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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 | |
| # MAX_SEED = np.iinfo(np.int32).max | |
| # def enable_lora(lora_add, basemodel): | |
| # 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) | |
| # print(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: | |
| # print(f"Error generando imagen: {e}") | |
| # return None, None | |
| # def get_upscale_finegrain(prompt, img_path, upscale_factor): | |
| # try: | |
| # 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] | |
| # except Exception as e: | |
| # print(f"Error escalando imagen: {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 | |
| # image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed) | |
| # if image is None: | |
| # return [None, None] | |
| # image_path = "temp_image.jpg" | |
| # image.save(image_path, format="JPEG") | |
| # if process_upscale: | |
| # upscale_image_path = get_upscale_finegrain(prompt, image_path, upscale_factor) | |
| # if upscale_image_path is not None: | |
| # upscale_image = Image.open(upscale_image_path) | |
| # upscale_image.save("upscale_image.jpg", format="JPEG") | |
| # return [image_path, "upscale_image.jpg"] | |
| # else: | |
| # print("Error: The scaled image path is None") | |
| # return [image_path, image_path] | |
| # else: | |
| # return [image_path, image_path] | |
| # css = """ | |
| # #col-container{ margin: 0 auto; max-width: 1024px;} | |
| # """ | |
| # with gr.Blocks(css=css) 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="Image Description") | |
| # basemodel_choice = gr.Dropdown(label="Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV", "enhanceaiteam/Flux-uncensored", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "city96/FLUX.1-dev-gguf"], value="black-forest-labs/FLUX.1-schnell") | |
| # lora_model_choice = gr.Dropdown(label="LoRA", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", "enhanceaiteam/Flux-uncensored"], value="XLabs-AI/flux-RealismLora") | |
| # process_lora = gr.Checkbox(label="LoRA Process") | |
| # process_upscale = gr.Checkbox(label="Scale Process") | |
| # upscale_factor = gr.Radio(label="Scaling Factor", choices=[2, 4, 8], value=2) | |
| # with gr.Accordion(label="Advanced Options", open=False): | |
| # 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) | |
| # btn = gr.Button("Generate") | |
| # 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() |