diff --git a/.gitignore b/.gitignore
index 68bc17f..0cda4e1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,16 @@
+raw/
+images/
+latent_*/
+vae/
+models/
+test.py
+test.png
+*.npy
+*.ckpt
+*.safetensors
+
+# default github .gitignore follows
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/interposer.py b/interposer.py
new file mode 100644
index 0000000..f35229d
--- /dev/null
+++ b/interposer.py
@@ -0,0 +1,31 @@
+import torch
+import torch.nn as nn
+import numpy as np
+
+class Interposer(nn.Module):
+	def __init__(self):
+		super().__init__()
+		
+		# it looks like a spaceship if you squint :P
+		module_list = [
+		#############)
+		#############)
+			    #||#
+			    #||#
+			nn.Conv2d(4, 32, kernel_size=5, padding=2),
+			nn.ReLU(),
+			nn.Conv2d(32, 128, kernel_size=7, padding=3),
+			nn.ReLU(),
+			nn.Conv2d(128, 32, kernel_size=7, padding=3),
+			nn.ReLU(),
+			nn.Conv2d(32, 4, kernel_size=5, padding=2),
+			    #||#
+			    #||#
+		#############)
+		#############)
+		]
+
+		self.sequential = nn.Sequential(*module_list)
+
+	def forward(self, x: torch.Tensor) -> torch.Tensor:
+		return self.sequential(x)
diff --git a/preprocess_images.py b/preprocess_images.py
new file mode 100644
index 0000000..1752625
--- /dev/null
+++ b/preprocess_images.py
@@ -0,0 +1,21 @@
+import os
+import hashlib
+from tqdm import tqdm
+from PIL import Image
+
+# target resolution [latent res * 8]
+resolution = 768
+
+if not os.path.isdir("images"):
+	os.mkdir("images")
+
+for i in tqdm(os.listdir("raw")):
+	src = os.path.join("raw", i)
+	md5 = hashlib.md5(open(src,'rb').read()).hexdigest()
+	out = os.path.join("images", f"{md5}.png")
+	if os.path.isfile(out):
+		continue
+	img = Image.open(src)
+	img = img.convert('RGB')
+	img = img.resize((resolution,resolution), Image.LANCZOS)
+	img.save(out)
diff --git a/preprocess_latents.py b/preprocess_latents.py
new file mode 100644
index 0000000..43b32c7
--- /dev/null
+++ b/preprocess_latents.py
@@ -0,0 +1,51 @@
+import os
+import torch
+import numpy as np
+from torchvision import transforms
+from diffusers import AutoencoderKL
+from tqdm import tqdm
+from PIL import Image
+
+from vae import get_vae
+
+def encode(vae, img):
+	"""image [PIL Image] -> latent [np array]"""
+	inp = transforms.ToTensor()(img).unsqueeze(0)
+	inp = inp.to("cuda") # move to GPU
+	latent = vae.encode(inp*2.0-1.0)
+	latent = latent.latent_dist.sample()
+	return latent.cpu().detach()
+
+def process_folder(vae, v):
+	if not os.path.isdir(f"latent_{v}"):
+		os.mkdir(f"latent_{v}")
+
+	vae.to("cuda")
+	for i in tqdm(os.listdir("images")):
+		src = os.path.join("images", i)
+		img = Image.open(src)
+		dst = os.path.join(f"latent_{v}", f"{os.path.splitext(i)[0]}.npy")
+		latent = encode(vae, img)
+		np.save(dst, latent)
+	vae.to("cpu")
+
+def run_v1(file_path=None):
+	vae = get_vae("v1", file_path)
+	process_folder(vae, "v1")
+	del vae
+
+def run_v2(file_path=None):
+	vae = get_vae("v2", file_path)
+	process_folder(vae, "v2")
+	del vae
+
+def run_xl(file_path=None):
+	vae = get_vae("xl", file_path)
+	process_folder(vae, "xl")
+	del vae
+
+if __name__ == "__main__":
+	# run_v1("./vae/ft-mse-840000.ckpt") # probably doesn't reflect internal SD latent
+	run_v1()
+	run_v2()
+	run_xl("./vae/sdxl_v0.9.safetensors") # 1.0 has artifacts 
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..bdf5091
--- /dev/null
+++ b/train.py
@@ -0,0 +1,95 @@
+import os
+import torch
+import torch.nn as nn
+import numpy as np
+import random
+from PIL import Image
+from tqdm import tqdm
+from safetensors.torch import save_file
+
+from interposer import Interposer
+from vae import get_vae
+
+# options
+target_dev = "cuda"
+target_steps = 500000
+save_every_n = 50000
+latent_src = "v1"
+latent_dst = "xl"
+
+class Latent:
+	def __init__(self, md5, lat_src, lat_dst, dev):
