app.py

#!/usr/bin/env python
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
import spaces
import subprocess

@spaces.GPU(required=True)
def install_flashattn():
    subprocess.run(['sh', './flashattn.sh'])
    
#install_flashattn()


import os

os.environ['PYTORCH_NVML_BASED_CUDA_CHECK'] = '1'
os.environ['TORCH_LINALG_PREFER_CUSOLVER'] = '1'
os.environ['PYTORCH_ALLOC_CONF'] = 'expandable_segments:True,pinned_use_background_threads:True'
os.environ["SAFETENSORS_FAST_GPU"] = "1"
os.environ['HF_HUB_ENABLE_HF_TRANSFER'] = '1'

import torch

torch.backends.cuda.matmul.allow_tf32 = False  #  torch 2.8
torch.backends.cudnn.allow_tf32 = False        #  torch 2.8

torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
#torch.backends.fp32_precision = "ieee"  torch 2.9
#torch.backends.cuda.matmul.fp32_precision = "ieee"  torch 2.9
#torch.backends.cudnn.fp32_precision = "ieee"  torch 2.9
#torch.backends.cudnn.conv.fp32_precision = "ieee"  torch 2.9
#torch.backends.cudnn.rnn.fp32_precision = "ieee"  torch 2.9
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False
torch.backends.cuda.preferred_blas_library="cublas"
torch.backends.cuda.preferred_linalg_library="cusolver"
torch.set_float32_matmul_precision("highest")

import random
import uuid
import gradio as gr
import numpy as np
from PIL import Image

from diffusers import AutoencoderKL, StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
from transformers import CLIPTextModelWithProjection, CLIPTextModel
from typing import Tuple
import paramiko
import datetime
from gradio import themes
from image_gen_aux import UpscaleWithModel
from ip_adapter import IPAdapterXL
from huggingface_hub import snapshot_download

FTP_HOST = 'noahcohn.com'
FTP_USER = 'ford442'
FTP_PASS = os.getenv("FTP_PASS")
FTP_DIR = 'img.noahcohn.com/stablediff/'

DESCRIPTIONXX = """
    ## ⚡⚡⚡⚡ REALVISXL V5.0 BF16 IP Adapter ⚡⚡⚡⚡
"""

examples = [

    "Many apples splashed with drops of water within a fancy bowl 4k, hdr  --v 6.0 --style raw",
    "A profile photo of a dog, brown background, shot on Leica M6 --ar 128:85 --v 6.0 --style raw",
]

MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))

BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))

device = torch.device("cuda:0")

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

styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
DEFAULT_STYLE_NAME = "Style Zero"
STYLE_NAMES = list(styles.keys())
HF_TOKEN = os.getenv("HF_TOKEN")

## load IP Adapter
repo_id = "ford442/SDXL-IP_ADAPTER"
subfolder = "image_encoder"
subfolder2 = "ip_adapter"
local_repo_path = snapshot_download(repo_id=repo_id, repo_type="model")
local_folder = os.path.join(local_repo_path, subfolder)
local_folder2 = os.path.join(local_repo_path, subfolder2) # Path to the ip_adapter dir
ip_ckpt = os.path.join(local_folder2, "ip-adapter_sdxl_vit-h.bin") # Correct path

upscaler = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device("cuda:0"))


def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
    if style_name in styles:
        p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    else:
        p, n = styles[DEFAULT_STYLE_NAME]
    if not negative:
        negative = ""
    return p.replace("{prompt}", positive), n + negative

def load_and_prepare_model():
    #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
    vaeX = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False, low_cpu_mem_usage=False, torch_dtype=torch.float32, token=True) #.to(device).to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
    pipe = StableDiffusionXLPipeline.from_pretrained(
        'ford442/RealVisXL_V5.0_BF16',
       #'ford442/Juggernaut-XI-v11-fp32',
      #   'SG161222/RealVisXL_V5.0',
        #'John6666/uber-realistic-porn-merge-xl-urpmxl-v3-sdxl',
        #torch_dtype=torch.bfloat16,
        add_watermarker=False,
       # custom_pipeline="lpw_stable_diffusion_xl",
        use_safetensors=True,
        token=HF_TOKEN,
        text_encoder=None,
        text_encoder_2=None,
        vae=None,
    )

