| import gradio as gr | |
| from convert_url_to_diffusers_sdxl_gr import ( | |
| convert_url_to_diffusers_repo, | |
| SCHEDULER_CONFIG_MAP, | |
| ) | |
| vaes = [""] | |
| loras = [""] | |
| schedulers = list(SCHEDULER_CONFIG_MAP.keys()) | |
| css = """""" | |
| with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo: | |
| gr.Markdown("# Download and convert any Stable Diffusion XL safetensors to Diffusers and create your repo") | |
| gr.Markdown( | |
| f""" | |
| The steps are the following: | |
| - Paste a write-access token from [hf.co/settings/tokens](https://huggingface.co/settings/tokens). | |
| - Input a model download url from the Hub or Civitai or other sites. | |
| - If you want to download a model from Civitai, paste a Civitai API Key. | |
| - Input your new repo name. e.g. 'yourid/newrepo'. | |
| - Click "Submit". | |
| - Patiently wait until the output changes. | |
| """ | |
| ) | |
| with gr.Column(): | |
| dl_url = gr.Textbox(label="URL to download", placeholder="https://...", value="", max_lines=1) | |
| repo_id = gr.Textbox(label="Your New Repo ID", placeholder="author/model", value="", max_lines=1) | |
| hf_token = gr.Textbox(label="Your HF write token", placeholder="", value="", max_lines=1) | |
| civitai_key = gr.Textbox(label="Your Civitai API Key (Optional)", info="If you download model from Civitai...", placeholder="", value="", max_lines=1) | |
| is_half = gr.Checkbox(label="Half precision", value=True) | |
| vae = gr.Dropdown(label="VAE", choices=vaes, value="", allow_custom_value=True) | |
| scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler a") | |
| lora1 = gr.Dropdown(label="LoRA1", choices=loras, value="", allow_custom_value=True) | |
| lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale") | |
| lora2 = gr.Dropdown(label="LoRA2", choices=loras, value="", allow_custom_value=True) | |
| lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale") | |
| lora3 = gr.Dropdown(label="LoRA3", choices=loras, value="", allow_custom_value=True) | |
| lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale") | |
| lora4 = gr.Dropdown(label="LoRA4", choices=loras, value="", allow_custom_value=True) | |
| lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale") | |
| lora5 = gr.Dropdown(label="LoRA5", choices=loras, value="", allow_custom_value=True) | |
| lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale") | |
| run_button = gr.Button(value="Submit") | |
| repo_urls = gr.CheckboxGroup(visible=False, choices=[], value=None) | |
| output_md = gr.Markdown(label="Output") | |
| gr.on( | |
| triggers=[run_button.click], | |
| fn=convert_url_to_diffusers_repo, | |
| inputs=[dl_url, repo_id, hf_token, civitai_key, repo_urls, is_half, vae, scheduler, | |
| lora1, lora1s, lora2, lora2s, lora3, lora3s, lora4, lora4s, lora5, lora5s], | |
| outputs=[repo_urls, output_md], | |
| ) | |
| demo.queue() | |
| demo.launch() | |