diff --git a/scripts/vqgan_codebook_visualizer.py b/scripts/vqgan_codebook_visualizer.py
new file mode 100644
index 0000000..24e4986
--- /dev/null
+++ b/scripts/vqgan_codebook_visualizer.py
@@ -0,0 +1,542 @@
+import argparse, os, sys, glob
+import torch
+import numpy as np
+import streamlit as st
+import matplotlib.pyplot as plt
+from omegaconf import OmegaConf
+from PIL import Image
+from main import instantiate_from_config, DataModuleFromConfig
+from torch.utils.data import DataLoader
+from torch.utils.data.dataloader import default_collate
+from einops import rearrange
+from torchvision.utils import make_grid
+
+
+rescale = lambda x: (x + 1.) / 2.
+
+
+def bchw_to_st(x):
+    return rescale(x.detach().cpu().numpy().transpose(0,2,3,1))
+
+def chw_to_st(x):
+    return rescale(x.detach().cpu().numpy().transpose(1,2,0))
+
+
+def custom_get_input(batch, key):
+    inputs = batch[key].permute(0, 3, 1, 2)
+    return inputs
+
+
+def vq_no_codebook_forward(model, x):
+    h = model.encoder(x)
+    h = model.quant_conv(h)
+    h = model.post_quant_conv(h)
+    xrec = model.decoder(h)
+    return xrec
+
+
+def save_img(x, fname):
+    I = (x.clip(0, 1) * 255).astype(np.uint8)
+    Image.fromarray(I).save(fname)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-r",
+        "--resume",
+        type=str,
+        nargs="?",
+        help="load from logdir or checkpoint in logdir",
+    )
+    parser.add_argument(
+        "-b",
+        "--base",
+        nargs="*",
+        metavar="base_config.yaml",
+        help="paths to base configs. Loaded from left-to-right. "
+        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
+        default=list(),
+    )
+    parser.add_argument(
+        "-c",
+        "--config",
+        nargs="?",
+        metavar="single_config.yaml",
+        help="path to single config. If specified, base configs will be ignored "
+        "(except for the last one if left unspecified).",
+        const=True,
+        default="",
+    )
+    parser.add_argument(
+        "--dataset_config",
+        type=str,
+        nargs="?",
+        default="",
+        help="path to dataset config"
+    )
+    return parser
+
+
+def load_model_from_config(config, sd, gpu=True, eval_mode=True):
+    model = instantiate_from_config(config)
+    if sd is not None:
+        model.load_state_dict(sd)
+    if gpu:
+        model.cuda()
+    if eval_mode:
+        model.eval()
+    return {"model": model}
+
+
+def get_data(config):
+    # get data
+    data = instantiate_from_config(config.data)
+    data.prepare_data()
+    data.setup()
+    return data
+
+
+@st.cache(allow_output_mutation=True)
+def load_model_and_dset(config, ckpt, gpu, eval_mode, delete_dataset_params=False):
+    # get data
+    if delete_dataset_params:
+        st.info("Deleting dataset parameters.")
+        del config["data"]["params"]["train"]["params"]
+        del config["data"]["params"]["validation"]["params"]
+
+    dsets = get_data(config)   # calls data.config ...
