diff --git a/README.md b/README.md
index 3738ddee5..7bdf52a17 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,9 @@
 </p>
 
 ## News
+
+### July 2022
+- Inference code and model weights to run our [retrieval-augmented diffusion models](https://arxiv.org/abs/2204.11824) are now available. See [this section](#retrieval-augmented-diffusion-models).
 ### April 2022
 - Thanks to [Katherine Crowson](https://github.com/crowsonkb), classifier-free guidance received a ~2x speedup and the [PLMS sampler](https://arxiv.org/abs/2202.09778) is available. See also [this PR](https://github.com/CompVis/latent-diffusion/pull/51).
 
@@ -42,6 +45,74 @@ conda activate ldm
 A general list of all available checkpoints is available in via our [model zoo](#model-zoo).
 If you use any of these models in your work, we are always happy to receive a [citation](#bibtex).
 
+## Retrieval Augmented Diffusion Models
+![rdm-figure](assets/rdm-preview.jpg)
+We include inference code to run our retrieval-augmented diffusion models (RDMs) as described in [https://arxiv.org/abs/2204.11824](https://arxiv.org/abs/2204.11824).
+
+
+To get started, install the additionally required python packages into your `ldm` environment
+```shell script
+pip install transformers==4.19.2 scann kornia==0.6.4 torchmetrics==0.6.0
+pip install git+https://github.com/arogozhnikov/einops.git
+```
+and download the trained weights (preliminary ceckpoints):
+
+```bash
+mkdir -p models/rdm/rdm768x768/
+wget -O models/rdm/rdm768x768/model.ckpt https://ommer-lab.com/files/rdm/model.ckpt
+```
+As these models are conditioned on a set of CLIP image embeddings, our RDMs support different inference modes, 
+which are described in the following.
+#### RDM with text-prompt only (no explicit retrieval needed)
+Since CLIP offers a shared image/text feature space, and RDMs learn to cover a neighborhood of a given
+example during training, we can directly take a CLIP text embedding of a given prompt and condition on it.
+Run this mode via
+```
+python scripts/knn2img.py  --prompt "a happy bear reading a newspaper, oil on canvas"
+```
+
+#### RDM with text-to-image retrieval
+
+To be able to run a RDM conditioned on a text-prompt and additionally images retrieved from this prompt, you will also need to download the corresponding retrieval database. 
+We provide two distinct databases extracted from the [Openimages-](https://storage.googleapis.com/openimages/web/index.html) and [ArtBench-](https://github.com/liaopeiyuan/artbench) datasets. 
+Interchanging the databases results in different capabilities of the model as visualized below, although the learned weights are the same in both cases. 
+
+Download the retrieval-databases which contain the retrieval-datasets ([Openimages](https://storage.googleapis.com/openimages/web/index.html) (~11GB) and [ArtBench](https://github.com/liaopeiyuan/artbench) (~82MB)) compressed into CLIP image embeddings:
+```bash
+mkdir -p data/rdm/retrieval_databases
+wget -O data/rdm/retrieval_databases/artbench.zip https://ommer-lab.com/files/rdm/artbench_databases.zip
+wget -O data/rdm/retrieval_databases/openimages.zip https://ommer-lab.com/files/rdm/openimages_database.zip
+unzip data/rdm/retrieval_databases/artbench.zip -d data/rdm/retrieval_databases/
+unzip data/rdm/retrieval_databases/openimages.zip -d data/rdm/retrieval_databases/
+```
+We also provide trained [ScaNN](https://github.com/google-research/google-research/tree/master/scann) search indices for ArtBench. Download and extract via
+```bash
+mkdir -p data/rdm/searchers
+wget -O data/rdm/searchers/artbench.zip https://ommer-lab.com/files/rdm/artbench_searchers.zip
+unzip data/rdm/searchers/artbench.zip -d data/rdm/searchers
+```
+
+Since the index for OpenImages is large (~21 GB), we provide a script to create and save it for usage during sampling. Note however,
+that sampling with the OpenImages database will not be possible without this index. Run the script via
+```bash
+python scripts/train_searcher.py
+```
+
+Retrieval based text-guided sampling with visual nearest neighbors can be started via 
+```
+python scripts/knn2img.py  --prompt "a happy pineapple" --use_neighbors --knn <number_of_neighbors> 
+```
+Note that the maximum supported number of neighbors is 20. 
