utils/inpainting.py

import sys
import cv2
import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from einops import repeat
from imwatermark import WatermarkEncoder
from pathlib import Path

from .ddim import DDIMSampler
from .util import instantiate_from_config


torch.set_grad_enabled(False)


def put_watermark(img, wm_encoder=None):
    if wm_encoder is not None:
        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
        img = wm_encoder.encode(img, 'dwtDct')
        img = Image.fromarray(img[:, :, ::-1])
    return img


def initialize_model(config, ckpt):
    config = OmegaConf.load(config)
    model = instantiate_from_config(config.model)

    model.load_state_dict(torch.load(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)

    return sampler


def make_batch_sd(
        image,
        mask,
        txt,
        device,
        num_samples=1):
    image = np.array(image.convert("RGB"))
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0

    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)

    masked_image = image * (mask < 0.5)

    batch = {
        "image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples),
        "txt": num_samples * [txt],
        "mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples),
        "masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples),
    }
    return batch

@torch.no_grad()
def inpaint(sampler, image, mask, prompt, seed, scale, ddim_steps, num_samples=1, w=512, h=512):
    device = torch.device(
        "cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = sampler.model

    print("Creating invisible watermark encoder (see https://github.com/ShieldMnt/invisible-watermark)...")
    wm = "SDV2"
    wm_encoder = WatermarkEncoder()
    wm_encoder.set_watermark('bytes', wm.encode('utf-8'))

    prng = np.random.RandomState(seed)
    start_code = prng.randn(num_samples, 4, h // 8, w // 8)
    start_code = torch.from_numpy(start_code).to(
        device=device, dtype=torch.float32)

    with torch.no_grad(), \
            torch.autocast("cuda"):
        batch = make_batch_sd(image, mask, txt=prompt,
                              device=device, num_samples=num_samples)

        c = model.cond_stage_model.encode(batch["txt"])

        c_cat = list()
        for ck in model.concat_keys:
            cc = batch[ck].float()
            if ck != model.masked_image_key:
                bchw = [num_samples, 4, h // 8, w // 8]
                cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
            else:
                cc = model.get_first_stage_encoding(
                    model.encode_first_stage(cc))
            c_cat.append(cc)
        c_cat = torch.cat(c_cat, dim=1)

        # cond
        cond = {"c_concat": [c_cat], "c_crossattn": [c]}

        # uncond cond
        uc_cross = model.get_unconditional_conditioning(num_samples, "")
        uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}

        shape = [model.channels, h // 8, w // 8]
        samples_cfg, intermediates = sampler.sample(
            ddim_steps,
            num_samples,
            shape,
            cond,
            verbose=False,
            eta=1.0,
            unconditional_guidance_scale=scale,
            unconditional_conditioning=uc_full,
            x_T=start_code,
        )
        x_samples_ddim = model.decode_first_stage(samples_cfg)

        result = torch.clamp((x_samples_ddim + 1.0) / 2.0,
                             min=0.0, max=1.0)

        result = result.cpu().numpy().transpose(0, 2, 3, 1) * 255
    return [put_watermark(Image.fromarray(img.astype(np.uint8)), wm_encoder) for img in result]

def pad_image(input_image):
    pad_w, pad_h = np.max(((2, 2), np.ceil(
        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
    im_padded = Image.fromarray(
        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
    return im_padded

def crop_image(input_image):
    crop_w, crop_h = np.floor(np.array(input_image.size) / 64).astype(int) * 64
    im_cropped = Image.fromarray(np.array(input_image)[:crop_h, :crop_w])
    return im_cropped

# sampler = initialize_model(sys.argv[1], sys.argv[2])
@torch.no_grad()
def predict(model, input_image, prompt, ddim_steps, num_samples, scale, seed):
    """_summary_

    Args:
        input_image (_type_): dict
            - image: PIL.Image. Input image.
            - mask: PIL.Image. Mask image.
        prompt (_type_): string to be used as prompt. 
        ddim_steps (_type_): typical 45
        num_samples (_type_): typical 4
        scale (_type_): typical 10.0 Guidance Scale.
        seed (_type_): typical 1529160519
    
    """
    init_image = input_image["image"].convert("RGB")
    init_mask = input_image["mask"].convert("RGB")
    image = pad_image(init_image) # resize to integer multiple of 32
    mask = pad_image(init_mask) # resize to integer multiple of 32
    width, height = image.size
    print("Inpainting...", width, height)

    result = inpaint(
        sampler=model,
        image=image,
        mask=mask,
        prompt=prompt,
        seed=seed,
        scale=scale,
        ddim_steps=ddim_steps,
        num_samples=num_samples,
        h=height, w=width
    )

    return result