app.py

import gradio as gr
import torch
import os
from PIL import Image
import numpy as np
from diffusers import StableDiffusionDepth2ImgPipeline
from pathlib import Path

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
is_gpu_associated = torch.cuda.is_available()
dept2img = StableDiffusionDepth2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-depth",
    torch_dtype=torch.float16,
).to(device)

css = '''
    .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
    .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
    #component-4, #component-3, #component-10{min-height: 0}
    .duplicate-button img{margin: 0}
'''


def pad_image(input_image):
    pad_w, pad_h = np.max(((2, 2), np.ceil(
        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
    im_padded = Image.fromarray(
        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
    w, h = im_padded.size
    if w == h:
        return im_padded
    elif w > h:
        new_image = Image.new(im_padded.mode, (w, w), (0, 0, 0))
        new_image.paste(im_padded, (0, (w - h) // 2))
        return new_image
    else:
        new_image = Image.new(im_padded.mode, (h, h), (0, 0, 0))
        new_image.paste(im_padded, ((h - w) // 2, 0))
        return new_image


def predict(input_image, prompt, negative_prompt, steps, num_samples, scale, seed, strength, depth_image=None):
    if not is_gpu_associated:
        raise gr.Error("Please associate a T4 GPU for this Space")
    torch.cuda.empty_cache()
    depth = None
    if depth_image is not None:
        depth_image = pad_image(depth_image)
        depth_image = depth_image.resize((512, 512))
        depth = np.array(depth_image.convert("L"))
        depth = depth.astype(np.float32) / 255.0
        depth = depth[None, None]
        depth = torch.from_numpy(depth)
    init_image = input_image.convert("RGB")
    image = pad_image(init_image)  # resize to integer multiple of 32
    image = image.resize((512, 512))
    result = dept2img(
        image=image,
        prompt=prompt,
        negative_prompt=negative_prompt,
        depth_image=depth,
        seed=seed,
        strength=strength,
        num_inference_steps=steps,
        guidance_scale=scale,
        num_images_per_prompt=num_samples,
    )
    return result['images']


block = gr.Blocks().queue()
with block:
    with gr.Box():
        if is_gpu_associated:
            top_description = gr.HTML(f'''
                <div style="text-align: center; max-width: 650px; margin: 0 auto;">
                <div>
                    <img class="logo" src="file/mirage.png" alt="Mirage Logo"
                        style="margin: auto; max-width: 7rem;">
                    <br />
                    <h1 style="font-weight: 900; font-size: 2.5rem;">
                    Depth2Img Web UI
                    </h1>
                    <br />
                    <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/MirageML/depth2img?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>
                </div>
                <br />
                <br />
                <p style="margin-bottom: 10px; font-size: 94%">
                Create variations of an image while preserving shape and depth!
                </p>
                </div>
            ''')
        else:
            top_description = gr.HTML(f'''
                <div style="text-align: center; max-width: 650px; margin: 0 auto;">
                <div>
                    <img class="logo" src="file/mirage.png" alt="Mirage Logo"
                        style="margin: auto; max-width: 7rem;">
                    <br />
                    <h1 style="font-weight: 900; font-size: 2.5rem;">
                    Depth2Img Web UI
                    </h1>
                    <br />
                    <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/MirageML/depth2img?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>
                </div>
                <br />
                <br />
                <p style="margin-bottom: 10px; font-size: 94%">
                Create variations of an image while preserving shape and depth!
                </p>
                <br />
                <p>There's only one step left before you can run the app: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a <b>T4 GPU</b> to it (via the Settings tab)</a> and run the app below. You will be billed by the minute from when you activate the GPU until it is turned it off.</p>
                </div>
            ''')

    with gr.Row():
        with gr.Column():
            input_image = gr.Image(source='upload', type="pil")
            # depth_image = gr.Image(
            #     source='upload', type="pil", label="Depth image Optional", value=None)
            depth_image = None
            prompt = gr.Textbox(label="Prompt")
            negative_prompt = gr.Textbox(label="Negative Pompt")

            run_button = gr.Button(label="Run")
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(
                    label="Images", minimum=1, maximum=4, value=1, step=1)
                steps = gr.Slider(label="Steps", minimum=1,
                                  maximum=100, value=50, step=1)
                scale = gr.Slider(
                    label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1
                )
                strength = gr.Slider(
                    label="Strength", minimum=0.0, maximum=1.0, value=0.9, step=0.01
                )
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=2147483647,
                    step=1,
                    randomize=True,
                )
        with gr.Column():
            gallery = gr.Gallery(label="Generated images", show_label=False).style(
                grid=[2], height="auto")
    if is_gpu_associated:
        gr.Examples(
            examples=[
                ["./examples/original_iso.png", "hogwarts castle",
                "", 50, 4, 10.0, 123123123, 0.8],
                ["./examples/original_sword.png", "flaming sword",
                "", 50, 4, 9.0, 1734133747, 0.8],

            ],
            inputs=[input_image, prompt, negative_prompt, steps,
                    num_samples, scale, seed, strength],
            outputs=[gallery],
            fn=predict,
            cache_examples=True,
        )
    run_button.click(fn=predict, inputs=[input_image, prompt, negative_prompt,
                     steps, num_samples, scale, seed, strength], outputs=[gallery])


block.launch(show_api=False)