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 huggingface_hub import login from themes import IndonesiaTheme # Import custom IndonesiaTheme from loras import loras MAX_SEED = np.iinfo(np.int32).max HF_TOKEN = os.getenv('HF_TOKEN_UPSCALER') HF_TOKEN_UPSCALER = os.getenv('HF_TOKEN_UPSCALER') 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 def generate_character_description(character_prompt): """Generate detailed character description using Qwen""" system_message = """You are a character description generator. Create detailed, vivid descriptions of characters including their physical appearance, personality, and notable features. Keep the description focused on visual elements that could be used for image generation.""" try: result = qwen_client.predict( message=character_prompt, param_2=system_message, param_3=128, param_4=0.7, param_5=0.95, api_name="/chat" ) '''result = qwen_client.predict( message=character_prompt, system_message=system_message, max_tokens=512, temperature=0.7, top_p=0.95, api_name="/chat" )''' return result except Exception as e: return f"Error generating description: {str(e)}" # 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 = generate_character_description(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_UPSCALER) 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 process, select the factor.") return [image_path] # CSS for styling the interface css = """ #col-left, #col-mid, #col-right { margin: 0 auto; max-width: 400px; padding: 10px; border-radius: 15px; background-color: #f9f9f9; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); } #banner { width: 100%; text-align: center; margin-bottom: 20px; } #run-button { background-color: #ff4b5c; color: white; font-weight: bold; padding: 10px; border-radius: 10px; cursor: pointer; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); } #footer { text-align: center; margin-top: 20px; color: silver; } """ # Creating Gradio interface with gr.Blocks(css=css, 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="📜 Image Description", placeholder="Write your prompt in any language, and it will be automatically translated into English.", elem_id="textbox-prompt" ) basemodel_choice = gr.Dropdown( label="🖼️ Select a Model", choices=[ "black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-merged", "dataautogpt3/FLUX-SyntheticAnime", "Shakker-Labs/FLUX.1-dev-LoRA-AntiBlur" "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "city96/FLUX.1-schnell-gguf" ], value="black-forest-labs/FLUX.1-schnell" ) lora_model_choice = gr.Dropdown( label="🎨 select a LoRA", choices=[ "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", "enhanceaiteam/Flux-uncensored", "Keltezaa/female-masturbation-fingering" ]+loaded_loras, value="XLabs-AI/flux-RealismLora" ) process_lora =gr.Checkbox(label="🎨 Use LoRA") process_upscale = gr.Checkbox(label="🔍 Upscale resolution") upscale_factor = gr.Radio(label="🔍 Upscale factor", 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="⚙️ Settings", open=True): width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=1280) height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=768) scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=8) steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=8) seed = gr.Number(label="Seed", value=-1) # Button to generate image btn = gr.Button("🚀 Bombs away!", 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=False).launch(show_api=True)