app.py

import gradio as gr
from convert_url_to_diffusers_multi_gr import convert_url_to_diffusers_repo, get_dtypes, FLUX_BASE_REPOS, SD35_BASE_REPOS
from presets import (DEFAULT_DTYPE, schedulers, clips, t5s, sdxl_vaes, sdxl_loras, sdxl_preset_dict, sdxl_set_presets,
                     sd15_vaes, sd15_loras, sd15_preset_dict, sd15_set_presets, flux_vaes, flux_loras, flux_preset_dict, flux_set_presets,
                     sd35_vaes, sd35_loras, sd35_preset_dict, sd35_set_presets)
import os


HF_USER = os.getenv("HF_USER", "")
HF_REPO = os.getenv("HF_REPO", "")
HF_URL = os.getenv("HF_URL", "")
HF_OW = os.getenv("HF_OW", False)
HF_PR = os.getenv("HF_PR", False)

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; }

.block.result p,
.block.result .prose,
.block-result * {
    color: aquamarine;
    font-family: "Atma", monospace !important;
    font-size: 1.06em;
    letter-spacing: 0.50px;
    word-spacing: 6px;
}
/* I know that I use a lot of !important instruction..
   Yeah, I know it's not really properly recommended to use !important instruction when writing CSS rules ..
   But it's because that Gradio interface use also a custom css-theme in gr.Blocks() from: "NoCrypt/miku@>=1.2.2"
   And all my !important instructions are for avoiding to get unexpected superceeded by that "NoCrypt/miku@>=1.2.2" css theme skin...
   No more, no less!
*/
.setting_tag::before {
    content: "::";
    font-family: system-ui, monospace !important;
    font-size: 1.2em !important;
    color: #736819 !important;
    font-weight: bold;
    display: block;
    text-align: center !important;
    margin: 0 auto !important;
}
.setting_tag {
    margin-top: 1em !important;
    font-size: 1.4em !important;
    color: darkgoldenrod !important;
    padding: 10px !important;
    font-weight: normal !important;
    border: 2px dotted gold !important;
    background: white !important;
    word-break: break-word !important;
    display: block;
    text-align: center !important;
    margin: 0 auto !important;
    text-shadow: 1px 1px 1px gold !important;
}
.setting_tag::after {
    content: "::";
    font-family: system-ui, monospace !important;
    font-size: 1.2em !important;
    color: #736819 !important;
    font-weight: bold !important;
    display: block !important;
    text-align: center !important;
    margin: 0 auto !important;
}
a.linkify_1,
a.linkify_1:focus,
a.linkify_1:visited {
    color: red !important;
    text-decoration: underline !important;
}
a.linkify_1:active,
a.linkify_1:hover {
    color: darkred !important;
    text-decoration: overline !important;
}
.details_info_block_expanded_override {
    font-size: 0.95em !important;
    color: grey !important;
    padding: 10px !important;
    font-weight: bold;
    border: none !important;
    box-shadow: 1px 1px 2px 3px whitesmoke, 3px 2px 1px 1px grey !important;
    background: whitesmoke !important;
    word-break: break-word !important;
}
.details_info_block {
    font-size: 0.95em !important;
    color: grey !important;
    padding: 10px !important;
    font-weight: bold;
    border: none !important;
    box-shadow: 1px 1px 2px 3px whitesmoke, 3px 2px 1px 1px grey !important;
    background: whitesmoke !important;
    word-break: break-word !important;
    height: initial !important;
    max-height: initial !important;
    overflow: auto !important;
}
.details_info_block[is-expanded="False"]::before {
    content: "(double-click to expand help...)";
    user-select: none;
