app.py

import gradio as gr
from stablepy import Model_Diffusers

from StableGR import (search_civitai, download_civitai, select_civitai_item, add_civitai_item, get_civitai_tag, select_civitai_all_item,
                           update_civitai_selection, update_civitai_checkbox, from_civitai_checkbox,
                           CIVITAI_TYPE, CIVITAI_BASEMODEL, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_FILETYPE, download_file)


css = """
.title { font-size: 3em; align-items: center; text-align: center; }
.info { align-items: center; text-align: center; }
.block.result { margin: 1em 0; padding: 1em; box-shadow: 0 0 3px 3px #664422, 0 0 3px 2px #664422 inset; border-radius: 6px; background: #665544; }
.desc [src$='#float'] { float: right; margin: 20px; }
"""




# Define the function to generate images
def generate_image(model_id, prompt, lora_A, num_steps, guidance_scale, sampler, img_width, img_height):
    model = Model_Diffusers(
        base_model_id=model_id,
        task_name='txt2img',
    )

    image, info_image = model(
        prompt=prompt,
        lora_A=lora_A,
        num_steps=num_steps,
        guidance_scale=guidance_scale,
        sampler=sampler,
        img_width=img_width,
        img_height=img_height,
    )
    return image[0]


with gr.Blocks(fill_width=True, css=css) as demo:
    with gr.Column():
        gr.Markdown("# StableGR", elem_classes="title")
        state = gr.State(value={})
        with gr.Accordion("Search Civitai", open=True):
            with gr.Row():
                search_civitai_type = gr.CheckboxGroup(label="Type", choices=CIVITAI_TYPE, value=["Checkpoint", "LORA"])
                search_civitai_basemodel = gr.CheckboxGroup(label="Base Model", choices=CIVITAI_BASEMODEL, value=[])
                search_civitai_filetype = gr.CheckboxGroup(label="File type", choices=CIVITAI_FILETYPE, value=["Model"])
            with gr.Row():
                search_civitai_sort = gr.Radio(label="Sort", choices=CIVITAI_SORT, value=CIVITAI_SORT[0])
                search_civitai_period = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="Month")
                search_civitai_limit = gr.Number(label="Limit", minimum=1, maximum=100, step=1, value=100)
                search_civitai_page = gr.Number(label="Page", info="If 0, retrieve all pages", minimum=0, maximum=10, step=1, value=1)
            with gr.Row(equal_height=True):
                search_civitai_query = gr.Textbox(label="Query", placeholder="flux", lines=1)
                search_civitai_tag = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True)
                search_civitai_user = gr.Textbox(label="Username", lines=1)
                search_civitai_submit = gr.Button("Search on Civitai")
            with gr.Accordion("Results", open=True):
                with gr.Row():
                    search_civitai_desc = gr.Markdown(value="", visible=False, elem_classes="desc")
                    search_civitai_json = gr.JSON(value={}, visible=False)
                with gr.Row(equal_height=True):
                    with gr.Column(scale=9):
                        with gr.Accordion("Select from Gallery", open=False):
                            search_civitai_gallery = gr.Gallery([], label="Results", allow_preview=False, columns=5, elem_id="gallery", show_share_button=False, interactive=False)
                        with gr.Accordion("Select by Checkbox", open=False):
                            search_civitai_result_checkbox = gr.CheckboxGroup(label="", choices=[], value=[])
                        search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value=[],
                                                            allow_custom_value=True, visible=True, multiselect=True)
                        search_civitai_result_info = gr.Markdown("Search result.", elem_classes="info")
                    with gr.Column(scale=1):
                        search_civitai_add = gr.Button("Add to download URLs")
                        search_civitai_select_all = gr.Button("Select All", variant="secondary", size="sm")
        with gr.Group():
            dl_url = gr.Textbox(label="Download URL(s)", placeholder="https://civitai.com/api/download/models/28907\n...", value="", lines=3, max_lines=255)
            with gr.Column():
                civitai_key = gr.Textbox(label="Your Civitai Key", value="", max_lines=1)
                gr.Markdown("Your Civitai API key is available at [https://civitai.com/user/account](https://civitai.com/user/account).", elem_classes="info")
        
        with gr.Row():
            run_base = gr.Button(value="Download Base Model", variant="primary")
            run_lora = gr.Button(value="Download Lora", variant="primary")
        uploaded_urls = gr.CheckboxGroup(visible=False, choices=[], value=None) # hidden
        urls_md = gr.Markdown("<br><br>", elem_classes="result")
        urls_remain = gr.Textbox("Remaining URLs", value="", show_copy_button=True, visible=False)
    with gr.Column():
        base_model = gr.File(label="Base Models")
        lora_A = gr.File(label="Lora")
        with gr.Row():
            prompt = gr.Textbox(label="Prompt", value="A highly detailed portrait of an underwater city, with towering spires and domes rising up from the ocean floor")
        
        num_steps = gr.Slider(label="Number of Steps", minimum=1, maximum=100, value=30, step=1)
        guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20.0, value=7.5, step=0.1)
        sampler = gr.Dropdown(label="Sampler", choices=["DPM++ 2M", "OtherSampler1", "OtherSampler2"], value="DPM++ 2M")
        img_width = gr.Slider(label="Image Width", minimum=64, maximum=2048, value=512, step=64)
        img_height = gr.Slider(label="Image Height", minimum=64, maximum=2048, value=1024, step=64)
        generate_button = gr.Button("Generate Image")
        output_image = gr.Image(label="output")

    gr.on(
        triggers=[run_base.click],
        fn=download_file,
        inputs=[dl_url, civitai_key],
        outputs=base_model,
        queue=True,
    )
    gr.on(
        triggers=[run_lora.click],
        fn=download_file,
        inputs=[dl_url, civitai_key],
        outputs=lora_A,
        queue=True,
    )
    gr.on(
        triggers=[generate_button.click],
        fn=generate_image,
        inputs=[prompt, num_steps, guidance_scale, sampler, img_width, img_height],
        outputs=output_image,
        queue=True,
    )
    gr.on(
        triggers=[search_civitai_submit.click, search_civitai_query.submit, search_civitai_user.submit],
        fn=search_civitai,
        inputs=[search_civitai_query, search_civitai_type, search_civitai_basemodel, search_civitai_sort,
                search_civitai_period, search_civitai_tag, search_civitai_user, search_civitai_limit,
                search_civitai_page, search_civitai_filetype, civitai_key, search_civitai_gallery, state],
        outputs=[search_civitai_result, search_civitai_desc, search_civitai_submit, search_civitai_query, search_civitai_gallery,
                 search_civitai_result_checkbox, search_civitai_result_info, state],
        queue=False,
        show_api=False,
    )
    search_civitai_result.change(select_civitai_item, [search_civitai_result, state], [search_civitai_desc, search_civitai_json, state], queue=False, show_api=False)\
    .success(update_civitai_checkbox, [search_civitai_result], [search_civitai_result_checkbox], queue=True, show_api=False)
    search_civitai_result_checkbox.select(from_civitai_checkbox, [search_civitai_result_checkbox], [search_civitai_result], queue=False, show_api=False)
    search_civitai_add.click(add_civitai_item, [search_civitai_result, dl_url], [dl_url], queue=False, show_api=False)
    search_civitai_select_all.click(select_civitai_all_item, [search_civitai_select_all, state], [search_civitai_select_all, search_civitai_result], queue=False, show_api=False)
    search_civitai_gallery.select(update_civitai_selection, [search_civitai_result, state], [search_civitai_result], queue=False, show_api=False)

demo.queue()
demo.launch(ssr_mode=False, share=True)