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# app.py
import gradio as gr
import torch, io, base64
from PIL import Image
from diffusers import StableDiffusionImg2ImgPipeline
from vtoonify_model import load_vtoonify # see below
# Load models
pipe_ghibli = StableDiffusionImg2ImgPipeline.from_pretrained(
"nitrosocke/Ghibli-Diffusion", torch_dtype=torch.float16
).to("cuda") # Ghibli-style fine-tuned SD :contentReference[oaicite:1]{index=1}
pipe_vtoonify = load_vtoonify().to("cuda") # cartoonization model loader
# Helpers for base64 conversion
def pil_to_b64(img: Image.Image) -> str:
buf = io.BytesIO()
img.save(buf, format="PNG")
return base64.b64encode(buf.getvalue()).decode()
def b64_to_pil(b64: str) -> Image.Image:
data = base64.b64decode(b64)
return Image.open(io.BytesIO(data)).convert("RGB")
# Core processor
def run_effect(input_b64: str, effect: str) -> dict:
img = b64_to_pil(input_b64)
if effect == "ghibli":
out = pipe_ghibli(prompt="ghibli style", image=img, strength=0.5, guidance_scale=7.5).images[0]
else:
out = pipe_vtoonify(img)
return {"output_b64": pil_to_b64(out)}
@gr.utils.decorators.thread_safe()
@spaces.GPU # enables GPU on ZeroGPU Infra
def api_process(input_b64, effect):
return run_effect(input_b64, effect)
def gradio_process(img: Image.Image, effect: str) -> Image.Image:
# Reuse logic, bypass base64
in_b64 = pil_to_b64(img)
return b64_to_pil(run_effect(in_b64, effect)["output_b64"])
with gr.Blocks() as demo:
gr.Markdown("# Ghibli & VToonify Effects 🎨")
with gr.Tab("Web UI"):
inp = gr.Image(type="pil", label="Upload Image")
eff = gr.Radio(["ghibli", "vtoonify"], label="Effect")
btn = gr.Button("Apply Effect")
out = gr.Image(label="Result")
btn.click(gradio_process, [inp, eff], out)
with gr.Tab("API (base64)"):
inp_b64 = gr.Textbox(lines=4, label="Input Image (base64)")
eff2 = gr.Radio(["ghibli", "vtoonify"], label="Effect")
btn2 = gr.Button("Run API")
out_b64 = gr.Textbox(lines=4, label="Output Image (base64)")
btn2.click(api_process, [inp_b64, eff2], out_b64)
demo.launch()
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