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import gradio as gr | |
import numpy as np | |
import random | |
from diffusers import DiffusionPipeline | |
from optimum.intel.openvino import OVStableDiffusionPipeline | |
import torch | |
model_id = "helenai/Linaqruf-anything-v3.0-ov" | |
negative_prompt = "score_6,score_5,score_4,source_furry,pathway,walkway,face mask,heterochromia,tattoos,muscular,deformed iris,deformed pupils,long body,long neck,text,error,print,signature,logo,watermark,deformed,distorted,disfigured,bad anatomy,wrong anatomy,ugly,disgusting,cropped,crooked teeth,multiple views,bad proportions,gross proportions,cloned face,worst quality,low quality,normal quality,bad quality,lowres,poorly drawn,semi-realistic,3d,render,cg,cgi,imperfect,partial,unfinished,incomplete,monochrome,grayscale,sepia,fat,wrinkle,fat leg,fat ass,loli,blurry,hazy,sagging breasts,loli,child,longbody,lowres,bad anatomy,bad hands,missing fingers,extra digit,fewer digits,worst quality,low quality,normal quality,watermark,artist name,signature," | |
pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile=False) | |
pipe.reshape( batch_size=1, height=256, width=256, num_images_per_prompt=1) | |
pipe.compile() | |
def infer(prompt, negative_prompt): | |
image = pipe( | |
prompt = prompt, | |
negative_prompt = negative_prompt, | |
width = 256, | |
height = 256, | |
).images[0] | |
return image | |
examples = [ | |
"A cute kitten, Japanese cartoon style.", | |
"A sweet family, dad stands next to mom, mom holds baby girl.", | |
"A delicious ceviche cheesecake slice", | |
] | |
css=""" | |
#col-container { | |
margin: 0 auto; | |
max-width: 520px; | |
} | |
""" | |
power_device = "CPU" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown(f""" | |
# Text-to-Image Gradio Template | |
Currently running on {power_device}. | |
""") | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
container=False, | |
) | |
run_button = gr.Button("Run", scale=0) | |
result = gr.Image(label="Result", show_label=False) | |
gr.Examples( | |
examples = examples, | |
inputs = [prompt] | |
) | |
run_button.click( | |
fn = infer, | |
inputs = [prompt, negative_prompt], | |
outputs = [result] | |
) | |
demo.queue().launch() |