| 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", |
| ) |
|
|
|
|
| |
| 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 |
|
|
|
|
| |
| 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) |
|
|
