add fast test for ldm

src/diffusers/models/embeddings.py

@@ -34,6 +34,7 @@ def get_timestep_embedding(timesteps, embedding_dim):
         emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
     return emb
 
+
 # unet_glide.py
 def timestep_embedding(timesteps, dim, max_period=10000):
     """

src/diffusers/models/unet_grad_tts.py

@@ -198,7 +198,6 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
         if not isinstance(spk, type(None)):
             s = self.spk_mlp(spk)
 
-        
         t = self.time_pos_emb(timesteps, scale=self.pe_scale)
         t = self.mlp(t)
 

tests/test_modeling_utils.py

@@ -694,6 +694,21 @@ class PipelineTesterMixin(unittest.TestCase):
         expected_slice = torch.tensor([0.7295, 0.7358, 0.7256, 0.7435, 0.7095, 0.6884, 0.7325, 0.6921, 0.6458])
         assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
 
+    @slow
+    def test_ldm_text2img_fast(self):
+        model_id = "fusing/latent-diffusion-text2im-large"
+        ldm = LatentDiffusionPipeline.from_pretrained(model_id)
+
+        prompt = "A painting of a squirrel eating a burger"
+        generator = torch.manual_seed(0)
+        image = ldm([prompt], generator=generator, num_inference_steps=20)
+
+        image_slice = image[0, -1, -3:, -3:].cpu()
+
+        assert image.shape == (1, 3, 256, 256)
+        expected_slice = torch.rensor([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
+        assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
+
     @slow
     def test_glide_text2img(self):
         model_id = "fusing/glide-base"