app.py

import spaces
import gradio as gr
import torch
from diffusers import FluxPipeline, FluxTransformer2DModel, FlowMatchEulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from PIL import Image
import requests
from translatepy import Translator
import numpy as np
import random

translator = Translator()

# Constants
model = "black-forest-labs/FLUX.1-dev"

CSS = """
footer {
    visibility: hidden;
}
"""

MAX_SEED = np.iinfo(np.int32).max

# Ensure model and scheduler are initialized in GPU-enabled function
if torch.cuda.is_available():
    transformer = FluxTransformer2DModel.from_single_file(
        "https://huggingface.co/aixonlab/flux.1-lumiere-alpha/blob/main/lumiere_flux_alpha-fp8.safetensors",
        torch_dtype=torch.bfloat16
    )
    pipe = FluxPipeline.from_pretrained(
        model, 
        transformer=transformer,
        torch_dtype=torch.bfloat16)
    pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(
        pipe.scheduler.config, use_beta_sigmas=True
    )
    pipe.to("cuda")
    


# Function 
@spaces.GPU()
def generate_image(
    prompt,
    width=768,
    height=1024,
    scale=3.5,
    steps=24,
    seed=-1,
    nums=1,
    progress=gr.Progress(track_tqdm=True)
):
    if seed == -1:
        seed = random.randint(0, MAX_SEED)
    seed = int(seed)

    generator = torch.Generator().manual_seed(seed)
    
    prompt = str(translator.translate(prompt, 'English'))

    print(f'prompt:{prompt}')
    
    image = pipe(
        prompt, 
        width=width,
        height=height,
        guidance_scale=scale,
        num_inference_steps=steps,
        generator=generator,
        output_type="pil",
        max_sequence_length=512,
        num_images_per_prompt=nums,
    ).images
    
    return image, seed

examples = [
    "close up portrait, Amidst the interplay of light and shadows in a photography studio,a soft spotlight traces the contours of a face,highlighting a figure clad in a sleek black turtleneck. The garment,hugging the skin with subtle luxury,complements the Caucasian model's understated makeup,embodying minimalist elegance. Behind,a pale gray backdrop extends,its fine texture shimmering subtly in the dim light,artfully balancing the composition and focusing attention on the subject. In a palette of black,gray,and skin tones,simplicity intertwines with profundity,as every detail whispers untold stories.",
    "Caucasian,The image features a young woman of European descent standing in an studio setting,surrounded by silk. (She is wearing a silk dress),paired with a bold. Her brown hair is wet and tousled,falling naturally around her face,giving her a raw and edgy look. The woman has an intense and direct gaze,adding to the dramatic feel of the image. The backdrop is flowing silk,big silk. The overall composition blends elements of fashion and nature,creating a striking and powerful visual",
    "A black and white portrait of a young woman with a captivating gaze. She's bundled up in a cozy black sweater,hands gently cupped near her face. The monochromatic tones highlight her delicate features and the contemplative mood of the image",
    "Fashion photography portrait,close up portrait,(a woman of European descent is surrounded by lava rock and magma from head to neck, red magma hair, wear volcanic lava rock magma outfit coat lava rock magma fashion costume with ruffled layers"
]


# Gradio Interface

with gr.Blocks(css=CSS, theme="ocean") as demo:
    gr.HTML("<h1><center>flux.1-lumiere</center></h1>")
    gr.HTML("<p><center><a href='https://huggingface.co/aixonlab/flux.1-lumiere-alpha</a></center></p>")
    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(label='Enter Your Prompt(multilingual)', scale=6)
            submit = gr.Button(scale=1, variant='primary')
    img = gr.Gallery(label="Gallery", columns = 1, preview=True, height=600)
    with gr.Accordion("Advanced Options", open=False):
        with gr.Row():
            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,
            )
        with gr.Row():
            scale = gr.Slider(
                label="Guidance Scale",
                minimum=0,
                maximum=50,
                step=0.1,
                value=3.0,
            )
            steps = gr.Slider(
                label="Steps",
                minimum=1,
                maximum=50,
                step=1,
                value=28,
            )
        with gr.Row():
            seed = gr.Slider(
                label="Seed(-1 Random)",
                minimum=-1,
                maximum=MAX_SEED,
                step=1,
                value=0,
                visible=True
            )
            nums = gr.Slider(
                label="Image Numbers",
                minimum=1,
                maximum=4,
                step=1,
                value=1,
                scale=1,
            )
    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=[img,seed],
        fn=generate_image,
        cache_examples=True,
        cache_mode='lazy'
    )

    gr.on(
        triggers=[
            prompt.submit,
            submit.click,
        ],
        fn=generate_image,
        inputs=[
            prompt, 
            width, 
            height, 
            scale, 
            steps, 
            seed, 
            nums
        ],
        outputs=[img, seed],
        api_name="run",
    )
    
demo.queue().launch()