+The database can be changed via the cmd parameter ``--database`` which can be `[openimages, artbench-art_nouveau, artbench-baroque, artbench-expressionism, artbench-impressionism, artbench-post_impressionism, artbench-realism, artbench-renaissance, artbench-romanticism, artbench-surrealism, artbench-ukiyo_e]`.
+For using `--database openimages`, the above script (`scripts/train_searcher.py`) must be executed before.
+Due to their relatively small size, the artbench datasetbases are best suited for creating more abstract concepts and do not work well for detailed text control. 
+
+
+#### Coming Soon
+- better models
+- more resolutions
+- image-to-image retrieval
+
 ## Text-to-Image
 ![text2img-figure](assets/txt2img-preview.png) 
 
@@ -273,6 +344,19 @@ Thanks for open-sourcing!
       archivePrefix={arXiv},
       primaryClass={cs.CV}
 }
+
+@misc{https://doi.org/10.48550/arxiv.2204.11824,
+  doi = {10.48550/ARXIV.2204.11824},
+  url = {https://arxiv.org/abs/2204.11824},
+  author = {Blattmann, Andreas and Rombach, Robin and Oktay, Kaan and Ommer, Björn},
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Retrieval-Augmented Diffusion Models},
+  publisher = {arXiv},
+  year = {2022},  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+
+
 ```
 
 
diff --git a/assets/rdm-preview.jpg b/assets/rdm-preview.jpg
new file mode 100644
index 000000000..3838b0f6b
Binary files /dev/null and b/assets/rdm-preview.jpg differ
diff --git a/configs/retrieval-augmented-diffusion/768x768.yaml b/configs/retrieval-augmented-diffusion/768x768.yaml
new file mode 100644
index 000000000..b51b1d837
--- /dev/null
+++ b/configs/retrieval-augmented-diffusion/768x768.yaml
@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 0.0001
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.0015
+    linear_end: 0.015
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: jpg
+    cond_stage_key: nix
+    image_size: 48
+    channels: 16
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_by_std: false
+    scale_factor: 0.22765929
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 48
+        in_channels: 16
+        out_channels: 16
+        model_channels: 448
+        attention_resolutions:
+        - 4
+        - 2
+        - 1
+        num_res_blocks: 2
+        channel_mult:
+        - 1
+        - 2
+        - 3
+        - 4
+        use_scale_shift_norm: false
+        resblock_updown: false
+        num_head_channels: 32
+        use_spatial_transformer: true
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: true
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        monitor: val/rec_loss
+        embed_dim: 16
+        ddconfig:
+          double_z: true
+          z_channels: 16
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 1
+          - 2
+          - 2
+          - 4
+          num_res_blocks: 2
+          attn_resolutions:
+          - 16
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+    cond_stage_config:
+      target: torch.nn.Identity
\ No newline at end of file
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index 506e39ba4..aa3031df6 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -1,6 +1,10 @@
 import torch
 import torch.nn as nn
 from functools import partial
+import clip
+from einops import rearrange, repeat
+import kornia
+
 
 from ldm.modules.x_transformer import Encoder, TransformerWrapper  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
 
@@ -129,3 +133,70 @@ def forward(self,x):
 
     def encode(self, x):
         return self(x)
+
+
+class FrozenCLIPTextEmbedder(nn.Module):
+    """
+    Uses the CLIP transformer encoder for text.
+    """
+    def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
+        super().__init__()
+        self.model, _ = clip.load(version, jit=False, device="cpu")
+        self.device = device
+        self.max_length = max_length
+        self.n_repeat = n_repeat
+        self.normalize = normalize
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        tokens = clip.tokenize(text).to(self.device)
+        z = self.model.encode_text(tokens)
+        if self.normalize:
+            z = z / torch.linalg.norm(z, dim=1, keepdim=True)
+        return z
+
+    def encode(self, text):
+        z = self(text)
+        if z.ndim==2:
+            z = z[:, None, :]
+        z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
+        return z
+
+
+class FrozenClipImageEmbedder(nn.Module):
+    """
+        Uses the CLIP image encoder.
