import os import numpy as np import random from pathlib import Path from PIL import Image import streamlit as st from huggingface_hub import InferenceClient, AsyncInferenceClient from gradio_client import Client, handle_file import asyncio from concurrent.futures import ThreadPoolExecutor MAX_SEED = np.iinfo(np.int32).max HF_TOKEN = os.environ.get("HF_TOKEN") HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER") client = AsyncInferenceClient() llm_client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") DATA_PATH = Path("./data") DATA_PATH.mkdir(exist_ok=True) def run_async(func): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) executor = ThreadPoolExecutor(max_workers=1) result = loop.run_in_executor(executor, func) return loop.run_until_complete(result) def enable_lora(lora_add, basemodel): return lora_add if lora_add else basemodel async def generate_image(combined_prompt, model, width, height, scales, steps, seed): try: if seed == -1: seed = random.randint(0, MAX_SEED) seed = int(seed) image = await client.text_to_image( prompt=combined_prompt, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model ) return image, seed except Exception as e: return f"Error al generar imagen: {e}", None def get_upscale_finegrain(prompt, img_path, upscale_factor): try: client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER) result = client.predict( input_image=handle_file(img_path), prompt=prompt, upscale_factor=upscale_factor ) return result[1] if isinstance(result, list) and len(result) > 1 else None except Exception as e: return None def save_prompt(prompt_text, seed): try: prompt_file_path = DATA_PATH / f"prompt_{seed}.txt" with open(prompt_file_path, "w") as prompt_file: prompt_file.write(prompt_text) return prompt_file_path except Exception as e: st.error(f"Error al guardar el prompt: {e}") return None async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora, process_enhancer): model = enable_lora(lora_model, basemodel) if process_lora else basemodel combined_prompt = prompt if process_enhancer: improved_prompt = await improve_prompt(prompt) combined_prompt = f"{prompt} {improved_prompt}" if seed == -1: seed = random.randint(0, MAX_SEED) seed = int(seed) progress_bar = st.progress(0) image, seed = await generate_image(combined_prompt, model, width, height, scales, steps, seed) progress_bar.progress(50) if isinstance(image, str) and image.startswith("Error"): progress_bar.empty() return [image, None, combined_prompt] image_path = save_image(image, seed) prompt_file_path = save_prompt(combined_prompt, seed) if process_upscale: upscale_image_path = get_upscale_finegrain(combined_prompt, image_path, upscale_factor) if upscale_image_path: upscale_image = Image.open(upscale_image_path) upscale_image.save(DATA_PATH / f"upscale_image_{seed}.jpg", format="JPEG") progress_bar.progress(100) image_path.unlink() return [str(DATA_PATH / f"upscale_image_{seed}.jpg"), str(prompt_file_path)] else: progress_bar.empty() return [str(image_path), str(prompt_file_path)] else: progress_bar.progress(100) return [str(image_path), str(prompt_file_path)] async def improve_prompt(prompt): try: instruction = ("With this idea, describe in English a detailed txt2img prompt in 500 characters at most, add ilumination, admosphere, cinematic and characters...") formatted_prompt = f"{prompt}: {instruction}" response = llm_client.text_generation(formatted_prompt, max_new_tokens=300) improved_text = response['generated_text'].strip() if 'generated_text' in response else response.strip() return improved_text[:300] if len(improved_text) > 300 else improved_text except Exception as e: return f"Error mejorando el prompt: {e}" def save_image(image, seed): try: image_path = DATA_PATH / f"image_{seed}.jpg" image.save(image_path, format="JPEG") return image_path except Exception as e: st.error(f"Error al guardar la imagen: {e}") return None def get_storage(): files = [file for file in DATA_PATH.glob("*.jpg") if file.is_file()] files.sort(key=lambda x: x.stat().st_mtime, reverse=True) usage = sum([file.stat().st_size for file in files]) return [str(file.resolve()) for file in files], f"Uso total: {usage/(1024.0 ** 3):.3f}GB" def get_prompts(): prompt_files = [file for file in DATA_PATH.glob("*.txt") if file.is_file()] return {file.stem.replace("prompt_", ""): file for file in prompt_files} def delete_image(image_path): try: if Path(image_path).exists(): Path(image_path).unlink() st.success(f"Imagen {image_path} borrada.") else: st.error("El archivo de imagen no existe.") except Exception as e: st.error(f"Error al borrar la imagen: {e}") def main(): st.set_page_config(layout="wide") st.title("FLUX with prompt enhancer and upscaler with LORA model training") prompt = st.sidebar.text_input("Descripción de la imagen", max_chars=200) process_enhancer = st.sidebar.checkbox("Mejorar Prompt", value=True) # Nuevo checkbox basemodel = st.sidebar.selectbox("Modelo Base", ["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"]) lora_model = st.sidebar.selectbox("LORA Realismo", ["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"]) format_option = st.sidebar.selectbox("Formato", ["9:16", "16:9"]) process_lora = st.sidebar.checkbox("Procesar LORA", value=True) process_upscale = st.sidebar.checkbox("Procesar Escalador", value=True) upscale_factor = st.sidebar.selectbox("Factor de Escala", [2, 4, 8], index=0) scales = st.sidebar.slider("Escalado", 1, 20, 10) steps = st.sidebar.slider("Pasos", 1, 100, 20) seed = st.sidebar.number_input("Semilla", value=-1) if format_option == "9:16": width = 720 height = 1280 else: width = 1280 height = 720 if st.sidebar.button("Generar Imagen"): with st.spinner("Mejorando y generando imagen..."): result = asyncio.run(gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora, process_enhancer)) image_paths = result[0] prompt_file = result[1] st.write(f"Image paths: {image_paths}") if image_paths: if Path(image_paths).exists(): st.image(image_paths, caption="Imagen Generada") else: st.error("El archivo de imagen no existe.") if prompt_file and Path(prompt_file).exists(): prompt_text = Path(prompt_file).read_text() st.write(f"Prompt utilizado: {prompt_text}") else: st.write("El archivo del prompt no está disponible.") files, usage = get_storage() st.text(usage) cols = st.columns(6) prompts = get_prompts() for idx, file in enumerate(files): with cols[idx % 6]: image = Image.open(file) prompt_file = prompts.get(Path(file).stem.replace("image_", ""), None) prompt_text = Path(prompt_file).read_text() if prompt_file else "No disponible" st.image(image, caption=f"Imagen {idx+1}") st.write(f"Prompt: {prompt_text}") if st.button(f"Borrar Imagen {idx+1}", key=f"delete_{idx}"): try: os.remove(file) if prompt_file: os.remove(prompt_file) st.success(f"Imagen {idx+1} y su prompt fueron borrados.") except Exception as e: st.error(f"Error al borrar la imagen o prompt: {e}") if __name__ == "__main__": main()