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import gradio as gr
import numpy as np
import random
from diffusers import StableDiffusionPipeline
import torch
import os
import logging
# Setup logging
logging.basicConfig(level=logging.INFO)
# Retrieve Hugging Face access token from environment variables
access_token = os.getenv("HF_ACCESS_TOKEN")
# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Global variable for the pipeline
pipe = None
def load_model():
global pipe
if pipe is None:
try:
logging.info("Loading the Stable Diffusion model...")
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium",
torch_dtype=torch.float16,
use_auth_token=access_token,
cache_dir="/path/to/cache" # specify cache directory if needed
)
pipe = pipe.to(device)
logging.info("Model loaded successfully.")
except Exception as e:
logging.error(f"Failed to load model: {e}")
pipe = None
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
load_model() # Ensure the model is loaded
if pipe is None:
raise RuntimeError("Model failed to load.")
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
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
power_device = "GPU" if torch.cuda.is_available() else "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 = gr.Textbox(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
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.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]
)
run_button.click(
fn=infer,
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result]
)
demo.queue().launch()
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