+        """
+    def __init__(
+            self,
+            model,
+            jit=False,
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+            antialias=False,
+        ):
+        super().__init__()
+        self.model, _ = clip.load(name=model, device=device, jit=jit)
+
+        self.antialias = antialias
+
+        self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
+        self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(x, (224, 224),
+                                   interpolation='bicubic',align_corners=True,
+                                   antialias=self.antialias)
+        x = (x + 1.) / 2.
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def forward(self, x):
+        # x is assumed to be in range [-1,1]
+        return self.model.encode_image(self.preprocess(x))
+
diff --git a/ldm/util.py b/ldm/util.py
index 51839cb14..8ba38853e 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -2,6 +2,13 @@
 
 import torch
 import numpy as np
+from collections import abc
+from einops import rearrange
+from functools import partial
+
+import multiprocessing as mp
+from threading import Thread
+from queue import Queue
 
 from inspect import isfunction
 from PIL import Image, ImageDraw, ImageFont
@@ -38,7 +45,7 @@ def ismap(x):
 
 
 def isimage(x):
-    if not isinstance(x,torch.Tensor):
+    if not isinstance(x, torch.Tensor):
         return False
     return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
 
@@ -64,7 +71,7 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
     return total_params
 
 
@@ -83,4 +90,114 @@ def get_obj_from_str(string, reload=False):
     if reload:
         module_imp = importlib.import_module(module)
         importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
\ No newline at end of file
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
+    # create dummy dataset instance
+
+    # run prefetching
+    if idx_to_fn:
+        res = func(data, worker_id=idx)
+    else:
+        res = func(data)
+    Q.put([idx, res])
+    Q.put("Done")
+
+
+def parallel_data_prefetch(
+        func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
+):
+    # if target_data_type not in ["ndarray", "list"]:
+    #     raise ValueError(
+    #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
+    #     )
+    if isinstance(data, np.ndarray) and target_data_type == "list":
+        raise ValueError("list expected but function got ndarray.")
+    elif isinstance(data, abc.Iterable):
+        if isinstance(data, dict):
+            print(
+                f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
+            )
+            data = list(data.values())
+        if target_data_type == "ndarray":
+            data = np.asarray(data)
+        else:
+            data = list(data)
+    else:
+        raise TypeError(
+            f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
+        )
+
+    if cpu_intensive:
+        Q = mp.Queue(1000)
+        proc = mp.Process
+    else:
+        Q = Queue(1000)
+        proc = Thread
+    # spawn processes
+    if target_data_type == "ndarray":
+        arguments = [
+            [func, Q, part, i, use_worker_id]
+            for i, part in enumerate(np.array_split(data, n_proc))
+        ]
+    else:
+        step = (
+            int(len(data) / n_proc + 1)
+            if len(data) % n_proc != 0
+            else int(len(data) / n_proc)
+        )
+        arguments = [
+            [func, Q, part, i, use_worker_id]
+            for i, part in enumerate(
+                [data[i: i + step] for i in range(0, len(data), step)]
+            )
+        ]
+    processes = []
+    for i in range(n_proc):
+        p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
+        processes += [p]
+
+    # start processes
+    print(f"Start prefetching...")
+    import time
+
+    start = time.time()
+    gather_res = [[] for _ in range(n_proc)]
+    try:
+        for p in processes:
+            p.start()
+
+        k = 0
+        while k < n_proc:
+            # get result
+            res = Q.get()
+            if res == "Done":
+                k += 1
+            else:
+                gather_res[res[0]] = res[1]
+
+    except Exception as e:
+        print("Exception: ", e)
+        for p in processes:
+            p.terminate()
+
+        raise e
+    finally:
+        for p in processes:
+            p.join()
+        print(f"Prefetching complete. [{time.time() - start} sec.]")
