inference_i2mv_sdxl.py

import argparse

import numpy as np
import torch
from diffusers import AutoencoderKL, DDPMScheduler, LCMScheduler, UNet2DConditionModel
from PIL import Image
from torchvision import transforms
from tqdm import tqdm
from transformers import AutoModelForImageSegmentation

from mvadapter.pipelines.pipeline_mvadapter_i2mv_sdxl import MVAdapterI2MVSDXLPipeline
from mvadapter.schedulers.scheduling_shift_snr import ShiftSNRScheduler
from mvadapter.utils import (
    get_orthogonal_camera,
    get_plucker_embeds_from_cameras_ortho,
    make_image_grid,
)


def prepare_pipeline(
    base_model,
    vae_model,
    unet_model,
    lora_model,
    adapter_path,
    scheduler,
    num_views,
    device,
    dtype,
):
    # Load vae and unet if provided
    pipe_kwargs = {}
    if vae_model is not None:
        pipe_kwargs["vae"] = AutoencoderKL.from_pretrained(vae_model)
    if unet_model is not None:
        pipe_kwargs["unet"] = UNet2DConditionModel.from_pretrained(unet_model)

    # Prepare pipeline
    pipe: MVAdapterI2MVSDXLPipeline
    pipe = MVAdapterI2MVSDXLPipeline.from_pretrained(base_model, **pipe_kwargs)

    # Load scheduler if provided
    scheduler_class = None
    if scheduler == "ddpm":
        scheduler_class = DDPMScheduler
    elif scheduler == "lcm":
        scheduler_class = LCMScheduler

    pipe.scheduler = ShiftSNRScheduler.from_scheduler(
        pipe.scheduler,
        shift_mode="interpolated",
        shift_scale=8.0,
        scheduler_class=scheduler_class,
    )
    pipe.init_custom_adapter(num_views=num_views)
    pipe.load_custom_adapter(
        adapter_path, weight_name="mvadapter_i2mv_sdxl.safetensors"
    )

    pipe.to(device=device, dtype=dtype)
    pipe.cond_encoder.to(device=device, dtype=dtype)

    # load lora if provided
    if lora_model is not None:
        model_, name_ = lora_model.rsplit("/", 1)
        pipe.load_lora_weights(model_, weight_name=name_)

    # vae slicing for lower memory usage
    pipe.enable_vae_slicing()

    return pipe

def remove_bg(image: Image.Image, net, transform, device, mask: Image.Image = None):
    """
    Applies a pre-existing mask to an image to make the background transparent.

    Args:
        image (PIL.Image.Image): The input image.
        net: Pre-trained neural network (not used but kept for compatibility).
        transform: Image transformation object (not used but kept for compatibility).
        device: Device used for inference (not used but kept for compatibility).
        mask (PIL.Image.Image, optional): The mask to use. Should be the same size
                                 as the input image, with values between 0 and 255 (or 0-1).
                                 If None, will return image with no changes.

    Returns:
        PIL.Image.Image: The modified image with transparent background.
    """
    if mask is None:
      return image
    
    image_size = image.size
    if mask.size != image_size:
        mask = mask.resize(image_size) # Resizing the mask if it is not the same size as image
    
    image.putalpha(mask)
    return image


def remove_bg(image, net, transform, device):
    image_size = image.size
    input_images = transform(image).unsqueeze(0).to(device)
    with torch.no_grad():
        preds = net(input_images)[-1].sigmoid().cpu()
        #preds = net(input_images)[-1] if isinstance(net(input_images), list) else net(input_images)
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(image_size)
    image.putalpha(mask)
    return image





def preprocess_image(image: Image.Image, height, width):
    
    alpha = image[..., 3] > 0
    # alpha = image

    #if image.mode in ("RGBA", "LA"):
    #    image = np.array(image)
    #    alpha = image[..., 3]  # Extract the alpha channel
    #elif image.mode in ("RGB"):
    #    image = np.array(image)
        # Create default alpha for non-alpha images
    #    alpha = np.ones(image[..., 0].shape, dtype=np.uint8) * 255 # Create
    H, W = alpha.shape
    # get the bounding box of alpha
    y, x = np.where(alpha)
    y0, y1 = max(y.min() - 1, 0), min(y.max() + 1, H)
    x0, x1 = max(x.min() - 1, 0), min(x.max() + 1, W)
    image_center = image[y0:y1, x0:x1]
    # resize the longer side to H * 0.9
    H, W, _ = image_center.shape
    if H > W:
        W = int(W * (height * 0.9) / H)
        H = int(height * 0.9)
    else:
        H = int(H * (width * 0.9) / W)
        W = int(width * 0.9)
    image_center = np.array(Image.fromarray(image_center).resize((W, H)))
    # pad to H, W
    start_h = (height - H) // 2
    start_w = (width - W) // 2
    image = np.zeros((height, width, 4), dtype=np.uint8)
    image[start_h : start_h + H, start_w : start_w + W] = image_center
    image = image.astype(np.float32) / 255.0
    image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5
    image = (image * 255).clip(0, 255).astype(np.uint8)
    image = Image.fromarray(image)

