Merge remote-tracking branch 'github/main' into main

configs/stable-diffusion/txt2img-multinode-clip-encoder-f16-256-pretraining.yaml

@@ -0,0 +1,131 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.001
+    linear_end: 0.015
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 16
+    channels: 16
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.22765929   # magic number
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 16    # not really needed
+        in_channels: 16
+        out_channels: 16
+        model_channels: 320    # TODO: scale model here
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ckpt_path: "models/first_stage_models/kl-f16/model.ckpt"
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+
+data:
+  target: ldm.data.laion.WebDataModuleFromConfig
+  params:
+    tar_base: "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/"
+    batch_size: 50  # TODO: max out
+    num_workers: 4
+    multinode: True
+    train:
+      shards: '{000000..231317}.tar -'
+      shuffle: 10000
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 256
+          interpolation: 3
+      - target: torchvision.transforms.RandomCrop
+        params:
+          size: 256
+
+    # NOTE use enough shards to avoid empty validation loops in workers
+    validation:
+      shards: '{231318..231349}.tar -'
+      shuffle: 0
+      image_key: jpg
+      image_transforms:
+      - target: torchvision.transforms.Resize
+        params:
+          size: 256
+          interpolation: 3
+      - target: torchvision.transforms.CenterCrop
+        params:
+          size: 256
+
+
+lightning:
+  callbacks:
+    image_logger:
+      target: main.ImageLogger
+      params:
+        batch_frequency: 5000
+        max_images: 4
+        increase_log_steps: False
+        log_first_step: False
+        log_images_kwargs:
+          use_ema_scope: False
+          inpaint: False
+          plot_progressive_rows: False
+          plot_diffusion_rows: False
+          N: 4
+          unconditional_guidance_scale: 3.0
+          unconditional_guidance_label: [""]
+
+  trainer:
+    replace_sampler_ddp: False  # TODO: check this
+    benchmark: True
+    val_check_interval: 5000000  # really sorry
+    num_sanity_val_steps: 0
+    accumulate_grad_batches: 2  # TODO: want accumulate on? --> wait for final batch-size

ldm/data/dummy.py

@@ -16,3 +16,19 @@ class DummyData(Dataset):
         letters = string.ascii_lowercase
         y = ''.join(random.choice(string.ascii_lowercase) for i in range(10))
         return {"jpg": x, "txt": y}
+
+
+class DummyDataWithEmbeddings(Dataset):
+    def __init__(self, length, size, emb_size):
+        self.length = length
+        self.size = size
+        self.emb_size = emb_size
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, i):
+        x = np.random.randn(*self.size)
+        y = np.random.randn(*self.emb_size).astype(np.float32)
+        return {"jpg": x, "txt": y}
+

ldm/models/diffusion/ddim.py

@@ -4,6 +4,7 @@ import torch
 import numpy as np
 from tqdm import tqdm
 from functools import partial
+from einops import rearrange
 
 from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
 
@@ -73,8 +74,8 @@ class DDIMSampler(object):
                x_T=None,
                log_every_t=100,
                unconditional_guidance_scale=1.,
-               unconditional_conditioning=None,
+               unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               dynamic_threshold=None,
                **kwargs
                ):
         if conditioning is not None:
@@ -106,6 +107,7 @@ class DDIMSampler(object):
                                                     log_every_t=log_every_t,
                                                     unconditional_guidance_scale=unconditional_guidance_scale,
                                                     unconditional_conditioning=unconditional_conditioning,
+                                                    dynamic_threshold=dynamic_threshold,
                                                     )
         return samples, intermediates
 