+		if lat_src == "v1": src = os.path.join("latent_v1", f"{md5}.npy")
+		if lat_src == "v2": src = os.path.join("latent_v2", f"{md5}.npy")
+		if lat_src == "xl": src = os.path.join("latent_xl", f"{md5}.npy")
+
+		if lat_dst == "v1": dst = os.path.join("latent_v1", f"{md5}.npy")
+		if lat_dst == "v2": dst = os.path.join("latent_v2", f"{md5}.npy")
+		if lat_dst == "xl": dst = os.path.join("latent_xl", f"{md5}.npy")
+
+		self.src = torch.from_numpy(np.load(src)).to(dev)
+		self.dst = torch.from_numpy(np.load(dst)).to(dev)
+
+vae = None
+def sample_decode(latent, filename, version):
+	global vae
+	if not vae:
+		vae = get_vae(version, fp16=True)
+		vae.to("cuda")
+
+	latent = latent.half().to("cuda")
+	out = vae.decode(latent).sample
+	out = out.cpu().detach().numpy()
+	out = np.squeeze(out, 0)
+	out = out.transpose((1, 2, 0))
+	out = np.clip(out, -1.0, 1.0)
+	out = (out+1)/2 * 255
+	out = out.astype(np.uint8)
+	out = Image.fromarray(out)
+	out.save(filename)
+
+if not os.path.isdir("models"): os.mkdir("models")
+
+if __name__ == "__main__":
+	print("Loading latents from disk")
+	latents = []
+	for i in tqdm(os.listdir("images")):
+		md5 = os.path.splitext(i)[0]
+		latents.append(Latent(md5, latent_src, latent_dst, target_dev))
+
+	if os.path.isfile(f"test_{latent_src}.npy"):
+		sample_latent = torch.from_numpy(np.load(f"test_{latent_src}.npy")).to(target_dev)
+	else:
+		sample_latent = random.choice(latents).src
+
+	model = Interposer()
+	model.to(target_dev)
+
+	criterion = torch.nn.MSELoss(size_average=False)
+	optimizer = torch.optim.SGD(model.parameters(), lr=1e-8)
+
+	for t in tqdm(range(target_steps)):
+		# io = latents[t%len(latents)]
+		io = random.choice(latents)
+
+		y_pred = model(io.src) # forward
+		loss = criterion(y_pred, io.dst) # loss
+
+		# backward
+		optimizer.zero_grad()
+		loss.backward()
+		optimizer.step()
+
+		# print loss
+		if t%1000 == 0: tqdm.write(f"{t} - {loss.data.item():.2f}")
+
+		# sample/save
+		if t%save_every_n == 0:
+			out = model(sample_latent)
+			output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{t/1000}k"
+			sample_decode(out, f"{output_name}.png", latent_dst)
+			save_file(model.state_dict(), f"{output_name}.safetensors")
+	# save final output
+	output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{target_steps/1000}k"
+	sample_decode(out, f"{output_name}.png", "v1")
+	save_file(model.state_dict(), f"{output_name}.safetensors")
diff --git a/vae.py b/vae.py
new file mode 100644
index 0000000..0bd4f9e
--- /dev/null
+++ b/vae.py
@@ -0,0 +1,48 @@
+import torch
+from diffusers import AutoencoderKL
+
+def get_vae(version, file_path=None, fp16=False):
+	"""Load VAE from file or default hf repo. fp16 only works from hf"""
+	vae = None
+	dtype = torch.float16 if fp16 else torch.float32
+	if version == "v1" and file_path:
+		vae = AutoencoderKL.from_single_file(
+			file_path,
+			image_size=512,
+		)
+	elif version == "v1":
+		vae = AutoencoderKL.from_pretrained(
+			"runwayml/stable-diffusion-v1-5",
+			subfolder="vae",
+			torch_dtype=dtype,
+		)
+	elif version == "v2" and file_path:
+		vae = AutoencoderKL.from_single_file(
+			file_path,
+			image_size=768,
+		)
+	elif version == "v2":
+		vae = AutoencoderKL.from_pretrained(
+			"stabilityai/stable-diffusion-2-1",
+			subfolder="vae",
+			torch_dtype=dtype,
+		)
+	elif version == "xl" and file_path:
+		vae = AutoencoderKL.from_single_file(
+			file_path,
+			image_size=1024
+		)
+	elif version == "xl" and fp16:
+		vae = AutoencoderKL.from_pretrained(
+			"madebyollin/sdxl-vae-fp16-fix",
+			torch_dtype=torch.float16,
+		)
+	elif version == "xl":
+		vae = AutoencoderKL.from_pretrained(
+			"stabilityai/stable-diffusion-xl-base-1.0",
+			subfolder="vae"
+		)
+	else:
+		input("Invalid VAE version. Press any key to exit")
+		exit(1)
+	return vae