app.py

import os
from huggingface_hub import login
import spaces
import gradio as gr
import torch
from PIL import Image
from diffusers import DiffusionPipeline
import random


# get access token
access_token = os.environ.get("ACCESS_TOKEN")

# login with access token
if access_token:
    login(token=access_token)
else:
    print("warning: no access token found")

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

lora_repo = "vincenthugging/flux-lora-leijun"
trigger_word = "leijun"  # Leave trigger_word blank if not used.
pipe.load_lora_weights(lora_repo)

pipe.to("cuda")

MAX_SEED = 2**32-1

@spaces.GPU(duration=80)
def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
    # Set random seed for reproducibility
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device="cuda").manual_seed(seed)

    # Update progress bar (0% saat mulai)
    progress(0, "Starting image generation...")

    # Generate image with progress updates
    for i in range(1, steps + 1):
        # Simulate the processing step (in a real scenario, you would integrate this with your image generation process)
        if i % (steps // 10) == 0:  # Update every 10% of the steps
            progress(i / steps * 100, f"Processing step {i} of {steps}...")

    # Generate image using the pipeline
    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]

    # Final update (100%)
    progress(100, "Completed!")

    yield image, seed

# Example cached image and settings
example_image_path = "example0.webp"  # Replace with the actual path to the example image
example_prompt = """photo of leijun, black t-shirt with  Logo of Xiaomi Company , face detailed, fit body,
looking at the viewer,holding a handwritten white sign with orange text that says, “Are you OK ?”,
light background,studio light"""

# example settings
example_cfg_scale = 3.5
example_steps = 28
example_width = 1024
example_height = 1024
example_seed = None
example_lora_scale = 0.85

def load_example():
    # Load example image from file
    example_image = Image.open(example_image_path)
    return example_prompt, example_cfg_scale, example_steps, True, example_seed, example_width, example_height, example_lora_scale, example_image

with gr.Blocks() as app:
    gr.Markdown("# Flux Image Generator for Leijun")
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.TextArea(label="Prompt", placeholder="Type a prompt", lines=5)
            generate_button = gr.Button("Generate")
            
            # Advanced options,with default values
            with gr.Accordion("Advanced options", open=False):
                cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=example_cfg_scale)
                steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=example_steps)
                width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=example_width)
                height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=example_height)
                randomize_seed = gr.Checkbox(True, label="Randomize seed")
                
                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=example_seed)
                lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=example_lora_scale)
        
        with gr.Column(scale=1):
            result = gr.Image(label="Generated Image")
            gr.Markdown("Generate images using Lora and a text prompt.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]")

    # Automatically load example data and image when the interface is launched
    app.load(load_example, inputs=[], outputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, result])
    
    generate_button.click(
        run_lora,
        inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale],
        outputs=[result, seed]
    )

app.queue()
app.launch()