@@ -115,7 +117,7 @@ class DDIMSampler(object):
                       callback=None, timesteps=None, quantize_denoised=False,
                       mask=None, x0=None, img_callback=None, log_every_t=100,
                       temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None,):
+                      unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None):
         device = self.model.betas.device
         b = shape[0]
         if x_T is None:
@@ -150,7 +152,8 @@ class DDIMSampler(object):
                                       noise_dropout=noise_dropout, score_corrector=score_corrector,
                                       corrector_kwargs=corrector_kwargs,
                                       unconditional_guidance_scale=unconditional_guidance_scale,
-                                      unconditional_conditioning=unconditional_conditioning)
+                                      unconditional_conditioning=unconditional_conditioning,
+                                      dynamic_threshold=dynamic_threshold)
             img, pred_x0 = outs
             if callback: callback(i)
             if img_callback: img_callback(pred_x0, i)
@@ -164,7 +167,8 @@ class DDIMSampler(object):
     @torch.no_grad()
     def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                       temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None):
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,
+                      dynamic_threshold=None):
         b, *_, device = *x.shape, x.device
 
         if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
@@ -194,6 +198,33 @@ class DDIMSampler(object):
         pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
         if quantize_denoised:
             pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+        if dynamic_threshold is not None:
+            # renorm
+            pred_max = pred_x0.max()
+            pred_min = pred_x0.min()
+            pred_x0 = (pred_x0-pred_min)/(pred_max-pred_min)   # 0 ... 1
+            pred_x0 = 2*pred_x0 - 1.   # -1 ... 1
+
+            s = torch.quantile(
+                rearrange(pred_x0, 'b ... -> b (...)').abs(),
+                dynamic_threshold,
+                dim=-1
+            )
+            s.clamp_(min=1.0)
+            s = s.view(-1, *((1,) * (pred_x0.ndim - 1)))
+
+            # clip by threshold
+            #pred_x0 = pred_x0.clamp(-s, s) / s  # needs newer pytorch  # TODO bring back to pure-gpu with min/max
+
+            # temporary hack: numpy on cpu
+            pred_x0 = np.clip(pred_x0.cpu().numpy(), -s.cpu().numpy(), s.cpu().numpy()) / s.cpu().numpy()
+            pred_x0 = torch.tensor(pred_x0).to(self.model.device)
+
+            # re.renorm
+            pred_x0 = (pred_x0 + 1.) / 2.   # 0 ... 1
+            pred_x0 = (pred_max-pred_min)*pred_x0 + pred_min  # orig range
+
         # direction pointing to x_t
         dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
         noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature

ldm/modules/encoders/modules.py

@@ -12,6 +12,10 @@ class AbstractEncoder(nn.Module):
     def encode(self, *args, **kwargs):
         raise NotImplementedError
 
+class IdentityEncoder(AbstractEncoder):
+
+    def encode(self, x):
+        return x
 
 
 class ClassEmbedder(nn.Module):

