diff --git a/sampling.py b/sampling.py
new file mode 100644
index 0000000..f07341a
--- /dev/null
+++ b/sampling.py
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+import torch
+import torchvision
+import os
+import gc
+import tqdm
+import matplotlib.pyplot as plt
+import torchvision.transforms as transforms
+from transformers import CLIPTextModel
+from peft import PeftModel, LoraConfig
+from lora_w2w import LoRAw2w
+from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel, LMSDiscreteScheduler
+from peft.utils.save_and_load import load_peft_weights, set_peft_model_state_dict
+from transformers import AutoTokenizer, PretrainedConfig
+from PIL import Image
+import warnings
+warnings.filterwarnings("ignore")
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    UNet2DConditionModel,
+    PNDMScheduler, 
+    StableDiffusionPipeline
+)
+
+
+
+######## Sampling utilities
+
+
+def sample_weights(unet, proj, mean, std, v, device, factor = 1.0): 
+    # get mean and standard deviation for each principal component
+    m = torch.mean(proj, 0)
+    standev = torch.std(proj, 0)
+    del proj
+    torch.cuda.empty_cache()
+    # sample
+    sample = torch.zeros([1, 1000]).to(device)
+    for i in range(1000):
+        sample[0, i] = torch.normal(m[i], factor*standev[i], (1,1))
+
+    # load weights into network
+    network = LoRAw2w( sample, mean, std, v, 
+                    unet,
+                    rank=1,
+                    multiplier=1.0,
+                    alpha=27.0,
+                    train_method="xattn-strict"
+                ).to(device, torch.bfloat16)
+        
+    return network
+
+
+
+