[ControlNet SDXL training] fixes in the training script (#4223)

* fix: #4206

* add: sdxl controlnet training smoketest.

* remove unnecessary token inits.

* add: licensing to model card.

* include SDXL licensing in the model card and make public visibility default

* debugging

* debugging

* disable local file download.

* fix: training test.

* fix: ckpt prefix.

examples/controlnet/train_controlnet_sdxl.py

@@ -124,7 +124,7 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step)
         for _ in range(args.num_validation_images):
             with torch.autocast("cuda"):
                 image = pipeline(
-                    validation_prompt, validation_image, num_inference_steps=20, generator=generator
+                    prompt=validation_prompt, image=validation_image, num_inference_steps=20, generator=generator
                 ).images[0]
             images.append(image)
 
@@ -178,7 +178,7 @@ def import_model_class_from_model_name_or_path(
     pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
 ):
     text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path, subfolder=subfolder, revision=revision, use_auth_token=True
+        pretrained_model_name_or_path, subfolder=subfolder, revision=revision
     )
     model_class = text_encoder_config.architectures[0]
 
@@ -226,6 +226,12 @@ inference: true
 
 These are controlnet weights trained on {base_model} with new type of conditioning.
 {img_str}
+"""
+    model_card += """
+
+## License
+
+[SDXL 0.9 Research License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/LICENSE.md)
 """
     with open(os.path.join(repo_folder, "README.md"), "w") as f:
         f.write(yaml + model_card)
@@ -798,10 +804,7 @@ def main(args):
 
         if args.push_to_hub:
             repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name,
-                exist_ok=True,
-                token=args.hub_token,
-                private=True,
+                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
             ).repo_id
 
     # Load the tokenizers
@@ -839,7 +842,7 @@ def main(args):
         revision=args.revision,
     )
     unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, use_auth_token=True
+        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
     )
 
     if args.controlnet_model_name_or_path:

examples/test_examples.py

@@ -1296,6 +1296,25 @@ class ExamplesTestsAccelerate(unittest.TestCase):
                 {"checkpoint-8", "checkpoint-10", "checkpoint-12"},
             )
 
+    def test_controlnet_sdxl(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+            examples/controlnet/train_controlnet_sdxl.py
+            --pretrained_model_name_or_path=hf-internal-testing/tiny-stable-diffusion-xl-pipe
+            --dataset_name=hf-internal-testing/fill10
+            --output_dir={tmpdir}
+            --resolution=64
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --controlnet_model_name_or_path=hf-internal-testing/tiny-controlnet-sdxl
+            --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.bin")))
+
     def test_custom_diffusion_checkpointing_checkpoints_total_limit(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             test_args = f"""

src/diffusers/models/controlnet.py

@@ -751,7 +751,6 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
         sample = self.conv_in(sample)
 
         controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
-
         sample = sample + controlnet_cond
 
         # 3. down