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""" |
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app.py |
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An interactive demo for text-guided panorama generation. |
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""" |
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import os |
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from os.path import join |
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from PIL import Image |
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import torch |
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import gradio as gr |
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from syncdiffusion.syncdiffusion_model import SyncDiffusion |
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from syncdiffusion.utils import seed_everything |
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device = torch.device("cuda") |
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syncdiffusion = SyncDiffusion(device, sd_version="2.0") |
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def run_inference( |
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prompt: str, |
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width: int = 2048, |
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sync_weight: float = 20.0, |
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sync_thres: int = 5, |
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seed: int = 0 |
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): |
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seed_everything(seed) |
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img = syncdiffusion.sample_syncdiffusion( |
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prompts = prompt, |
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negative_prompts = "", |
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height = 512, |
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width = width, |
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num_inference_steps = 50, |
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guidance_scale = 7.5, |
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sync_weight = sync_weight, |
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sync_decay_rate = 0.99, |
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sync_freq = 1, |
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sync_thres = sync_thres, |
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stride = 16 |
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) |
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return [img] |
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if __name__=="__main__": |
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title = "SyncDiffusion: Text-Guided Panorama Generation" |
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description_text = ''' |
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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>. |
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Please refer to our <a href="https://syncdiffusion.github.io/">project page</a> for details. |
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(Note: Generation takes about 100 seconds for the default setting below, with the current A10G GPU.) |
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''' |
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with gr.Blocks(title=title) as demo: |
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gr.Markdown(description_text) |
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with gr.Row(): |
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with gr.Column(): |
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run_button = gr.Button(label="Generate") |
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prompt = gr.Textbox(label="Text Prompt", value='a cinematic view of a castle in the sunset') |
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width = gr.Slider(label="Width", minimum=512, maximum=3072, value=2048, step=128) |
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sync_weight = gr.Slider(label="Sync Weight", minimum=0.0, maximum=30.0, value=20.0, step=5.0) |
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sync_thres = gr.Slider(label="Sync Threshold (If N, apply SyncDiffusion for the first N steps)", minimum=0, maximum=15, value=5, step=1) |
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seed = gr.Number(label="Seed", value=0) |
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with gr.Column(): |
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result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') |
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examples = gr.Examples( |
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examples=[ |
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['a cinematic view of a castle in the sunset', 2048, 20.0, 5, 1], |
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['natural landscape in anime style illustration', 2048, 20.0, 5, 2], |
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['a photo of a lake under the northern lights', 2048, 20.0, 5, 6] |
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], |
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inputs=[prompt, width, sync_weight, sync_thres, seed], |
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) |
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with gr.Row(): |
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gr.Image(Image.open(join("assets", "result_castle_seed_1.png")), label="Sample output 1") |
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with gr.Row(): |
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gr.Textbox(label="Prompt", type="text", value="a cinematic view of a castle in the sunset") |
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with gr.Row(): |
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gr.Image(Image.open(join("assets", "result_natural_seed_2.png")), label="Sample output 2") |
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with gr.Row(): |
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gr.Textbox(label="Prompt", type="text", value="natural landscape in anime style illustration") |
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with gr.Row(): |
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gr.Image(Image.open(join("assets", "result_northern_seed_6.png")), label="Sample output 3") |
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with gr.Row(): |
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gr.Textbox(label="Prompt", type="text", value="a photo of a lake under the northern lights") |
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ips = [prompt, width, sync_weight, sync_thres, seed] |
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run_button.click(fn=run_inference, inputs=ips, outputs=[result_gallery]) |
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demo.queue(max_size=30) |
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demo.launch() |
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