    pipe.vae=vaeX
    pipe.to(device=device, dtype=torch.bfloat16)
    pipe.vae.set_default_attn_processor()
    print(f'Pipeline: ')
    print(f'image_processor: {pipe.image_processor}')
    print(f'init noise scale: {pipe.scheduler.init_noise_sigma}')
    pipe.watermark=None
    pipe.safety_checker=None
    return pipe

# Preload and compile both models
pipe = load_and_prepare_model()
ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
text_encoder=CLIPTextModel.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='text_encoder',token=True).to(device=device, dtype=torch.bfloat16)
text_encoder_2=CLIPTextModelWithProjection.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='text_encoder_2',token=True).to(device=device, dtype=torch.bfloat16)

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

neg_prompt_2 = " 'non-photorealistic':1.5, 'unrealistic skin','unattractive face':1.3, 'low quality':1.1, ('dull color scheme', 'dull colors', 'digital noise':1.2),'amateurish', 'poorly drawn face':1.3, 'poorly drawn', 'distorted face', 'low resolution', 'simplistic' "

def upload_to_ftp(filename):
    try:
        transport = paramiko.Transport((FTP_HOST, 22))
        destination_path=FTP_DIR+filename
        transport.connect(username = FTP_USER, password = FTP_PASS)
        sftp = paramiko.SFTPClient.from_transport(transport)
        sftp.put(filename, destination_path)
        sftp.close()
        transport.close()
        print(f"Uploaded {filename} to FTP server")
    except Exception as e:
        print(f"FTP upload error: {e}")

def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name,optimize=False,compress_level=0)
    return unique_name

def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
    filename= f'IP_{timestamp}.txt'
    with open(filename, "w") as f:
        f.write(f"Realvis 5.0 IP Adapter \n")
        f.write(f"Date/time: {timestamp} \n")
        f.write(f"Prompt: {prompt} \n")
        f.write(f"Steps: {num_inference_steps} \n")
        f.write(f"Guidance Scale: {guidance_scale} \n")
        f.write(f"SPACE SETUP: \n")
        f.write(f"Use Model Dtype: no \n")
        f.write(f"Model Scheduler: Euler_a all_custom before cuda \n")
        f.write(f"Model VAE: sdxl-vae to bfloat safetensor=false before cuda then attn_proc / scale factor 8 \n")
        f.write(f"Model UNET: ford442/RealVisXL_V5.0_BF16 \n")
    upload_to_ftp(filename)

# ----------------- CHANGE START -----------------
# We no longer need this function as gr.Image will act as its own preview.
# def display_image(file):
#     if file is not None:
#         return Image.open(file.name)
#     else:
#         return None
# ----------------- CHANGE END -------------------


@spaces.GPU(duration=40)
def generate_30(
    prompt: str = "",
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    style_selection: str = "",
    width: int = 768,
    height: int = 768,
    guidance_scale: float = 4,
    num_inference_steps: int = 125,
    # ----------------- CHANGE START -----------------
    # Changed default from gr.File() to None. The input will be a string filepath.
    latent_file = None,
    latent_file_2 = None,
    latent_file_3 = None,
    latent_file_4 = None,
    latent_file_5 = None,
    # ----------------- CHANGE END -------------------
    text_scale: float = 1.0,
    ip_scale: float = 1.0,
    latent_file_1_scale: float = 1.0,
    latent_file_2_scale: float = 1.0,
    latent_file_3_scale: float = 1.0,
    latent_file_4_scale: float = 1.0,
    latent_file_5_scale: float = 1.0,
    samples=1,
    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
):
    pipe.text_encoder=text_encoder
    pipe.text_encoder_2=text_encoder_2
    seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device='cuda').manual_seed(seed)
    torch.cuda.empty_cache()
    torch.cuda.reset_peak_memory_stats()
    
