app.py

#Save ZeroGPU limited resources, switch to InferenceAPI
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

translator = Translator()
HF_TOKEN = os.environ.get("HF_TOKEN", None)
# Constants
basemodel = "black-forest-labs/FLUX.1-dev"
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):
    if not lora_add:
        return basemodel
    else:
        return lora_add

async def generate_image(
    prompt:str,
    model:str,
    lora_word:str,
    width:int=768,
    height:int=1024,
    scales:float=3.5,
    steps:int=24,
    seed:int=-1):

    if seed == -1:
        seed = random.randint(0, MAX_SEED)
    seed = int(seed)
    print(f'prompt:{prompt}')
    
    text = str(translator.translate(prompt, 'English')) + "," + lora_word

    client = AsyncInferenceClient()
    try:
        image = await client.text_to_image(
            prompt=text,
            height=height,
            width=width,
            guidance_scale=scales,
            num_inference_steps=steps,
            model=model,
        )
    except Exception as e:
        raise gr.Error(f"Error in {e}")
    
    return image, seed

async def gen(
    prompt:str,
    lora_add:str="",
    lora_word:str="",
    width:int=768,
    height:int=1024,
    scales:float=3.5,
    steps:int=24,
    seed:int=-1,
    progress=gr.Progress(track_tqdm=True)
):
    model = enable_lora(lora_add)
    print(model)
    image, seed = await generate_image(prompt,model,lora_word,width,height,scales,steps,seed)
    return image, seed
     
examples = [
    ["a seal holding a beach ball in a pool, in the style of BSstyle004","bingbangboom/flux_dreamscape","in the style of BSstyle004"],
    ["1980s anime screengrab, VHS quality, a woman with her face glitching and disorted, a halo above her head","dataautogpt3/FLUX-SyntheticAnime","1980s anime screengrab, VHS quality"],
    ["photograph, background of Earth from space, red car on the Moon watching Earth","martintomov/retrofuturism-flux","retrofuturism"],
    ["a living room interior","fofr/flux-80s-cyberpunk","80s cyberpunk"],
    ["Shrek, a lovable green ogre with a big smile, sitting on a moss-covered rock while enjoying a plate of freshly picked vegetables, in a magical forest filled with whimsical creatures, dappled sunlight filtering through the trees, surrounded by curious fairies peeking out from behind leaves", "alvarobartt/ghibli-characters-flux-lora","Ghibli style "],
    ["a tourist in London, illustration in the style of VCTRNDRWNG, Victorian-era drawing","dvyio/flux-lora-victorian-drawing","illustration in the style of VCTRNDRWNG"],
    ["an African American and a caucasian man petting a cat at a busy electronic store. flikr photo from 2012. three people working in the background","kudzueye/boreal-flux-dev-v2","photo"],
    ["mgwr/cine, woman silhouette, morning light, sun rays, indoor scene, soft focus, golden hour, stretching pose, peaceful mood, cozy atmosphere, window light, shadows and highlights, backlit figure, minimalistic interior, warm tones, contemplative moment, calm energy, serene environment, yoga-inspired, elegant posture, natural light beams, artistic composition. <lora:MGWR_Cine:0.8>","mgwr/Cine-Aesthetic","atmospheric lighting and a dreamy, surreal vibe"]
]


# Gradio Interface

with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
    gr.HTML("<h1><center>Flux Lab Light</center></h1>")
    gr.HTML("<p><center>Powered By HF Inference API</center></p>")
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Row():
                img = gr.Image(type="filepath", label='flux Generated Image', height=600)
            with gr.Row():
                prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6)
                sendBtn = gr.Button(scale=1, variant='primary')
        with gr.Accordion("Advanced Options", open=True):
            with gr.Column(scale=1):
                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,
                )
                lora_add = gr.Textbox(
                    label="Add Flux LoRA",
                    info="Copy the HF LoRA model name here",
                    lines=1,
                    placeholder="Please use Warm status model",
                )
                lora_word = gr.Textbox(
                    label="Add Flux LoRA Trigger Word",
                    info="Add the Trigger Word",
                    lines=1,
                    value="",
                )

    gr.Examples(
        examples=examples,
        inputs=[prompt,lora_add,lora_word],
        outputs=[img, seed],
        fn=gen,
        cache_examples="lazy",
        examples_per_page=4,
    )

    gr.on(
        triggers=[
            prompt.submit,
            sendBtn.click,
        ],
        fn=gen,
        inputs=[
            prompt,
            lora_add,
            lora_word,
            width, 
            height, 
            scales, 
            steps, 
            seed
        ],
        outputs=[img, seed],
        api_name="run",
    )
    

    
demo.queue().launch()