[Tests] add: tests for t2i adapter training. (#4947)

add: tests for t2i adapter training.

examples/t2i_adapter/train_t2i_adapter_sdxl.py

@@ -245,6 +245,13 @@ def parse_args(input_args=None):
         default=None,
         help="Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.",
     )
+    parser.add_argument(
+        "--adapter_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained adapter model or model identifier from huggingface.co/models."
+        " If not specified adapter weights are initialized w.r.t the configurations of SDXL.",
+    )
     parser.add_argument(
         "--revision",
         type=str,
@@ -840,7 +847,11 @@ def main(args):
         args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
-    logger.info("Initializing t2iadapter weights from unet")
+    if args.adapter_model_name_or_path:
+        logger.info("Loading existing adapter weights.")
+        t2iadapter = T2IAdapter.from_pretrained(args.adapter_model_name_or_path)
+    else:
+        logger.info("Initializing t2iadapter weights.")
         t2iadapter = T2IAdapter(
             in_channels=3,
             channels=(320, 640, 1280, 1280),

examples/test_examples.py

@@ -1528,6 +1528,25 @@ class ExamplesTestsAccelerate(unittest.TestCase):
 
             self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))
 
+    def test_t2i_adapter_sdxl(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+            examples/t2i_adapter/train_t2i_adapter_sdxl.py
+            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
+            --adapter_model_name_or_path=hf-internal-testing/tiny-adapter
+            --dataset_name=hf-internal-testing/fill10
+            --output_dir={tmpdir}
+            --resolution=64
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --max_train_steps=9
+            --checkpointing_steps=2
+            """.split()
+
+            run_command(self._launch_args + test_args)
+
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))
+
     def test_custom_diffusion_checkpointing_checkpoints_total_limit(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""