Upload hf_demo.py

frontend/webui/hf_demo.py

@@ -0,0 +1,161 @@
+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
+import PIL
+from backend.models.lcmdiffusion_setting import DiffusionTask
+from pprint import pprint
+
+lcm_text_to_image = LCMTextToImage()
+lcm_lora = LCMLora(
+    base_model_id="Lykon/dreamshaper-7",
+    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,
+    use_seed,
+):
+    print(f"prompt - {prompt}")
+    lcm_diffusion_setting = LCMDiffusionSetting()
+    lcm_diffusion_setting.diffusion_task = DiffusionTask.text_to_image.value
+    lcm_diffusion_setting.openvino_lcm_model_id = "rupeshs/sd-turbo-openvino"
+    lcm_diffusion_setting.use_lcm_lora = False
+    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 = use_seed
+    lcm_diffusion_setting.use_safety_checker = True
+    lcm_diffusion_setting.use_tiny_auto_encoder = True
+    # lcm_diffusion_setting.image_width = 320 if is_openvino_device() else 512
+    # lcm_diffusion_setting.image_height = 320 if is_openvino_device() else 512
+    lcm_diffusion_setting.image_width = 512
+    lcm_diffusion_setting.image_height = 512
+    lcm_diffusion_setting.use_openvino = True
+    lcm_diffusion_setting.use_tiny_auto_encoder = True
+    pprint(lcm_diffusion_setting.model_dump())
+    lcm_text_to_image.init(lcm_diffusion_setting=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]  # .resize([512, 512], PIL.Image.ANTIALIAS)
+
+
+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>"
+    )
+    warning_msg = "<p><b> Please note that this is a minimal demo app.</b> </p><br>"
+    return warning_msg + footer_msg
+
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="container"):
+        use_openvino = "" if is_openvino_device() else ""
+        gr.Markdown(
+            f"""# FastSD CPU demo {use_openvino}
+               **Device : {DEVICE.upper()} , {get_device_name()}**
+            """,
+            elem_id="intro",
+        )
+        gr.HTML(
+            f"""
+            <p id="project-links" align="center">
+                <a href='https://github.com/rupeshs/fastsdcpu'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
+            </p> 
+                    """
+        )
+
+        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=1,
+                minimum=1,
+                maximum=3,
+                step=1,
+            )
+            seed = gr.Slider(
+                randomize=True,
+                minimum=0,
+                maximum=999999999,
+                label="Seed",
+                step=1,
+            )
+            seed_checkbox = gr.Checkbox(
+                label="Use seed",
+                value=False,
+                interactive=True,
+            )
+        gr.HTML(_get_footer_message())
+
+        inputs = [prompt, steps, seed, seed_checkbox]
+        generate_btn.click(fn=predict, inputs=inputs, outputs=image)
+
+
+def start_demo_text_to_image(share=False):
+    demo.queue()
+    demo.launch(share=share)