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import torch
from diffusers import StableDiffusionInstructPix2PixPipeline
from diffusers.utils import load_image
from PIL import Image as im
import requests
import io
import gradio as gr
API_URL = "https://api-inference.huggingface.co/models/ZB-Tech/Text-to-Image"
headers = {"Authorization": "Bearer HF_TOKEN"}
model_id = "instruction-tuning-sd/cartoonizer"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
model_id, torch_dtype=torch.float16, use_auth_token=True
).to(device)
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.content
def cartoonizer(input_img,bg_prompt):
if input_img is not None:
data = im.fromarray(input_img)
data = data.resize((300,300))
org_image = load_image(data)
cart_image = pipeline("Cartoonize the following image", image=org_image).images[0]
if len(bg_prompt) !=0:
image_bytes = query({
"inputs": bg_prompt,
})
else:
image_bytes = query({
"inputs": "orange background image",
})
bg_image = im.open(io.BytesIO(image_bytes))
return [cart_image,bg_image]
else:
gr.Warning("Please upload an Input Image!")
return [input_img,input_img]
with gr.Blocks(theme = gr.themes.Citrus()) as cart:
gr.HTML("""<h1 align="center">Cartoonize your Image with best backgrounds!</h1>""")
with gr.Tab("Cartoonize"):
with gr.Row():
image_input = gr.Image()
image_output = gr.Image()
text_img_output = gr.Image()
txt_label = gr.Label("Enter your photo frame description:")
txt_input = gr.Textbox()
image_btn = gr.Button("Convert")
image_btn.click(cartoonizer,inputs = [image_input,txt_input],outputs=[image_output,text_img_output])
cart.launch()
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