import os import gradio as gr import numpy as np import random from huggingface_hub import AsyncInferenceClient from translatepy import Translator from gradio_client import Client, handle_file from PIL import Image from lora_saver import main as backup_loras os.system('quent_models.py') from loras import loras from huggingface_hub import login from themes import IndonesiaTheme # Import custom IndonesiaTheme from lorify import Lorify from css import css2 MAX_SEED = np.iinfo(np.int32).max HF_TOKEN = os.getenv('HF_TOKEN') HF_TOKEN_UPSCALER = os.getenv('HF_TOKEN') qwen_client = Client("K00B404/HugChatWrap",hf_token=HF_TOKEN) loaded_loras=[] for lora in loras: print(lora.get('repo')) loaded_loras.append(lora.get('repo')) # Function to enable LoRA if selected def enable_lora(lora_add, basemodel): print(f"[-] Determining model: LoRA {'enabled' if lora_add else 'disabled'}, base model: {basemodel}") return basemodel if not lora_add else lora_add # Function to generate image async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed): try: if seed == -1: seed = random.randint(0, MAX_SEED) seed = int(seed) print(f"[-] Menerjemahkan prompt: {prompt}") text = str(Translator().translate(prompt, 'English')) + "," + lora_word print(f"[-] Generating image with prompt: {text}, model: {model}") client = AsyncInferenceClient() image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model) return image, seed except Exception as e: print(f"[-] Error generating image: {e}") return None, None # Function to upscale image def get_upscale_finegrain(prompt, img_path, upscale_factor): try: print(f"[-] Memulai proses upscaling dengan faktor {upscale_factor} untuk gambar {img_path}") client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN) result = client.predict( input_image=handle_file(img_path), prompt=prompt, negative_prompt="worst quality, low quality, normal quality", upscale_factor=upscale_factor, controlnet_scale=0.6, controlnet_decay=1, condition_scale=6, denoise_strength=0.35, num_inference_steps=18, solver="DDIM", api_name="/process" ) print(f"[-] Proses upscaling berhasil.") return result[1] # Return upscale image path except Exception as e: print(f"[-] Error scaling image: {e}") return None # Main function to generate images and optionally upscale async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora): print(f"[-] Memulai generasi gambar dengan prompt: {prompt}") model = enable_lora(lora_model, basemodel) if process_lora else basemodel print(f"[-] Menggunakan model: {model}") image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed) if image is None: print("[-] Image generation failed.") return [] image_path = "temp_image.jpg" print(f"[-] Menyimpan gambar sementara di: {image_path}") image.save(image_path, format="JPEG") upscale_image_path = None if process_upscale: print(f"[-] Memproses upscaling dengan faktor: {upscale_factor}") upscale_image_path = get_upscale_finegrain(prompt, image_path, upscale_factor) if upscale_image_path is not None and os.path.exists(upscale_image_path): print(f"[-] Proses upscaling selesai. Gambar tersimpan di: {upscale_image_path}") return [image_path, upscale_image_path] # Return both images else: print("[-] Upscaling gagal, jalur gambar upscale tidak ditemukan.") return [image_path] base_models=[ "black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", #"city96/FLUX.1-dev-gguf" ] loras_list_custom=[ "Keltezaa/anal-riding-missionary", "Keltezaa/Fingering", "Keltezaa/Spreading", "Keltezaa/flux-prone-ass-spread-hd", "Keltezaa/Flux_P", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", ]# + loaded_loras # add loras loaded from file backup_loras(loras_list_custom) # Creating Gradio interface with gr.Blocks(css=css2, theme=IndonesiaTheme()) as WallpaperFluxMaker: # Displaying the application title gr.HTML('') with gr.Column(elem_id="col-container"): # Output section (replacing ImageSlider with gr.Gallery) with gr.Row(): output_res = gr.Gallery(label="⚡ Flux / Upscaled Image ⚡", elem_id="output-res", columns=2, height="auto") # User input section split into two columns with gr.Row(): # Column 1: Input prompt, LoRA, and base model with gr.Column(scale=1, elem_id="col-left"): prompt = gr.Textbox( label="📜 Description", placeholder="Tuliskan prompt Anda dalam bahasa apapun, yang akan langsung diterjemahkan ke bahasa Inggris.", elem_id="textbox-prompt" ) basemodel_choice = gr.Dropdown( label="🖼️ Select Model", choices=base_models, value=base_models[0] ) lora_model_choice = gr.Dropdown( label="🎨 Select LoRA", choices=loras_list_custom, value=loras_list_custom[0] ) process_lora = gr.Checkbox(label="🎨 Aktifkan LoRA") process_upscale = gr.Checkbox(label="🔍 Aktifkan Peningkatan Resolusi") upscale_factor = gr.Radio(label="🔍 Faktor Peningkatan Resolusi", choices=[2, 4, 8], value=2) # Column 2: Advanced options (always open) with gr.Column(scale=1, elem_id="col-right"): with gr.Accordion(label="⚙️ Opsi Lanjutan", open=True): width = gr.Slider(label="Lebar", minimum=512, maximum=1280, step=8, value=1280) height = gr.Slider(label="Tinggi", minimum=512, maximum=1280, step=8, value=768) scales = gr.Slider(label="Skala", minimum=1, maximum=20, step=1, value=8) steps = gr.Slider(label="Langkah", minimum=1, maximum=100, step=1, value=8) seed = gr.Number(label="Seed", value=-1) # Button to generate image btn = gr.Button("🚀 Buat Gambar", elem_id="generate-btn") # Running the `gen` function when "Generate" button is pressed btn.click(fn=gen, inputs=[ prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora ], outputs=output_res) # Launching the Gradio app WallpaperFluxMaker.queue(api_open=True).launch(show_api=True)