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import torch
from diffusers import StableDiffusionPipeline
import gradio as gr
# Load the model
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
# Define placeholder functions and variables
DEFAULT_STYLE_NAME = "default"
default_negative = ""
NUM_IMAGES_PER_PROMPT = 1
def check_text(prompt, negative_prompt):
# Implement your text check logic here
return False
def apply_style(style, prompt, negative_prompt):
# Implement your style application logic here
return prompt, negative_prompt
def randomize_seed_fn(seed, randomize_seed):
# Implement your seed randomization logic here
return seed
def save_image(image):
# Implement your image saving logic here
return image
def generate_image(prompt, negative_prompt="", use_negative_prompt=False, style=DEFAULT_STYLE_NAME, seed=0, width=1024, height=1024, guidance_scale=3, randomize_seed=False, use_resolution_binning=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 = ""
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
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()
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