app.py

import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline

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

# Initialize the base model
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
pipe.to("cuda")

def update_selection(evt: gr.SelectData):
 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}) ✨"
 return (
 gr.update(placeholder=new_placeholder),
 updated_text,
 evt.index
 )

@spaces.GPU(duration=90)
def run_lora(prompt, cfg_scale, steps, selected_index, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
 if selected_index is None:
 raise gr.Error("You must select a LoRA before proceeding.")

 selected_lora = loras[selected_index]
 lora_path = selected_lora["repo"]
 trigger_word = selected_lora["trigger_word"]

 # Load LoRA weights
 pipe.load_lora_weights(lora_path)

 # Set random seed for reproducibility
 generator = torch.Generator(device="cuda").manual_seed(seed)

 # Generate image
 image = pipe(
 prompt=f"{prompt} {trigger_word}",
 #negative_prompt=negative_prompt,
 num_inference_steps=steps,
 guidance_scale=cfg_scale,
 width=width,
 height=height,
 generator=generator,
 #cross_attention_kwargs={"scale": lora_scale},
 ).images[0]

 # Unload LoRA weights
 pipe.unload_lora_weights()

 return image

with gr.Blocks(theme=gr.themes.Soft()) as app:
 gr.Markdown("# FLUX.1 LoRA the Explorer")
 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):
 generate_button = gr.Button("Generate", variant="primary")
 with gr.Row():
 with gr.Column(scale=1):
 selected_info = gr.Markdown("")
 gallery = gr.Gallery(
 [(item["image"], item["title"]) for item in loras],
 label="LoRA Gallery",
 allow_preview=False,
 columns=2
 )
 
 with gr.Column(scale=2):
 result = gr.Image(label="Generated Image")

 with gr.Row():
 #with gr.Column():
 #prompt_title = gr.Markdown("### Click on a LoRA in the gallery to select it")
 #negative_prompt = gr.Textbox(label="Negative Prompt", lines=2, value="low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry")

 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=100, step=1, value=30)
 
 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():
 seed = gr.Slider(label="Seed", minimum=0, maximum=2**32-1, step=1, value=0, randomize=True)
 lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=1)

 gallery.select(update_selection, outputs=[prompt, selected_info, selected_index])
 
 generate_button.click(
 fn=run_lora,
 inputs=[prompt, cfg_scale, steps, selected_index, seed, width, height, lora_scale],
 outputs=[result]
 )

app.queue()
app.launch()