    cursor: pointer;
    font-family: "Atma" !important;
    font-weight: 500 !important;
    letter-spacing: 2px !important;
    word-spacing: 5px !important;
    color: darkmagenta !important;
    display: inline-block !important;
    font-size: 0.99em !important;
    background: whitesmoke !important;
    padding: 10px !important;
    border: 2px solid black !important;
}
.details_info_block[is-expanded="False"] {
    transition: 1.2s all;
    height: 80px !important;
    max-height: 80px !important;
    overflow: hidden !important;
}
.details_info_block[is-expanded="True"]::before {
    content: "(double-click to reduce help...)";
    user-select: none;
    cursor: pointer;
    font-family: "Atma" !important;
    font-weight: 500 !important;
    letter-spacing: 2px !important;
    word-spacing: 5px !important;
    color: white !important;
    display: block !important;
    font-size: 0.99em !important;
    background: #1c9e5c !important;
    padding: 10px !important;
    border: 2px solid black !important;
    border-radius: 0px !important;
    margin-bottom: 1em !important;
}
.details_info_block[is-expanded="True"] {
    transition: 1.2s all;
    height: initial !important;
    max-height: initial !important;
    overflow: auto !important;
    margin-bottom: 1em !important;
}
.em_warning {
    font-family: "Atma", system-ui !important;
    font-weight: 400 !important;
    font-style: normal !important;
    font-size: 1.3em !important;
    word-spacing: 3.2px !important;
    color: orangered !important;
}
.spanify_safetensors_base_model {
    font-family: "Nunito", system-ui, monospace !important;
    font-weight: 600;
    color: green !important;
}
.spanify_safetensors_checkpoint_model {
    font-family: "Nunito", system-ui, monospace !important;
    font-weight: 600;
    color: orange !important;
}
.spanify_vae_model {
    font-family: "Nunito", system-ui, monospace !important;
    font-weight: 600;
    color: deeppink !important;
}
.spanify_lora_checkpoint_model {
    font-family: "Nunito", system-ui, monospace !important;
    font-weight: 600;
    color: #8c627b !important;
}
.spanify_other_model {
    font-family: "Nunito", system-ui, monospace !important;
    font-weight: 600;
    color: silver !important;
}
.setting_tag_as_mini {
    font-family: "Nunito", serif !important;
    font-optical-sizing: auto !important;
    font-weight: 600 !important;
    font-size: 1.2em !important;
    color: darkgoldenrod !important;
    padding: 4px !important;
    border: 2px dashed gold !important;
    background: white !important;
    word-break: break-word !important;
    display: inline-block !important;
    text-shadow: 1px 1px 1px gold !important;
}
li.has_divider_1 {
    list-style: none;
}
li.has_divider_1 > div.is_divider_1 {
    display: inline-block;
    width: 100%;
    height: 0.4em;
    background: burlywood;
}
.accordion-element,
.accordion-element button:not(.reset-button):not(.primary),
.accordion-element button:not(.reset-button):not(.primary) * {
    background: navy !important;
    color: white !important;
    font-family: "Nunito", serif !important;
    font-optical-sizing: auto !important;
    font-weight: 600 !important;
    font-size: 1.2em !important;
}
/* the issue with a Gradio gr.Tab() component,
is that if we specify a CSS class for it through
the declaration of the well-said component, then,
it would be useless as it don't permit to targetting
the TRUE button tab clickable DOMElement..
