app.py

import gradio as gr
import torch
import random
import os
import time
from PIL import Image
from deep_translator import GoogleTranslator
from diffusers import DiffusionPipeline
from huggingface_hub import hf_hub_download

# Project by Nymbo with LoRA integration

# Model and LoRA configuration
BASE_MODEL = "black-forest-labs/FLUX.1-dev"
LORA_REPO = "burhansyam/uncen"
LORA_WEIGHTS_NAME = "uncen.safetensors"  # Adjust if different
torch_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16

# Initialize the pipeline with LoRA
def init_pipeline():
    pipe = DiffusionPipeline.from_pretrained(
        BASE_MODEL,
        torch_dtype=torch_dtype
    )
    
    # Load LoRA weights
    pipe.load_lora_weights(
        hf_hub_download(repo_id=LORA_REPO, filename=LORA_WEIGHTS_NAME),
        adapter_name="uncen"
    )
    
    # Enable model offloading if needed
    if torch.cuda.is_available():
        pipe.to("cuda")
        pipe.enable_xformers_memory_efficient_attention()
    
    return pipe

pipe = init_pipeline()

def convert_to_png(image):
    """Convert any image format to true PNG format"""
    png_buffer = io.BytesIO()
    if image.mode == 'RGBA':
        image.save(png_buffer, format='PNG', optimize=True)
    else:
        if image.mode != 'RGB':
            image = image.convert('RGB')
        image.save(png_buffer, format='PNG', optimize=True)
    png_buffer.seek(0)
    return Image.open(png_buffer)

def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", 
          seed=-1, strength=0.7, width=1024, height=1024):
    if not prompt:
        return None

    key = random.randint(0, 999)
    
    # Translate prompt
    try:
        prompt = GoogleTranslator(source='id', target='en').translate(prompt)
        print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
        prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    except Exception as e:
        print(f"Translation error: {e}")
    
    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
    
    # Set random seed if not specified
    generator = None
    if seed != -1:
        generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
    else:
        seed = random.randint(1, 1000000000)
        generator = torch.Generator(device="cuda" if torch.cuda.is_available() else "cpu").manual_seed(seed)
    
    # Map sampler names to Diffusers scheduler names
    sampler_map = {
        "DPM++ 2M Karras": "dpmsolver++",
        "DPM++ SDE Karras": "dpmsolver++",
        "Euler": "euler",
        "Euler a": "euler_a",
        "Heun": "heun",
        "DDIM": "ddim"
    }
    
    try:
        # Generate image with LoRA
        image = pipe(
            prompt=prompt,
            negative_prompt=is_negative if is_negative else None,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            generator=generator,
            strength=strength,
            width=width,
            height=height,
            cross_attention_kwargs={"scale": 0.8},  # LoRA strength adjustment
        ).images[0]
        
        png_img = convert_to_png(image)
        print(f'\033[1mGeneration {key} completed as PNG!\033[0m')
        return png_img
        
    except Exception as e:
        print(f"Generation error: {e}")
        raise gr.Error(f"Image generation failed: {str(e)}")

# Light theme CSS (same as before)
css = """
#app-container {
    max-width: 800px;
    margin: 0 auto;
    padding: 20px;
    background: #ffffff;
}
#prompt-text-input, #negative-prompt-text-input {
    font-size: 14px;
    background: #f9f9f9;
}
#gallery {
    min-height: 512px;
    background: #ffffff;
    border: 1px solid #e0e0e0;
}
#gen-button {
    margin: 10px 0;
    background: #4CAF50;
    color: white;
}
.accordion {
    background: #f5f5f5;
    border: 1px solid #e0e0e0;
}
h1 {
    color: #333333;
}
"""

with gr.Blocks(theme=gr.themes.Default(primary_hue="green"), css=css) as app:
    gr.HTML("<center><h1>FLUX.1-Dev with LoRA (PNG Output)</h1></center>")
    
    with gr.Column(elem_id="app-container"):
        with gr.Row():
            with gr.Column(elem_id="prompt-container"):
                with gr.Row():
                    text_prompt = gr.Textbox(
                        label="Prompt", 
                        placeholder="Masukkan prompt dalam Bahasa Indonesia", 
                        lines=2,
                        elem_id="prompt-text-input"
                    )
                
                with gr.Accordion("Advanced Settings", open=False):
                    negative_prompt = gr.Textbox(
                        label="Negative Prompt", 
                        value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
                        lines=3
                    )
                    with gr.Row():
                        width = gr.Slider(1024, label="Width", minimum=512, maximum=1536, step=64)
                        height = gr.Slider(1024, label="Height", minimum=512, maximum=1536, step=64)
                    with gr.Row():
                        steps = gr.Slider(35, label="Steps", minimum=10, maximum=100, step=1)
                        cfg = gr.Slider(7.0, label="CFG Scale", minimum=1.0, maximum=20.0, step=0.5)
                    with gr.Row():
                        strength = gr.Slider(0.7, label="Strength", minimum=0.1, maximum=1.0, step=0.01)
                        seed = gr.Number(-1, label="Seed (-1 for random)")
                    method = gr.Radio(
                        ["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"],
                        value="DPM++ 2M Karras",
                        label="Sampling Method"
                    )

        generate_btn = gr.Button("Generate Image", variant="primary")
        
        with gr.Row():
            output_image = gr.Image(
                type="pil",
                label="Generated PNG Image",
                format="png",
                elem_id="gallery"
            )
        
        generate_btn.click(
            fn=query,
            inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height],
            outputs=output_image
        )

app.launch(server_name="0.0.0.0", server_port=7860, share=True)