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import gradio as gr
from backend.lcm_text_to_image import LCMTextToImage
from backend.models.lcmdiffusion_setting import LCMLora, LCMDiffusionSetting
from constants import DEVICE, LCM_DEFAULT_MODEL_OPENVINO
from time import perf_counter
import numpy as np
from cv2 import imencode
import base64
from backend.device import get_device_name
from constants import APP_VERSION
from backend.device import is_openvino_device
lcm_text_to_image = LCMTextToImage()
lcm_lora = LCMLora(
base_model_id="Lykon/dreamshaper-8",
lcm_lora_id="latent-consistency/lcm-lora-sdv1-5",
)
# https://github.com/gradio-app/gradio/issues/2635#issuecomment-1423531319
def encode_pil_to_base64_new(pil_image):
image_arr = np.asarray(pil_image)[:, :, ::-1]
_, byte_data = imencode(".png", image_arr)
base64_data = base64.b64encode(byte_data)
base64_string_opencv = base64_data.decode("utf-8")
return "data:image/png;base64," + base64_string_opencv
# monkey patching encode pil
gr.processing_utils.encode_pil_to_base64 = encode_pil_to_base64_new
def predict(
prompt,
steps,
seed,
):
lcm_diffusion_setting = LCMDiffusionSetting()
lcm_diffusion_setting.openvino_lcm_model_id = "rupeshs/LCM-dreamshaper-v7-openvino"
lcm_diffusion_setting.prompt = prompt
lcm_diffusion_setting.guidance_scale = 1.0
lcm_diffusion_setting.inference_steps = steps
lcm_diffusion_setting.seed = seed
lcm_diffusion_setting.use_seed = True
lcm_diffusion_setting.image_width = 256 if is_openvino_device() else 512
lcm_diffusion_setting.image_height = 256 if is_openvino_device() else 512
lcm_diffusion_setting.use_openvino = True if is_openvino_device() else False
lcm_text_to_image.init(
DEVICE,
lcm_diffusion_setting,
)
start = perf_counter()
images = lcm_text_to_image.generate(lcm_diffusion_setting)
latency = perf_counter() - start
print(f"Latency: {latency:.2f} seconds")
return images[0]
css = """
#container{
margin: 0 auto;
max-width: 40rem;
}
#intro{
max-width: 100%;
text-align: center;
margin: 0 auto;
}
#generate_button {
color: white;
border-color: #007bff;
background: #007bff;
width: 200px;
height: 50px;
}
footer {
visibility: hidden
}
"""
def _get_footer_message() -> str:
version = f"<center><p> {APP_VERSION} "
footer_msg = version + (
' © 2023 <a href="https://github.com/rupeshs">'
" Rupesh Sreeraman</a></p></center>"
)
return footer_msg
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="container"):
use_openvino = "- OpenVINO" if is_openvino_device() else ""
gr.Markdown(
f"""# Realtime FastSD CPU {use_openvino}
**Device : {DEVICE} , {get_device_name()}**
""",
elem_id="intro",
)
with gr.Row():
with gr.Row():
prompt = gr.Textbox(
placeholder="Describe the image you'd like to see",
scale=5,
container=False,
)
generate_btn = gr.Button(
"Generate",
scale=1,
elem_id="generate_button",
)
image = gr.Image(type="filepath")
with gr.Accordion("Advanced options", open=False):
steps = gr.Slider(
label="Steps",
value=4 if is_openvino_device() else 3,
minimum=1,
maximum=6,
step=1,
)
seed = gr.Slider(
randomize=True,
minimum=0,
maximum=999999999,
label="Seed",
step=1,
)
gr.HTML(_get_footer_message())
inputs = [prompt, steps, seed]
prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False)
generate_btn.click(
fn=predict, inputs=inputs, outputs=image, show_progress=False
)
steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
def start_realtime_text_to_image(share=False):
demo.queue()
demo.launch(share=share)
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