scripts/prompts/prompts-with-wings.txt

@@ -0,0 +1,64 @@
+A portrait of Abraham Lincoln
+A portrait of Barack Obama
+A portrait of a nekomimi girl smiling
+a portrait of isaac newton the alchemist
+A portrait of Friedrich Nietzsche wearing an open double breasted suit with a bowtie
+Portrait of a cyberpunk cyborg man wearing alternate reality goggles
+Portrait of a woman screaming
+A portrait of a man in a flight jacket leaning against a biplane
+a cold landscape by Albert Bierstadt
+the monument of the ancients by van gogh
+the universal library
+a vision of paradise. unreal engine
+matte painting of cozy underground bunker wholefoods aisle, trending on artstation
+illustration of wooly mammoths reclaiming the arctic, trending on artstation
+a mountain range in the desert, Provia, Velvia
+the gateway between dreams, trending on ArtStation
+a cityscape at night
+starry night by cyberpunk
+A fantasy painting of a city in a deep valley by Ivan Aivazovsky
+An oil painting of The New York City Skyline by Natalia Goncharova
+a rainy city street in the style of cyberpunk noir, trending on ArtStation
+an astral city in the style of cyberpunk noir art deco
+The Golden Gate Bridge in the style of art deco
+a city on a 70s science fiction novel cover
+An oil painting of A Vase Of Flowers
+still life oil painting of a smooth silver steel tungsten square cube box by Albrecht Dürer
+An oil painting of a bookshelf crammed with books, trending on artstation
+An N95 respirator mask in the style of art deco
+a surreal and organic stone monument to a plutonium atom
+oil painting of a candy dish of glass candies, mints, and other assorted sweets
+illustration of a ford model-t in pristine condition, trending on artstation
+illustration of DEC minicomputer console monitor retrocomputing teletype interdata PDP-11 univac, trending on artstation
+The Rise Of Consciousness
+The Human Utility Function
+Revolution of the Souls
+a good amphetamine spirit
+Control The Soul
+The Lunatic, The Lover, and The Poet
+A Planet Ruled By Angels
+the Tower of Babel by J.M.W. Turner
+sketch of a 3D printer by Leonardo da Vinci
+In The Style Of M.C. Escher
+A cup of coffee by Picasso
+The US Capitol Building in the style of Kandinsky
+A Mysterious Orb by Andy Warhol
+The everlasting zero, a glimpse of a million, by Salvador Dali
+a painting of a haunted house with Halloween decorations by Giovanni Paolo Panini
+a painting of drops of Venus by Vincent van Gogh
+ascii art of a man riding a bicycle
+cyberpunk noir art deco detective in space
+a cyborg angel in the style of ukiyo-e
+Hell in the style of pointillism
+Moloch in the style of socialist realism
+Metaphysics in the style of WPAP
+advertisement for a psychedelic virtual reality headset, 16 bit sprite pixel art
+a watercolor painting of a Christmas tree
+control room monitors televisions screens computers hacker lab, concept art, matte painting, trending on artstation
+a group of surgeons wait to cryonically suspend a patient
+technological singularity cult by James Gurney
+an autogyro flying car, trending on artstation
+illustration of airship zepplins in the skies, trending on artstation
+watercolor illustration of a martian colony geodesic dome aquaponics farming on the surface, trending on artstation
+humanity is killed by AI, by James Gurney
+the Vitruvian Man as a propaganda poster for transhumanism

scripts/txt2img.py

@@ -4,6 +4,7 @@ import numpy as np
 from omegaconf import OmegaConf
 from PIL import Image
 from tqdm import tqdm, trange
+from itertools import islice
 from einops import rearrange
 from torchvision.utils import make_grid
 
@@ -12,6 +13,11 @@ from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
 
 
+def chunk(it, size):
+    it = iter(it)
+    return iter(lambda: tuple(islice(it, size)), ())
+
+
 def load_model_from_config(config, ckpt, verbose=False):
     print(f"Loading model from {ckpt}")
     pl_sd = torch.load(ckpt, map_location="cpu")
@@ -51,7 +57,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--ddim_steps",
         type=int,
-        default=200,
+        default=50,
         help="number of ddim sampling steps",
     )
 
@@ -91,8 +97,8 @@ if __name__ == "__main__":
     parser.add_argument(
         "--n_samples",
         type=int,
-        default=4,
+        default=8,
-        help="how many samples to produce for the given prompt",
+        help="how many samples to produce for each given prompt. A.k.a batch size",
     )
 
     parser.add_argument(
@@ -101,11 +107,35 @@ if __name__ == "__main__":
         default=5.0,
         help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
     )
+
+    parser.add_argument(
+        "--dyn",
+        type=float,
+        help="dynamic thresholding from Imagen, in latent space (TODO: try in pixel space with intermediate decode)",
+    )
+    parser.add_argument(
+        "--from-file",
+        type=str,
+        help="if specified, load prompts from this file",
+    )
+
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="logs/f8-kl-clip-encoder-256x256-run1/configs/2022-06-01T22-11-40-project.yaml",
+        help="path to config which constructs model",
+    )
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="logs/f8-kl-clip-encoder-256x256-run1/checkpoints/last.ckpt",
+        help="path to checkpoint of model",
+    )
     opt = parser.parse_args()
 