so this is a workaround which assume the TAB is at that XPath (at least for Gradio version 5) */
/* any NOT active Gradio tab : */
.tab-wrapper .tab-container[role="tablist"] button[role="tab"]:not(.selected) {
    background: red !important;
    color: white !important;
    font-family: "Nunito", serif !important;
    font-optical-sizing: auto !important;
    font-weight: 600 !important;
    font-size: 1.2em !important;
}
/* the CURRENT active Gradio tab : */
.tab-wrapper .tab-container[role="tablist"] button[role="tab"].selected {
    background: green !important;
    color: white !important;
    font-family: "Nunito", serif !important;
    font-optical-sizing: auto !important;
    font-weight: 600 !important;
    font-size: 1.6em !important;
}
/* here we only target Gradio button
that are reset-button
IN an accordion-element classe */
.accordion-element button.reset-button {
    background: #803e3e !important;
    color: white !important;
    font-family: "Atma", serif !important;
    font-weight: 700 !important;
    font-style: normal !important;
}
/* here we only target Gradio button
that are primary
IN an accordion-element classe */
.accordion-element button.primary {
    background: #5f925e !important;
    font-size: 1.6em !important;
    font-style: oblique !important;
    font-weight: normal !important;
    text-transform: uppercase !important;
    letter-spacing: 1px !important;
    border-bottom-right-radius: 100em !important;
    border-bottom-left-radius: 100em !important;
    font-family: "Atma", serif !important;
    font-weight: 700 !important;
    font-style: normal !important;
}
"""


help_dict = {
    "hf_username": """
        <article class="setting_tag" use-webfont="wf-nunito-regular">
            (hf_username)
        </article>
        <div class="details_info_block_expanded_override"
            use-webfont="wf-atma-light"
            >
            <em>Your HuggingFace username, no more, no less</em>
        </div>""",
    "hf_write_token_access": """
        <article class="setting_tag" use-webfont="wf-nunito-regular">
            (hf_write_token_access)
        </article>
        <div class="details_info_block"
            is-expanded="False"
            ondblclick="makeExpandable(this);"
            use-webfont="wf-atma-light"
            >
            <em>Your HuggingFace Token with WRITE access</em>
            <br>
            <br>
            - Your Token with WRITE access can be created for free at <a class="linkify_1" target="_blank" href="https://huggingface.co/settings/tokens">https://huggingface.co/settings/tokens</a>.
            <br>
            <br>
            <em class=\"em_warning\">
                please, note once created, note its value somewhere you can retrieve later,
                <br>
                because afterwards it would be no more possible to see its value from the
                <br>
                tokens HuggingFace account page!
            </em>
        </div>""",
}

def help(key):
    with gr.Accordion("Help", open=False) as help:
        gr.HTML(value=help_dict.get(key, ""))
    return help


with gr.Blocks(theme="theNeofr/Syne", fill_width=True, css=css, delete_cache=(60, 3600)) as demo:
    gr.Markdown("# Download SDXL / SD 1.5 / SD 3.5 / FLUX.1 safetensors and convert to HF🤗 Diffusers format and create your repo", elem_classes="title")
    gr.Markdown(f"""
### ⚠️IMPORTANT NOTICE⚠️<br>
It's dangerous to expose your access token or key to others.
If you do use it, I recommend that you duplicate this space on your own HF account in advance.
Keys and tokens could be set to **Secrets** (`HF_TOKEN`, `CIVITAI_API_KEY`) if it's placed in your own space.
It saves you the trouble of typing them in.<br>
It barely works in the CPU space, but larger files can be converted if duplicated on the more powerful **Zero GPU** space.
In particular, conversion of FLUX.1 or SD 3.5 is almost impossible in CPU space.