+
+    # now load the specified checkpoint
+    if ckpt:
+        pl_sd = torch.load(ckpt, map_location="cpu")
+        global_step = pl_sd["global_step"]
+    else:
+        pl_sd = {"state_dict": None}
+        global_step = None
+    model = load_model_from_config(config.model,
+                                   pl_sd["state_dict"],
+                                   gpu=gpu,
+                                   eval_mode=eval_mode)["model"]
+    return dsets, model, global_step
+
+
+@torch.no_grad()
+def get_image_embeddings(model, dset, used_codebook, used_indices):
+    import plotly.graph_objects as go
+    batch_size = st.number_input("Batch size for embedding visualization", min_value=1, value=4)
+    start_index = st.number_input("Start index", value=0,
+                                          min_value=0,
+                                          max_value=len(dset) - batch_size)
+    if st.sidebar.button("Sample Batch"):
+        indices = np.random.choice(len(dset), batch_size)
+    else:
+        indices = list(range(start_index, start_index + batch_size))
+
+    st.write(f"Indices: {indices}")
+    batch = default_collate([dset[i] for i in indices])
+    x = model.get_input(batch, "image")
+    x = x.to(model.device)
+
+    # get reconstruction from non-quantized and quantized, compare
+    z_pre_quant = model.encode_to_prequant(x)
+    z_quant, emb_loss, info = model.quantize(z_pre_quant)
+    indices = info[2].detach().cpu().numpy()
+    #indices = rearrange(indices, '(b d) -> b d', b=batch_size)
+    unique_indices = np.unique(indices)
+    st.write(f"Unique indices in batch: {unique_indices.shape[0]}")
+
+    x1 = used_codebook[:, 0].cpu().numpy()
+    x2 = used_codebook[:, 1].cpu().numpy()
+    x3 = used_codebook[:, 2].cpu().numpy()
+
+    zp1 = rearrange(z_pre_quant, 'b c h w -> (b h w) c')[:, 0].cpu().numpy()
+    zp2 = rearrange(z_pre_quant, 'b c h w -> (b h w) c')[:, 1].cpu().numpy()
+    zp3 = rearrange(z_pre_quant, 'b c h w -> (b h w) c')[:, 2].cpu().numpy()
+
+    zq1 = rearrange(z_quant, 'b c h w -> (b h w) c')[:, 0].cpu().numpy()
+    zq2 = rearrange(z_quant, 'b c h w -> (b h w) c')[:, 1].cpu().numpy()
+    zq3 = rearrange(z_quant, 'b c h w -> (b h w) c')[:, 2].cpu().numpy()
+
+    fig = go.Figure(data=[go.Scatter3d(x=x1, y=x2, z=x3, mode='markers', marker=dict(size=1.4, line=dict(width=1.,
+                                                                                                         color="Blue")),
+                                       name="All Used Codebook Entries"),
+
+                          ])
+    trace2 = go.Scatter3d(x=zp1, y=zp2, z=zp3, mode='markers', marker=dict(size=1., line=dict(width=1., color=indices)),
+                          name="Pre-Quant Codes")
+    trace3 = go.Scatter3d(x=zq1, y=zq2, z=zq3, mode='markers',
+                          marker=dict(size=2., line=dict(width=10., color=indices)), name="Quantized Codes")
+
+    fig.add_trace(trace2)
+    fig.add_trace(trace3)
+
+    fig.update_layout(
+        autosize=False,
+        width=1000,
+        height=1000,
+    )
+
+    x_rec_no_quant = model.decode(z_pre_quant)
+    x_rec_quant = model.decode(z_quant)
+    delta_x = x_rec_no_quant - x_rec_quant
+
+    st.text("Fitting Gaussian...")
