Update app.py
Browse files
app.py
CHANGED
@@ -1,6 +1,10 @@
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#!/usr/bin/env python
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from __future__ import annotations
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import os
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import random
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import time
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@@ -55,6 +59,21 @@ pipe.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{tae
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pipe.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
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pipe.compile()
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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@@ -73,6 +92,7 @@ def save_images(image_array, profile: gr.OAuthProfile | None, metadata: dict):
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def generate(
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prompt: str,
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seed: int = 0,
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guidance_scale: float = 8.0,
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num_inference_steps: int = 4,
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@@ -88,6 +108,7 @@ def generate(
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seed = randomize_seed_fn(seed, randomize_seed)
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np.random.seed(seed)
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start_time = time.time()
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result = pipe(
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prompt=prompt,
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width=width,
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@@ -99,7 +120,7 @@ def generate(
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).images
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paths = save_images(result, profile, metadata={"prompt": prompt, "seed": seed, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
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print(time.time() - start_time)
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return paths, seed
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examples = [
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"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
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@@ -115,6 +136,7 @@ with gr.Blocks(css="style.css") as demo:
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elem_id="duplicate-button",
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
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)
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with gr.Group():
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with gr.Row():
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prompt = gr.Text(
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@@ -153,6 +175,15 @@ with gr.Blocks(css="style.css") as demo:
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step=1,
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value=4,
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)
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gr.Examples(
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examples=examples,
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@@ -171,15 +202,17 @@ with gr.Blocks(css="style.css") as demo:
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inputs=[
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prompt,
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seed,
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guidance_scale,
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num_inference_steps,
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randomize_seed
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],
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outputs=[result, seed],
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api_name="run",
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)
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if __name__ == "__main__":
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demo.queue(api_open=False)
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# demo.queue(max_size=20).launch()
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demo.launch()
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#!/usr/bin/env python
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from __future__ import annotations
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import requests
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import re
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import threading
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import os
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import random
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import time
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pipe.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
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pipe.compile()
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# Personal Thing-----------------------------------
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api_url = None
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def make_api_request():
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global api_url
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response = requests.get("https://genielamp-image8.hf.space/")
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api_url = response.text
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match = re.search(r'"root"\s*:\s*"([^"]+)"', response.text)
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api_url = match.group(1) + "/file="
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print(api_url)
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def delayed_api_request():
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threading.Timer(10, make_api_request).start()
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#------------------------------------------------------
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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def generate(
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prompt: str,
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url: str,
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seed: int = 0,
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guidance_scale: float = 8.0,
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num_inference_steps: int = 4,
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seed = randomize_seed_fn(seed, randomize_seed)
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np.random.seed(seed)
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start_time = time.time()
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url = api_url
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result = pipe(
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prompt=prompt,
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width=width,
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).images
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paths = save_images(result, profile, metadata={"prompt": prompt, "seed": seed, "width": width, "height": height, "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps})
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print(time.time() - start_time)
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return paths, seed, url
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examples = [
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"portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
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elem_id="duplicate-button",
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
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)
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with gr.Group():
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with gr.Row():
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prompt = gr.Text(
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step=1,
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value=4,
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)
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url = gr.Text(
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label="url",
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value="Null",
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show_label=False,
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placeholder="Null",
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max_lines=1,
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container=False,
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interactive=False,
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)
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gr.Examples(
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examples=examples,
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inputs=[
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prompt,
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seed,
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url,
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guidance_scale,
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num_inference_steps,
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randomize_seed
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],
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outputs=[result, seed, url],
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api_name="run",
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)
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if __name__ == "__main__":
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demo.queue(api_open=False)
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delayed_api_request()
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# demo.queue(max_size=20).launch()
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demo.launch()
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