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import argparse
import gradio as gr
import numpy as np
import torch
from PIL import Image
import constants
import utils
from ldm.util import instantiate_from_config
from omegaconf import OmegaConf
from zipfile import ZipFile
import os
import requests
import shutil
def download_model(url):
os.makedirs("models", exist_ok=True)
local_filename = url.split('/')[-1]
with requests.get(url, stream=True) as r:
with open(os.path.join("models", local_filename), 'wb') as file:
shutil.copyfileobj(r.raw, file)
with ZipFile("models/gqa_inpaint.zip", 'r') as zObject:
zObject.extractall(path="models/")
os.remove("models/gqa_inpaint.zip")
MODEL = None
def inference(image: np.ndarray, instruction: str, center_crop: bool):
if not instruction.lower().startswith("remove the"):
raise gr.Error("Instruction should start with 'Remove the' !")
image = Image.fromarray(image)
cropped_image, image = utils.preprocess_image(image, center_crop=center_crop)
output_image = MODEL.inpaint(image, instruction, num_steps=100, device="cuda", return_pil=True, seed=0)
return cropped_image, output_image
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--config",
type=str,
default="configs/latent-diffusion/gqa-inpaint-ldm-vq-f8-256x256.yaml",
help="Path of the model config file",
)
parser.add_argument(
"--checkpoint",
type=str,
default="models/gqa_inpaint/ldm/model.ckpt",
help="Path of the model checkpoint file",
)
args = parser.parse_args()
print("## Downloading the model file")
download_model("https://huggingface.co/abyildirim/inst-inpaint-models/resolve/main/gqa_inpaint.zip")
print("## Download is completed")
print("## Running the demo")
parsed_config = OmegaConf.load(args.config)
MODEL = instantiate_from_config(parsed_config["model"])
model_state_dict = torch.load(args.checkpoint, map_location="cpu")["state_dict"]
MODEL.load_state_dict(model_state_dict)
MODEL.eval()
MODEL.to("cuda")
sample_image, sample_instruction, sample_step = constants.EXAMPLES[3]
gr.Interface(
fn=inference,
inputs=[
gr.Image(type="numpy", value=sample_image, label="Source Image").style(
height=256
),
gr.Textbox(
label="Instruction",
lines=1,
value=sample_instruction,
),
gr.Checkbox(value=True, label="Center Crop", interactive=False),
],
outputs=[
gr.Image(type="pil", label="Cropped Image").style(height=256),
gr.Image(type="pil", label="Output Image").style(height=256),
],
allow_flagging="never",
examples=constants.EXAMPLES,
cache_examples=True,
title=constants.TITLE,
description=constants.DESCRIPTION,
).launch()
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