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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)
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; }
"""
with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", 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="", max_lines=1)
with gr.Group():
with gr.Row():
hf_user = gr.Textbox(label="Your HF user ID", placeholder="username", value="", max_lines=1)
hf_repo = gr.Textbox(label="New repo name", placeholder="reponame", info="If empty, auto-complete", value="", 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")
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)
is_overwrite = gr.Checkbox(label="Overwrite repo", value=False)
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)
with gr.Row():
sdxl_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value=DEFAULT_DTYPE)
sdxl_ema = gr.Checkbox(label="Extract EMA", info="For SD 1.5", value=True, visible=False)
sdxl_base_repo = gr.Dropdown(label="Base repo ID", choices=FLUX_BASE_REPOS, value=FLUX_BASE_REPOS[0], allow_custom_value=True, visible=False)
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_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
sdxl_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True, visible=False)
sdxl_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler a")
with gr.Row():
with gr.Column():
sdxl_lora1 = gr.Dropdown(label="LoRA1", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
sdxl_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
with gr.Column():
sdxl_lora2 = gr.Dropdown(label="LoRA2", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
sdxl_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
with gr.Column():
sdxl_lora3 = gr.Dropdown(label="LoRA3", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
sdxl_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
with gr.Column():
sdxl_lora4 = gr.Dropdown(label="LoRA4", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
sdxl_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
with gr.Column():
sdxl_lora5 = gr.Dropdown(label="LoRA5", choices=sdxl_loras, value="", allow_custom_value=True, min_width=320)
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)
with gr.Row():
sd15_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value=DEFAULT_DTYPE)
sd15_ema = gr.Checkbox(label="Extract EMA", info="For SD 1.5", value=True, visible=True)
sd15_base_repo = gr.Dropdown(label="Base repo ID", choices=FLUX_BASE_REPOS, value=FLUX_BASE_REPOS[0], allow_custom_value=True, visible=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_clip = gr.Dropdown(label="CLIP", choices=clips, value="", allow_custom_value=True)
sd15_t5 = gr.Dropdown(label="T5", choices=t5s, value="", allow_custom_value=True, visible=False)
sd15_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=schedulers, value="Euler")
with gr.Row():
with gr.Column():
sd15_lora1 = gr.Dropdown(label="LoRA1", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
sd15_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
with gr.Column():
sd15_lora2 = gr.Dropdown(label="LoRA2", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
sd15_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
with gr.Column():
sd15_lora3 = gr.Dropdown(label="LoRA3", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
sd15_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
with gr.Column():
sd15_lora4 = gr.Dropdown(label="LoRA4", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
sd15_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
with gr.Column():
sd15_lora5 = gr.Dropdown(label="LoRA5", choices=sd15_loras, value="", allow_custom_value=True, min_width=320)
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)
with gr.Row():
flux_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value="bf16")
flux_ema = gr.Checkbox(label="Extract EMA", info="For SD 1.5", value=True, visible=False)
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_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)
flux_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=[""], value="", visible=False)
with gr.Row():
with gr.Column():
flux_lora1 = gr.Dropdown(label="LoRA1", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
flux_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
with gr.Column():
flux_lora2 = gr.Dropdown(label="LoRA2", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
flux_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
with gr.Column():
flux_lora3 = gr.Dropdown(label="LoRA3", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
flux_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
with gr.Column():
flux_lora4 = gr.Dropdown(label="LoRA4", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
flux_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
with gr.Column():
flux_lora5 = gr.Dropdown(label="LoRA5", choices=flux_loras, value="", allow_custom_value=True, min_width=320)
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)
with gr.Row():
sd35_dtype = gr.Radio(label="Output data type", choices=get_dtypes(), value="bf16")
sd35_ema = gr.Checkbox(label="Extract EMA", info="For SD 1.5", value=True, visible=False)
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_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)
sd35_scheduler = gr.Dropdown(label="Scheduler (Sampler)", choices=[""], value="", visible=False)
with gr.Row():
with gr.Column():
sd35_lora1 = gr.Dropdown(label="LoRA1", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
sd35_lora1s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA1 weight scale")
with gr.Column():
sd35_lora2 = gr.Dropdown(label="LoRA2", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
sd35_lora2s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA2 weight scale")
with gr.Column():
sd35_lora3 = gr.Dropdown(label="LoRA3", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
sd35_lora3s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA3 weight scale")
with gr.Column():
sd35_lora4 = gr.Dropdown(label="LoRA4", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
sd35_lora4s = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA4 weight scale")
with gr.Column():
sd35_lora5 = gr.Dropdown(label="LoRA5", choices=sd35_loras, value="", allow_custom_value=True, min_width=320)
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")
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.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, is_overwrite, is_upload_sf, repo_urls,
sdxl_dtype, sdxl_vae, sdxl_clip, sdxl_t5, sdxl_scheduler, sdxl_ema, sdxl_base_repo, sdxl_mtype,
sdxl_lora1, sdxl_lora1s, sdxl_lora2, sdxl_lora2s, sdxl_lora3, sdxl_lora3s, sdxl_lora4, sdxl_lora4s, sdxl_lora5, sdxl_lora5s],
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, is_overwrite, is_upload_sf, repo_urls,
sd15_dtype, sd15_vae, sd15_clip, sd15_t5, sd15_scheduler, sd15_ema, sd15_base_repo, sd15_mtype,
sd15_lora1, sd15_lora1s, sd15_lora2, sd15_lora2s, sd15_lora3, sd15_lora3s, sd15_lora4, sd15_lora4s, sd15_lora5, sd15_lora5s],
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, is_overwrite, is_upload_sf, repo_urls,
flux_dtype, flux_vae, flux_clip, flux_t5, flux_scheduler, flux_ema, 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],
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, is_overwrite, is_upload_sf, repo_urls,
sd35_dtype, sd35_vae, sd35_clip, sd35_t5, sd35_scheduler, sd35_ema, 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],
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()
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