+    h, w = z_quant.shape[2], z_quant.shape[3]
+    from sklearn.mixture import GaussianMixture
+    gaussian = GaussianMixture(n_components=1)
+    gaussian.fit(rearrange(z_pre_quant, 'b c h w -> (b h w) c').cpu().numpy())
+    samples, _ = gaussian.sample(n_samples=batch_size*h*w)
+    samples = rearrange(samples, '(b h w) c -> b h w c', b=batch_size, h=h, w=w, c=3)
+    samples = rearrange(samples, 'b h w c -> b c h w')
+    samples = torch.tensor(samples).to(z_quant)
+    samples, _, _ = model.quantize(samples)
+    x_sample = model.decode(samples)
+
+    all_img = torch.stack([x, x_rec_quant, x_rec_no_quant, delta_x, x_sample])   # 5 b 3 H W
+
+    all_img = rearrange(all_img, 'n b c h w -> b n c h w')
+    all_img = rearrange(all_img, 'b n c h w -> (b n) c h w')
+    grid = make_grid(all_img, nrow=5)
+
+    st.write("** Input | Rec. (w/ quant) | Rec. (no quant) | Delta(quant, no_quant) **")
+    st.image(chw_to_st(grid), clamp=True, output_format="PNG")
+
+    st.write(fig)
+    # 2d projections
+    import matplotlib.pyplot as plt
+    pairs = [(1, 0), (2, 0), (2, 1)]
+    fig2, ax = plt.subplots(1, 3, figsize=(21, 7))
+    for d in range(3):
+        d1, d2 = pairs[d]
+        #ax[d].scatter(used_codebook[:, d1].cpu().numpy(),
+        #              used_codebook[:, d2].cpu().numpy(),
+        #              label="All Used Codebook Entries", s=10.0, c=used_indices)
+        ax[d].scatter(rearrange(z_quant, 'b c h w -> (b h w) c')[:, d1].cpu().numpy(),
+                      rearrange(z_quant, 'b c h w -> (b h w) c')[:, d2].cpu().numpy(),
+                      label="Quantized Codes", alpha=0.9, s=8.0, c=indices)
+        ax[d].scatter(rearrange(z_pre_quant, 'b c h w -> (b h w) c')[:, d1].cpu().numpy(),
+                      rearrange(z_pre_quant, 'b c h w -> (b h w) c')[:, d2].cpu().numpy(),
+                      label="Pre-Quant Codes", alpha=0.5, s=1.0, c=indices)
+        ax[d].set_title(f"dim {d2} vs dim {d1}")
+        ax[d].legend()
+    st.write(fig2)
+
+    plt.close()
+
+    # from scipy.spatial import Voronoi, voronoi_plot_2d
+    # fig3 = plt.figure(figsize=(10,10))
+    # points = rearrange(z_pre_quant, 'b c h w -> (b h w) c')[:, :2].cpu().numpy()
+    # vor = Voronoi(points)
+    # # plot
+    # voronoi_plot_2d(vor)
+    # # colorize
+    # for region in vor.regions:
+    #     if not -1 in region:
+    #         polygon = [vor.vertices[i] for i in region]
+    #         plt.fill(*zip(*polygon))
+    # plt.savefig("voronoi_test.png")
+    # st.write(fig3)
+
+
+@torch.no_grad()
+def get_used_indices(model, dset, batch_size=20):
+    dloader = torch.utils.data.DataLoader(dset, shuffle=True, batch_size=batch_size, drop_last=False)
+    data = list()
+    info = st.empty()
+    for i, batch in enumerate(dloader):
+        x = model.get_input(batch, "image")
+        x = x.to(model.device)
+        zq, _, zi = model.encode(x)
+        indices = zi[2]
+        indices = indices.reshape(zq.shape[0], -1).detach().cpu().numpy()
+        data.append(indices)
+
+        unique = np.unique(data)
+        info.text(f"iteration {i} [{batch_size*i}/{len(dset)}]: unique indices found so far: {unique.size}")
+
+    unique = np.unique(data)
+    #np.save(outpath, unique)
+    st.write(f"end of data: found **{unique.size} unique indices.**")
+    print(f"end of data: found {unique.size} unique indices.")
