Let users jointhe LoRA Library organization

app_training.py

@@ -48,8 +48,7 @@ def create_training_demo(trainer: Trainer,
                         choices=[_.value for _ in UploadTarget],
                         value=UploadTarget.LORA_LIBRARY.value)
                     gr.Markdown('''
-                    - By default, trained models will be uploaded to [LoRA Library](https://huggingface.co/lora-library) (See [this example model](https://huggingface.co/lora-library/lora-dreambooth-sample-dog)).
-                      Note that it will fail if you are not a member of the organization. So please join the org first. In the case uploading failed, you can use the "Upload" tab to upload your model later.
+                    - By default, trained models will be uploaded to [LoRA Library](https://huggingface.co/lora-library) (see [this example model](https://huggingface.co/lora-library/lora-dreambooth-sample-dog)).
                     - You can also choose "Personal Profile", in which case, the model will be uploaded to https://huggingface.co/{your_username}/{model_name}.
                     ''')
 

trainer.py

@@ -16,6 +16,8 @@ from huggingface_hub import HfApi
 from app_upload import LoRAModelUploader
 from utils import save_model_card
 
+URL_TO_JOIN_LORA_LIBRARY_ORG = 'https://huggingface.co/organizations/lora-library/share/hjetHAcKjnPHXhHfbeEcqnBqmhgilFfpOL'
+
 
 def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
     w, h = image.size
@@ -33,6 +35,7 @@ def pad_image(image: PIL.Image.Image) -> PIL.Image.Image:
 
 class Trainer:
     def __init__(self, hf_token: str | None = None):
+        self.hf_token = hf_token
         self.api = HfApi(token=hf_token)
         self.model_uploader = LoRAModelUploader(hf_token)
 
@@ -48,6 +51,12 @@ class Trainer:
             out_path = instance_data_dir / f'{i:03d}.jpg'
             image.save(out_path, format='JPEG', quality=100)
 
+    def join_lora_library_org(self) -> None:
+        subprocess.run(
+            shlex.split(
+                f'curl -X POST -H "Authorization: Bearer {self.hf_token}" -H "Content-Type: application/json" {URL_TO_JOIN_LORA_LIBRARY_ORG}'
+            ))
+
     def run(
         self,
         instance_images: list | None,
@@ -97,6 +106,9 @@ class Trainer:
         instance_data_dir = repo_dir / 'training_data' / output_model_name
         self.prepare_dataset(instance_images, resolution, instance_data_dir)
 
+        if upload_to_hub:
+            self.join_lora_library_org()
+
         command = f'''
         accelerate launch train_dreambooth_lora.py \
           --pretrained_model_name_or_path={base_model}  \