import spaces import gradio as gr from gradio_pannellum import Pannellum import torch from huggingface_hub import snapshot_download from txt2panoimg import Text2360PanoramaImagePipeline from PIL import Image # Download the model model_path = snapshot_download("archerfmy0831/sd-t2i-360panoimage") # Initialize pipelines txt2panoimg = Text2360PanoramaImagePipeline(model_path, torch_dtype=torch.float16) @spaces.GPU(duration=200) def text_to_pano(prompt, upscale): input_data = {'prompt': prompt, 'upscale': upscale, 'refinement': False} output = txt2panoimg(input_data) return output, output title = """

SD-T2I-360PanoImage

360° Panorama Image Generation

[Github] [Models]

""" with gr.Blocks(theme='bethecloud/storj_theme') as demo: gr.HTML(title) with gr.Row(): with gr.Column(): t2p_input = gr.Textbox(label="Enter your prompt", lines=3) t2p_upscale = gr.Checkbox(label="Upscale (takes about 60 seconds 6144x3072 resolution)") t2p_generate = gr.Button("Generate Panorama") with gr.Column(variant="default"): t2p_output = Pannellum(show_label=False, interactive=True) with gr.Row(): t2p_image_output = gr.Image(label="Generated Image") # Add a hidden component to store a random value update_trigger = gr.State(value=0) def generate_with_update(prompt, upscale, trigger): output, image = text_to_pano(prompt, upscale) return output, image, trigger + 1 t2p_generate.click( generate_with_update, inputs=[t2p_input, t2p_upscale, update_trigger], outputs=[t2p_output, t2p_image_output, update_trigger] ) demo.launch()