app.py

import json
import random
import requests
import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline, LCMScheduler
from PIL import Image
import os

# Load the JSON data
with open("sdxl_lora.json", "r") as file:
    data = json.load(file)
    sdxl_loras_raw = [
        {
            "image": item["image"],
            "title": item["title"],
            "repo": item["repo"],
            "trigger_word": item["trigger_word"],
            "weights": item["weights"],
            "is_pivotal": item.get("is_pivotal", False),
            "text_embedding_weights": item.get("text_embedding_weights", None),
            "likes": item.get("likes", 0),
        }
        for item in data
    ]

# Sort the loras by likes
sdxl_loras_raw = sorted(sdxl_loras_raw, key=lambda x: x["likes"], reverse=True)

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "stabilityai/stable-diffusion-xl-base-1.0"

pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("jasperai/flash-sdxl", adapter_name="lora")
pipe.to(device=DEVICE, dtype=torch.float16)

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

def update_selection(selected_state: gr.SelectData, gr_sdxl_loras):
    lora_id = gr_sdxl_loras[selected_state.index]["repo"]
    trigger_word = gr_sdxl_loras[selected_state.index]["trigger_word"]
    return lora_id, trigger_word

def get_image(image_data):
    if isinstance(image_data, str):
        return image_data
    
    if isinstance(image_data, dict):
        local_path = image_data.get('local_path')
        hf_url = image_data.get('hf_url')
    else:
        print(f"Unexpected image_data format: {type(image_data)}")
        return None

    if local_path and os.path.exists(local_path):
        try:
            Image.open(local_path).verify()
            return local_path
        except Exception as e:
            print(f"Error loading local image {local_path}: {e}")

    if hf_url:
        try:
            response = requests.get(hf_url)
            if response.status_code == 200:
                img = Image.open(requests.get(hf_url, stream=True).raw)
                img.verify()
                img.save(local_path)
                return local_path
            else:
                print(f"Failed to fetch image from URL {hf_url}. Status code: {response.status_code}")
        except Exception as e:
            print(f"Error loading image from URL {hf_url}: {e}")

    print(f"Failed to load image for {image_data}")
    return None

@spaces.GPU
def infer(
    pre_prompt,
    prompt,
    seed,
    randomize_seed,
    num_inference_steps,
    negative_prompt,
    guidance_scale,
    user_lora_selector,
    user_lora_weight,
    progress=gr.Progress(track_tqdm=True),
):
    flash_sdxl_id = "jasperai/flash-sdxl"

    new_adapter_id = user_lora_selector.replace("/", "_")
    loaded_adapters = pipe.get_list_adapters()

    if new_adapter_id not in loaded_adapters["unet"]:
        gr.Info("Swapping LoRA")
        pipe.unload_lora_weights()
        pipe.load_lora_weights(flash_sdxl_id, adapter_name="lora")
        pipe.load_lora_weights(user_lora_selector, adapter_name=new_adapter_id)

    pipe.set_adapters(["lora", new_adapter_id], adapter_weights=[1.0, user_lora_weight])
    gr.Info("LoRA setup done")

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    if pre_prompt != "":
        prompt = f"{pre_prompt} {prompt}"

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
    ).images[0]

    return image

css = """
body {
    background-color: #1a1a1a;
    color: #ffffff;
}
.container {
    max-width: 900px;
    margin: auto;
    padding: 20px;
}
h1, h2 {
    color: #4CAF50;
    text-align: center;
}
.gallery {
    display: flex;
    flex-wrap: wrap;
    justify-content: center;
}
.gallery img {
    margin: 10px;
    border-radius: 10px;
    transition: transform 0.3s ease;
}
.gallery img:hover {
    transform: scale(1.05);
}
.gradio-slider input[type="range"] {
    background-color: #4CAF50;
}
.gradio-button {
    background-color: #4CAF50 !important;
}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(
        """
        # ⚡ FlashDiffusion: Araminta K's FlashLoRA Showcase ⚡

        This interactive demo showcases [Araminta K's models](https://huggingface.co/alvdansen) using [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) technology.