    # ----------------- CHANGE START -----------------
    # We now check if latent_file (which is a string path) is not None.
    # And we open it directly, instead of using latent_file.name
    if latent_file is not None:  # Check if a latent file is provided
        sd_image_a = Image.open(latent_file).convert('RGB') # Use latent_file directly
        sd_image_a.resize((height,width), Image.LANCZOS)
        if latent_file_2 is not None:  # Check if a latent file is provided
            sd_image_b = Image.open(latent_file_2).convert('RGB') # Use latent_file_2 directly
            sd_image_b.resize((height,width), Image.LANCZOS)
        else:
            sd_image_b = None
        if latent_file_3 is not None:  # Check if a latent file is provided
            sd_image_c = Image.open(latent_file_3).convert('RGB') # Use latent_file_3 directly
            sd_image_c.resize((height,width), Image.LANCZOS)
        else:
            sd_image_c = None
        if latent_file_4 is not None:  # Check if a latent file is provided
            sd_image_d = Image.open(latent_file_4).convert('RGB') # Use latent_file_4 directly
            sd_image_d.resize((height,width), Image.LANCZOS)
        else:
            sd_image_d = None
        if latent_file_5 is not None:  # Check if a latent file is provided
            sd_image_e = Image.open(latent_file_5).convert('RGB') # Use latent_file_5 directly
            sd_image_e.resize((height,width), Image.LANCZOS)
        else:
            sd_image_e = None
    # ----------------- CHANGE END -------------------
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        filename= f'rv_IP_{timestamp}.png'
        print("-- using image file --")
        print('-- generating image --')
        sd_image = ip_model.generate(
                pil_image_1=sd_image_a,
                pil_image_2=sd_image_b,
                pil_image_3=sd_image_c,
                pil_image_4=sd_image_d,
                pil_image_5=sd_image_e,
                prompt=prompt,
                negative_prompt=negative_prompt,
                text_scale=text_scale,
                ip_scale=ip_scale,
                scale_1=latent_file_1_scale,
                scale_2=latent_file_2_scale,
                scale_3=latent_file_3_scale,
                scale_4=latent_file_4_scale,
                scale_5=latent_file_5_scale,
                num_samples=samples,
                seed=seed,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
        )
        sd_image[0].save(filename,optimize=False,compress_level=0)
        upload_to_ftp(filename)
        uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
        torch.set_float32_matmul_precision("medium")
        torch.cuda.empty_cache()
        torch.cuda.reset_peak_memory_stats()
        with torch.no_grad():
            upscale = upscaler(sd_image, tiling=True, tile_width=256, tile_height=256)
        downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
        downscale_path = f"rvIP_upscale_{timestamp}.png"
        downscale1.save(downscale_path,optimize=False,compress_level=0)
        upload_to_ftp(downscale_path)
        image_paths = [save_image(downscale1)]
    else:
        print('-- IMAGE REQUIRED --')
        image_paths = [] # Return an empty list if no image was provided
    return image_paths

@spaces.GPU(duration=70)
def generate_60(
    prompt: str = "",
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    style_selection: str = "",
    width: int = 768,
    height: int = 768,
    guidance_scale: float = 4,
    num_inference_steps: int = 125,
    # ----------------- CHANGE START -----------------
    latent_file = None,
    latent_file_2 = None,
    latent_file_3 = None,
    latent_file_4 = None,
    latent_file_5 = None,
    # ----------------- CHANGE END -------------------
    text_scale: float = 1.0,
    ip_scale: float = 1.0,
    latent_file_1_scale: float = 1.0,
    latent_file_2_scale: float = 1.0,
    latent_file_3_scale: float = 1.0,
    latent_file_4_scale: float = 1.0,
    latent_file_5_scale: float = 1.0,
    samples=1,
    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
):
    pipe.text_encoder=text_encoder
    pipe.text_encoder_2=text_encoder_2
    seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device='cuda').manual_seed(seed)
    torch.cuda.empty_cache()
    torch.cuda.reset_peak_memory_stats()
    
