diff --git a/entry.py b/entry.py
index 7e230e5..758fef7 100644
--- a/entry.py
+++ b/entry.py
@@ -1,4 +1,6 @@
 import os
+import math
+import numpy as np
 import torch
 import safetensors.torch
 
@@ -7,6 +9,68 @@ from sgm.util import instantiate_from_config
 
 from sgm.modules.diffusionmodules.sampling import EulerAncestralSampler
 
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list(set([x.input_key for x in conditioner.embedders]))
+
+
+def get_batch(keys, value_dict, N, device="cuda"):
+    # Hardcoded demo setups; might undergo some changes in the future
+
+    batch = {}
+    batch_uc = {}
+
+    for key in keys:
+        if key == "txt":
+            batch["txt"] = (
+                np.repeat([value_dict["prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
+            )
+            batch_uc["txt"] = (
+                np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
+            )
+        elif key == "original_size_as_tuple":
+            batch["original_size_as_tuple"] = (
+                torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+        elif key == "crop_coords_top_left":
+            batch["crop_coords_top_left"] = (
+                torch.tensor(
+                    [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
+                )
+                .to(device)
+                .repeat(*N, 1)
+            )
+        elif key == "aesthetic_score":
+            batch["aesthetic_score"] = (
+                torch.tensor([value_dict["aesthetic_score"]]).to(device).repeat(*N, 1)
+            )
+            batch_uc["aesthetic_score"] = (
+                torch.tensor([value_dict["negative_aesthetic_score"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+
+        elif key == "target_size_as_tuple":
+            batch["target_size_as_tuple"] = (
+                torch.tensor([value_dict["target_height"], value_dict["target_width"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+        else:
+            batch[key] = value_dict[key]
+
+    for key in batch.keys():
+        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+            batch_uc[key] = torch.clone(batch[key])
+    return batch, batch_uc
+
+
 sampler = EulerAncestralSampler(
     num_steps=40,
     discretization_config={
@@ -31,4 +95,23 @@ model.eval()
 sd = safetensors.torch.load_file('./sd_xl_base_1.0.safetensors')
 model.load_state_dict(sd, strict=False)
 
+model.conditioner.cuda()
+
+value_dict = {
+    "prompt": "a handsome man in forest", "negative_prompt": "ugly, bad", "orig_height": 1024, "orig_width": 1024,
+    "crop_coords_top": 0, "crop_coords_left": 0, "target_height": 1024, "target_width": 1024, "aesthetic_score": 7.5,
+    "negative_aesthetic_score": 2.0,
+}
+
+batch, batch_uc = get_batch(
+    get_unique_embedder_keys_from_conditioner(model.conditioner),
+    value_dict,
+    1,
+)
+
+c, uc = model.conditioner.get_unconditional_conditioning(
+    batch,
+    batch_uc=batch_uc)
+model.conditioner.cpu()
+
 a = 0