app.py

import gradio as gr
import numpy as np
import torch
import os
from PIL import Image
from typing import Optional, Union
from diffusers import StableDiffusionUpscalePipeline


PIPE: Optional[StableDiffusionUpscalePipeline] = None


def get_pipe() -> StableDiffusionUpscalePipeline:
    global PIPE
    if PIPE is not None:
        return PIPE
    dtype = torch.float16 if torch.cuda.is_available() else torch.float32
    model_id = "stabilityai/stable-diffusion-x4-upscaler"
    PIPE = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=dtype)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    PIPE = PIPE.to(device)
    return PIPE


def upscale(image: Union[str, np.ndarray], scale: float = 4.0) -> np.ndarray:
    """Upscale using Stable Diffusion x4 Upscaler and return an RGB numpy array.

    The pipeline inherently performs 4x upscaling; if a smaller scale is requested,
    we will downscale the 4x result to the requested size.
    """
    if image is None:
        raise gr.Error("No image provided")

    scale = max(1.0, min(float(scale or 4.0), 4.0))
    pipe = get_pipe()

    # Accept either a filepath (preferred for robustness) or a numpy array
    if isinstance(image, str):
        if not os.path.exists(image):
            raise gr.Error("Uploaded image not found. Please re-upload and try again.")
        pil = Image.open(image).convert("RGB")
    else:
        pil = Image.fromarray(image)

    # Run the upscaler (4x)
    # Use neutral prompt and zero guidance for faithful upscaling
    result = pipe(prompt="", image=pil, num_inference_steps=20, guidance_scale=0.0)
    out: Image.Image = result.images[0]

    if scale < 4.0:
        w, h = pil.size
        target = (int(round(w * scale)), int(round(h * scale)))
        out = out.resize(target, Image.LANCZOS)

    return np.array(out)


demo = gr.Interface(
    fn=upscale,
    inputs=[
        gr.Image(type="filepath", label="image"),
        gr.Slider(1.0, 4.0, step=0.5, value=4.0, label="scale"),
    ],
    outputs=gr.Image(type="numpy", label="output"),
    title="SD x4 Upscaler API",
    description=(
        "Upscale images using Stability AI's Stable Diffusion x4 upscaler. The input 'scale' controls the final size (<4x downscales the 4x result). "
        "Use the 'View API' button on this Space to see the exact /predict schema."
    ),
    allow_flagging="never",
)

if __name__ == "__main__":
    demo.queue().launch()