Last Commit.txt

import spaces
import gradio as gr
import torch
from PIL import Image
from diffusers import DiffusionPipeline
import random
import uuid
from typing import Tuple
import numpy as np

DESCRIPTIONz = """## FLUX REALISM 🔥"""


DESCRIPTIONy = """
            <p align="left">
            <a title="Github" href="https://github.com/PRITHIVSAKTHIUR/FLUX-REALPIX" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
                <img src="https://img.shields.io/github/stars/PRITHIVSAKTHIUR/FLUX-REALPIX?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
            </a>
            </p>
"""

def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name

def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

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

if not torch.cuda.is_available():
    DESCRIPTIONz += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>"

base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)

lora_repo = "prithivMLmods/Canopus-LoRA-Flux-FaceRealism"
trigger_word = "Realism"  # Leave trigger_word blank if not used.
pipe.load_lora_weights(lora_repo)

pipe.to("cuda")

style_list = [
    {
        "name": "3840 x 2160",
        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
    },
    {
        "name": "2560 x 1440",
        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
    },
    {
        "name": "HD+",
        "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
    },
    {
        "name": "Style Zero",
        "prompt": "{prompt}",
    },
]

styles = {k["name"]: k["prompt"] for k in style_list}

DEFAULT_STYLE_NAME = "3840 x 2160"
STYLE_NAMES = list(styles.keys())

def apply_style(style_name: str, positive: str) -> str:
    return styles.get(style_name, styles[DEFAULT_STYLE_NAME]).replace("{prompt}", positive)

@spaces.GPU(duration=60, enable_queue=True)
def generate(
    prompt: str,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    randomize_seed: bool = False,
    style_name: str = DEFAULT_STYLE_NAME,
    progress=gr.Progress(track_tqdm=True),
):
    seed = int(randomize_seed_fn(seed, randomize_seed))

    positive_prompt = apply_style(style_name, prompt)
    
    if trigger_word:
        positive_prompt = f"{trigger_word} {positive_prompt}"

    images = pipe(
        prompt=positive_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=16,
        num_images_per_prompt=1,
        output_type="pil",
    ).images
    image_paths = [save_image(img) for img in images]
    print(image_paths)
    return image_paths, seed


def load_predefined_images():
    predefined_images = [
        "assets/11.png",
        "assets/22.png",
        "assets/33.png",
        "assets/44.png",
        "assets/55.webp",
        "assets/66.png",
        "assets/77.png",
        "assets/88.png",
        "assets/99.png",
    ]
    return predefined_images



examples = [
    "A portrait of an attractive woman in her late twenties with light brown hair and purple, wearing large a a yellow sweater. She is looking directly at the camera, standing outdoors near trees.. --ar 128:85 --v 6.0 --style raw",
    "A photo of the model wearing a white bodysuit and beige trench coat, posing in front of a train station with hands on head, soft light, sunset, fashion photography, high resolution, 35mm lens, f/22, natural lighting, global illumination. --ar 85:128 --v 6.0 --style raw",
]


css = '''
.gradio-container{max-width: 575px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
    gr.Markdown(DESCRIPTIONz)
    gr.Markdown(DESCRIPTIONy)
    with gr.Row():
        prompt = gr.Text(
            label="Prompt",
            show_label=False,
            max_lines=1,
            placeholder="Enter your prompt",
            container=False,
        )
        run_button = gr.Button("Run", scale=0)
    result = gr.Gallery(label="Result", columns=1, show_label=False) 
    
    with gr.Accordion("Advanced options", open=False, visible=True):
        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
            visible=True
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        
        with gr.Row(visible=True):
            width = gr.Slider(
                label="Width",
                minimum=512,
                maximum=2048,
                step=64,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=512,
                maximum=2048,
                step=64,
                value=1024,
            )
        
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=20.0,
                step=0.1,
                value=3.0,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=40,
                step=1,
                value=16,
            )

        style_selection = gr.Radio(
            show_label=True,
            container=True,
            interactive=True,
            choices=STYLE_NAMES,
            value=DEFAULT_STYLE_NAME,
            label="Quality Style",
        )



    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=[result, seed],
        fn=generate,
        cache_examples=False,
    )

    gr.on(
        triggers=[
            prompt.submit,
            run_button.click,
        ],
        fn=generate,
        inputs=[
            prompt,
            seed,
            width,
            height,
            guidance_scale,
            randomize_seed,
            style_selection,
        ],
        outputs=[result, seed],
        api_name="run",
    )
    
    gr.Markdown("### Generated Images")
    predefined_gallery = gr.Gallery(label="Generated Images", columns=3, show_label=False, value=load_predefined_images())


    gr.Markdown("**Disclaimer/Note:**")
    
    gr.Markdown("🔥This space provides realistic image generation, which works better for human faces and portraits. Realistic trigger works properly, better for photorealistic trigger words, close-up shots, face diffusion, male, female characters.")
   
    gr.Markdown("🔥users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.")
    
if __name__ == "__main__":
    demo.queue(max_size=40).launch()