### The steps are the following:
1. Paste a write-access token from [hf.co/settings/tokens](https://huggingface.co/settings/tokens).
1. Input a model download url of the Hugging Face or Civitai or other sites.
1. If you want to download a model from Civitai, paste a Civitai API Key.
1. Input your HF user ID. e.g. 'yourid'.
1. Input your new repo name. If empty, auto-complete. e.g. 'newrepo'.
1. Set the parameters. If not sure, just use the defaults.
1. Click "Submit".
1. Patiently wait until the output changes. It takes approximately 2 to 3 minutes (on SDXL models downloading from HF).
            """)
    with gr.Column():
        dl_url = gr.Textbox(label="URL to download", placeholder="https://huggingface.co/bluepen5805/blue_pencil-XL/blob/main/blue_pencil-XL-v7.0.0.safetensors",
                            value=HF_URL, max_lines=1)
        with gr.Group():
            with gr.Row():
                with gr.Column():
                    hf_user = gr.Textbox(label="Your HF user ID", placeholder="username", value=HF_USER, max_lines=1)
                    help("hf_username")
                with gr.Column():
                    hf_repo = gr.Textbox(label="New repo name", placeholder="reponame", info="If empty, auto-complete", value=HF_REPO, max_lines=1)
            with gr.Row(equal_height=True):
                with gr.Column():
                    hf_token = gr.Textbox(label="Your HF write token", placeholder="hf_...", value="", max_lines=1)
                    gr.Markdown("Your token is available at [hf.co/settings/tokens](https://huggingface.co/settings/tokens).", elem_classes="info")
                    help("hf_write_token_access")
                with gr.Column():
                    civitai_key = gr.Textbox(label="Your Civitai API Key (Optional)", info="If you download model from Civitai...", placeholder="", 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():
                is_upload_sf = gr.Checkbox(label="Upload single safetensors file into new repo", value=False)
                is_private = gr.Checkbox(label="Create private repo", value=True)
                gated = gr.Radio(label="Create gated repo", info="Gated repo must be public", choices=["auto", "manual", "False"], value="False")
            with gr.Row():
                is_overwrite = gr.Checkbox(label="Overwrite repo", value=HF_OW)
                is_pr = gr.Checkbox(label="Create PR", value=HF_PR)
        with gr.Tab("SDXL"):
            with gr.Group():
                sdxl_presets = gr.Radio(label="Presets", choices=list(sdxl_preset_dict.keys()), value=list(sdxl_preset_dict.keys())[0])
                sdxl_mtype = gr.Textbox(value="SDXL", visible=False)
                sdxl_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value=DEFAULT_DTYPE)
                with gr.Accordion("Advanced settings", open=False):
                    with gr.Row():
                        sdxl_vae = gr.Dropdown(label="VAE", choices=sdxl_vaes, value="", allow_custom_value=True)
                        sdxl_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler a")
                        sdxl_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
                    with gr.Column():
                        with gr.Row():
                            sdxl_lora1 = gr.Dropdown(label="LoRA1", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sdxl_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
                        with gr.Row():
                            sdxl_lora2 = gr.Dropdown(label="LoRA2", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sdxl_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
                        with gr.Row():
                            sdxl_lora3 = gr.Dropdown(label="LoRA3", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sdxl_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
                        with gr.Row():
                            sdxl_lora4 = gr.Dropdown(label="LoRA4", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sdxl_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
                        with gr.Row():
                            sdxl_lora5 = gr.Dropdown(label="LoRA5", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sdxl_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
            sdxl_run_button = gr.Button(value="Submit", variant="primary")
        with gr.Tab("SD 1.5"):
            with gr.Group():
                sd15_presets = gr.Radio(label="Presets", choices=list(sd15_preset_dict.keys()), value=list(sd15_preset_dict.keys())[0])
                sd15_mtype = gr.Textbox(value="SD 1.5", visible=False)
                sd15_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value=DEFAULT_DTYPE)
                with gr.Row():
                    sd15_ema = gr.Checkbox(label="Extract EMA", value=True, visible=True)
                    sd15_isize = gr.Radio(label="Image size", choices=["768", "512"], value="768")
                    sd15_sc = gr.Checkbox(label="Safety checker", value=False)
                with gr.Accordion("Advanced settings", open=False):
                    with gr.Row():
