# https://huggingface.co/yuntian-deng/latex2im_ss_finetunegptneo import gradio as gr import numpy as np import requests import base64 import os API_ENDPOINT = os.getenv('API_ENDPOINT') API_KEY = os.getenv('API_KEY') title = "

Markup-to-Image Diffusion Models with Scheduled Sampling

" authors = "
Yuntian Deng, Noriyuki Kojima, Alexander M. Rush
" info = '
Paper Code
' with gr.Blocks() as demo: gr.Markdown(title) gr.Markdown(authors) gr.Markdown(info) with gr.Row(): with gr.Column(scale=2): textbox = gr.Textbox(label=r'Type LaTeX formula below and click "Generate"', lines=1, max_lines=1, placeholder='Type LaTeX formula here and click "Generate"', value=r'\sum_{t=1}^T\E_{y_t \sim {\tilde P(y_t| y_0)}} \left\| \frac{y_t - \sqrt{\bar{\alpha}_t}y_0}{\sqrt{1-\bar{\alpha}_t}} - \epsilon_\theta(y_t, t)\right\|^2.') submit_btn = gr.Button("Generate", elem_id="btn") with gr.Column(scale=3): slider = gr.Slider(0, 1000, value=0, label='step (out of 1000)') image = gr.Image(label="Rendered Image", show_label=False, elem_id="image") inputs = [textbox] outputs = [slider, image, submit_btn] def infer(formula): data = {'formula': formula, 'api_key': API_KEY} try: with requests.post(url=API_ENDPOINT, data=data, timeout=600, stream=True) as r: i = 0 for line in r.iter_lines(): response = line.decode('ascii').strip() r = base64.decodebytes(response.encode('ascii')) q = np.frombuffer(r, dtype=np.float32).reshape((64, 320, 3)) i += 1 yield i, q, submit_btn.update(visible=False) yield i, q, submit_btn.update(visible=True) except Exception as e: yield 1000, 255*np.ones((64, 320, 3)), submit_btn.update(visible=True) submit_btn.click(fn=infer, inputs=inputs, outputs=outputs) demo.queue(concurrency_count=20, max_size=200).launch(enable_queue=True)