+
+    if target_data_type == 'ndarray':
+        if not isinstance(gather_res[0], np.ndarray):
+            return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
+
+        # order outputs
+        return np.concatenate(gather_res, axis=0)
+    elif target_data_type == 'list':
+        out = []
+        for r in gather_res:
+            out.extend(r)
+        return out
+    else:
+        return gather_res
diff --git a/scripts/knn2img.py b/scripts/knn2img.py
new file mode 100644
index 000000000..e6eaaecab
--- /dev/null
+++ b/scripts/knn2img.py
@@ -0,0 +1,398 @@
+import argparse, os, sys, glob
+import clip
+import torch
+import torch.nn as nn
+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, repeat
+from torchvision.utils import make_grid
+import scann
+import time
+from multiprocessing import cpu_count
+
+from ldm.util import instantiate_from_config, parallel_data_prefetch
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.plms import PLMSSampler
+from ldm.modules.encoders.modules import FrozenClipImageEmbedder, FrozenCLIPTextEmbedder
+
+DATABASES = [
+    "openimages",
+    "artbench-art_nouveau",
+    "artbench-baroque",
+    "artbench-expressionism",
+    "artbench-impressionism",
+    "artbench-post_impressionism",
+    "artbench-realism",
+    "artbench-romanticism",
+    "artbench-renaissance",
+    "artbench-surrealism",
+    "artbench-ukiyo_e",
+]
+
+
+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")
+    if "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+    sd = pl_sd["state_dict"]
+    model = instantiate_from_config(config.model)
+    m, u = model.load_state_dict(sd, strict=False)
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    model.cuda()
+    model.eval()
+    return model
+
+
+class Searcher(object):
+    def __init__(self, database, retriever_version='ViT-L/14'):
+        assert database in DATABASES
+        # self.database = self.load_database(database)
+        self.database_name = database
+        self.searcher_savedir = f'data/rdm/searchers/{self.database_name}'
+        self.database_path = f'data/rdm/retrieval_databases/{self.database_name}'
+        self.retriever = self.load_retriever(version=retriever_version)
+        self.database = {'embedding': [],
+                         'img_id': [],
+                         'patch_coords': []}
+        self.load_database()
+        self.load_searcher()
+
+    def train_searcher(self, k,
+                       metric='dot_product',
+                       searcher_savedir=None):
+
+        print('Start training searcher')
+        searcher = scann.scann_ops_pybind.builder(self.database['embedding'] /
+                                                  np.linalg.norm(self.database['embedding'], axis=1)[:, np.newaxis],
+                                                  k, metric)
+        self.searcher = searcher.score_brute_force().build()
+        print('Finish training searcher')
+
+        if searcher_savedir is not None:
+            print(f'Save trained searcher under "{searcher_savedir}"')
+            os.makedirs(searcher_savedir, exist_ok=True)
+            self.searcher.serialize(searcher_savedir)
+
+    def load_single_file(self, saved_embeddings):
+        compressed = np.load(saved_embeddings)
+        self.database = {key: compressed[key] for key in compressed.files}
+        print('Finished loading of clip embeddings.')
+
+    def load_multi_files(self, data_archive):
+        out_data = {key: [] for key in self.database}
+        for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
+            for key in d.files:
+                out_data[key].append(d[key])
+
+        return out_data
+
+    def load_database(self):
+
+        print(f'Load saved patch embedding from "{self.database_path}"')
+        file_content = glob.glob(os.path.join(self.database_path, '*.npz'))
+
+        if len(file_content) == 1:
+            self.load_single_file(file_content[0])
+        elif len(file_content) > 1:
+            data = [np.load(f) for f in file_content]
+            prefetched_data = parallel_data_prefetch(self.load_multi_files, data,
+                                                     n_proc=min(len(data), cpu_count()), target_data_type='dict')
+
+            self.database = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in
+                             self.database}
+        else:
+            raise ValueError(f'No npz-files in specified path "{self.database_path}" is this directory existing?')
+
+        print(f'Finished loading of retrieval database of length {self.database["embedding"].shape[0]}.')
+
+    def load_retriever(self, version='ViT-L/14', ):
+        model = FrozenClipImageEmbedder(model=version)
+        if torch.cuda.is_available():
+            model.cuda()
+        model.eval()
+        return model
+
+    def load_searcher(self):
+        print(f'load searcher for database {self.database_name} from {self.searcher_savedir}')
+        self.searcher = scann.scann_ops_pybind.load_searcher(self.searcher_savedir)
+        print('Finished loading searcher.')
