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import os
import gradio as gr
import json
from gradio_client import Client
with open('loras.json', 'r') as f:
loras = json.load(f)
def infer (prompt, style_prompt, inf_steps, guidance_scale, width, height, seed, lora_weight, progress=gr.Progress(track_tqdm=True)):
custom_model="lichorosario/dott_remastered_style_lora_sdxl"
weight_name="dott_style.safetensors"
client = Client("fffiloni/sd-xl-custom-model")
result = client.predict(
custom_model=custom_model,
api_name="/load_model"
)
client = Client("fffiloni/sd-xl-custom-model")
prompt = "dott style. "+prompt+". "+style_prompt
result = client.predict(
custom_model=custom_model,
weight_name=weight_name,
prompt=prompt,
inf_steps=inf_steps,
guidance_scale=guidance_scale,
width=width,
height=height,
seed=seed,
lora_weight=lora_weight,
api_name="/infer"
)
new_result = result + (prompt, )
return new_result
css="""
#col-container{
margin: 0 auto;
max-width: 720px;
text-align: left;
}
div#warning-duplicate {
background-color: #ebf5ff;
padding: 0 16px 16px;
margin: 20px 0;
}
div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
color: #0f4592!important;
}
div#warning-duplicate strong {
color: #0f4592;
}
p.actions {
display: flex;
align-items: center;
margin: 20px 0;
}
div#warning-duplicate .actions a {
display: inline-block;
margin-right: 10px;
}
button#load_model_btn{
height: 46px;
}
#status_info{
font-size: 0.9em;
}
.custom-color {
color: #030303 !important;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown("# lichorosario LoRA Portfolio")
gr.Markdown(
"### This is my portfolio.\n"
"**Note**: Generation quality may vary. For best results, adjust the parameters.\n"
"Special thanks to [@artificialguybr](https://huggingface.co/artificialguybr) and [@fffiloni](https://huggingface.co/fffiloni)."
)
with gr.Row():
with gr.Column(scale=2):
prompt_in = gr.Textbox(
label="Your Prompt",
info = "Dont' forget to include your trigger word if necessary"
)
style_prompt_in = gr.Textbox(
label="Your Style Prompt"
)
used_prompt = gr.Textbox(
label="Used prompt"
)
with gr.Column(scale=1):
gallery = gr.Gallery(
[(item["image"], item["title"]) for item in loras],
label="LoRA Gallery",
allow_preview=False,
columns=2
)
with gr.Column(elem_id="col-container"):
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
inf_steps = gr.Slider(
label="Inference steps",
minimum=12,
maximum=50,
step=1,
value=25
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=50.0,
step=0.1,
value=7.5
)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=2048,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=2048,
step=32,
value=1024,
)
with gr.Row():
seed = gr.Slider(
label="Seed",
info = "-1 denotes a random seed",
minimum=-1,
maximum=423538377342,
step=1,
value=-1
)
last_used_seed = gr.Number(
label = "Last used seed",
info = "the seed used in the last generation",
)
lora_weight = gr.Slider(
label="LoRa weigth",
minimum=0.0,
maximum=1.0,
step=0.01,
value=1.0
)
submit_btn = gr.Button("Submit")
image_out = gr.Image(label="Image output")
submit_btn.click(
fn = infer,
inputs = [prompt_in, style_prompt_in, inf_steps, guidance_scale, width, height, seed, lora_weight],
outputs = [image_out, last_used_seed, used_prompt]
)
demo.launch()
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