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import gradio as gr
import numpy as np
import random
import spaces
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/stable-diffusion-3.5-large"
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU(duration=65)
def infer(
prompt,
negative_prompt="",
seed=42,
randomize_seed=False,
width=1024,
height=1024,
guidance_scale=4.5,
num_inference_steps=40,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
# Enhanced examples with creative prompts
examples = [
"A capybara wearing a suit holding a sign that reads Hello World",
"A steampunk-style flying ship made of brass and wood, floating through cotton candy clouds",
"A magical library where books are flying and glowing, with a wise owl librarian",
"A cyberpunk street food vendor selling neon-colored dumplings in the rain",
"A group of penguins having a formal tea party in the Antarctic",
"A treehouse city at sunset with bioluminescent plants and floating lanterns"
]
# Custom CSS with modern styling
css = """
:root {
--primary-color: #7B2CBF;
--secondary-color: #9D4EDD;
--background-color: #10002B;
--text-color: #E0AAFF;
--card-bg: #240046;
}
#col-container {
max-width: 850px !important;
margin: 0 auto;
padding: 20px;
background: var(--background-color);
border-radius: 15px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.main-title {
color: var(--text-color) !important;
text-align: center;
font-size: 2.5em !important;
margin-bottom: 1em !important;
text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3);
}
.gradio-container {
background: var(--background-color) !important;
color: var(--text-color) !important;
}
.gr-button {
background: var(--primary-color) !important;
border: none !important;
color: white !important;
transition: transform 0.2s !important;
}
.gr-button:hover {
transform: translateY(-2px) !important;
background: var(--secondary-color) !important;
}
.gr-input, .gr-box {
background: var(--card-bg) !important;
border: 1px solid var(--primary-color) !important;
color: var(--text-color) !important;
}
.footer-custom a {
color: var(--text-color);
text-decoration: none;
margin: 0 10px;
transition: color 0.3s;
}
.footer-custom a:hover {
color: var(--secondary-color);
text-decoration: underline;
}
"""
# Footer HTML
footer = """
<div class="footer-custom" style="text-align: center; margin-top: 20px; color: #f8f8f2;">
<a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> |
<a href="https://github.com/arad1367" target="_blank">GitHub</a> |
<a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a> |
<a href="https://huggingface.co/stabilityai/stable-diffusion-3.5-large" target="_blank">stable-diffusion-3.5-large model</a> |
<a href="https://huggingface.co/spaces/stabilityai/stable-diffusion-3.5-large-turbo" target="_blank">stable-diffusion-3.5-large-turbo</a> |
<a href="https://stability.ai/license" target="_blank">Stability.ai licence</a>
<br>
<p style="margin-top: 10px;">Made with 💖 by Pejman Ebrahimi</p>
</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(
'<h1 class="main-title">Stable Diffusion 3.5 Large (8B)</h1>'
'<div style="text-align: center; margin-bottom: 20px;">'
'<a href="https://stability.ai" target="_blank" style="color: #E0AAFF;">Visit Stability.ai</a>'
'</div>'
)
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Generate", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=7.5,
step=0.1,
value=4.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=40,
)
gr.Examples(
examples=examples,
inputs=[prompt],
outputs=[result, seed],
fn=infer,
cache_examples=True,
cache_mode="lazy"
)
gr.HTML(footer)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch() |