 
-    config = OmegaConf.load("configs/latent-diffusion/txt2img-1p4B-eval.yaml")  # TODO: Optionally download from same location as ckpt and chnage this logic
+    config = OmegaConf.load(f"{opt.config}")
-    model = load_model_from_config(config, "models/ldm/text2img-large/model.ckpt")  # TODO: check path
+    model = load_model_from_config(config, f"{opt.ckpt}")
 
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     model = model.to(device)
@@ -118,48 +148,62 @@ if __name__ == "__main__":
     os.makedirs(opt.outdir, exist_ok=True)
     outpath = opt.outdir
 
-    prompt = opt.prompt
+    batch_size = opt.n_samples
+    if not opt.from_file:
+        prompt = opt.prompt
+        assert prompt is not None
+        data = [batch_size * [prompt]]
 
+    else:
+        print(f"reading prompts from {opt.from_file}")
+        with open(opt.from_file, "r") as f:
+            data = f.read().splitlines()
+            data = list(chunk(data, batch_size))
 
     sample_path = os.path.join(outpath, "samples")
     os.makedirs(sample_path, exist_ok=True)
     base_count = len(os.listdir(sample_path))
+    grid_count = len(os.listdir(outpath)) - 1
+
 
-    all_samples=list()
     with torch.no_grad():
         with model.ema_scope():
-            uc = None
-            if opt.scale != 1.0:
-                uc = model.get_learned_conditioning(opt.n_samples * [""])
             for n in trange(opt.n_iter, desc="Sampling"):
-                c = model.get_learned_conditioning(opt.n_samples * [prompt])
+                all_samples = list()
-                shape = [4, opt.H//8, opt.W//8]
+                for prompts in tqdm(data, desc="data"):
-                samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
+                    uc = None
-                                                 conditioning=c,
+                    if opt.scale != 1.0:
-                                                 batch_size=opt.n_samples,
+                        uc = model.get_learned_conditioning(batch_size * [""])
-                                                 shape=shape,
+                    c = model.get_learned_conditioning(prompts)
-                                                 verbose=False,
+                    shape = [4, opt.H//8, opt.W//8]
-                                                 unconditional_guidance_scale=opt.scale,
+                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
-                                                 unconditional_conditioning=uc,
+                                                     conditioning=c,
-                                                 eta=opt.ddim_eta)
+                                                     batch_size=opt.n_samples,
+                                                     shape=shape,
+                                                     verbose=False,
+                                                     unconditional_guidance_scale=opt.scale,
+                                                     unconditional_conditioning=uc,
+                                                     eta=opt.ddim_eta,
+                                                     dynamic_threshold=opt.dyn)
 
-                x_samples_ddim = model.decode_first_stage(samples_ddim)
+                    x_samples_ddim = model.decode_first_stage(samples_ddim)
-                x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)
+                    x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)
 
-                for x_sample in x_samples_ddim:
+                    for x_sample in x_samples_ddim:
-                    x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
-                    Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:04}.png"))
+                        Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:05}.png"))
-                    base_count += 1
+                        base_count += 1
-                all_samples.append(x_samples_ddim)
+                    all_samples.append(x_samples_ddim)
 
 
-    # additionally, save as grid
+                # additionally, save as grid
-    grid = torch.stack(all_samples, 0)
+                grid = torch.stack(all_samples, 0)
-    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+                grid = rearrange(grid, 'n b c h w -> (n b) c h w')
-    grid = make_grid(grid, nrow=opt.n_samples)
+                grid = make_grid(grid, nrow=opt.n_samples)
 
-    # to image
+                # to image
-    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
+                grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
-    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'{prompt.replace(" ", "-")}.png'))
+                Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
+                grid_count += 1
 
-    print(f"Your samples are ready and waiting four you here: \n{outpath} \nEnjoy.")
+    print(f"Your samples are ready and waiting for you here: \n{outpath} \nEnjoy.")

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)