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| import os | |
| import gradio as gr | |
| import numpy as np | |
| import random | |
| from huggingface_hub import AsyncInferenceClient | |
| from translatepy import Translator | |
| import requests | |
| import re | |
| import asyncio | |
| from PIL import Image | |
| from gradio_client import Client, handle_file | |
| from huggingface_hub import login | |
| from gradio_imageslider import ImageSlider | |
| translator = Translator() | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| basemodel = "black-forest-labs/FLUX.1-schnell" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| CSS = "footer { visibility: hidden; }" | |
| JS = "function () { gradioURL = window.location.href; if (!gradioURL.endsWith('?__theme=dark')) { window.location.replace(gradioURL + '?__theme=dark'); } }" | |
| def enable_lora(lora_add): | |
| return basemodel if not lora_add else lora_add | |
| 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) | |
| text = str(translator.translate(prompt, 'English')) + "," + lora_word | |
| 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: | |
| raise gr.Error(f"Error en {e}") | |
| async def gen(prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, process_upscale): | |
| model = enable_lora(lora_add) | |
| image, seed = await generate_image(prompt, model, lora_word, width, height, scales, steps, seed) | |
| image_path = "temp_image.png" | |
| image.save(image_path) | |
| if process_upscale: | |
| upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor) | |
| else: | |
| upscale_image = image_path | |
| return [image_path, upscale_image] | |
| def get_upscale_finegrain(prompt, img_path, upscale_factor): | |
| client = Client("finegrain/finegrain-image-enhancer") | |
| 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] | |
| css = """ | |
| #col-container{ | |
| margin: 0 auto; | |
| max-width: 1024px; | |
| } | |
| """ | |
| with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown("Flux Upscaled +LORA") | |
| with gr.Group(): | |
| prompt = gr.Textbox(label="Prompt") | |
| with gr.Row(): | |
| lora_add = gr.Textbox(label="Add Flux LoRA", info="Modelo Lora", lines=1, value="XLabs-AI/flux-RealismLora") | |
| lora_word = gr.Textbox(label="Add Flux LoRA Trigger Word", info="Add the Trigger Word", lines=1, value="") | |
| width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=768) | |
| height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=1024) | |
| scales = gr.Slider(label="Guidance", minimum=3.5, maximum=7, step=0.1, value=3.5) | |
| steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=24) | |
| seed = gr.Slider(label="Seeds", minimum=-1, maximum=MAX_SEED, step=1, value=-1) | |
| upscale_factor = gr.Radio(label="UpScale Factor", choices=[2, 3, 4], value=2, scale=2) | |
| process_upscale = gr.Checkbox(label="Process Upscale", value=True) | |
| submit_btn = gr.Button("Submit", scale=1) | |
| output_res = ImageSlider(label="Flux / Upscaled") | |
| submit_btn.click( | |
| fn=lambda: None, | |
| inputs=None, | |
| outputs=[output_res], | |
| queue=False | |
| ).then( | |
| fn=gen, | |
| inputs=[prompt, lora_add, lora_word, width, height, scales, steps, seed, upscale_factor, process_upscale], | |
| outputs=[output_res] | |
| ) | |
| demo.launch() |