                        sd15_vae = gr.Dropdown(label="VAE", choices=sd15_vaes, value="", allow_custom_value=True)
                        sd15_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler")
                        sd15_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
                    with gr.Column():
                        with gr.Row():
                            sd15_lora1 = gr.Dropdown(label="LoRA1", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd15_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
                        with gr.Row():
                            sd15_lora2 = gr.Dropdown(label="LoRA2", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd15_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
                        with gr.Row():
                            sd15_lora3 = gr.Dropdown(label="LoRA3", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd15_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
                        with gr.Row():
                            sd15_lora4 = gr.Dropdown(label="LoRA4", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd15_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
                        with gr.Row():
                            sd15_lora5 = gr.Dropdown(label="LoRA5", choices=sd15_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd15_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
            sd15_run_button = gr.Button(value="Submit", variant="primary")
        with gr.Tab("FLUX.1"):
            with gr.Group():
                flux_presets = gr.Radio(label="Presets", choices=list(flux_preset_dict.keys()), value=list(flux_preset_dict.keys())[0])
                flux_mtype = gr.Textbox(value="FLUX", visible=False)
                flux_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value="bf16")
                flux_base_repo = gr.Dropdown(label="Base repo ID", choices=FLUX_BASE_REPOS, value=FLUX_BASE_REPOS[0], allow_custom_value=True, visible=True)
                with gr.Accordion("Advanced settings", open=False):
                    with gr.Row():
                        flux_vae = gr.Dropdown(label="VAE", choices=flux_vaes, value="", allow_custom_value=True)
                        flux_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=[""], value="", visible=False)
                    with gr.Row():
                        flux_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
                        flux_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True)
                    with gr.Column():
                        with gr.Row():
                            flux_lora1 = gr.Dropdown(label="LoRA1", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            flux_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
                        with gr.Row():
                            flux_lora2 = gr.Dropdown(label="LoRA2", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            flux_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
                        with gr.Row():
                            flux_lora3 = gr.Dropdown(label="LoRA3", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            flux_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
                        with gr.Row():
                            flux_lora4 = gr.Dropdown(label="LoRA4", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            flux_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
                        with gr.Row():
                            flux_lora5 = gr.Dropdown(label="LoRA5", choices=flux_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            flux_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
            flux_run_button = gr.Button(value="Submit", variant="primary")
        with gr.Tab("SD 3.5"):
            with gr.Group():
                sd35_presets = gr.Radio(label="Presets", choices=list(sd35_preset_dict.keys()), value=list(sd35_preset_dict.keys())[0])
                sd35_mtype = gr.Textbox(value="SD 3.5", visible=False)
                sd35_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value="bf16")
                sd35_base_repo = gr.Dropdown(label="Base repo ID", choices=SD35_BASE_REPOS, value=SD35_BASE_REPOS[0], allow_custom_value=True, visible=True)
                with gr.Accordion("Advanced settings", open=False):
                    with gr.Row():
                        sd35_vae = gr.Dropdown(label="VAE", choices=sd35_vaes, value="", allow_custom_value=True)
                        sd35_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=[""], value="", visible=False)
                    with gr.Row():
                        sd35_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
                        sd35_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True)
                    with gr.Column():
                        with gr.Row():
                            sd35_lora1 = gr.Dropdown(label="LoRA1", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd35_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
