File size: 3,556 Bytes
db1e5fb 8956d86 db1e5fb 8956d86 db1e5fb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
#!/usr/bin/env python
from __future__ import annotations
import pathlib
import gradio as gr
import slugify
from constants import UploadTarget
from uploader import Uploader
from utils import find_exp_dirs
class LoRAModelUploader(Uploader):
def upload_lora_model(self, folder_path: str, repo_name: str,
upload_to: str, private: bool,
delete_existing_repo: bool) -> str:
if not repo_name:
repo_name = pathlib.Path(folder_path).name
repo_name = slugify.slugify(repo_name)
if upload_to == UploadTarget.PERSONAL_PROFILE.value:
organization = ''
elif upload_to == UploadTarget.LORA_LIBRARY.value:
organization = 'lora-library'
else:
raise ValueError
return self.upload(folder_path,
repo_name,
organization=organization,
private=private,
delete_existing_repo=delete_existing_repo)
def load_local_lora_model_list() -> dict:
choices = find_exp_dirs(ignore_repo=True)
return gr.update(choices=choices, value=choices[0] if choices else None)
def create_upload_demo(hf_token: str | None) -> gr.Blocks:
uploader = LoRAModelUploader(hf_token)
model_dirs = find_exp_dirs(ignore_repo=True)
with gr.Blocks() as demo:
with gr.Box():
gr.Markdown('Local Models')
reload_button = gr.Button('Reload Model List')
model_dir = gr.Dropdown(
label='LoRA Model ID',
choices=model_dirs,
value=model_dirs[0] if model_dirs else None)
gr.Markdown(
'- Models uploaded in training time will not be shown here.')
with gr.Box():
gr.Markdown('Upload Settings')
with gr.Row():
use_private_repo = gr.Checkbox(label='Private', value=False)
delete_existing_repo = gr.Checkbox(
label='Delete existing repo of the same name', value=False)
upload_to = gr.Radio(label='Upload to',
choices=[_.value for _ in UploadTarget],
value=UploadTarget.PERSONAL_PROFILE.value)
model_name = gr.Textbox(label='Model Name')
upload_button = gr.Button('Upload')
gr.Markdown('''
- You can upload your trained model to your personal profile (i.e. https://huggingface.co/{your_username}/{model_name}) or to the public [LoRA Concepts Library](https://huggingface.co/lora-library) (i.e. https://huggingface.co/lora-library/{model_name}).
''')
with gr.Box():
gr.Markdown('Output message')
output_message = gr.Markdown()
reload_button.click(fn=load_local_lora_model_list,
inputs=None,
outputs=model_dir)
upload_button.click(fn=uploader.upload_lora_model,
inputs=[
model_dir,
model_name,
upload_to,
use_private_repo,
delete_existing_repo,
],
outputs=output_message)
return demo
if __name__ == '__main__':
import os
hf_token = os.getenv('HF_TOKEN')
demo = create_upload_demo(hf_token)
demo.queue(max_size=1).launch(share=False)
|