    return image


def run_pipeline(
    pipe,
    num_views,
    text,
    image,
    height,
    width,
    num_inference_steps,
    guidance_scale,
    seed,
    remove_bg_fn=None,
    reference_conditioning_scale=1.0,
    negative_prompt="watermark, ugly, deformed, noisy, blurry, low contrast",
    lora_scale=1.0,
    device="cuda",
):
    # Prepare cameras
    cameras = get_orthogonal_camera(
        elevation_deg=[0, 0, 0, 0, 0, 0],
        distance=[1.8] * num_views,
        left=-0.55,
        right=0.55,
        bottom=-0.55,
        top=0.55,
        azimuth_deg=[x - 90 for x in [0, 45, 90, 180, 270, 315]],
        device=device,
    )

    plucker_embeds = get_plucker_embeds_from_cameras_ortho(
        cameras.c2w, [1.1] * num_views, width
    )
    control_images = ((plucker_embeds + 1.0) / 2.0).clamp(0, 1)

    # Prepare image
    reference_image = Image.open(image) if isinstance(image, str) else image
    if remove_bg_fn is not None:
        reference_image = remove_bg_fn(reference_image)
        reference_image = preprocess_image(reference_image, height, width)
    elif reference_image.mode == "RGBA":
        reference_image = preprocess_image(reference_image, height, width)

    pipe_kwargs = {}
    if seed != -1 and isinstance(seed, int):
        pipe_kwargs["generator"] = torch.Generator(device=device).manual_seed(seed)

    images = pipe(
        text,
        height=height,
        width=width,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        num_images_per_prompt=num_views,
        control_image=control_images,
        control_conditioning_scale=1.0,
        reference_image=reference_image,
        reference_conditioning_scale=reference_conditioning_scale,
        negative_prompt=negative_prompt,
        cross_attention_kwargs={"scale": lora_scale},
        **pipe_kwargs,
    ).images

    return images, reference_image


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Models
    parser.add_argument(
        "--base_model", type=str, default="stabilityai/stable-diffusion-xl-base-1.0"
    )
    parser.add_argument(
        "--vae_model", type=str, default="madebyollin/sdxl-vae-fp16-fix"
    )
    parser.add_argument("--unet_model", type=str, default=None)
    parser.add_argument("--scheduler", type=str, default=None)
    parser.add_argument("--lora_model", type=str, default=None)
    parser.add_argument("--adapter_path", type=str, default="huanngzh/mv-adapter")
    parser.add_argument("--num_views", type=int, default=6)
    # Device
    parser.add_argument("--device", type=str, default="cuda")
    # Inference
    parser.add_argument("--image", type=str, required=True)
    parser.add_argument("--text", type=str, default="high quality")
    parser.add_argument("--num_inference_steps", type=int, default=50)
    parser.add_argument("--guidance_scale", type=float, default=3.0)
    parser.add_argument("--seed", type=int, default=-1)
    parser.add_argument("--lora_scale", type=float, default=1.0)
    parser.add_argument("--reference_conditioning_scale", type=float, default=1.0)
    parser.add_argument(
        "--negative_prompt",
        type=str,
        default="watermark, ugly, deformed, noisy, blurry, low contrast",
    )
    parser.add_argument("--output", type=str, default="output.png")
    # Extra
    parser.add_argument("--remove_bg", action="store_true", help="Remove background")
    args = parser.parse_args()

    pipe = prepare_pipeline(
        base_model=args.base_model,
        vae_model=args.vae_model,
        unet_model=args.unet_model,
        lora_model=args.lora_model,
        adapter_path=args.adapter_path,
        scheduler=args.scheduler,
        num_views=args.num_views,
        device=args.device,
        dtype=torch.float16,
    )

    if args.remove_bg:
        birefnet = AutoModelForImageSegmentation.from_pretrained(
            "ZhengPeng7/BiRefNet", trust_remote_code=True
        )
        birefnet.to(args.device)
        transform_image = transforms.Compose(
            [
                transforms.Resize((1024, 1024)),
                transforms.ToTensor(),
                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
            ]
        )
        remove_bg_fn = lambda x: remove_bg(x, birefnet, transform_image, args.device)
    else:
        remove_bg_fn = None

    images, reference_image = run_pipeline(
        pipe,
        num_views=args.num_views,
        text=args.text,
        image=args.image,
        height=768,
        width=768,
        num_inference_steps=args.num_inference_steps,
        guidance_scale=args.guidance_scale,
        seed=args.seed,
        lora_scale=args.lora_scale,
        reference_conditioning_scale=args.reference_conditioning_scale,
        negative_prompt=args.negative_prompt,
        device=args.device,
        remove_bg_fn=remove_bg_fn,
    )
    make_image_grid(images, rows=1).save(args.output)
    reference_image.save(args.output.rsplit(".", 1)[0] + "_reference.png")