src/pipelines/inpainting.py

import torch
import gradio as gr
from src.util.base import *
from src.util.params import *
from diffusers import AutoPipelineForInpainting

# inpaint_pipe = AutoPipelineForInpainting.from_pretrained(inpaint_model_path).to(torch_device)
inpaint_pipe = AutoPipelineForInpainting.from_pipe(pipe).to(torch_device)


def inpaint(dict, num_inference_steps, seed, prompt="", progress=gr.Progress()):
    progress(0)
    mask = dict["mask"].convert("RGB").resize((imageHeight, imageWidth))
    init_image = dict["image"].convert("RGB").resize((imageHeight, imageWidth))
    output = inpaint_pipe(
        prompt=prompt,
        image=init_image,
        mask_image=mask,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=torch.Generator().manual_seed(seed),
    )
    progress(1)

    fname = "inpainting"
    tab_config = {
        "Tab": "Inpainting",
        "Prompt": prompt,
        "Number of Inference Steps per Image": num_inference_steps,
        "Seed": seed,
    }

    imgs_list = []
    imgs_list.append((output.images[0], "Inpainted Image"))
    imgs_list.append((mask, "Mask"))

    export_as_zip(imgs_list, fname, tab_config)
    return output.images[0], f"outputs/{fname}.zip"


__all__ = ["inpaint"]