app.py

import spaces
import os
import numpy as np
import gradio as gr
import json
import torch
from diffusers import DiffusionPipeline
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast

# Define the device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Use the 'waffles' environment variable as the access token
hf_token = os.getenv('waffles')

# Ensure the token is loaded correctly
if not hf_token:
    raise ValueError("Hugging Face API token not found. Please set the 'waffles' environment variable.")

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

# Initialize the base model with authentication and specify the device
pipe = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
    token=hf_token
).to(device)

# Define MAX_SEED
MAX_SEED = 2**32 - 1

@spaces.GPU(duration=90)
def run_lora(prompt, cfg_scale, steps, selected_repo, randomize_seed, seed, width, height, lora_scale):
    if not selected_repo:
        raise gr.Error("You must select a LoRA before proceeding.")

    selected_lora = next((lora for lora in loras if lora["repo"] == selected_repo), None)
    if not selected_lora:
        raise gr.Error("Selected LoRA not found.")

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

    # Load LoRA weights
    if "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
    if randomize_seed:
        seed = torch.randint(0, MAX_SEED, (1,)).item()

    # Generate image
    generator = torch.Generator(device=device).manual_seed(seed)
    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]

    # Reset the model to CPU and unload LoRA weights to free up memory
    pipe.to("cpu")
    pipe.unload_lora_weights()

    return image, seed

# Custom CSS for GUI styling
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}
'''

def update_selection(index, width, height):
    selected_lora = loras[index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{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, width, height

with gr.Blocks(theme="Nymbo/Nymbo_Theme", 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
            )
        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)
                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.95)

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

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

app.queue()
app.launch()