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