app.py

import gradio as gr
from gradio_imageslider import ImageSlider
import torch
from diffusers import DiffusionPipeline, AutoencoderKL
from PIL import Image
from torchvision import transforms
import tempfile
import os
import time
import uuid

LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    custom_pipeline="pipeline_demofusion_sdxl.py",
    custom_revision="main",
    torch_dtype=dtype,
    variant="fp16",
    use_safetensors=True,
    vae=vae,
)
pipe = pipe.to(device)


def load_and_process_image(pil_image):
    transform = transforms.Compose(
        [
            transforms.Resize((1024, 1024)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
        ]
    )
    image = transform(pil_image)
    image = image.unsqueeze(0).half()
    return image


def pad_image(image):
    w, h = image.size
    if w == h:
        return image
    elif w > h:
        new_image = Image.new(image.mode, (w, w), (0, 0, 0))
        pad_w = 0
        pad_h = (w - h) // 2
        new_image.paste(image, (0, pad_h))
        return new_image
    else:
        new_image = Image.new(image.mode, (h, h), (0, 0, 0))
        pad_w = (h - w) // 2
        pad_h = 0
        new_image.paste(image, (pad_w, 0))
        return new_image


def predict(
    input_image,
    prompt,
    negative_prompt,
    seed,
    scale=2,
    progress=gr.Progress(track_tqdm=True),
):
    if input_image is None:
        raise gr.Error("Please upload an image.")
    padded_image = pad_image(input_image).resize((1024, 1024)).convert("RGB")
    image_lr = load_and_process_image(padded_image).to(device)
    generator = torch.manual_seed(seed)
    last_time = time.time()
    images = pipe(
        prompt,
        negative_prompt=negative_prompt,
        image_lr=image_lr,
        width=1024 * scale,
        height=1024 * scale,
        view_batch_size=16,
        stride=64,
        generator=generator,
        num_inference_steps=25,
        guidance_scale=7.5,
        cosine_scale_1=3,
        cosine_scale_2=1,
        cosine_scale_3=1,
        sigma=0.8,
        multi_decoder=True,
        show_image=False,
        lowvram=LOW_MEMORY,
    )
    print(f"Time taken: {time.time() - last_time}")
    images_path = tempfile.mkdtemp()
    paths = []
    uuid_name = uuid.uuid4()
    for i, img in enumerate(images):
        img.save(images_path + f"/img_{uuid_name}_{img.size[0]}.jpg")
        paths.append(images_path + f"/img_{uuid_name}_{img.size[0]}.jpg")
    return (images[0], images[-1]), paths


css = """
#intro{
    max-width: 32rem;
    text-align: center;
    margin: 0 auto;
}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(
        """
# Zoom and Enhance with DemoFusion SDXL

[DemoFusion](https://github.com/PRIS-CV/DemoFusion) enables higher-resolution image generation.  
You can upload an initial image and prompt to generate an enhanced version. 
[Duplicate Space](https://huggingface.co/spaces/radames/Enhance-This-DemoFusion-SDXL?duplicate=true) to avoid the queue.  
<small>
*Note*: The author advises against the term "super resolution" because it's more like image-to-image generation than enhancement, but it's still a lot of fun!
</small>
        """,
        elem_id="intro",
    )
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(type="pil", label="Input Image")
            prompt = gr.Textbox(
                label="Prompt",
                info="The prompt is very important to get the desired results. Please try to describe the image as best as you can.",
            )
            negative_prompt = gr.Textbox(
                label="Negative Prompt",
                value="blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
            )
            scale = gr.Slider(
                minimum=1,
                maximum=5,
                value=2,
                step=1,
                label="x Scale",
                interactive=False,
            )
            seed = gr.Slider(
                minimum=0,
                maximum=2**64 - 1,
                value=1415926535897932,
                step=1,
                label="Seed",
                randomize=True,
            )
            btn = gr.Button()
        with gr.Column(scale=2):
            image_slider = ImageSlider()
            files = gr.Files()
    inputs = [image_input, prompt, negative_prompt, seed, scale]
    # inputs = [image_input, prompt, negative_prompt, seed]
    outputs = [image_slider, files]
    btn.click(predict, inputs=inputs, outputs=outputs, concurrency_limit=1)
    gr.Examples(
        fn=predict,
        examples=[
            [
                "./examples/lara.jpeg",
                "photography of lara croft 8k high definition award winning",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                1528069323235669750,
                2,
            ],
            [
                "./examples/cybetruck.jpeg",
                "photo of tesla cybertruck futuristic car 8k high definition on a sand dune in mars, future",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                9257959681232992980,
                3,
            ],
            [
                "./examples/jesus.png",
                "a photorealistic painting of Jesus Christ, 4k high definition",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                13317204146129588000,
                2,
            ],
            [
                "./examples/anna-sullivan-DioLM8ViiO8-unsplash.jpg",
                "A crowded stadium with enthusiastic fans watching a daytime sporting event, the stands filled with colorful attire and the sun casting a warm glow",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                8398712905087378000,
                3,
            ],
        ],
        inputs=inputs,
        outputs=outputs,
        cache_examples=True,
    )


demo.queue(api_open=False)
demo.launch(show_api=False)