add vae test for data preprocess

.github/workflows/test.yml

@@ -20,6 +20,7 @@ jobs:
 
       - name: Install dependencies
         run: |
+          pip install setuptools
           pip install --upgrade pip
           pip install packaging ninja && pip install flash-attn==2.7.0.post2 --no-build-isolation 
           pip install -e .

tests/test_data_preprocess.py

@@ -0,0 +1,45 @@
+import os
+from transformers import AutoTokenizer, T5EncoderModel
+from diffusers import AutoencoderKLHunyuanVideo
+import torch
+
+init_dict =  {
+            "in_channels": 3,
+            "out_channels": 3,
+            "latent_channels": 4,
+            "down_block_types": (
+                "HunyuanVideoDownBlock3D",
+                "HunyuanVideoDownBlock3D",
+                "HunyuanVideoDownBlock3D",
+                "HunyuanVideoDownBlock3D",
+            ),
+            "up_block_types": (
+                "HunyuanVideoUpBlock3D",
+                "HunyuanVideoUpBlock3D",
+                "HunyuanVideoUpBlock3D",
+                "HunyuanVideoUpBlock3D",
+            ),
+            "block_out_channels": (8, 8, 8, 8),
+            "layers_per_block": 1,
+            "act_fn": "silu",
+            "norm_num_groups": 4,
+            "scaling_factor": 0.476986,
+            "spatial_compression_ratio": 8,
+            "temporal_compression_ratio": 4,
+            "mid_block_add_attention": True,
+        }
+os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1"
+text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+model = AutoencoderKLHunyuanVideo(**init_dict)
+
+input_tensor = torch.rand(1, 3, 9, 16, 16)
+
+vae_encoder_output =  model.encoder(input_tensor)
+
+# vae_decoder_output =  model.decoder(vae_encoder_output)
+
+assert vae_encoder_output.shape == (1,8,3,2,2)
+
+# print(vae_decoder_output.shape)