| 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() |