    # ----------------- CHANGE START -----------------
    if latent_file is not None:
        sd_image_a = Image.open(latent_file).convert('RGB') # Use latent_file directly
        sd_image_a.resize((height,width), Image.LANCZOS)
        if latent_file_2 is not None:
            sd_image_b = Image.open(latent_file_2).convert('RGB') # Use latent_file_2 directly
            sd_image_b.resize((height,width), Image.LANCZOS)
        else:
            sd_image_b = None
        if latent_file_3 is not None:
            sd_image_c = Image.open(latent_file_3).convert('RGB') # Use latent_file_3 directly
            sd_image_c.resize((height,width), Image.LANCZOS)
        else:
            sd_image_c = None
        if latent_file_4 is not None:
            sd_image_d = Image.open(latent_file_4).convert('RGB') # Use latent_file_4 directly
            sd_image_d.resize((height,width), Image.LANCZOS)
        else:
            sd_image_d = None
        if latent_file_5 is not None:
            sd_image_e = Image.open(latent_file_5).convert('RGB') # Use latent_file_5 directly
            sd_image_e.resize((height,width), Image.LANCZOS)
        else:
            sd_image_e = None
    # ----------------- CHANGE END -------------------
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        filename= f'rv_IP_{timestamp}.png'
        print("-- using image file --")
        print('-- generating image --')
        sd_image = ip_model.generate(
                pil_image_1=sd_image_a,
                pil_image_2=sd_image_b,
                pil_image_3=sd_image_c,
                pil_image_4=sd_image_d,
                pil_image_5=sd_image_e,
                prompt=prompt,
                negative_prompt=negative_prompt,
                text_scale=text_scale,
                ip_scale=ip_scale,
                scale_1=latent_file_1_scale,
                scale_2=latent_file_2_scale,
                scale_3=latent_file_3_scale,
                scale_4=latent_file_4_scale,
                scale_5=latent_file_5_scale,
                num_samples=samples,
                seed=seed,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
        )
        sd_image[0].save(filename,optimize=False,compress_level=0)
        upload_to_ftp(filename)
        uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
        torch.set_float32_matmul_precision("medium")
        torch.cuda.empty_cache()
        torch.cuda.reset_peak_memory_stats()
        with torch.no_grad():
            upscale = upscaler(sd_image, tiling=True, tile_width=256, tile_height=256)
        downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
        downscale_path = f"rvIP_upscale_{timestamp}.png"
        downscale1.save(downscale_path,optimize=False,compress_level=0)
        upload_to_ftp(downscale_path)
        image_paths = [save_image(downscale1)]
    else:
        print('-- IMAGE REQUIRED --')
        image_paths = []
    return image_paths

@spaces.GPU(duration=100)
def generate_90(
    prompt: str = "",
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    style_selection: str = "",
    width: int = 768,
    height: int = 768,
    guidance_scale: float = 4,
    num_inference_steps: int = 125,
    # ----------------- CHANGE START -----------------
    latent_file = None,
    latent_file_2 = None,
    latent_file_3 = None,
    latent_file_4 = None,
    latent_file_5 = None,
    # ----------------- CHANGE END -------------------
    text_scale: float = 1.0,
    ip_scale: float = 1.0,
    latent_file_1_scale: float = 1.0,
    latent_file_2_scale: float = 1.0,
    latent_file_3_scale: float = 1.0,
    latent_file_4_scale: float = 1.0,
    latent_file_5_scale: float = 1.0,
    samples=1,
    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
):
    pipe.text_encoder=text_encoder
    pipe.text_encoder_2=text_encoder_2
    seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device='cuda').manual_seed(seed)
    torch.cuda.empty_cache()
    torch.cuda.reset_peak_memory_stats()
    
