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import gradio as gr
import numpy as np
import random
import uuid
from PIL import Image
import spaces
from diffusers import DiffusionPipeline
import torch
DESCRIPTIONx = """## SD-3.5 LARGE TURBO """
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]
grid_sizes = {
"2x1": (2, 1),
"1x2": (1, 2),
"2x2": (2, 2),
"2x3": (2, 3),
"3x2": (3, 2),
"1x1": (1, 1)
}
@spaces.GPU(duration=60, enable_queue=True)
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",
grid_size="1x1",
progress=gr.Progress(track_tqdm=True),
):
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)
grid_size_x, grid_size_y = grid_sizes.get(grid_size, (2, 2))
num_images = grid_size_x * grid_size_y
images = []
for _ in range(num_images):
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]
images.append(image)
# Create a grid image
grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y))
for i, img in enumerate(images[:num_images]):
grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height))
# Save the grid image
unique_name = str(uuid.uuid4()) + ".png"
grid_img.save(unique_name)
return unique_name, seed
examples = [
"A capybara wearing a suit holding a sign that reads Hello World",
]
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:
gr.Markdown(DESCRIPTIONx)
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):
grid_size_selection = gr.Dropdown(
choices=["2x1", "1x2", "2x2", "2x3", "3x2", "1x1"],
value="1x1",
label="Grid Size"
)
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):
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, cache_mode="lazy")
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,
grid_size_selection,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()