+    return unique
+
+
+def visualize3d(codebook, used_indices):
+    import matplotlib.pyplot as plt
+    from mpl_toolkits.mplot3d import Axes3D
+
+    codebook = codebook.cpu().numpy()
+    selected_codebook = codebook[used_indices, :]
+    z_dim = codebook.shape[1]
+    assert z_dim == 3
+
+    pairs = [(1,0), (2,0), (2,1)]
+    fig, ax = plt.subplots(1,3, figsize=(15,5))
+    for d in range(3):
+        d1, d2 = pairs[d]
+        ax[d].scatter(selected_codebook[:, d1], selected_codebook[:, d2])
+        ax[d].set_title(f"dim {d2} vs dim {d1}")
+    st.write(fig)
+
+    # # plot 3D
+    # fig = plt.figure(1)
+    # ax = Axes3D(fig)
+    # ax.scatter(codebook[:, 0], codebook[:, 1], codebook[:, 2], s=10., alpha=0.8, label="all entries")
+    # ax.scatter(selected_codebook[:, 0], selected_codebook[:, 1], selected_codebook[:, 2], s=3., alpha=1.0, label="used entries")
+    # plt.legend()
+    # #st.write(fig)
+    # st.pyplot(fig)
+
+    # plot histogram of vector norms
+    fig = plt.figure(2, figsize=(6,5))
+    norms = np.linalg.norm(selected_codebook, axis=1)
+    plt.hist(norms, bins=100, edgecolor="black", lw=1.1)
+    plt.title("Distribution of norms of used codebook entries")
+    st.write(fig)
+
+    # plot 3D with plotly
+    import pandas as pd
+    import plotly.graph_objects as go
+    x = selected_codebook[:, 0]
+    y = selected_codebook[:, 1]
+    z = selected_codebook[:, 2]
+    fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z, mode='markers', marker=dict(size=2., line=dict(width=1.,
+                                                                                                     color="Blue"))
+                                       )
+                          ]
+                    )
+
+    fig.update_layout(
+        autosize=False,
+        width=1000,
+        height=1000,
+    )
+    st.write(fig)
+
+
+@torch.no_grad()
+def get_fixed_points(model, dset):
+    n_iter = st.number_input("Number of Iterations for FP-Analysis", min_value=1, value=25)
+    batch_size = st.number_input("Batch size for fixed-point visualization", min_value=1, value=4)
+    start_index = st.number_input("Start index", value=0, min_value=0, max_value=len(dset) - batch_size)
+    clip_decoded = st.checkbox("Clip decoded image", False)
+    quantize_decoded = st.checkbox("Quantize decoded image (e.g. map back to uint8)", False)
+    factor = st.sidebar.number_input("image size", value=1., min_value=0.1)
+    if st.sidebar.button("Sample Batch"):
+        indices = np.random.choice(len(dset), batch_size)
+    else:
+        indices = list(range(start_index, start_index + batch_size))
+
+    st.write(f"Indices: {indices}")
+    batch = default_collate([dset[i] for i in indices])
+    x = model.get_input(batch, "image")
+    x = x.to(model.device)
+
+    progress = st.empty()
+    progress_cb = lambda k: progress.write(f"iteration {k}/{n_iter}")
+
+    image_progress = st.empty()  # TODO
+
+    input = x
+    img_quant = x
+    img_noquant = x
+    delta_img = img_quant - img_noquant
+
+    st.write("** Input | Rec. (w/ quant) | Rec. (no quant) | Delta(quant, no_quant) **")
+
+    def display(input, img_quant, img_noquant, delta_img):
+        all_img = torch.stack([input, img_quant, img_noquant, delta_img])  # 4 b 3 H W
+        all_img = rearrange(all_img, 'n b c h w -> b n c h w')
+        all_img = rearrange(all_img, 'b n c h w -> (b n) c h w')
+        grid = make_grid(all_img, nrow=4)
+        image_progress.image(chw_to_st(grid), clamp=True, output_format="PNG", width=int(factor*grid.shape[2]))
+
+    display(input, img_quant, img_noquant, delta_img)
+    for n in range(n_iter):
+        # get reconstruction from non-quantized and quantized, compare via iteration
+
+        # quantized_stream
+        z_pre_quant = model.encode_to_prequant(img_quant)
+        z_quant, emb_loss, info = model.quantize(z_pre_quant)
+
+        # non_quantized stream
+        z_noquant = model.encode_to_prequant(img_noquant)
+
+        img_quant = model.decode(z_quant)
+        img_noquant = model.decode(z_noquant)
+        if clip_decoded:
+            img_quant = torch.clamp(img_quant, -1., 1.)
+            img_noquant = torch.clamp(img_noquant, -1., 1.)
