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Update app.py
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import os
import random
import numpy as np
import gradio as gr
from utils.t2i import t2i_gen
MAX_SEED = np.iinfo(np.int32).max
MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512"))
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
with gr.Blocks(
title="🪄 LayerDiffuse - Flux version",
theme="CultriX/gradio-theme"
) as demo:
gr.Markdown(
"""
# 🪄 LayerDiffuse - Flux version
A Flux version implementation of LayerDiffuse ([LayerDiffuse](https://github.com/lllyasviel/LayerDiffuse))
**Feel free to open a PR and contribute to this demo to help improve it!**
"""
)
prompt = gr.Text(
label="Prompt",
info="Your prompt here",
placeholder="E.g: glass bottle, high quality"
)
negative_prompt = gr.Text(
info="Your negative prompt here",
label="Negative Prompt",
placeholder="(Optional)"
)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=MIN_IMAGE_SIZE,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=MIN_IMAGE_SIZE,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=1,
maximum=20,
step=0.1,
value=3.5,
)
num_inference_steps = gr.Slider(
label="Steps",
minimum=10,
maximum=100,
step=1,
value=50,
)
t2i_gen_bttn = gr.Button("Generate")
t2i_result = gr.Image(
label="Result",
show_label=False,
format="png"
)
gr.on(
triggers=[
t2i_gen_bttn.click
],
fn=lambda: gr.update(interactive=False, value="Generating..."),
outputs=t2i_gen_bttn,
api_name=False
).then(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False
).then(
fn=t2i_gen,
inputs=[
prompt,
negative_prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps
],
outputs=t2i_result
).then(
fn=lambda: gr.update(interactive=True, value="Generate"),
outputs=t2i_gen_bttn,
api_name=False
)
if __name__ == "__main__":
demo.queue(max_size=20).launch(show_error=True)