Spaces:
Runtime error
Runtime error
File size: 6,841 Bytes
2a777d2 eda7afa 2a777d2 abcf349 8a32fe2 2a777d2 abcf349 2a777d2 0904b5e 2a777d2 abcf349 2a777d2 abcf349 2a777d2 abcf349 2a777d2 eda7afa 2a777d2 8a32fe2 2a777d2 b2646f8 8a32fe2 abcf349 2a777d2 abcf349 2a777d2 b8b8034 2a777d2 abcf349 2a777d2 abcf349 7db39ea 2a777d2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 |
from contextlib import nullcontext
import gradio as gr
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
context = autocast if device == "cuda" else nullcontext
dtype = torch.float16 if device == "cuda" else torch.float32
pipe = StableDiffusionPipeline.from_pretrained("lambdalabs/sd-naruto-diffusers", torch_dtype=dtype)
pipe = pipe.to(device)
# Sometimes the nsfw checker is confused by the Naruto images, you can disable
# it at your own risk here
# disable_safety = True
# if disable_safety:
# def null_safety(images, **kwargs):
# return images, False
# pipe.safety_checker = null_safety
def infer(prompt, n_samples, steps, scale):
with context("cuda"):
images = pipe(n_samples*[prompt], guidance_scale=scale, num_inference_steps=steps).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 .logo{ filter: invert(1); }
.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 = [
[
'Yoda',
2,
7.5,
],
[
'Abraham Lincoln',
2,
7.5,
],
[
'George Washington',
2,
7,
],
]
with block:
gr.HTML(
"""
<div style="text-align: center; max-width: 650px; margin: 0 auto;">
<div>
<img class="logo" src="https://lambdalabs.com/hubfs/logos/lambda-logo.svg" alt="Lambda Logo"
style="margin: auto; max-width: 7rem;">
<h1 style="font-weight: 900; font-size: 3rem;">
Naruto text to image
</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
Generate new Naruto anime character from a text description,
<a href="https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/">created by Lambda Labs</a>.
</p>
</div>
"""
)
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),
)
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style(grid=[2], height="auto")
with gr.Row(elem_id="advanced-options"):
samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1)
steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=50, step=5)
scale = gr.Slider(
label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
)
ex = gr.Examples(examples=examples, fn=infer, inputs=[text, samples, scale], outputs=gallery, cache_examples=False)
ex.dataset.headers = [""]
text.submit(infer, inputs=[text, samples, steps, scale], outputs=gallery)
btn.click(infer, inputs=[text, samples, steps, scale], outputs=gallery)
gr.HTML(
"""
<div class="footer">
<p> Gradio Demo by 🤗 Hugging Face and Lambda Labs
</p>
</div>
<div class="acknowledgments">
<p> Put in a text prompt and generate your own Naruto anime character, no "prompt engineering" required!
<p>If you want to find out how we made this model read about it in <a href="https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/">this blog post</a>.
<p>And if you want to train your own Stable Diffusion variants, see our <a href="https://github.com/LambdaLabsML/examples/tree/main/stable-diffusion-finetuning">Examples Repo</a>!
<p>Trained by Eole Cervenka at <a href="https://lambdalabs.com/">Lambda Labs</a>.</p>
</div>
"""
)
block.launch() |