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import gradio as gr
import torch
import requests
import json
# from diffusers import StableDiffusionLDM3DPipeline
import gradio as gr
import torch
from PIL import Image
import base64
from io import BytesIO
from tempfile import NamedTemporaryFile
from pathlib import Path
from gradio_client import Client
theme = gr.themes.Base(
primary_hue=gr.themes.Color(
c100="#dbeafe", c200="#bfdbfe", c300="#93c5fd", c400="#60a5fa", c50="#eff6ff", c500="#0054ae", c600="#00377c", c700="#00377c", c800="#1e40af", c900="#1e3a8a", c950="#0a0c2b"),
secondary_hue=gr.themes.Color(
c100="#dbeafe", c200="#bfdbfe", c300="#93c5fd", c400="#60a5fa", c50="#eff6ff", c500="#0054ae", c600="#0054ae", c700="#0054ae", c800="#1e40af", c900="#1e3a8a", c950="#1d3660"),
).set(
body_background_fill_dark='*primary_950',
body_text_color_dark='#FFFFFF',
body_text_color='#000000',
border_color_accent='*primary_700',
border_color_accent_dark='*neutral_800',
block_background_fill_dark='*primary_950',
block_border_width='2px',
block_border_width_dark='2px',
button_primary_background_fill_dark='*primary_500',
button_primary_border_color_dark='*primary_500'
)
css='''
@font-face {
font-family: IntelOne;
src: url("file/assets/intelone-bodytext-font-family-regular.ttf");
}
'''
html_title = '''
<table>
<tr style="height:150px">
<td style="border-bottom:0"><img src="file/assets/intel-labs.png" height="100" width="100"></td>
<td style="border-bottom:0; vertical-align:middle">
<p style="font-size:xx-large;font-family:IntelOne, Georgia, sans-serif;color: var(--body-text-color);">
Cognitive AI:
<br>
LDM3D: Latent Diffusion Model for 3D
</p>
</td>
<td style="border-bottom:0;"><img src="file/assets/gaudi.png" width="100" height="100"></td>
<td style="border-bottom:0;"><img src="file/assets/xeon.png" width="100" height="100"></td>
</tr>
</table>
'''
client = Client("http://198.175.88.247:17810/")
def generate(
prompt: str,
negative_prompt: str,
guidance_scale: float = 5.0,
seed: int = 0,
randomize_seed: bool = True,
):
result = client.predict(
prompt, # str in 'Prompt' Textbox component
negative_prompt, # str in 'Negative Prompt' Textbox component
guidance_scale, # int | float (numeric value between 0 and 10) in 'Guidance Scale' Slider component
seed, # int | float (numeric value between 0 and 18446744073709551615) in 'Seed' Slider component
randomize_seed, # bool in 'Randomize Seed' Checkbox component
False, # bool in 'Upscale' Checkbox component
fn_index=1
)
return result
with gr.Blocks(theme=theme, css=css) as demo:
gr.HTML(value=html_title)
gr.Markdown(
"""
[Model card](https://huggingface.co/Intel/ldm3d-pano<br>)
[Diffusers docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/ldm3d_diffusion)
For better results, specify "360 view of" or "panoramic view of" in the prompt
"""
)
with gr.Row():
with gr.Column(scale=1):
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(label="Negative Prompt")
guidance_scale = gr.Slider(
label="Guidance Scale", minimum=0, maximum=10, step=0.1, value=5.0
)
randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
seed = gr.Slider(label="Seed", minimum=0,
maximum=2**64 - 1, step=1)
generated_seed = gr.Number(label="Generated Seed")
markdown = gr.Markdown(label="Output Box")
with gr.Row():
new_btn = gr.Button("New Image")
with gr.Column(scale=2):
html = gr.HTML()
with gr.Row():
rgb = gr.Image(label="RGB Image", type="filepath")
depth = gr.Image(label="Depth Image", type="filepath")
gr.Examples(
examples=[
["360 view of a large bedroom", "", 7.0, 42, False]],
inputs=[prompt, negative_prompt, guidance_scale, seed, randomize_seed],
outputs=[rgb, depth, generated_seed, html],
fn=generate,
cache_examples=False)
new_btn.click(
fn=generate,
inputs=[prompt, negative_prompt, guidance_scale, seed, randomize_seed],
outputs=[rgb, depth, generated_seed, html],
)
demo.launch(
allowed_paths=["assets/", "static/"]
)
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