app.py

import torch
from diffusers import StableDiffusionPipeline
import gradio as gr

model_id = "SG161222/RealVisXL_V4.0"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.to("cpu")  # Use "cuda" if GPU is available

def generate_image(
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    style: str = DEFAULT_STYLE_NAME,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    randomize_seed: bool = False,
    use_resolution_binning: bool = True,
    progress=gr.Progress(track_tqdm=True),
):
    if check_text(prompt, negative_prompt):
        raise ValueError("Prompt contains restricted words.")
    
    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator().manual_seed(seed)

    if not use_negative_prompt:
        negative_prompt = ""  # type: ignore
    negative_prompt += default_negative    

    options = {
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "width": width,
        "height": height,
        "guidance_scale": guidance_scale,
        "num_inference_steps": 25,
        "generator": generator,
        "num_images_per_prompt": NUM_IMAGES_PER_PROMPT,
        "use_resolution_binning": use_resolution_binning,
        "output_type": "pil",
    }
    
    images = pipe(**options).images + pipe2(**options).images

    image_paths = [save_image(img) for img in images]
    return image_paths, seed

def chatbot(prompt):
    # Generate the image based on the user's input
    image = generate_image(prompt)
    return image

# Create the Gradio interface
interface = gr.Interface(
    fn=chatbot,
    inputs="text",
    outputs="image",
    title="RealVisXL V4.0 Text-to-Image Chatbot",
    description="Enter a text prompt and get an AI-generated image."
)

# Launch the interface
interface.launch()