debug_vae_from_images.py

# Original from https://gist.github.com/Poiuytrezay1/db6b98672675456bed39d45077d44179
# Credit to Poiuytrezay1

import argparse
import os
from collections import defaultdict
from pathlib import Path

import numpy as np
import torch
import tqdm
from PIL import Image
from torchvision import transforms

import library.model_util as model_util
import library.sdxl_train_util as sdxl_train_util

IMAGE_TRANSFORMS = transforms.Compose(
    [
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def load_image(image_path):
    image = Image.open(image_path)
    if not image.mode == "RGB":
        image = image.convert("RGB")
    img = np.array(image, np.uint8)
    return img, image.info


def process_images_group(vae, images_group):
    with torch.no_grad():
        # Stack the tensors from the same size group
        img_tensors = torch.stack(images_group, dim=0).to(vae.device)

        # Encode and decode the images
        latents = vae.encode(img_tensors).latent_dist.sample()

    return latents


def process_latents_from_images(vae, input_file_or_dir, output_dir, args):
    if args.consistency_decoder:
        from consistencydecoder import ConsistencyDecoder

        decoder_consistency = ConsistencyDecoder(device=vae.device)

    input = Path(input_file_or_dir)
    output = Path(output_dir)

    os.makedirs(str(output.absolute()), exist_ok=True)

    if input.is_dir():
        image_files = [
            file
            for file in input.iterdir()
            if file.suffix in [".jpg", ".jpeg", ".png", ".webp", ".bmp", ".avif"]
        ]
    else:
        image_files = [input]

    size_to_images = defaultdict(list)
    file_names = []  # List to keep track of file names

    for image_file in image_files:
        image, _ = load_image(image_file)
        transformed_image = IMAGE_TRANSFORMS(image)
        size_to_images[transformed_image.shape[1:]].append(transformed_image)
        file_names.append(image_file)  # Save the file name

    total_images = len(file_names)
    # batch_size = args.batch_size
    batch_size = 1
    print("Temporarily limiting batch size to 1")

    vae_name = Path(args.vae).stem if args.vae is not None else None

    with tqdm.tqdm(total=total_images) as progress_bar:
        for i, (size, images_group) in enumerate(size_to_images.items()):
            batch_file_names = file_names[i : i + batch_size]

            # Get the batch file names
            latents = process_images_group(vae, images_group)

            if args.consistency_decoder:
                consistencydecoder_and_save(
                    decoder_consistency,
                    latents,
                    batch_file_names,
                    output,
                    device=vae.device,
                )
            else:
                decode_vae_and_save(
                    vae,
                    latents,
                    batch_file_names,
                    output,
                    gif=args.gif,
                    vae_name=vae_name,
                )

            progress_bar.update(1)


def decode_vae_and_save(vae, latents, filenames, output, gif=False, vae_name=None):
    with torch.no_grad():
        decoded_images = []
        for i in range(0, 1, 1):
            decoded_images.append(
                vae.decode(
                    latents[i : i + 1] if i > 1 else latents[i].unsqueeze(0)
                ).sample
            )
        decoded_images = torch.cat(decoded_images)

    # Rescale images from [-1, 1] to [0, 255] and save
    decoded_images = (
        ((decoded_images / 2 + 0.5).clamp(0, 1) * 255)
        .cpu()
        .permute(0, 2, 3, 1)
        .numpy()
        .astype("uint8")
    )

    vae_file_part = f"-{vae_name}" if vae_name is not None else ""

    for j, decoded_image in enumerate(decoded_images):
        original_file = filenames[j]  # Get the original file name for each image

        output_file = (
            output.absolute()
            / original_file.with_name(
                f"{original_file.stem}-latents-decoded{vae_file_part}.png"
            ).name
        )

        output_image = Image.fromarray(decoded_image)
        print(f"Saving to {output_file}")
        output_image.save(output_file)

        if gif:
            output_gif_file = (
                output.absolute()
                / original_file.with_name(
                    f"{original_file.stem}-latents-decoded{vae_file_part}.gif"
                ).name
            )

            Image.open(original_file).save(
                output_gif_file,
                save_all=True,
                append_images=[output_image],
                duration=500,
                loop=0,
            )


def consistencydecoder_and_save(
    decoder_consistency, latents, filenames, output_dir, device
):
    from consistencydecoder import save_image

    with torch.no_grad():
        sample_consistences = decoder_consistency(latents)

        for j, decoded_image in enumerate(sample_consistences):
            original_file_name = filenames[
                j
            ]  # Get the original file name for each image
            original_name_without_extension = os.path.splitext(original_file_name)[0]
            save_image(
                decoded_image,
                os.path.join(
                    output_dir,
                    f"{original_name_without_extension}-latents-decoded-consistency.png",
                ),
            )


def main(args):
    device = torch.device(args.device)

    if args.vae is None:
        if args.sdxl:
            # putting this in here just to be able to pass the argument
            from accelerate import Accelerator

            accelerator = Accelerator()

            _, _, _, vae, _, _, _ = sdxl_train_util.load_target_model(
                args,
                accelerator,
                args.pretrained_model_name_or_path,
                torch.float16,
            )
        else:
            # Load model's VAE
            _, vae, _ = model_util.load_models_from_stable_diffusion_checkpoint(
                args.v2,
                args.pretrained_model_name_or_path,
            )
            vae.to(device, dtype=torch.float32)
    else:
        vae = model_util.load_vae(args.vae, torch.float32).to(device)

    # Save image decoded latents
    process_latents_from_images(vae, args.input_file_or_dir, args.output_dir, args)


if __name__ == "__main__":
    argparser = argparse.ArgumentParser()
    argparser.add_argument("--device", default="cpu")
    argparser.add_argument(
        "--input_file_or_dir", help="Input file or directory to load the images from"
    )
    argparser.add_argument(
        "--output_dir", help="Output directory to put the VAE decoded images"
    )
    argparser.add_argument(
        "--vae", default="", help="Path to VAE file or hugging face VAE path"
    )
    argparser.add_argument(
        "--pretrained_model_name_or_path",
        default="",
        help="Stable diffusion model name or path to load the VAE from.",
    )
    argparser.add_argument(
        "--gif",
        action="store_true",
        help="Make a gif of the decoded image with the original",
    )

    argparser.add_argument(
        "--v2", action="store_true", help="Is a Stable Diffusion v2 model."
    )

    argparser.add_argument(
        "--batch_size", type=int, default=1, help="Batch size to process the images."
    )

    argparser.add_argument(
        "--sdxl", action="store_true", help="(NOTWORKING) SDXL model"
    )
    argparser.add_argument("--lowram", type=int, default=1, help="SDXL low ram option")

    argparser.add_argument(
        "--full_fp16", type=int, default=1, help="SDXL use full fp16"
    )
    argparser.add_argument(
        "--full_bf16", type=int, default=1, help="SDXL use full bf16"
    )

    argparser.add_argument(
        "--consistency_decoder",
        action="store_true",
        help="Use Consistency Decoder from OpenAI https://github.com/openai/consistencydecoder",
    )

    args = argparser.parse_args()
    main(args)

no module called "library"

@Seedmanc sorry, would need https://github.com/kohya-ss/sd-scripts setup for this to work. This requirement does the model loading (see main function) but you could use any PyTorch model loading like diffusers/transformers libraries to remove this sd-scripts requirement.

Edit: General approach originally was to put this file in the sd-scripts directory and install dependencies and run from there.