        ## Acknowledgments
        - Original Flash Diffusion technology by the Jasper AI team
        - Based on the paper: [Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) by Clément Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin
        - Models showcased here are created by Araminta K at Alvdansen Labs

        Explore the power of FlashLoRA with Araminta K's unique artistic styles!
        """
    )
    gr.Markdown(
        "If you enjoy this showcase, please support open-source development by giving a ⭐ to the Flash Diffusion repo: [![GitHub Stars](https://img.shields.io/github/stars/gojasper/flash-diffusion?style=social)](https://github.com/gojasper/flash-diffusion)"
    )

    gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
    gr_lora_id = gr.State(value="")

    with gr.Row():
        with gr.Column(scale=2):
            gallery = gr.Gallery(
                value=[(get_image(item["image"]), item["title"]) for item in sdxl_loras_raw if get_image(item["image"]) is not None],
                label="SDXL LoRA Gallery",
                show_label=False,
                elem_id="gallery",
                columns=3,
                height=600,
            )
            
            user_lora_selector = gr.Textbox(
                label="Current Selected LoRA",
                max_lines=1,
                interactive=False,
            )

            user_lora_weight = gr.Slider(
                label="Selected LoRA Weight",
                minimum=0.5,
                maximum=3,
                step=0.1,
                value=1,
            )

        with gr.Column(scale=3):
            prompt = gr.Textbox(
                label="Prompt",
                placeholder="Enter your prompt",
                lines=3,
            )

            with gr.Row():
                run_button = gr.Button("Run", variant="primary")
                clear_button = gr.Button("Clear")

            result = gr.Image(label="Result", height=512)

            with gr.Accordion("Advanced Settings", open=False):
                pre_prompt = gr.Textbox(
                    label="Pre-Prompt",
                    placeholder="Pre Prompt from the LoRA config",
                    lines=2,
                )

                with gr.Row():
                    seed = gr.Slider(
                        label="Seed",
                        minimum=0,
                        maximum=MAX_SEED,
                        step=1,
                        value=0,
                    )
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=4,
                    maximum=8,
                    step=1,
                    value=4,
                )

                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=6,
                    step=0.5,
                    value=1,
                )

                hint_negative = gr.Markdown(
                    """💡 _Hint : Negative Prompt will only work with Guidance > 1 but the model was 
                    trained to be used with guidance = 1 (ie. without guidance).
                    Can degrade the results, use cautiously._"""
                )

                negative_prompt = gr.Textbox(
                    label="Negative Prompt",
                    placeholder="Enter a negative Prompt",
                    lines=2,
                )

    gr.on(
        [
            run_button.click,
            seed.change,
            randomize_seed.change,
            prompt.submit,
            negative_prompt.change,
            negative_prompt.submit,
            guidance_scale.change,
        ],
        fn=infer,
        inputs=[
            pre_prompt,
            prompt,
            seed,
            randomize_seed,
            num_inference_steps,
            negative_prompt,
            guidance_scale,
            user_lora_selector,
            user_lora_weight,
        ],
        outputs=[result],
    )

    clear_button.click(lambda: "", outputs=[prompt, result])

    gallery.select(
        fn=update_selection,
        inputs=[gr_sdxl_loras],
        outputs=[user_lora_selector, pre_prompt],
    )

    gr.Markdown(
        """
        ## Unleash Your Creativity!

        This showcase brings together the speed of Flash Diffusion and the artistic flair of Araminta K's models. 
        Craft your prompts, adjust the settings, and watch as AI brings your ideas to life in stunning detail.

        Remember to use this tool ethically and respect copyright and individual privacy.

        Enjoy exploring these unique artistic styles!

        **Disclaimer:**
        This demo is for research purposes only. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards.
        """
    )

demo.queue().launch()