Spaces:
Sleeping
Sleeping
File size: 4,367 Bytes
639c25d 2881ba6 639c25d 2881ba6 639c25d 2149360 639c25d 2149360 639c25d 2881ba6 2149360 639c25d 2881ba6 639c25d 2149360 639c25d 2149360 639c25d 2881ba6 2149360 2881ba6 639c25d 2149360 639c25d 2149360 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
#!/usr/bin/env python
from __future__ import annotations
import os
import random
import gradio as gr
import numpy as np
import torch
from model import Model
DESCRIPTION = "# [UniDiffuser](https://github.com/thu-ml/unidiffuser)"
SPACE_ID = os.getenv("SPACE_ID")
if SPACE_ID is not None:
DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶</p>"
model = Model()
MAX_SEED = np.iinfo(np.int32).max
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
def create_demo(mode_name: str) -> gr.Blocks:
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
mode = gr.Dropdown(
label="Mode",
choices=[
"t2i",
"i2t",
"joint",
"i",
"t",
"i2t2i",
"t2i2t",
],
value=mode_name,
visible=False,
)
prompt = gr.Text(label="Prompt", max_lines=1, visible=mode_name in ["t2i", "t2i2t"])
image = gr.Image(label="Input image", type="pil", visible=mode_name in ["i2t", "i2t2i"])
run_button = gr.Button("Run")
with gr.Accordion("Advanced options", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
num_steps = gr.Slider(
label="Steps",
minimum=1,
maximum=100,
value=20,
step=1,
)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.1,
maximum=30.0,
value=8.0,
step=0.1,
)
with gr.Column():
result_image = gr.Image(label="Generated image", visible=mode_name in ["t2i", "i", "joint", "i2t2i"])
result_text = gr.Text(label="Generated text", visible=mode_name in ["i2t", "t", "joint", "t2i2t"])
inputs = [
mode,
prompt,
image,
seed,
num_steps,
guidance_scale,
]
outputs = [
result_image,
result_text,
]
prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
).then(
fn=model.run,
inputs=inputs,
outputs=outputs,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
).then(
fn=model.run,
inputs=inputs,
outputs=outputs,
api_name=f"run_{mode_name}",
)
return demo
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
with gr.Tabs():
with gr.TabItem("text2image"):
create_demo("t2i")
with gr.TabItem("image2text"):
create_demo("i2t")
with gr.TabItem("image variation"):
create_demo("i2t2i")
with gr.TabItem("joint generation"):
create_demo("joint")
with gr.TabItem("image generation"):
create_demo("i")
with gr.TabItem("text generation"):
create_demo("t")
with gr.TabItem("text variation"):
create_demo("t2i2t")
if __name__ == "__main__":
demo.queue(max_size=15).launch()
|