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| import os | |
| import gradio as gr | |
| import numpy as np | |
| import random | |
| from huggingface_hub import AsyncInferenceClient, InferenceClient | |
| from PIL import Image | |
| from gradio_client import Client, handle_file | |
| from gradio_imageslider import ImageSlider | |
| 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") | |
| def enable_lora(lora_add, basemodel): | |
| return basemodel if not lora_add else lora_add | |
| 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, negative_prompt="", | |
| seed=42, upscale_factor=upscale_factor, controlnet_scale=0.6, | |
| controlnet_decay=1, condition_scale=6, tile_width=112, | |
| tile_height=144, denoise_strength=0.35, num_inference_steps=18, | |
| solver="DDIM", api_name="/process" | |
| ) | |
| return result[1] if isinstance(result, list) and len(result) > 1 else None | |
| except Exception as e: | |
| return None | |
| async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora): | |
| model = enable_lora(lora_model, basemodel) if process_lora else basemodel | |
| improved_prompt = await improve_prompt(prompt) | |
| combined_prompt = f"{prompt} {improved_prompt}" | |
| if seed == -1: | |
| seed = random.randint(0, MAX_SEED) | |
| seed = int(seed) | |
| image, seed = await generate_image(combined_prompt, model, width, height, scales, steps, seed) | |
| if isinstance(image, str) and image.startswith("Error"): | |
| return [image, None] | |
| image_path = "temp_image.jpg" | |
| image.save(image_path, format="JPEG") | |
| 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("upscale_image.jpg", format="JPEG") | |
| return [image_path, "upscale_image.jpg"] | |
| else: | |
| return [image_path, image_path] | |
| else: | |
| return [image_path, image_path] | |
| async def improve_prompt(prompt): | |
| try: | |
| instruction = ("With this idea, describe in English a detailed img2vid prompt in a single paragraph of up to 200 characters maximum, developing atmosphere, characters, lighting, and cameras.") | |
| formatted_prompt = f"{prompt}: {instruction}" | |
| response = llm_client.text_generation(formatted_prompt, max_new_tokens=200) | |
| improved_text = response['generated_text'].strip() if 'generated_text' in response else response.strip() | |
| return improved_text | |
| except Exception as e: | |
| return f"Error mejorando el prompt: {e}" | |
| css = """ | |
| #col-container{ margin: 0 auto; max-width: 1024px;} | |
| """ | |
| with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo: | |
| with gr.Column(elem_id="col-container"): | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| output_res = ImageSlider(label="Flux / Upscaled") | |
| with gr.Column(scale=2): | |
| prompt = gr.Textbox(label="Descripción de imágen") | |
| basemodel_choice = gr.Dropdown( | |
| label="Modelo", | |
| choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], | |
| value="black-forest-labs/FLUX.1-schnell" | |
| ) | |
| lora_model_choice = gr.Dropdown( | |
| label="LORA Realismo", | |
| choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"], | |
| value="XLabs-AI/flux-RealismLora" | |
| ) | |
| with gr.Row(): | |
| process_lora = gr.Checkbox(label="Procesar LORA") | |
| process_upscale = gr.Checkbox(label="Procesar Escalador") | |
| improved_prompt = gr.Textbox(label="Prompt Mejorado", interactive=False) | |
| improve_btn = gr.Button("Mejora mi prompt") | |
| improve_btn.click(fn=improve_prompt, inputs=[prompt], outputs=improved_prompt) | |
| with gr.Accordion(label="Opciones Avanzadas", open=False): | |
| width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=1280) | |
| height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=768) | |
| upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2) | |
| scales = gr.Slider(label="Escalado", minimum=1, maximum=20, step=1, value=10) | |
| steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=20) | |
| seed = gr.Number(label="Semilla", value=-1) | |
| btn = gr.Button("Generar") | |
| 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 | |
| ) | |
| demo.launch() |