    # ----------------- CHANGE START -----------------
    if latent_file is not None:
        sd_image_a = Image.open(latent_file).convert('RGB') # Use latent_file directly
        sd_image_a.resize((height,width), Image.LANCZOS)
        if latent_file_2 is not None:
            sd_image_b = Image.open(latent_file_2).convert('RGB') # Use latent_file_2 directly
            sd_image_b.resize((height,width), Image.LANCZOS)
        else:
            sd_image_b = None
        if latent_file_3 is not None:
            sd_image_c = Image.open(latent_file_3).convert('RGB') # Use latent_file_3 directly
            sd_image_c.resize((height,width), Image.LANCZOS)
        else:
            sd_image_c = None
        if latent_file_4 is not None:
            sd_image_d = Image.open(latent_file_4).convert('RGB') # Use latent_file_4 directly
            sd_image_d.resize((height,width), Image.LANCZOS)
        else:
            sd_image_d = None
        if latent_file_5 is not None:
            sd_image_e = Image.open(latent_file_5).convert('RGB') # Use latent_file_5 directly
            sd_image_e.resize((height,width), Image.LANCZOS)
        else:
            sd_image_e = None
    # ----------------- CHANGE END -------------------
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        filename= f'rv_IP_{timestamp}.png'
        print("-- using image file --")
        print('-- generating image --')
        sd_image = ip_model.generate(
                pil_image_1=sd_image_a,
                pil_image_2=sd_image_b,
                pil_image_3=sd_image_c,
                pil_image_4=sd_image_d,
                pil_image_5=sd_image_e,
                prompt=prompt,
                negative_prompt=negative_prompt,
                text_scale=text_scale,
                ip_scale=ip_scale,
                scale_1=latent_file_1_scale,
                scale_2=latent_file_2_scale,
                scale_3=latent_file_3_scale,
                scale_4=latent_file_4_scale,
                scale_5=latent_file_5_scale,
                num_samples=samples,
                seed=seed,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
        )
        sd_image[0].save(filename,optimize=False,compress_level=0)
        upload_to_ftp(filename)
        uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
        torch.set_float32_matmul_precision("medium")
        torch.cuda.empty_cache()
        torch.cuda.reset_peak_memory_stats()
        with torch.no_grad():
            upscale = upscaler(sd_image, tiling=True, tile_width=256, tile_height=256)
        downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
        downscale_path = f"rvIP_upscale_{timestamp}.png"
        downscale1.save(downscale_path,optimize=False,compress_level=0)
        upload_to_ftp(downscale_path)
        image_paths = [save_image(downscale1)]
    else:
        print('-- IMAGE REQUIRED --')
        image_paths = []
    return image_paths

def load_predefined_images1():
    predefined_images1 = [
        "assets/7.png",
        "assets/8.png",
        "assets/9.png",
        "assets/1.png",
        "assets/2.png",
        "assets/3.png",
        "assets/4.png",
        "assets/5.png",
        "assets/6.png",
    ]
    return predefined_images1

css = '''
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
h1{text-align:center}
footer {
    visibility: hidden
}
body {
  background-color: green;
}
'''

