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import os |
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import gradio as gr |
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import numpy as np |
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import random |
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from huggingface_hub import AsyncInferenceClient |
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from translatepy import Translator |
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import requests |
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import re |
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import asyncio |
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from PIL import Image |
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translator = Translator() |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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basemodel = "black-forest-labs/FLUX.1-dev" |
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MAX_SEED = np.iinfo(np.int32).max |
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CSS = """ |
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footer { |
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visibility: hidden; |
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} |
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""" |
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JS = """function () { |
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gradioURL = window.location.href |
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if (!gradioURL.endsWith('?__theme=dark')) { |
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window.location.replace(gradioURL + '?__theme=dark'); |
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} |
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}""" |
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def enable_lora(lora_add): |
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if not lora_add: |
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return basemodel |
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else: |
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return lora_add |
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async def generate_image( |
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prompt:str, |
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model:str, |
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lora_word:str, |
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width:int=768, |
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height:int=1024, |
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scales:float=3.5, |
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steps:int=24, |
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seed:int=-1): |
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if seed == -1: |
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seed = random.randint(0, MAX_SEED) |
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seed = int(seed) |
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print(f'prompt:{prompt}') |
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text = str(translator.translate(prompt, 'English')) + "," + lora_word |
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client = AsyncInferenceClient() |
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try: |
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image = await client.text_to_image( |
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prompt=text, |
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height=height, |
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width=width, |
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guidance_scale=scales, |
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num_inference_steps=steps, |
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model=model, |
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) |
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except Exception as e: |
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raise gr.Error(f"Error in {e}") |
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return image, seed |
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async def gen( |
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prompt:str, |
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lora_add:str="", |
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lora_word:str="", |
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width:int=768, |
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height:int=1024, |
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scales:float=3.5, |
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steps:int=24, |
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seed:int=-1, |
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progress=gr.Progress(track_tqdm=True) |
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): |
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model = enable_lora(lora_add) |
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print(model) |
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image, seed = await generate_image(prompt,model,lora_word,width,height,scales,steps,seed) |
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return image, seed |
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examples = [ |
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["a seal holding a beach ball in a pool","bingbangboom/flux_dreamscape","in the style of BSstyle004"], |
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["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"], |
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["photograph, background of Earth from space, red car on the Moon watching Earth","martintomov/retrofuturism-flux","retrofuturism"], |
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["a living room interior","fofr/flux-80s-cyberpunk","80s cyberpunk"], |
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["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"], |
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["a tourist in London, illustration in the style of VCTRNDRWNG, Victorian-era drawing","dvyio/flux-lora-victorian-drawing","illustration in the style of VCTRNDRWNG"], |
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["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"], |
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["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","mgwr/Cine-Aesthetic","atmospheric lighting and a dreamy, surreal vibe"] |
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] |
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with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo: |
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gr.HTML("<h1><center>Flux Lab Light</center></h1>") |
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gr.HTML("<p><center>Powered By HF Inference API</center></p>") |
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with gr.Row(): |
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with gr.Column(scale=4): |
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with gr.Row(): |
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img = gr.Image(type="filepath", label='flux Generated Image', height=600) |
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with gr.Row(): |
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prompt = gr.Textbox(label='Enter Your Prompt (Multi-Languages)', placeholder="Enter prompt...", scale=6) |
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sendBtn = gr.Button(scale=1, variant='primary') |
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with gr.Accordion("Advanced Options", open=True): |
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with gr.Column(scale=1): |
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width = gr.Slider( |
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label="Width", |
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minimum=512, |
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maximum=1280, |
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step=8, |
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value=768, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=512, |
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maximum=1280, |
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step=8, |
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value=1024, |
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) |
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scales = gr.Slider( |
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label="Guidance", |
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minimum=3.5, |
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maximum=7, |
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step=0.1, |
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value=3.5, |
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) |
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steps = gr.Slider( |
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label="Steps", |
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minimum=1, |
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maximum=100, |
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step=1, |
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value=24, |
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) |
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seed = gr.Slider( |
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label="Seeds", |
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minimum=-1, |
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maximum=MAX_SEED, |
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step=1, |
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value=-1, |
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) |
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lora_add = gr.Textbox( |
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label="Add Flux LoRA", |
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info="Copy the HF LoRA model name here", |
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lines=1, |
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placeholder="Please use Warm status model", |
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) |
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lora_word = gr.Textbox( |
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label="Add Flux LoRA Trigger Word", |
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info="Add the Trigger Word", |
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lines=1, |
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value="", |
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) |
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gr.Examples( |
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examples=examples, |
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inputs=[prompt,lora_add,lora_word], |
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outputs=[img, seed], |
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fn=gen, |
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cache_examples="lazy", |
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examples_per_page=4, |
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) |
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gr.on( |
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triggers=[ |
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prompt.submit, |
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sendBtn.click, |
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], |
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fn=gen, |
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inputs=[ |
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prompt, |
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lora_add, |
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lora_word, |
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width, |
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height, |
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scales, |
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steps, |
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seed |
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], |
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outputs=[img, seed] |
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) |
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if __name__ == "__main__": |
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demo.queue(api_open=False).launch(show_api=False, share=False) |
