app.py

import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny
from huggingface_hub import hf_hub_download

def feifeimodload():

    dtype = torch.bfloat16
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    pipe = DiffusionPipeline.from_pretrained(
        "aifeifei798/DarkIdol-flux-v1.1", torch_dtype=dtype
    ).to(device)
    
    pipe.vae.enable_slicing()
    pipe.vae.enable_tiling()
    pipe.unload_lora_weights()
    torch.cuda.empty_cache()
    return pipe
    
pipe = feifeimodload()


MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    #prompt = f"{prompt}, slight smile, Master of Light and Shadow."
    image = pipe(
            prompt = prompt, 
            width = width,
            height = height,
            num_inference_steps = num_inference_steps, 
            generator = generator,
            guidance_scale=3.5
    ).images[0] 
    return image, seed
 
examples = [
    "real model girl in real life"
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""# DarkIdol-flux
DarkIdol-flux is a text-to-image AI model designed to create aesthetic, detailed and diverse images from textual prompts in just 6-8 steps. It offers enhanced performance in image quality, typography, understanding complex prompts, and resource efficiency.
        """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=12,
                placeholder="Enter your prompt",
                container=False,
            )
            
        run_button = gr.Button("Run")
        
        result = gr.Image(label="Result", show_label=False,height=520)
        
        with gr.Accordion("Advanced Settings", open=False):
            
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=832,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1280,
                )
            
            with gr.Row():
                
  
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=6,
                )
        
        gr.Examples(
            examples = examples,
            fn = infer,
            inputs = [prompt],
            outputs = [result, seed],
            cache_examples=False
        )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
        outputs = [result, seed]
    )

demo.launch()