with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
    gr.Markdown(DESCRIPTIONXX)
    with gr.Row():
        prompt = gr.Text(
            label="Prompt",
            show_label=False,
            max_lines=1,
            placeholder="Enter your prompt",
            container=False,
        )
        text_strength =  gr.Slider(
            label="Text Strength",
            minimum=0.0,
            maximum=16.0,
            step=0.01,
            value=1.0,
        )
        run_button_30 = gr.Button("Run 30 Seconds", scale=0)
        run_button_60 = gr.Button("Run 60 Seconds", scale=0)
        run_button_90 = gr.Button("Run 90 Seconds", scale=0)
    result = gr.Gallery(label="Result", columns=1, show_label=False)
    ip_strength =  gr.Slider(
            label="Image Strength",
            minimum=0.0,
            maximum=16.0,
            step=0.01,
            value=1.0,
    )
    with gr.Row():
        # ----------------- CHANGE START -----------------
        # Replaced gr.File and gr.Image (preview) with a single gr.Image component
        # Added sources=["upload", "clipboard"] and type="filepath"
        with gr.Column():
            latent_file = gr.Image(label="Image Prompt (Required)", sources=["upload", "clipboard"], type="filepath")
        file_1_strength =  gr.Slider(
            label="Img 1 %",
            minimum=0.0,
            maximum=16.0,
            step=0.01,
            value=1.0,
        )
        with gr.Column():
            latent_file_2 = gr.Image(label="Image Prompt 2 (Optional)", sources=["upload", "clipboard"], type="filepath")
        file_2_strength =  gr.Slider(
            label="Img 2 %",
            minimum=0.0,
            maximum=16.0,
            step=0.01,
            value=1.0,
        )
        with gr.Column():
            latent_file_3 = gr.Image(label="Image Prompt 3 (Optional)", sources=["upload", "clipboard"], type="filepath")
        file_3_strength =  gr.Slider(
            label="Img 3 %",
            minimum=0.0,
            maximum=16.0,
            step=0.01,
            value=1.0,
        )
        with gr.Column():
            latent_file_4 = gr.Image(label="Image Prompt 4 (Optional)", sources=["upload", "clipboard"], type="filepath")
        file_4_strength =  gr.Slider(
            label="Img 4 %",
            minimum=0.0,
            maximum=16.0,
            step=0.01,
            value=1.0,
        )
        with gr.Column():
            latent_file_5 = gr.Image(label="Image Prompt 5 (Optional)", sources=["upload", "clipboard"], type="filepath")
        file_5_strength =  gr.Slider(
            label="Img 5 %",
            minimum=0.0,
            maximum=16.0,
            step=0.01,
            value=1.0,
        )
        # ----------------- CHANGE END -------------------
        style_selection = gr.Radio(
            show_label=True,
            container=True,
            interactive=True,
            choices=STYLE_NAMES,
            value=DEFAULT_STYLE_NAME,
            label="Quality Style",
        )
        with gr.Row():
            with gr.Column(scale=1):
                use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
                negative_prompt = gr.Text(
                    label="Negative prompt",
                    max_lines=5,
                    lines=4,
                    placeholder="Enter a negative prompt",
                    value="('deformed', 'distorted', 'disfigured':1.3),'not photorealistic':1.5, 'poorly drawn', 'bad anatomy', 'wrong anatomy', 'extra limb', 'missing limb', 'floating limbs', 'poorly drawn hands', 'poorly drawn feet', 'poorly drawn face':1.3, 'out of frame', 'extra limbs', 'bad anatomy', 'bad art', 'beginner',  'distorted face','amateur'",
                    visible=True,
                )
        samples = gr.Slider(
            label="Samples",
            minimum=0,
            maximum=20,
            step=1,
            value=1,
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        with gr.Row():
            width = gr.Slider(
                label="Width",
                minimum=448,
                maximum=MAX_IMAGE_SIZE,
                step=64,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=448,
                maximum=MAX_IMAGE_SIZE,
                step=64,
                value=1024,
            )
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=30,
                step=0.1,
                value=3.75,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=10,
                maximum=1000,
                step=10,
                value=200,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        cache_examples=False
    )

    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        api_name=False,
    )

