app.py

import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
import torch
import spaces

device = "cuda"

torch.cuda.max_memory_allocated(device=device)
pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash")
pipe = pipe.to(device)
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

@spaces.GPU(duration=20,queue=False)
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
 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;
}
"""

with gr.Blocks(css=css) as demo:
 
 with gr.Column(elem_id="col-container"):
 gr.Markdown("""# SDXL Flash""")
 
 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)
 
 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",
 value = "Ugly, malformed, noise, blur, watermark",
 )
 
 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=3.0,
 )
 
 num_inference_steps = gr.Slider(
 label="Number of inference steps",
 minimum=1,
 maximum=12,
 step=1,
 value=5,
 )
 
 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()