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import gradio as gr
import torch
import numpy as np
from PIL import Image
from diffusers import DiffusionPipeline
# Initialize the DiffusionPipeline model with LoRA weights
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
pipeline.load_lora_weights("ostris/super-cereal-sdxl-lora")
def text_to_image(prompt):
# Generate image using the DiffusionPipeline
output = pipeline(prompt)
generated_img_tensor = output.images[0]
# Convert torch tensor to numpy array
generated_img_array = generated_img_tensor.cpu().numpy().transpose((1, 2, 0))
return generated_img_array
def create_cereal_box(input_image):
# Convert the input numpy array to PIL Image
cover_img = Image.fromarray((input_image.astype(np.uint8)))
# Load the template image
template_img = Image.open('CerealBoxMaker/template.jpeg')
# Simplified cereal box creation logic
scaling_factor = 1.5
rect_height = int(template_img.height * 0.32)
new_width = int(rect_height * 0.70)
cover_resized = cover_img.resize((new_width, rect_height), Image.LANCZOS)
new_width_scaled = int(new_width * scaling_factor)
new_height_scaled = int(rect_height * scaling_factor)
cover_resized_scaled = cover_resized.resize((new_width_scaled, new_height_scaled), Image.LANCZOS)
left_x = int(template_img.width * 0.085)
left_y = int((template_img.height - new_height_scaled) // 2 + template_img.height * 0.012)
left_position = (left_x, left_y)
right_x = int(template_img.width * 0.82) - new_width_scaled
right_y = left_y
right_position = (right_x, right_y)
template_copy = template_img.copy()
template_copy.paste(cover_resized_scaled, left_position)
template_copy.paste(cover_resized_scaled, right_position)
# Convert the PIL Image back to a numpy array
template_copy_array = np.array(template_copy)
return template_copy_array
def combined_function(prompt):
generated_img_array = text_to_image(prompt)
final_img = create_cereal_box(generated_img_array)
return final_img
# Create a Gradio Interface
gr.Interface(fn=combined_function, inputs="text", outputs="image").launch() |