,
+    LatentDiffusionUncondPipeline,
     NCSNpp,
     PNDMPipeline,
     PNDMScheduler,
@@ -46,7 +47,6 @@
     UNetLDMModel,
     UNetModel,
     VQModel,
-    AutoencoderKL,
 )
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
@@ -915,7 +915,7 @@ def prepare_init_args_and_inputs_for_common(self):
             "out_ch": 3,
             "resolution": 32,
             "z_channels": 4,
-            "attn_resolutions": []
+            "attn_resolutions": [],
         }
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
@@ -925,7 +925,7 @@ def test_forward_signature(self):
 
     def test_training(self):
         pass
-    
+
     def test_from_pretrained_hub(self):
         model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
         self.assertIsNotNone(model)
@@ -1151,6 +1151,19 @@ def test_score_sde_vp_pipeline(self):
         assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2
         assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4
 
+    @slow
+    def test_ldm_uncond(self):
+        ldm = LatentDiffusionUncondPipeline.from_pretrained("fusing/latent-diffusion-celeba-256")
+
+        generator = torch.manual_seed(0)
+        image = ldm(generator=generator, num_inference_steps=5)
+
+        image_slice = image[0, -1, -3:, -3:].cpu()
+
+        assert image.shape == (1, 3, 256, 256)
+        expected_slice = torch.tensor([0.5025, 0.4121, 0.3851, 0.4806, 0.3996, 0.3745, 0.4839, 0.4559, 0.4293])
+        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)