                        with gr.Row():
                            sd35_lora2 = gr.Dropdown(label="LoRA2", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd35_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
                        with gr.Row():
                            sd35_lora3 = gr.Dropdown(label="LoRA3", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd35_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
                        with gr.Row():
                            sd35_lora4 = gr.Dropdown(label="LoRA4", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd35_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
                        with gr.Row():
                            sd35_lora5 = gr.Dropdown(label="LoRA5", choices=sd35_loras, value="", allow_custom_value=True, min_width=320, scale=2)
                            sd35_lora5s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA5 weight scale")
            sd35_run_button = gr.Button(value="Submit", variant="primary")
        adv_args = gr.Textbox(label="Advanced arguments", value="", visible=False)
        with gr.Group():
            repo_urls = gr.CheckboxGroup(visible=False, choices=[], value=[])
            output_md = gr.Markdown(label="Output", value="<br><br>", elem_classes="result")
            clear_button = gr.Button(value="Clear Output", variant="secondary")
        gr.DuplicateButton(value="Duplicate Space")

    gr.Markdown("This webui was redesigned with ❤ by [theNeofr](https://huggingface.co/theNeofr)")
    gr.on(
        triggers=[sdxl_run_button.click],
        fn=convert_url_to_diffusers_repo,
        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, gated, is_overwrite, is_pr, is_upload_sf, repo_urls,
                sdxl_dtype, sdxl_vae, sdxl_clip, flux_t5, sdxl_scheduler, sd15_ema, sd15_isize, sd15_sc, flux_base_repo, sdxl_mtype,
                sdxl_lora1, sdxl_lora1s, sdxl_lora2, sdxl_lora2s, sdxl_lora3, sdxl_lora3s, sdxl_lora4, sdxl_lora4s, sdxl_lora5, sdxl_lora5s, adv_args],
        outputs=[repo_urls, output_md],
    )
    sdxl_presets.change(
        fn=sdxl_set_presets,
        inputs=[sdxl_presets],
        outputs=[sdxl_dtype, sdxl_vae, sdxl_scheduler, sdxl_lora1, sdxl_lora1s, sdxl_lora2, sdxl_lora2s, sdxl_lora3, sdxl_lora3s,
                 sdxl_lora4, sdxl_lora4s, sdxl_lora5, sdxl_lora5s],
        queue=False,
    )
    gr.on(
        triggers=[sd15_run_button.click],
        fn=convert_url_to_diffusers_repo,
        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, gated, is_overwrite, is_pr, is_upload_sf, repo_urls,
                sd15_dtype, sd15_vae, sd15_clip, flux_t5, sd15_scheduler, sd15_ema, sd15_isize, sd15_sc, flux_base_repo, sd15_mtype,
                sd15_lora1, sd15_lora1s, sd15_lora2, sd15_lora2s, sd15_lora3, sd15_lora3s, sd15_lora4, sd15_lora4s, sd15_lora5, sd15_lora5s, adv_args],
        outputs=[repo_urls, output_md],
    )
    sd15_presets.change(
        fn=sd15_set_presets,
        inputs=[sd15_presets],
        outputs=[sd15_dtype, sd15_vae, sd15_scheduler, sd15_lora1, sd15_lora1s, sd15_lora2, sd15_lora2s, sd15_lora3, sd15_lora3s,
                 sd15_lora4, sd15_lora4s, sd15_lora5, sd15_lora5s, sd15_ema],
        queue=False,
    )
    gr.on(
        triggers=[flux_run_button.click],
        fn=convert_url_to_diffusers_repo,
        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, gated, is_overwrite, is_pr, is_upload_sf, repo_urls,
                flux_dtype, flux_vae, flux_clip, flux_t5, flux_scheduler, sd15_ema, sd15_isize, sd15_sc, flux_base_repo, flux_mtype,
                flux_lora1, flux_lora1s, flux_lora2, flux_lora2s, flux_lora3, flux_lora3s, flux_lora4, flux_lora4s, flux_lora5, flux_lora5s, adv_args],
        outputs=[repo_urls, output_md],
    )
    flux_presets.change(
        fn=flux_set_presets,
        inputs=[flux_presets],
        outputs=[flux_dtype, flux_vae, flux_scheduler, flux_lora1, flux_lora1s, flux_lora2, flux_lora2s, flux_lora3, flux_lora3s,
                 flux_lora4, flux_lora4s, flux_lora5, flux_lora5s, flux_base_repo],
        queue=False,
    )
    gr.on(
        triggers=[sd35_run_button.click],
        fn=convert_url_to_diffusers_repo,
        inputs=[dl_url, hf_user, hf_repo, hf_token, civitai_key, is_private, gated, is_overwrite, is_pr, is_upload_sf, repo_urls,
                sd35_dtype, sd35_vae, sd35_clip, sd35_t5, sd35_scheduler, sd15_ema, sd15_isize, sd15_sc, sd35_base_repo, sd35_mtype,
                sd35_lora1, sd35_lora1s, sd35_lora2, sd35_lora2s, sd35_lora3, sd35_lora3s, sd35_lora4, sd35_lora4s, sd35_lora5, sd35_lora5s, adv_args],
        outputs=[repo_urls, output_md],
    )
    sd35_presets.change(
        fn=sd35_set_presets,
        inputs=[sd35_presets],
        outputs=[sd35_dtype, sd35_vae, sd35_scheduler, sd35_lora1, sd35_lora1s, sd35_lora2, sd35_lora2s, sd35_lora3, sd35_lora3s,
                 sd35_lora4, sd35_lora4s, sd35_lora5, sd35_lora5s, sd35_base_repo],
        queue=False,
    )
    clear_button.click(lambda: ([], "<br><br>"), None, [repo_urls, output_md], queue=False, show_api=False)

demo.queue()
demo.launch(ssr_mode=False)