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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-turbo"
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
# Define styles
style_list = [
{
"name": "3840 x 2160",
"prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
},
{
"name": "2560 x 1440",
"prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
},
{
"name": "HD+",
"prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
},
{
"name": "Style Zero",
"prompt": "{prompt}",
"negative_prompt": "",
},
]
STYLE_NAMES = [style["name"] for style in style_list]
DEFAULT_STYLE_NAME = STYLE_NAMES[0]
@spaces.GPU(duration=60)
def infer(
prompt,
negative_prompt="",
seed=42,
randomize_seed=False,
width=1024,
height=1024,
guidance_scale=0.0,
num_inference_steps=4,
style="Style Zero",
progress=gr.Progress(track_tqdm=True),
):
# Apply selected style
selected_style = next(s for s in style_list if s["name"] == style)
styled_prompt = selected_style["prompt"].format(prompt=prompt)
styled_negative_prompt = selected_style["negative_prompt"]
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=styled_prompt,
negative_prompt=styled_negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
examples = [
"Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic oil --ar 2:3 --q 2 --s 750 --v 5 --ar 2:3 --q 2 --s 750 --v 5",
"3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)",
"Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K, Photo-Realistic",
"Food photography of a milk shake with flying strawberrys against a pink background, professionally studio shot with cinematic lighting. The image is in the style of a professional studio shot --ar 85:128 --v 6.0 --style raw"
]
css = '''
.gradio-container{max-width: 585px !important}
h1{text-align:center}
footer {
visibility: hidden
}
'''
with gr.Blocks(css=css, theme="prithivMLmods/Minecraft-Theme") as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("## SD3.5-Turbo")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Row(visible=True):
style_selection = gr.Radio(
show_label=True,
container=True,
interactive=True,
choices=STYLE_NAMES,
value=DEFAULT_STYLE_NAME,
label="Quality Style",
)
with gr.Accordion("Advanced Settings", open=False, visible=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=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
gr.Examples(examples=examples,
inputs=[prompt],
outputs=[result, seed],
fn=infer,
cache_examples=True)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
style_selection,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch() |