Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,261 Bytes
9a5dc44 88b57b3 9a5dc44 88b57b3 9a5dc44 88b57b3 9a5dc44 88b57b3 9a5dc44 85f94cd 88b57b3 85f94cd 88b57b3 85f94cd 88b57b3 85f94cd 88b57b3 85f94cd 88b57b3 85f94cd 9a5dc44 85f94cd 9a5dc44 85f94cd 2423f10 85f94cd 88b57b3 85f94cd 88b57b3 |
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 |
#!/usr/bin/env python
from __future__ import annotations
import os
import gradio as gr
import torch
from model import Model
DESCRIPTION = '# [Multiresolution Textual Inversion](https://github.com/giannisdaras/multires_textual_inversion)'
DETAILS = '''
- To run the Semi Resolution-Dependent sampler, use the format: `<jane(number)>`.
- To run the Fully Resolution-Dependent sampler, use the format: `<jane[number]>`.
- To run the Fixed Resolution sampler, use the format: `<jane|number|>`.
For this demo, only `<jane>`, `<gta5-artwork>` and `<cat-toy>` are available.
Also, `number` should be an integer in [0, 9].
'''
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv(
'CACHE_EXAMPLES') == '1'
model = Model()
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label='Prompt')
with gr.Row():
num_images = gr.Slider(
label='Number of images',
minimum=1,
maximum=9,
step=1,
value=1,
)
with gr.Row():
num_steps = gr.Slider(label='Number of inference steps',
minimum=1,
maximum=50,
step=1,
value=10)
with gr.Row():
seed = gr.Slider(label='Seed',
minimum=0,
maximum=100000,
step=1,
value=100)
with gr.Row():
run_button = gr.Button('Run')
with gr.Column():
result = gr.Gallery(label='Result', object_fit='scale-down')
with gr.Row():
with gr.Group():
fn = lambda x: model.run(x, 2, 10, 100)
with gr.Row():
gr.Examples(
label='Examples 1',
examples=[
['an image of <gta5-artwork(0)>'],
['an image of <jane(0)>'],
['an image of <jane(3)>'],
['an image of <cat-toy(0)>'],
],
inputs=prompt,
outputs=result,
fn=fn,
cache_examples=CACHE_EXAMPLES,
)
with gr.Row():
gr.Examples(
label='Examples 2',
examples=[
[
'an image of a cat in the style of <gta5-artwork(0)>'
],
['a painting of a dog in the style of <jane(0)>'],
['a painting of a dog in the style of <jane(5)>'],
[
'a painting of a <cat-toy(0)> in the style of <jane(3)>'
],
],
inputs=prompt,
outputs=result,
fn=fn,
cache_examples=CACHE_EXAMPLES,
)
with gr.Row():
gr.Examples(
label='Examples 3',
examples=[
['an image of <jane[0]>'],
['an image of <jane|0|>'],
['an image of <jane|3|>'],
],
inputs=prompt,
outputs=result,
fn=fn,
cache_examples=CACHE_EXAMPLES,
)
inputs = [
prompt,
num_images,
num_steps,
seed,
]
prompt.submit(
fn=model.run,
inputs=inputs,
outputs=result,
api_name=False,
)
run_button.click(
fn=model.run,
inputs=inputs,
outputs=result,
api_name='run',
)
with gr.Accordion('About available prompts', open=False):
gr.Markdown(DETAILS)
demo.queue(max_size=10).launch()
|