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app.py
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import spaces
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import gradio as gr
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import torch
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from huggingface_hub import snapshot_download
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from txt2panoimg import Text2360PanoramaImagePipeline
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from PIL import Image
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from gradio import themes
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# Download the model
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model_path = snapshot_download("archerfmy0831/sd-t2i-360panoimage")
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output = txt2panoimg(input_data)
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return output, output
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<div style="display: flex; align-items: center; justify-content: space-between;">
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<img src="https://ant.dpu.ac.th/wp-content/uploads/2024/04/dpulogo.png" width="150" height="50">
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<div>
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<h1 align="center">SD-T2I-360PanoImage</h1>
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<p align="center">AI FOR VR 360° Panorama Image Generator</p>
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</div>
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<div style="font-size:12px; text-align: right;">
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<a href="https://github.com/ArcherFMY/SD-T2I-360PanoImage/" target="_blank">[Github]</a>
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<a href="https://huggingface.co/archerfmy0831/sd-t2i-360panoimage" target="_blank">[Models]</a>
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</div>
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</div>
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""")
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# Credits and Information
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gr.Markdown(
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"""
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This study was conducted in the College of Creative Design and Entertainment Technology laboratory at Dhurakij Pundit University by Asst. Prof. Banyapon Poolsawas. The project builds upon the SD-T2I-360PanoImage repository (https://github.com/ArcherFMY/SD-T2I-360PanoImage/) for Text-to-360Panorama Sample and Build it with aframe VR.
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"""
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)
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with gr.Row():
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with gr.Column():
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t2p_input = gr.Textbox(label="Enter your prompt", lines=3)
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t2p_upscale = gr.Checkbox(label="Upscale (takes about 60 seconds 6144x3072 resolution)")
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t2p_generate = gr.Button("Generate Panorama")
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# A-Frame Preview (HTML) and Generated Image
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with gr.Row():
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t2p_image_output = gr.Image(label="Generated
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t2p_iframe = gr.HTML(label="A-Frame Preview")
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#
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update_trigger = gr.State(value=0)
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# Modified Generate Function
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def generate_with_update(prompt, upscale, trigger):
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output, image = text_to_pano(prompt, upscale)
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# A-Frame Preview Generation
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iframe_html = f"""
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<html>
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<head>
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<script src="https://aframe.io/releases/1.6.0/aframe.min.js"></script>
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</head>
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<body>
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<a-scene>
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<a-sky src="{image}" rotation="0 -130 0"></a-sky>
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<a-text font="kelsonsans" value="Create from AI" width="6" position="-2.5 0.25 -1.5" rotation="0 15 0"></a-text>
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</a-scene>
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</body>
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</html>
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"""
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return image, iframe_html, trigger + 1 # Return the image path instead of Pannellum output
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t2p_generate.click(
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generate_with_update,
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inputs=[t2p_input, t2p_upscale, update_trigger],
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outputs=[
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)
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demo.launch()
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import spaces
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import gradio as gr
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from gradio_pannellum import Pannellum
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import torch
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from huggingface_hub import snapshot_download
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from txt2panoimg import Text2360PanoramaImagePipeline
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from PIL import Image
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# Download the model
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model_path = snapshot_download("archerfmy0831/sd-t2i-360panoimage")
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output = txt2panoimg(input_data)
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return output, output
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title = """<h1 align="center">SD-T2I-360PanoImage</h1>
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<p align="center">360° Panorama Image Generation</p>
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<p><center>
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<a href="https://github.com/ArcherFMY/SD-T2I-360PanoImage/" target="_blank">[Github]</a>
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<a href="https://huggingface.co/archerfmy0831/sd-t2i-360panoimage" target="_blank">[Models]</a>
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</center></p>
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"""
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with gr.Blocks(theme='bethecloud/storj_theme') as demo:
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gr.HTML(title)
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with gr.Row():
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with gr.Column():
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t2p_input = gr.Textbox(label="Enter your prompt", lines=3)
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t2p_upscale = gr.Checkbox(label="Upscale (takes about 60 seconds 6144x3072 resolution)")
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t2p_generate = gr.Button("Generate Panorama")
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with gr.Column(variant="default"):
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t2p_output = Pannellum(show_label=False, interactive=True)
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with gr.Row():
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t2p_image_output = gr.Image(label="Generated Image")
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# Add a hidden component to store a random value
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update_trigger = gr.State(value=0)
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def generate_with_update(prompt, upscale, trigger):
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output, image = text_to_pano(prompt, upscale)
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return output, image, trigger + 1
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t2p_generate.click(
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generate_with_update,
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inputs=[t2p_input, t2p_upscale, update_trigger],
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outputs=[t2p_output, t2p_image_output, update_trigger]
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)
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demo.launch()
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