diff --git a/scripts/inpaint_gradio.py b/scripts/inpaint_gradio.py
new file mode 100644
index 000000000..55cab55af
--- /dev/null
+++ b/scripts/inpaint_gradio.py
@@ -0,0 +1,138 @@
+from omegaconf import OmegaConf
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+import torchvision.transforms.functional as F
+from main import instantiate_from_config
+from ldm.models.diffusion.ddim import DDIMSampler
+import gradio as gr
+
+
+def run(
+    *,
+    image,
+    mask,
+    device,
+    model,
+    sampler,
+    steps,
+):
+
+    # Transpose image if needed according to EXIF data
+    image = ImageOps.exif_transpose(image)
+
+    # Save original image size
+    orig_size = image.size
+    print(f"Original image size: {orig_size}")
+
+    # Convert image from PIL Image to torch tensor
+    image = np.array(image.convert("RGB"))
+    image = image.astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+
+    # Convert mask from PIL Image to torch tensor
+    mask = np.array(mask.convert("L"))
+    mask = mask.astype(np.float32) / 255.0
+    mask = mask[None, None]
+    mask[mask < 0.5] = 0
+    mask[mask >= 0.5] = 1
+    mask = torch.from_numpy(mask)
+
+    # Rescale image and mask if needed, saving unscaled original image and mask
+    orig_image = image
+    orig_mask = mask
+
+    if orig_size != (512, 512):
+        print("Resize image an mask to 512x512")
+        image = F.resize(image, (512, 512), interpolation=F.InterpolationMode.BICUBIC)
+        mask = F.resize(mask, (512, 512), interpolation=F.InterpolationMode.BICUBIC)
+
+    # Compute the masked image
+    masked_image = (1 - mask) * image
+
+    # Saving tensors in a batch dict and move them to the GPU
+    batch = {"image": image, "mask": mask, "masked_image": masked_image}
+    for k in batch:
+        batch[k] = batch[k].to(device=device)
+        batch[k] = batch[k] * 2.0 - 1.0
+
+    # encode masked image and concat downsampled mask
+    c = model.cond_stage_model.encode(batch["masked_image"])
+    cc = torch.nn.functional.interpolate(batch["mask"], size=c.shape[-2:])
+    c = torch.cat((c, cc), dim=1)
+
+    # Predict image
+    shape = (c.shape[1] - 1,) + c.shape[2:]
+    samples_ddim, _ = sampler.sample(
+        S=steps, conditioning=c, batch_size=c.shape[0], shape=shape, verbose=False
+    )
+    x_samples_ddim = model.decode_first_stage(samples_ddim)
+
+    image = torch.clamp((batch["image"] + 1.0) / 2.0, min=0.0, max=1.0)
+    mask = torch.clamp((batch["mask"] + 1.0) / 2.0, min=0.0, max=1.0)
+    predicted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+
+    # Get final image tensor by adding the original masked image with the
+    # prediction inside the mask - resizing prediction image if needed
+    if orig_size == (512, 512):
+        inpainted = (1 - mask) * image + mask * predicted_image
+        inpainted = inpainted.cpu()
+    else:
+        w, h = orig_size
+        print(f"Resize prediction to {w}x{h}")
+        predicted_image = F.resize(
+            predicted_image, (h, w), interpolation=F.InterpolationMode.BICUBIC
+        )
+        inpainted = (1 - orig_mask) * orig_image + orig_mask * predicted_image.cpu()
+
+    # Convert final image back to a PIL Image
+    inpainted = inpainted.numpy().transpose(0, 2, 3, 1)[0] * 255
+    image_result = Image.fromarray(inpainted.astype(np.uint8))
+
+    return image_result
+
+
+if __name__ == "__main__":
+
+    config = OmegaConf.load("models/ldm/inpainting_big/config.yaml")
+    model = instantiate_from_config(config.model)
+    model.load_state_dict(
+        torch.load("models/ldm/inpainting_big/last.ckpt")["state_dict"], strict=False
+    )
+
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model = model.to(device)
+    sampler = DDIMSampler(model)
+
+    def gradio_run(sketch, nb_steps):
+
+        image = sketch["image"]
+        mask = sketch["mask"]
+
+        generated = run(
+            image=image,
+            mask=mask,
+            device=device,
+            model=model,
+            sampler=sampler,
+            steps=nb_steps,
+        )
+
+        return generated
+
+    inpaint_interface = gr.Interface(
+        gradio_run,
+        inputs=[
+            gr.Image(interactive=True, type="pil", tool="sketch"),
+            gr.Slider(minimum=1, maximum=200, value=50, label="Number of steps"),
+        ],
+        outputs=[
+            gr.Image(),
+        ],
+        article="To avoid rescaling, use an image of dimensions **512x512**.",
+    )
+
+    with torch.no_grad():
+        with model.ema_scope():
+            inpaint_interface.launch()