+
+    def search(self, x, k):
+        if self.searcher is None and self.database['embedding'].shape[0] < 2e4:
+            self.train_searcher(k)   # quickly fit searcher on the fly for small databases
+        assert self.searcher is not None, 'Cannot search with uninitialized searcher'
+        if isinstance(x, torch.Tensor):
+            x = x.detach().cpu().numpy()
+        if len(x.shape) == 3:
+            x = x[:, 0]
+        query_embeddings = x / np.linalg.norm(x, axis=1)[:, np.newaxis]
+
+        start = time.time()
+        nns, distances = self.searcher.search_batched(query_embeddings, final_num_neighbors=k)
+        end = time.time()
+
+        out_embeddings = self.database['embedding'][nns]
+        out_img_ids = self.database['img_id'][nns]
+        out_pc = self.database['patch_coords'][nns]
+
+        out = {'nn_embeddings': out_embeddings / np.linalg.norm(out_embeddings, axis=-1)[..., np.newaxis],
+               'img_ids': out_img_ids,
+               'patch_coords': out_pc,
+               'queries': x,
+               'exec_time': end - start,
+               'nns': nns,
+               'q_embeddings': query_embeddings}
+
+        return out
+
+    def __call__(self, x, n):
+        return self.search(x, n)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # TODO: add n_neighbors and modes (text-only, text-image-retrieval, image-image retrieval etc)
+    # TODO: add 'image variation' mode when knn=0 but a single image is given instead of a text prompt?
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        nargs="?",
+        default="a painting of a virus monster playing guitar",
+        help="the prompt to render"
+    )
+
+    parser.add_argument(
+        "--outdir",
+        type=str,
+        nargs="?",
+        help="dir to write results to",
+        default="outputs/txt2img-samples"
+    )
+
+    parser.add_argument(
+        "--skip_grid",
+        action='store_true',
+        help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
+    )
+
+    parser.add_argument(
+        "--ddim_steps",
+        type=int,
+        default=50,
+        help="number of ddim sampling steps",
+    )
+
+    parser.add_argument(
+        "--n_repeat",
+        type=int,
+        default=1,
+        help="number of repeats in CLIP latent space",
+    )
+
+    parser.add_argument(
+        "--plms",
+        action='store_true',
+        help="use plms sampling",
+    )
+
+    parser.add_argument(
+        "--ddim_eta",
+        type=float,
+        default=0.0,
+        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
+    )
+    parser.add_argument(
+        "--n_iter",
+        type=int,
+        default=1,
+        help="sample this often",
+    )
+
+    parser.add_argument(
+        "--H",
+        type=int,
+        default=768,
+        help="image height, in pixel space",
+    )
+
+    parser.add_argument(
+        "--W",
+        type=int,
+        default=768,
+        help="image width, in pixel space",
+    )
+
+    parser.add_argument(
+        "--n_samples",
+        type=int,
+        default=3,
+        help="how many samples to produce for each given prompt. A.k.a batch size",
+    )
+
+    parser.add_argument(
+        "--n_rows",
+        type=int,
+        default=0,
+        help="rows in the grid (default: n_samples)",
+    )
+
+    parser.add_argument(
+        "--scale",
+        type=float,
+        default=5.0,
+        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
+    )
+
+    parser.add_argument(
+        "--from-file",
+        type=str,
+        help="if specified, load prompts from this file",
+    )
+
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="configs/retrieval-augmented-diffusion/768x768.yaml",
+        help="path to config which constructs model",
+    )
+
+    parser.add_argument(
+        "--ckpt",
+        type=str,
+        default="models/rdm/rdm768x768/model.ckpt",
+        help="path to checkpoint of model",
+    )
+
+    parser.add_argument(
+        "--clip_type",
+        type=str,
+        default="ViT-L/14",
+        help="which CLIP model to use for retrieval and NN encoding",
+    )
+    parser.add_argument(
+        "--database",
+        type=str,
+        default='artbench-surrealism',
+        choices=DATABASES,
+        help="The database used for the search, only applied when --use_neighbors=True",
+    )
+    parser.add_argument(
+        "--use_neighbors",
+        default=False,
+        action='store_true',
+        help="Include neighbors in addition to text prompt for conditioning",
+    )
+    parser.add_argument(
+        "--knn",
+        default=10,
+        type=int,
+        help="The number of included neighbors, only applied when --use_neighbors=True",
+    )
+
+    opt = parser.parse_args()
+
+    config = OmegaConf.load(f"{opt.config}")
+    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)
+
+    clip_text_encoder = FrozenCLIPTextEmbedder(opt.clip_type).to(device)
+
+    if opt.plms:
+        sampler = PLMSSampler(model)
+    else:
+        sampler = DDIMSampler(model)
+
+    os.makedirs(opt.outdir, exist_ok=True)
