from contextlib import nullcontext
import gradio as gr
import torch
from torch import autocast
from diffusers import SemanticStableDiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to(device)
gen = torch.Generator(device=device)
# Sometimes the nsfw checker is confused by the Pokémon images, you can disable
# it at your own risk here
disable_safety = False
if disable_safety:
def null_safety(images, **kwargs):
return images, False
pipe.safety_checker = null_safety
def infer(prompt, steps, scale, seed, editing_prompt_1 = None, reverse_editing_direction_1 = False, edit_warmup_steps_1=10, edit_guidance_scale_1=5, edit_threshold_1=0.95,
editing_prompt_2 = None, reverse_editing_direction_2 = False, edit_warmup_steps_2=10, edit_guidance_scale_2=5, edit_threshold_2=0.95,
edit_momentum_scale=0.5, edit_mom_beta=0.6):
gen.manual_seed(seed)
images = pipe(prompt, guidance_scale=scale, num_inference_steps=steps, generator=gen).images
editing_prompt = [editing_prompt_1, editing_prompt_2]
reverse_editing_direction = [reverse_editing_direction_1, reverse_editing_direction_2]
edit_warmup_steps = [edit_warmup_steps_1, edit_warmup_steps_2]
edit_guidance_scale = [edit_guidance_scale_1, edit_guidance_scale_2]
edit_threshold = [edit_threshold_1, edit_threshold_2]
indices = [ind for ind, val in enumerate(editing_prompt) if val is None or len(val) <= 1]
for index in sorted(indices, reverse=True):
del editing_prompt[index]
del reverse_editing_direction[index]
del edit_warmup_steps[index]
del edit_guidance_scale[index]
del edit_threshold[index]
gen.manual_seed(seed)
images.extend(pipe(prompt, guidance_scale=scale, num_inference_steps=steps, generator=gen,
editing_prompt=editing_prompt, reverse_editing_direction=reverse_editing_direction, edit_warmup_steps=edit_warmup_steps, edit_guidance_scale=edit_guidance_scale,
edit_momentum_scale=edit_momentum_scale, edit_mom_beta=edit_mom_beta
).images)
return images
css = """
a {
color: inherit;
text-decoration: underline;
}
.gradio-container {
font-family: 'IBM Plex Sans', sans-serif;
}
.gr-button {
color: white;
border-color: #9d66e5;
background: #9d66e5;
}
input[type='range'] {
accent-color: #9d66e5;
}
.dark input[type='range'] {
accent-color: #dfdfdf;
}
.container {
max-width: 730px;
margin: auto;
padding-top: 1.5rem;
}
#gallery {
min-height: 22rem;
margin-bottom: 15px;
margin-left: auto;
margin-right: auto;
border-bottom-right-radius: .5rem !important;
border-bottom-left-radius: .5rem !important;
}
#gallery>div>.h-full {
min-height: 20rem;
}
.details:hover {
text-decoration: underline;
}
.gr-button {
white-space: nowrap;
}
.gr-button:focus {
border-color: rgb(147 197 253 / var(--tw-border-opacity));
outline: none;
box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
--tw-border-opacity: 1;
--tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
--tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
--tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
--tw-ring-opacity: .5;
}
#advanced-options {
margin-bottom: 20px;
}
.footer {
margin-bottom: 45px;
margin-top: 35px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.acknowledgments h4{
margin: 1.25em 0 .25em 0;
font-weight: bold;
font-size: 115%;
}
"""
block = gr.Blocks(css=css)
examples = [
[
'a photo of a cat',
50,
7,
3,
'sunglasses',
False,
10,
6,
0.95,
'',
False,
10,
5,
0.95
],
[
'an image of a crowded boulevard, realistic, 4k',
50,
7,
9,
'crowd, crowded, people',
True,
10,
8.3,
0.9,
'',
False,
10,
5,
0.95
],
[
'a castle next to a river',
50,
7,
48,
'boat on a river',
False,
15,
6,
0.9,
'monet, impression, sunrise',
False,
18,
6,
0.8
],
[
'a portrait of a king, full body shot, 8k',
50,
7,
33,
'male',
True,
5,
5,
0.9,
'female',
False,
5,
5,
0.9
],
[
'a photo of a flowerpot',
50,
7,
2,
'glasses',
False,
12,
5,
0.975,
'',
False,
10,
5,
0.95
],
[
'a photo of the face of a woman',
50,
7,
21,
'smiling, smile',
False,
15,
3,
0.99,
'curls, wavy hair, curly hair',
False,
13,
3,
0.925
],
]
with block:
gr.HTML(
"""
"""
)
gr.HTML("""
For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
""")
with gr.Group():
with gr.Box():
with gr.Row().style(mobile_collapse=False, equal_height=True):
text = gr.Textbox(
label="Enter your prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
btn = gr.Button("Generate image").style(
margin=False,
rounded=(False, True, True, False),
)
with gr.Box():
with gr.Row().style(mobile_collapse=False, equal_height=True):
edit_1 = gr.Textbox(
label="Edit Prompt 1",
show_label=False,
max_lines=1,
placeholder="Enter your 1st edit prompt",
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
with gr.Group():
with gr.Row().style(mobile_collapse=False, equal_height=True):
rev_1 = gr.Checkbox(
label='Reverse')
warmup_1 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=10, step=1, interactive=True)
scale_1 = gr.Slider(label='Scale', minimum=1, maximum=10, value=5, step=0.25, interactive=True)
threshold_1 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01, interactive=True)
with gr.Row().style(mobile_collapse=False, equal_height=True):
edit_2 = gr.Textbox(
label="Edit Prompt 2",
show_label=False,
max_lines=1,
placeholder="Enter your 2nd edit prompt",
).style(
border=(True, False, True, True),
rounded=(True, False, False, True),
container=False,
)
with gr.Group():
with gr.Row().style(mobile_collapse=False, equal_height=True):
rev_2 = gr.Checkbox(
label='Reverse')
warmup_2 = gr.Slider(label='Warmup', minimum=0, maximum=50, value=10, step=1, interactive=True)
scale_2 = gr.Slider(label='Scale', minimum=1, maximum=10, value=5, step=0.25, interactive=True)
threshold_2 = gr.Slider(label='Threshold', minimum=0.5, maximum=0.99, value=0.95, steps=0.01, interactive=True)
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style(grid=[2], height="auto")
with gr.Row(elem_id="advanced-options"):
scale = gr.Slider(label="Scale", minimum=3, maximum=15, value=7, step=1)
steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=50, step=5, interactive=False)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=2147483647,
step=1,
#randomize=True,
)
ex = gr.Examples(examples=examples, fn=infer, inputs=[text, steps, scale, seed, edit_1, rev_1, warmup_1, scale_1, threshold_1, edit_2, rev_2, warmup_2, scale_2, threshold_2], outputs=gallery, cache_examples=False)
ex.dataset.headers = [""]
text.submit(infer, inputs=[text, steps, scale, seed, edit_1, rev_1, warmup_1, scale_1, threshold_1, edit_2, rev_2, warmup_2, scale_2, threshold_2], outputs=gallery)
btn.click(infer, inputs=[text, steps, scale, seed, edit_1, rev_1, warmup_1, scale_1, threshold_1, edit_2, rev_2, warmup_2, scale_2, threshold_2], outputs=gallery)
gr.HTML(
"""
"""
)
block.launch() 