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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
import base64
from io import BytesIO
from PIL import Image
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
MAX_IMAGE_SIZE = 1024
def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
prompt = f"{prompt_part1} {color} colored {dress_type} with {design} design, {prompt_part5}"
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]
# Convert the PIL image to base64
buffered = BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
return img_str
examples = [
["red", "t-shirt", "yellow stripes"],
["blue", "hoodie", "minimalist"],
["red", "sweatshirt", "geometric design"],
]
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():
prompt_part1 = gr.Textbox(
value="a single",
label="Prompt Part 1",
show_label=False,
interactive=False,
container=False,
elem_id="prompt_part1",
visible=False,
)
prompt_part2 = gr.Textbox(
label="color",
show_label=False,
max_lines=1,
placeholder="color (e.g., color category)",
container=False,
)
prompt_part3 = gr.Textbox(
label="dress_type",
show_label=False,
max_lines=1,
placeholder="dress_type (e.g., t-shirt, sweatshirt, shirt, hoodie)",
container=False,
)
prompt_part4 = gr.Textbox(
label="design",
show_label=False,
max_lines=1,
placeholder="design",
container=False,
)
prompt_part5 = gr.Textbox(
value="hanging on the plain grey wall",
label="Prompt Part 5",
show_label=False,
interactive=False,
container=False,
elem_id="prompt_part5",
visible=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.Textbox(
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=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=12,
step=1,
value=2,
)
gr.Examples(
examples=examples,
inputs=[prompt_part2, prompt_part3, prompt_part4]
)
run_button.click(
fn=infer,
inputs=[prompt_part1, prompt_part2, prompt_part3, prompt_part4, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result]
)
demo.queue().launch()
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