app.py

import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline
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
models = ["camenduru/FLUX.1-dev-diffusers", "black-forest-labs/FLUX.1-schnell",
           "sayakpaul/FLUX.1-merged", "John6666/blue-pencil-flux1-v001-fp8-flux"]
base_model = models[0]
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)

MAX_SEED = 2**32-1

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, width, height):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    if "aspect" in selected_lora:
        if selected_lora["aspect"] == "portrait":
            width = 768
            height = 1024
        elif selected_lora["aspect"] == "landscape":
            width = 1024
            height = 768
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
    )

@spaces.GPU(duration=70)
def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    
    with calculateDuration("Generating image"):
        # Generate image
        image = pipe(
            prompt=f"{prompt} {trigger_word}",
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            joint_attention_kwargs={"scale": lora_scale},
        ).images[0]
    return image

def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
              lora_scale, lora_repo, lora_weights, lora_trigger, progress=gr.Progress(track_tqdm=True)):
    #if selected_index is None and not lora_repo:
    #    raise gr.Error("You must select a LoRA before proceeding.")

    if selected_index is not None and not lora_repo:
        selected_lora = loras[selected_index]
        lora_path = selected_lora["repo"]
        trigger_word = selected_lora["trigger_word"]
    else: # override
        selected_lora = loras[0]
        lora_path = lora_repo
        trigger_word = lora_trigger

    # Load LoRA weights
    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
        if selected_index is None and not lora_repo: # override
            pass
        elif lora_weights: # override
            pipe.load_lora_weights(lora_path, weight_name=lora_weights)
        elif "weights" in selected_lora:
            pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
        else:
            pipe.load_lora_weights(lora_path)
        
    # Set random seed for reproducibility
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
    
    image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
    pipe.to("cpu")
    if selected_index is not None or lora_repo: pipe.unload_lora_weights()
    return image, seed  

run_lora.zerogpu = True

def get_repo_safetensors(repo_id: str):
    from huggingface_hub import HfApi
    api = HfApi()
    try:
        if " " in repo_id or not api.repo_exists(repo_id): return gr.update(value="", choices=[])
        files = api.list_repo_files(repo_id=repo_id)
    except Exception as e:
        print(f"Error: Failed to get {repo_id}'s info. ")
        print(e)
        return gr.update(choices=[])
    files = [f for f in files if f.endswith(".safetensors")]
    if len(files) == 0: return gr.update(value="", choices=[])
    else: return gr.update(value=files[0], choices=files)

def change_base_model(repo_id: str):
    from huggingface_hub import HfApi
    global pipe
    api = HfApi()
    try:
        if " " in repo_id or not api.repo_exists(repo_id): return
        pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
    except Exception as e:
        print(e)

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}
'''
with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
    title = gr.HTML(
        """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> FLUX LoRA the Explorer</h1>""",
        elem_id="title",
    )
    selected_index = gr.State(None)
    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, elem_id="gen_column"):
            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
    with gr.Row():
        with gr.Column(scale=3):
            selected_info = gr.Markdown("")
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA Gallery",
                allow_preview=False,
                columns=3,
                elem_id="gallery"
            )
            
        with gr.Column(scale=4):
            result = gr.Image(label="Generated Image")

    with gr.Row():
        with gr.Accordion("Advanced Settings", open=False):
            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)

                with gr.Row():
                    lora_repo = gr.Dropdown(label="LoRA Repo", choices=[], info="Input LoRA Repo ID", value="", allow_custom_value=True)
                    lora_weights = gr.Dropdown(label="LoRA Filename", choices=[], info="Optional", value="", allow_custom_value=True)
                    lora_trigger = gr.Textbox(label="LoRA Trigger Prompt", value="")
                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.95)

                with gr.Row():
                    model_name = gr.Dropdown(label="Base Model", choices=models, value=models[0], allow_custom_value=True)

    gallery.select(
        update_selection,
        inputs=[width, height],
        outputs=[prompt, selected_info, selected_index, width, height]
    )

    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=run_lora,
        inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
                 lora_scale, lora_repo, lora_weights, lora_trigger],
        outputs=[result, seed]
    )

    lora_repo.change(get_repo_safetensors, [lora_repo], [lora_weights])
    model_name.change(change_base_model, [model_name], None)


app.queue()
app.launch()