    # ----------------- CHANGE START -----------------
    # We no longer need the .change() events for latent_file,
    # as the gr.Image component handles its own preview.
    #
    # latent_file.change(...)
    # latent_file_2.change(...)
    # latent_file_3.change(...)
    # latent_file_4.change(...)
    # latent_file_5.change(...)
    #
    # ----------------- CHANGE END -------------------


    gr.on(
        triggers=[
            run_button_30.click,
        ],
      #  api_name="generate",  # Add this line
        fn=generate_30,
        inputs=[
            prompt,
            negative_prompt,
            use_negative_prompt,
            style_selection,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            latent_file,
            latent_file_2,
            latent_file_3,
            latent_file_4,
            latent_file_5,
            text_strength,
            ip_strength,
            file_1_strength,
            file_2_strength,
            file_3_strength,
            file_4_strength,
            file_5_strength,
            samples,
        ],
        outputs=[result],
    )

    gr.on(
        triggers=[
            run_button_60.click,
        ],
      #  api_name="generate",  # Add this line
        fn=generate_60,
        inputs=[
            prompt,
            negative_prompt,
            use_negative_prompt,
            style_selection,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            latent_file,
            latent_file_2,
            latent_file_3,
            latent_file_4,
            latent_file_5,
            text_strength,
            ip_strength,
            file_1_strength,
            file_2_strength,
            file_3_strength,
            file_4_strength,
            file_5_strength,
            samples,
        ],
        outputs=[result],
    )

    gr.on(
        triggers=[
            run_button_90.click,
        ],
      #  api_name="generate",  # Add this line
        fn=generate_90,
        inputs=[
            prompt,
            negative_prompt,
            use_negative_prompt,
            style_selection,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            latent_file,
            latent_file_2,
            latent_file_3,
            latent_file_4,
            latent_file_5,
            text_strength,
            ip_strength,
            file_1_strength,
            file_2_strength,
            file_3_strength,
            file_4_strength,
            file_5_strength,
            samples,
        ],
        outputs=[result],
    )

    gr.Markdown("### REALVISXL V5.0")
    predefined_gallery = gr.Gallery(label="REALVISXL V5.0", columns=3, show_label=False, value=load_predefined_images1())

    #gr.Markdown("### LIGHTNING V5.0")
    #predefined_gallery = gr.Gallery(label="LIGHTNING V5.0", columns=3, show_label=False, value=load_predefined_images())

    gr.Markdown(
    """
    <div style="text-align: justify;">
    ⚡Models used in the playground <a href="https://huggingface.co/SG161222/RealVisXL_V5.0">[REALVISXL V5.0]</a>, <a href="https://huggingface.co/SG161222/RealVisXL_V5.0_Lightning">[REALVISXL V5.0 LIGHTNING]</a> for image generation. Stable Diffusion XL piped (SDXL) model HF. This is the demo space for generating images using the Stable Diffusion XL models, with multiple different variants available.
    </div>
    """)

    gr.Markdown(
    """
    <div style="text-align: justify;">
    ⚡This is the demo space for generating images using Stable Diffusion XL with quality styles, different models, and types. Try the sample prompts to generate higher quality images. Try the sample prompts for generating higher quality images.
    <a href='https://huggingface.co/spaces/prithivMLmods/Top-Prompt-Collection' target='_blank'>Try prompts</a>.
    </div>
    """)

    gr.Markdown(
    """
    <div style="text-align: justify;">
    ⚠️ Users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.
    </div>
    """)

def text_generation(input_text, seed):
    full_prompt = "Text Generator Application by ecarbo"
    return full_prompt

title = "Text Generator Demo GPT-Neo"
description = "Text Generator Application by ecarbo"

if __name__ == "__main__":
    demo_interface = demo.queue(max_size=50)  # Remove .launch() here
    text_gen_interface = gr.Interface(
        fn=text_generation,
        inputs=[
            gr.Textbox(lines=1, label="Expand the following prompt to be more detailed and descriptive for image generation: "),
            gr.Number(value=10, label="Enter seed number")
        ],
        outputs=gr.Textbox(label="Text Generated"),
        title=title,
        description=description,
    )
    combined_interface = gr.TabbedInterface([demo_interface, text_gen_interface], ["Image Generation", "Text Generation"])
    combined_interface.launch(show_api=False)