Modelgen1 / app.py
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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to(device)
else:
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
DEFAULT_IMAGE_SIZE = 512
MAX_IMAGE_SIZE = 1024
# Define the default parts of the prompt
DEFAULT_PREFIX = "a single"
DEFAULT_SUFFIX = "hanging on the grey wall"
CATEGORIES = ["T-shirt", "Sweatshirt", "Shirt", "Hoodie"]
EXAMPLES = [
["T-shirt", "floral pattern"],
["Sweatshirt", "abstract design"],
["Shirt", "geometric shapes"],
["Hoodie", "urban graffiti"],
]
def infer(category, design, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
prompt = f"{DEFAULT_PREFIX} {category} with {design} {DEFAULT_SUFFIX}"
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
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Text-to-Image Gradio Template
Currently running on {power_device}.
""")
with gr.Row():
category = gr.Dropdown(label="Category", choices=CATEGORIES, value=CATEGORIES[0])
design = gr.Text(
label="Design/Graphic",
show_label=True,
max_lines=1,
placeholder="Enter design or graphic",
container=False,
)
run_button = gr.Button("Run", scale=0)
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",
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=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=DEFAULT_IMAGE_SIZE,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=DEFAULT_IMAGE_SIZE,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=12,
step=1,
value=50,
)
gr.Examples(
examples=EXAMPLES,
inputs=[category, design]
)
run_button.click(
fn=infer,
inputs=[category, design, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result]
)
demo.queue().launch()