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import gradio as gr
from optimum.intel.openvino.modeling_diffusion import OVPipelineForText2Image
import torch
model_id = "AIFunOver/stable-diffusion-3.5-large-turbo-openvino-8bit"
HIGH=1024
WIDTH=512
batch_size = -1
pipe = OVPipelineForText2Image.from_pretrained(
model_id,
compile = False,
ov_config = {"CACHE_DIR":""},
torch_dtype=torch.uint8,
safety_checker=None,
use_safetensors=False,
)
print(pipe.scheduler.compatibles)
pipe.reshape( batch_size=-1, height=HIGH, width=WIDTH, num_images_per_prompt=1)
pipe.compile()
prompt=""
negative_prompt=f"EasyNegative, cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly,"
def infer(prompt,negative_prompt):
image = pipe(
prompt = f",hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic, ",
negative_prompt = negative_prompt,
width = WIDTH,
height = HIGH,
guidance_scale=1.0,
num_inference_steps=6,
num_images_per_prompt=1,
).images[0]
return image
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"""
# {model_id.split('/')[1]} {WIDTH}x{HIGH}
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)
run_button.click(
fn = infer,
inputs = [prompt],
outputs = [result]
)
demo.queue().launch() |