+    outpath = opt.outdir
+
+    batch_size = opt.n_samples
+    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
+    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
+
+    print(f"sampling scale for cfg is {opt.scale:.2f}")
+
+    searcher = None
+    if opt.use_neighbors:
+        searcher = Searcher(opt.database)
+
+    with torch.no_grad():
+        with model.ema_scope():
+            for n in trange(opt.n_iter, desc="Sampling"):
+                all_samples = list()
+                for prompts in tqdm(data, desc="data"):
+                    print("sampling prompts:", prompts)
+                    if isinstance(prompts, tuple):
+                        prompts = list(prompts)
+                    c = clip_text_encoder.encode(prompts)
+                    uc = None
+                    if searcher is not None:
+                        nn_dict = searcher(c, opt.knn)
+                        c = torch.cat([c, torch.from_numpy(nn_dict['nn_embeddings']).cuda()], dim=1)
+                    if opt.scale != 1.0:
+                        uc = torch.zeros_like(c)
+                    if isinstance(prompts, tuple):
+                        prompts = list(prompts)
+                    shape = [16, opt.H // 16, opt.W // 16]  # note: currently hardcoded for f16 model
+                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
+                                                     conditioning=c,
+                                                     batch_size=c.shape[0],
+                                                     shape=shape,
+                                                     verbose=False,
+                                                     unconditional_guidance_scale=opt.scale,
+                                                     unconditional_conditioning=uc,
+                                                     eta=opt.ddim_eta,
+                                                     )
+
+                    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)
+
+                    for x_sample in x_samples_ddim:
+                        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:05}.png"))
+                        base_count += 1
+                    all_samples.append(x_samples_ddim)
+
+                if not opt.skip_grid:
+                    # additionally, save as grid
+                    grid = torch.stack(all_samples, 0)
+                    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+                    grid = make_grid(grid, nrow=n_rows)
+
+                    # to image
+                    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
+                    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 for you here: \n{outpath} \nEnjoy.")
diff --git a/scripts/train_searcher.py b/scripts/train_searcher.py
new file mode 100644
index 000000000..1e7904889
--- /dev/null
+++ b/scripts/train_searcher.py
@@ -0,0 +1,147 @@
+import os, sys
+import numpy as np
+import scann
+import argparse
+import glob
+from multiprocessing import cpu_count
+from tqdm import tqdm
+
+from ldm.util import parallel_data_prefetch
+
+
+def search_bruteforce(searcher):
+    return searcher.score_brute_force().build()
+
+
+def search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k,
+                        partioning_trainsize, num_leaves, num_leaves_to_search):
+    return searcher.tree(num_leaves=num_leaves,
+                         num_leaves_to_search=num_leaves_to_search,
+                         training_sample_size=partioning_trainsize). \
+        score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(reorder_k).build()
+
+
+def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k):
+    return searcher.score_ah(dims_per_block, anisotropic_quantization_threshold=aiq_threshold).reorder(
+        reorder_k).build()
+
+def load_datapool(dpath):
+
+
+    def load_single_file(saved_embeddings):
+        compressed = np.load(saved_embeddings)
+        database = {key: compressed[key] for key in compressed.files}
+        return database
+
+    def load_multi_files(data_archive):
+        database = {key: [] for key in data_archive[0].files}
+        for d in tqdm(data_archive, desc=f'Loading datapool from {len(data_archive)} individual files.'):
+            for key in d.files:
+                database[key].append(d[key])
+
+        return database
+
+    print(f'Load saved patch embedding from "{dpath}"')
+    file_content = glob.glob(os.path.join(dpath, '*.npz'))
+
+    if len(file_content) == 1:
+        data_pool = load_single_file(file_content[0])
+    elif len(file_content) > 1:
+        data = [np.load(f) for f in file_content]
+        prefetched_data = parallel_data_prefetch(load_multi_files, data,
+                                                 n_proc=min(len(data), cpu_count()), target_data_type='dict')
+
+        data_pool = {key: np.concatenate([od[key] for od in prefetched_data], axis=1)[0] for key in prefetched_data[0].keys()}
+    else:
+        raise ValueError(f'No npz-files in specified path "{dpath}" is this directory existing?')
