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"""
app.py
An interactive demo for text-guided panorama generation.
"""
import os
from os.path import join
import torch
import gradio as gr
from syncdiffusion.syncdiffusion_model import SyncDiffusion
from syncdiffusion.utils import seed_everything
# set device
device = torch.device("cuda")
# load SyncDiffusion model
syncdiffusion = SyncDiffusion(device, sd_version="2.0")
def run_inference(
prompt: str,
width: int = 2048,
sync_weight: float = 20.0,
sync_thres: int = 5,
seed: int = 0
):
# set random seed
seed_everything(seed)
img = syncdiffusion.sample_syncdiffusion(
prompts = prompt,
negative_prompts = "",
height = 512,
width = width,
num_inference_steps = 50,
guidance_scale = 7.5,
sync_weight = sync_weight,
sync_decay_rate = 0.99,
sync_freq = 1,
sync_thres = sync_thres,
stride = 16
)
return [img]
if __name__=="__main__":
title = "SyncDiffusion: Text-Guided Panorama Generation"
description_text = '''
This demo features text-guided panorama generation from our work <a href="https://arxiv.org/abs/2306.05178">SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions, NeurIPS 2023</a>.
Please refer to our <a href="https://syncdiffusion.github.io/">project page</a> for details.
'''
# create UI
with gr.Blocks(title=title) as demo:
# description of demo
gr.Markdown(description_text)
# inputs
with gr.Row():
with gr.Column():
run_button = gr.Button(label="Generate")
prompt = gr.Textbox(label="Text Prompt", value='a cinematic view of a castle in the sunset')
width = gr.Slider(label="Width", minimum=512, maximum=3072, value=2048, step=128)
sync_weight = gr.Slider(label="Sync Weight", minimum=0.0, maximum=30.0, value=20.0, step=5.0)
sync_thres = gr.Slider(label="Sync Threshold (If N, apply SyncDiffusion for the first N steps)", minimum=0, maximum=15, value=5, step=1)
seed = gr.Number(label="Seed", value=0)
with gr.Column():
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
# display examples
examples = gr.Examples(
examples=[
['a cinematic view of a castle in the sunset', 2048, 20.0, 5, 1],
['natural landscape in anime style illustration', 2048, 20.0, 5, 2],
['a photo of a lake under the northern lights', 2048, 20.0, 5, 6]
],
inputs=[prompt, width, sync_weight, sync_thres, seed],
outputs=[
join(os.path.dirname(__file__), "assets", "result_castle_seed_1.png"),
join(os.path.dirname(__file__), "assets", "result_natural_seed_2.png"),
join(os.path.dirname(__file__), "assets", "result_northern_seed_6.png"),
]
)
ips = [prompt, width, sync_weight, sync_thres, seed]
run_button.click(fn=run_inference, inputs=ips, outputs=[result_gallery])
demo.queue(max_size=30)
demo.launch()
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