app.py

import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time

# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
    loras = json.load(f)

# Initialize the base model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "black-forest-labs/FLUX.1-dev"

taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
                                                      vae=good_vae,
                                                      transformer=pipe.transformer,
                                                      text_encoder=pipe.text_encoder,
                                                      tokenizer=pipe.tokenizer,
                                                      text_encoder_2=pipe.text_encoder_2,
                                                      tokenizer_2=pipe.tokenizer_2,
                                                      torch_dtype=dtype
                                                     )

MAX_SEED = 2**32-1

pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def update_selection(evt: gr.SelectData, selected_indices, width, height):
    selected_index = evt.index
    selected_indices = selected_indices or []
    if selected_index in selected_indices:
        # LoRA is already selected, remove it
        selected_indices.remove(selected_index)
    else:
        if len(selected_indices) < 2:
            selected_indices.append(selected_index)
        else:
            raise gr.Error("You can select up to 2 LoRAs only.")

    # Initialize outputs
    selected_info_1 = ""
    selected_info_2 = ""
    lora_scale_1 = 0.95
    lora_scale_2 = 0.95
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
        lora_image_2 = lora2['image']

    # Update prompt placeholder based on last selected LoRA
    if selected_indices:
        last_selected_lora = loras[selected_indices[-1]]
        new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
    else:
        new_placeholder = "Type a prompt after selecting a LoRA"

    return (
        gr.update(placeholder=new_placeholder),
        selected_info_1,
        selected_info_2,
        selected_indices,
        lora_scale_1,
        lora_scale_2,
        width,
        height,
        lora_image_1,
        lora_image_2,
    )

def remove_lora_1(selected_indices):
    selected_indices = selected_indices or []
    if len(selected_indices) >= 1:
        selected_indices.pop(0)
    # Update selected_info_1 and selected_info_2
    selected_info_1 = ""
    selected_info_2 = ""
    lora_scale_1 = 0.95
    lora_scale_2 = 0.95
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

def remove_lora_2(selected_indices):
    selected_indices = selected_indices or []
    if len(selected_indices) >= 2:
        selected_indices.pop(1)
    # Update selected_info_1 and selected_info_2
    selected_info_1 = ""
    selected_info_2 = ""
    lora_scale_1 = 0.95
    lora_scale_2 = 0.95
    lora_image_1 = None
    lora_image_2 = None
    if len(selected_indices) >= 1:
        lora1 = loras[selected_indices[0]]
        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
        lora_image_1 = lora1['image']
    if len(selected_indices) >= 2:
        lora2 = loras[selected_indices[1]]
        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
        lora_image_2 = lora2['image']
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

def randomize_loras(selected_indices):
    if len(loras) < 2:
        raise gr.Error("Not enough LoRAs to randomize.")
    selected_indices = random.sample(range(len(loras)), 2)
    lora1 = loras[selected_indices[0]]
    lora2 = loras[selected_indices[1]]
    selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
    selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
    lora_scale_1 = 0.95
    lora_scale_2 = 0.95
    lora_image_1 = lora1['image']
    lora_image_2 = lora2['image']
    return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

# ... (rest of your code remains unchanged)

# Update your UI components to include image previews
run_lora.zerogpu = True

css = '''
#gen_btn{height: 100%}
#title{text-align: center}
#title h1{font-size: 3em; display:inline-flex; align-items:center}
#title img{width: 100px; margin-right: 0.5em}
#gallery .grid-wrap{height: 10vh}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.custom_lora_card{margin-bottom: 1em}
.card_internal{display: flex;height: 100px;margin-top: .5em}
.card_internal img{margin-right: 1em}
.styler{--form-gap-width: 0px !important}
#progress{height:30px}
#progress .generating{display:none}
.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
'''

with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
    title = gr.HTML(
        """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> LoRA Lab</h1>""",
        elem_id="title",
    )
    selected_indices = gr.State([])
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
        with gr.Column(scale=1):
            generate_button = gr.Button("Generate", variant="primary")
    with gr.Row():
        with gr.Column(scale=1):
            randomize_button = gr.Button("🎲", variant="secondary", scale=1, min_width=50)
        with gr.Column(scale=4):
            lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False)
            selected_info_1 = gr.Markdown("Select a LoRA 1")
            lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=0.95)
            remove_button_1 = gr.Button("Remove LoRA 1")
        with gr.Column(scale=4):
            lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False)
            selected_info_2 = gr.Markdown("Select a LoRA 2")
            lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=0.95)
            remove_button_2 = gr.Button("Remove LoRA 2")
    with gr.Row():
        with gr.Column():
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA Gallery",
                allow_preview=False,
                columns=3,
                elem_id="gallery"
            )
            with gr.Group():
                custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
                gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
            custom_lora_info = gr.HTML(visible=False)
            custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
        with gr.Column():
            progress_bar = gr.Markdown(elem_id="progress", visible=False)
            result = gr.Image(label="Generated Image")
    with gr.Row():
        with gr.Accordion("Advanced Settings", open=False):
            with gr.Row():
                input_image = gr.Image(label="Input image", type="filepath")
                image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
            with gr.Column():
                with gr.Row():
                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
                
                with gr.Row():
                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
                
                with gr.Row():
                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
    
    gallery.select(
        update_selection,
        inputs=[selected_indices, width, height],
        outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2]
    )
    remove_button_1.click(
        remove_lora_1,
        inputs=[selected_indices],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    remove_button_2.click(
        remove_lora_2,
        inputs=[selected_indices],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    randomize_button.click(
        randomize_loras,
        inputs=[selected_indices],
        outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    custom_lora.change(
        add_custom_lora,
        inputs=[custom_lora, selected_indices],
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    custom_lora_button.click(
        remove_custom_lora,
        inputs=[custom_lora_info, custom_lora_button, selected_indices],
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
    )
    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=run_lora,
        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height],
        outputs=[result, seed, progress_bar]
    )

app.queue()
app.launch()