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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() | |