+        if quantize_decoded:
+            device = img_quant.device
+            img_quant = (2*torch.Tensor(((img_quant.cpu().numpy()+1.)*127.5).astype(np.uint8))/255. - 1.).to(device)
+            img_noquant = (2*torch.Tensor(((img_noquant.cpu().numpy()+1.)*127.5).astype(np.uint8))/255. - 1.).to(device)
+        delta_img = img_quant - img_noquant
+        display(input, img_quant, img_noquant, delta_img)
+        progress_cb(n + 1)
+
+
+@torch.no_grad()
+def get_fixed_points_kl_ae(model, dset):
+    n_iter = st.number_input("Number of Iterations for FP-Analysis", min_value=1, value=25)
+    batch_size = st.number_input("Batch size for fixed-point visualization", min_value=1, value=4)
+    start_index = st.number_input("Start index", value=0, min_value=0, max_value=len(dset) - batch_size)
+    clip_decoded = st.checkbox("Clip decoded image", False)
+    quantize_decoded = st.checkbox("Quantize decoded image (e.g. map back to uint8)", False)
+    sample_posterior = st.checkbox("Sample from encoder posterior", False)
+    factor = st.sidebar.number_input("image size", value=1., min_value=0.1)
+    if st.sidebar.button("Sample Batch"):
+        indices = np.random.choice(len(dset), batch_size)
+    else:
+        indices = list(range(start_index, start_index + batch_size))
+
+    st.write(f"Indices: {indices}")
+    batch = default_collate([dset[i] for i in indices])
+    x = model.get_input(batch, "image")
+    x = x.to(model.device)
+
+    progress = st.empty()
+    progress_cb = lambda k: progress.write(f"iteration {k}/{n_iter}")
+
+    st.write("** Input | Rec. (no quant) | Delta(input, iter_rec) **")
+    image_progress = st.empty()
+
+    input = x
+    img_noquant = x
+    delta_img = input - img_noquant
+
+    def display(input, img_noquant, delta_img):
+        all_img = torch.stack([input, img_noquant, delta_img])  # 3 b 3 H W
+        all_img = rearrange(all_img, 'n b c h w -> b n c h w')
+        all_img = rearrange(all_img, 'b n c h w -> (b n) c h w')
+        grid = make_grid(all_img, nrow=3)
+        image_progress.image(chw_to_st(grid), clamp=True, output_format="PNG", width=int(factor*grid.shape[2]))
+
+    fig, ax = plt.subplots()
+
+    distribution_progress = st.empty()
+    def display_latent_distribution(latent_z, alpha=1., title=""):
+        flatz = latent_z.reshape(-1).cpu().detach().numpy()
+        #fig, ax = plt.subplots()
+        ax.hist(flatz, bins=42, alpha=alpha, lw=.1, edgecolor="black")
+        ax.set_title(title)
+        distribution_progress.pyplot(fig)
+
+    display(input, img_noquant, delta_img)
+    for n in range(n_iter):
+        # get reconstructions
+
+        posterior = model.encode(img_noquant)
+        if sample_posterior:
+            z = posterior.sample()
+        else:
+            z = posterior.mode()
+
+        if n==0:
+            flatz_init = z.reshape(-1).cpu().detach().numpy()
+            std_init = flatz_init.std()
+            max_init, min_init = flatz_init.max(), flatz_init.min()
+
+        display_latent_distribution(z, alpha=np.sqrt(1/(n+1)),
+                                    title=f"initial z: std/min/max: {std_init:.2f}/{min_init:.2f}/{max_init:.2f}")
+
+        img_noquant = model.decode(z)
+        if clip_decoded:
+            img_noquant = torch.clamp(img_noquant, -1., 1.)
