app.py

import gradio as gr
import numpy as np
import random

import spaces  # [uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

if torch.cuda.is_available():
    torch_dtype = torch.float16
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


@spaces.GPU  # [uncomment to use ZeroGPU]
def generate_images(prompt, seed, steps, pipe, pruned_pipe):
    # Run the model and return images directly
    g_cpu = torch.Generator("cuda").manual_seed(seed)
    original_image = pipe(prompt=prompt, generator=g_cpu, num_inference_steps=steps).images[0]
    g_cpu = torch.Generator("cuda").manual_seed(seed)
    ecodiff_image = pruned_pipe(prompt=prompt, generator=g_cpu, num_inference_steps=steps).images[0]
    return original_image, ecodiff_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: 640px;
}
"""
header = """
# 🌱 Text-to-Image Generation with EcoDiff Pruned SD-XL (20% Pruning Ratio)
# Under Construction!!!
<div style="text-align: center; display: flex; justify-content: left; gap: 5px;">
<a href="https://arxiv.org/abs/2412.02852"><img src="https://img.shields.io/badge/ariXv-Paper-A42C25.svg" alt="arXiv"></a>
<a href="https://huggingface.co/zhangyang-0123/EcoDiffPrunedModels"><img src="https://img.shields.io/badge/🤗-Model-ffbd45.svg" alt="HuggingFace"></a>
<a href="https://github.com/YaNgZhAnG-V5/EcoDiff"><img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub"></a>
</div>
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(header)
    with gr.Row():
        prompt = gr.Textbox(
            label="Prompt",
            value="A clock tower floating in a sea of clouds",
            scale=3,
        )
        seed = gr.Number(label="Seed", value=44, precision=0, scale=1)
        steps = gr.Slider(
            label="Number of Steps",
            minimum=1,
            maximum=100,
            value=50,
            step=1,
            scale=1,
        )
        generate_btn = gr.Button("Generate Images")
    gr.Examples(
        examples=[
            "A clock tower floating in a sea of clouds",
            "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
            "An astronaut riding a green horse",
            "A delicious ceviche cheesecake slice",
            "A sprawling cyberpunk metropolis at night, with towering skyscrapers emitting neon lights of every color, holographic billboards advertising alien languages",
        ],
        inputs=[prompt],
    )
    with gr.Row():
        original_output = gr.Image(label="Original Output")
        ecodiff_output = gr.Image(label="EcoDiff Output")
    gr.on(
        triggers=[generate_btn.click, prompt.submit],
        fn=generate_images,
        inputs=[
            prompt,
            seed,
            steps,
            pipe,
            pipe,
        ],
        outputs=[original_output, ecodiff_output],
    )

if __name__ == "__main__":
    demo.launch()