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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, 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,
width = width,
height = height,
num_inference_steps = num_inference_steps,
generator = generator,
guidance_scale=0.0
).images[0]
return image, seed
examples = [
"Create a new logo for a tech startup",
"Design an engaging Instagram post for a fashion brand",
"Create a new character for a social media campaign",
"Generate a marketing advertisement for a new product launch",
"Design a social media banner for a charity event",
"Create a new branding concept for a luxury hotel",
"Design a promotional video thumbnail for a movie premiere",
"Generate a marketing campaign for a sustainable lifestyle brand"
]
css = """
#col-container {
margin: 0 auto;
max-width: 800px;
padding: 20px;
border-radius: 10px;
box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
}
#title {
text-align: center;
font-size: 32px;
font-weight: bold;
margin-bottom: 20px;
}
#prompt {
margin-bottom: 20px;
}
#result {
margin-bottom: 20px;
}
#advanced-settings {
margin-bottom: 20px;
}
#footer {
text-align: center;
font-size: 14px;
color: #888;
}
"""
footer = """
<div id="footer">
<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/black-forest-labs/FLUX.1-schnell" target="_blank">black-forest-labs/FLUX.1-schnell</a>
<br>
Made with 💖 by Pejman Ebrahimi
</div>
"""
with gr.Blocks(css=css, theme='gradio/soft') as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("""
# FLUX.1 Schnell Marketing Assistant
This app uses the FLUX.1 Schnell model to generate high-quality images based on your prompt. Use it to create new logos, social media content, marketing advertisements, and more.
""", elem_id="title")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
elem_id="prompt"
)
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False, elem_id="result")
with gr.Accordion("Advanced Settings", open=False, elem_id="advanced-settings"):
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=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
gr.Examples(
examples = examples,
fn = infer,
inputs = [prompt],
outputs = [result, seed],
cache_examples="lazy"
)
gr.HTML(footer)
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
outputs = [result, seed]
)
demo.launch()
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