+        if quantize_decoded:
+            img_noquant = (2*torch.Tensor(((img_noquant.cpu().numpy()+1.)*127.5).astype(np.uint8))/255. - 1.).to(model.device)
+        delta_img = img_noquant - input
+        display(input, img_noquant, delta_img)
+        progress_cb(n + 1)
+
+
+
+if __name__ == "__main__":
+    from ldm.models.autoencoder import AutoencoderKL
+    # VISUALIZE USED AND ALL INDICES of VQ-Model. VISUALIZE FIXED POINTS OF KL MODEL
+    sys.path.append(os.getcwd())
+
+    parser = get_parser()
+    opt, unknown = parser.parse_known_args()
+
+    ckpt = None
+    if opt.resume:
+        if not os.path.exists(opt.resume):
+            raise ValueError("Cannot find {}".format(opt.resume))
+        if os.path.isfile(opt.resume):
+            paths = opt.resume.split("/")
+            try:
+                idx = len(paths)-paths[::-1].index("logs")+1
+            except ValueError:
+                idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
+            logdir = "/".join(paths[:idx])
+            ckpt = opt.resume
+        else:
+            assert os.path.isdir(opt.resume), opt.resume
+            logdir = opt.resume.rstrip("/")
+            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
+
+        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
+        opt.base = base_configs+opt.base
+
+    if opt.config:
+        if type(opt.config) == str:
+            opt.base = [opt.config]
+        else:
+            opt.base = [opt.base[-1]]
+
+    configs = [OmegaConf.load(cfg) for cfg in opt.base]
+    cli = OmegaConf.from_dotlist(unknown)
+    config = OmegaConf.merge(*configs, cli)
+
+    if opt.dataset_config:
+        dcfg = OmegaConf.load(opt.dataset_config)
+        print("Replacing data config with:")
+        print(dcfg.pretty())
+        dcfg = OmegaConf.to_container(dcfg)
+        config["data"] = dcfg["data"]
+
+    st.sidebar.text(ckpt)
+    gs = st.sidebar.empty()
+    gs.text(f"Global step: ?")
+    st.sidebar.text("Options")
+    gpu = st.sidebar.checkbox("GPU", value=True)
+    eval_mode = st.sidebar.checkbox("Eval Mode", value=True)
+    show_config = st.sidebar.checkbox("Show Config", value=False)
+    if show_config:
+        st.info("Checkpoint: {}".format(ckpt))
+        st.json(OmegaConf.to_container(config))
+
+    delelete_dataset_parameters = st.sidebar.checkbox("Delete parameters of dataset.")
+    dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode,
+                                                    delete_dataset_params=delelete_dataset_parameters)
+    gs.text(f"Global step: {global_step}")
+
+    split = st.sidebar.radio("Split", sorted(dsets.datasets.keys())[::-1])
+    dset = dsets.datasets[split]
+
+    batch_size = st.sidebar.number_input("Batch size", min_value=1, value=20)
+    num_batches = st.sidebar.number_input("Number of batches", min_value=1, value=5)
+    data_size = batch_size*num_batches
+    dset = torch.utils.data.Subset(dset, np.random.choice(np.arange(len(dset)), size=(data_size,), replace=False))
+
+    if not isinstance(model, AutoencoderKL):
+        # VQ MODEL
+        codebook = model.quantize.embedding.weight.data
+        st.write(f"VQ-Model has codebook of dimensionality **{codebook.shape[0]} x {codebook.shape[1]} (num_entries x z_dim)**")
+        st.write(f"Evaluating codebook-usage on **{config['data']['params'][split]['target']}**")
+        st.write("**Select ONE of the following options**")
+
+        if st.checkbox("Show Codebook Statistics", False):
+            used_indices = get_used_indices(model, dset, batch_size=batch_size)
+            visualize3d(codebook, used_indices)
+        if st.checkbox("Show Batch Encodings", False):
+            used_indices = get_used_indices(model, dset, batch_size=batch_size)
+            get_image_embeddings(model, dset, codebook[used_indices, :], used_indices)
+        if st.checkbox("Show Fixed Points of Data", False):
+            get_fixed_points(model, dset)
+
+    else:
+        st.info("Detected a KL model")
+        get_fixed_points_kl_ae(model, dset)
\ No newline at end of file