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: FlashLoRA ⚡
        This is an interactive demo of [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) **on top of** existing LoRAs.
        
        The distillation method proposed in [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* from Jasper Research.
        The LoRAs can be added **without** any retraining for similar results in most cases. Feel free to tweak the parameters and use your own LoRAs by giving a look at the [Github Repo](https://github.com/gojasper/flash-diffusion)
        """
    )
    gr.Markdown(
        "If you enjoy the space, please also promote *open-source* by giving a ⭐ to our 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("**Disclaimer:**")
    gr.Markdown(
        "This demo is only for research purpose. 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()