+
+    print(f'Finished loading of retrieval database of length {data_pool["embedding"].shape[0]}.')
+    return data_pool
+
+
+def train_searcher(opt,
+                   metric='dot_product',
+                   partioning_trainsize=None,
+                   reorder_k=None,
+                   # todo tune
+                   aiq_thld=0.2,
+                   dims_per_block=2,
+                   num_leaves=None,
+                   num_leaves_to_search=None,):
+
+    data_pool = load_datapool(opt.database)
+    k = opt.knn
+
+    if not reorder_k:
+        reorder_k = 2 * k
+
+    # normalize
+    # embeddings =
+    searcher = scann.scann_ops_pybind.builder(data_pool['embedding'] / np.linalg.norm(data_pool['embedding'], axis=1)[:, np.newaxis], k, metric)
+    pool_size = data_pool['embedding'].shape[0]
+
+    print(*(['#'] * 100))
+    print('Initializing scaNN searcher with the following values:')
+    print(f'k: {k}')
+    print(f'metric: {metric}')
+    print(f'reorder_k: {reorder_k}')
+    print(f'anisotropic_quantization_threshold: {aiq_thld}')
+    print(f'dims_per_block: {dims_per_block}')
+    print(*(['#'] * 100))
+    print('Start training searcher....')
+    print(f'N samples in pool is {pool_size}')
+
+    # this reflects the recommended design choices proposed at
+    # https://github.com/google-research/google-research/blob/aca5f2e44e301af172590bb8e65711f0c9ee0cfd/scann/docs/algorithms.md
+    if pool_size < 2e4:
+        print('Using brute force search.')
+        searcher = search_bruteforce(searcher)
+    elif 2e4 <= pool_size and pool_size < 1e5:
+        print('Using asymmetric hashing search and reordering.')
+        searcher = search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
+    else:
+        print('Using using partioning, asymmetric hashing search and reordering.')
+
+        if not partioning_trainsize:
+            partioning_trainsize = data_pool['embedding'].shape[0] // 10
+        if not num_leaves:
+            num_leaves = int(np.sqrt(pool_size))
+
+        if not num_leaves_to_search:
+            num_leaves_to_search = max(num_leaves // 20, 1)
+
+        print('Partitioning params:')
+        print(f'num_leaves: {num_leaves}')
+        print(f'num_leaves_to_search: {num_leaves_to_search}')
+        # self.searcher = self.search_ah(searcher, dims_per_block, aiq_thld, reorder_k)
+        searcher = search_partioned_ah(searcher, dims_per_block, aiq_thld, reorder_k,
+                                                 partioning_trainsize, num_leaves, num_leaves_to_search)
+
+    print('Finish training searcher')
+    searcher_savedir = opt.target_path
+    os.makedirs(searcher_savedir, exist_ok=True)
+    searcher.serialize(searcher_savedir)
+    print(f'Saved trained searcher under "{searcher_savedir}"')
+
+if __name__ == '__main__':
+    sys.path.append(os.getcwd())
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--database',
+                        '-d',
+                        default='data/rdm/retrieval_databases/openimages',
+                        type=str,
+                        help='path to folder containing the clip feature of the database')
+    parser.add_argument('--target_path',
+                        '-t',
+                        default='data/rdm/searchers/openimages',
+                        type=str,
+                        help='path to the target folder where the searcher shall be stored.')
+    parser.add_argument('--knn',
+                        '-k',
+                        default=20,
+                        type=int,
+                        help='number of nearest neighbors, for which the searcher shall be optimized')
+
+    opt, _  = parser.parse_known_args()
+
+    train_searcher(opt,)
\ No newline at end of file