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import gradio as gr
import numpy as np
import random
import spaces
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "tensorart/stable-diffusion-3.5-large-TurboX"
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype, use_fast=True)
pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_repo_id, subfolder="scheduler", shift=5)
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=1.5,
num_inference_steps=8,
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
css = """
body {
background: linear-gradient(135deg, #f9e2e6 0%, #e8f3fc 50%, #e2f9f2 100%);
background-attachment: fixed;
min-height: 100vh;
}
#col-container {
margin: 0 auto;
max-width: 640px;
background-color: rgba(255, 255, 255, 0.85);
border-radius: 16px;
box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
padding: 24px;
backdrop-filter: blur(10px);
}
.gradio-container {
background: transparent !important;
}
.gr-button-primary {
background: linear-gradient(90deg, #6b9dfc, #8c6bfc) !important;
border: none !important;
transition: all 0.3s ease;
}
.gr-button-primary:hover {
transform: translateY(-2px);
box-shadow: 0 5px 15px rgba(108, 99, 255, 0.3);
}
.gr-form {
border-radius: 12px;
background-color: rgba(255, 255, 255, 0.7);
}
.gr-accordion {
border-radius: 12px;
overflow: hidden;
}
h1 {
background: linear-gradient(90deg, #6b9dfc, #8c6bfc);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-weight: 800;
}
"""
with gr.Blocks(theme="apriel", css=css) as demo:
with gr.Column(elem_id="col-container"):
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt copied from the previous website",
container=False,
)
run_button = gr.Button("Run", 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",
)
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=1.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=8,
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
"cartoon styled korean" + prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch(mcp_server=True) |