|
import gradio as gr |
|
import numpy as np |
|
import random |
|
import torch |
|
from diffusers import DiffusionPipeline |
|
import spaces |
|
|
|
|
|
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 |
|
|
|
|
|
EXAMPLES = [ |
|
{ |
|
"title": "Smart Coffee Machine", |
|
"prompt": """A sleek industrial design concept for a coffee machine: |
|
- Curved metallic body with minimal bezel |
|
- Touchscreen panel for settings |
|
- Modern matte black finish |
|
- Hand-drawn concept sketch style""", |
|
"width": 1024, |
|
"height": 1024 |
|
}, |
|
{ |
|
"title": "AI Speaker", |
|
"prompt": """A futuristic AI speaker concept: |
|
- Cylindrical shape with LED ring near top |
|
- Voice assistant concept, floating panel controls |
|
- Smooth glossy finish with minimal seams |
|
- Techy, modern look in grayscale""", |
|
"width": 1024, |
|
"height": 1024 |
|
}, |
|
{ |
|
"title": "Next-Gen Smartphone", |
|
"prompt": """A wireframe-style concept for a bezel-less smartphone: |
|
- Edge-to-edge display |
|
- Integrated camera under screen |
|
- Metallic frame, minimal ports |
|
- Sleek, glossy black design""", |
|
"width": 1024, |
|
"height": 1024 |
|
}, |
|
{ |
|
"title": "Futuristic Electric Bicycle", |
|
"prompt": """An industrial design sketch of an electric bike: |
|
- Lightweight carbon frame, aerodynamic lines |
|
- Integrated battery, sleek display on handlebars |
|
- Neon color highlights on wheels |
|
- High-tech vibe, minimal clutter""", |
|
"width": 1024, |
|
"height": 1024 |
|
}, |
|
{ |
|
"title": "Concept Car Interior", |
|
"prompt": """A luxurious and futuristic car interior concept: |
|
- Wrap-around digital dashboard |
|
- Minimalistic steering control, seat controls on touchscreen |
|
- Ambient LED accent lights |
|
- Soft leather seats, bright accent stitching""", |
|
"width": 1024, |
|
"height": 1024 |
|
} |
|
] |
|
|
|
|
|
GRADIO_EXAMPLES = [ |
|
[example["prompt"], example["width"], example["height"]] |
|
for example in EXAMPLES |
|
] |
|
|
|
@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 |
|
|
|
|
|
css = """ |
|
.container { |
|
display: flex; |
|
flex-direction: row; |
|
height: 100%; |
|
} |
|
.input-column { |
|
flex: 1; |
|
padding: 20px; |
|
border-right: 2px solid #eee; |
|
max-width: 800px; |
|
} |
|
.examples-column { |
|
flex: 1; |
|
padding: 20px; |
|
overflow-y: auto; |
|
background: #f7f7f7; |
|
} |
|
.title { |
|
text-align: center; |
|
color: #2a2a2a; |
|
padding: 20px; |
|
font-size: 2.5em; |
|
font-weight: bold; |
|
background: linear-gradient(90deg, #f0f0f0 0%, #ffffff 100%); |
|
border-bottom: 3px solid #ddd; |
|
margin-bottom: 30px; |
|
} |
|
.subtitle { |
|
text-align: center; |
|
color: #666; |
|
margin-bottom: 30px; |
|
} |
|
.input-box { |
|
background: white; |
|
padding: 20px; |
|
border-radius: 10px; |
|
box-shadow: 0 2px 10px rgba(0,0,0,0.1); |
|
margin-bottom: 20px; |
|
width: 100%; |
|
} |
|
.input-box textarea { |
|
width: 100% !important; |
|
min-width: 600px !important; |
|
font-size: 14px !important; |
|
line-height: 1.5 !important; |
|
padding: 12px !important; |
|
} |
|
.example-card { |
|
background: white; |
|
padding: 15px; |
|
margin: 10px 0; |
|
border-radius: 8px; |
|
box-shadow: 0 2px 5px rgba(0,0,0,0.05); |
|
} |
|
.example-title { |
|
font-weight: bold; |
|
color: #2a2a2a; |
|
margin-bottom: 10px; |
|
} |
|
.contain { |
|
max-width: 1400px !important; |
|
margin: 0 auto !important; |
|
} |
|
.input-area { |
|
flex: 2 !important; |
|
} |
|
.examples-area { |
|
flex: 1 !important; |
|
} |
|
""" |
|
|
|
with gr.Blocks(css=css) as demo: |
|
gr.Markdown( |
|
""" |
|
<div class="title">GINI Design</div> |
|
<div class="subtitle">Generate sleek industrial/product design concepts with FLUX AI</div> |
|
""") |
|
|
|
with gr.Row(equal_height=True): |
|
|
|
with gr.Column(elem_id="input-column", scale=2): |
|
with gr.Group(elem_classes="input-box"): |
|
prompt = gr.Text( |
|
label="Design Prompt", |
|
placeholder="Enter your product design concept details...", |
|
lines=10, |
|
elem_classes="prompt-input" |
|
) |
|
run_button = gr.Button("Generate Design", variant="primary") |
|
result = gr.Image(label="Generated Design") |
|
|
|
with gr.Accordion("Advanced Settings", open=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=256, |
|
maximum=MAX_IMAGE_SIZE, |
|
step=32, |
|
value=1024, |
|
) |
|
height = gr.Slider( |
|
label="Height", |
|
minimum=256, |
|
maximum=MAX_IMAGE_SIZE, |
|
step=32, |
|
value=1024, |
|
) |
|
|
|
num_inference_steps = gr.Slider( |
|
label="Number of inference steps", |
|
minimum=1, |
|
maximum=50, |
|
step=1, |
|
value=4, |
|
) |
|
|
|
|
|
with gr.Column(elem_id="examples-column", scale=1): |
|
gr.Markdown("### Example Product Designs") |
|
for example in EXAMPLES: |
|
with gr.Group(elem_classes="example-card"): |
|
gr.Markdown(f"#### {example['title']}") |
|
gr.Markdown(f"```\n{example['prompt']}\n```") |
|
|
|
def create_example_handler(ex): |
|
def handler(): |
|
return { |
|
prompt: ex["prompt"], |
|
width: ex["width"], |
|
height: ex["height"] |
|
} |
|
return handler |
|
|
|
gr.Button("Use This Example", size="sm").click( |
|
fn=create_example_handler(example), |
|
outputs=[prompt, width, height] |
|
) |
|
|
|
|
|
gr.on( |
|
triggers=[run_button.click, prompt.submit], |
|
fn=infer, |
|
inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps], |
|
outputs=[result, seed] |
|
) |
|
|
|
if __name__ == "__main__": |
|
demo.queue() |
|
demo.launch( |
|
server_name="0.0.0.0", |
|
server_port=7860, |
|
share=False, |
|
show_error=True, |
|
debug=True |
|
) |
|
|