diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..fc34f9a
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "synthius/TabDiff"]
+	path = synthius/TabDiff
+	url = git@github.com:calgo-lab/TabDiff.git
diff --git a/README.md b/README.md
index 8686857..e1c1313 100644
--- a/README.md
+++ b/README.md
@@ -34,4 +34,7 @@ Mac users may encounter errors during installation. To resolve these issues, ins
 
 
 ## Acknowledgments
-Special thanks to all contributors and the libraries used in this project.
\ No newline at end of file
+Special thanks to all contributors and the libraries used in this project.
+
+This package includes TabDiff (Copyright 2024 Minkai Xu) under the MIT License.
+For more details, see synthius/TabDiff/LICENSE.
\ No newline at end of file
diff --git a/examples/data/adult_subset.csv b/examples/data/adult_subset.csv
index 94b5b56..1f8e3c8 100644
--- a/examples/data/adult_subset.csv
+++ b/examples/data/adult_subset.csv
@@ -1,21 +1,21 @@
-,Unnamed: 0,Age,Workclass,Fnlwgt,Education,Education-num,Marital-status,Occupation,Relationship,Race,Sex,Capital-gain,Capital-loss,Hours-per-week,Native-country,Income
-0,12,30,Private,149507,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,42,United-States,True
-1,142,52,Private,72743,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,50,United-States,True
-2,170,44,Private,142968,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,40,United-States,True
-3,247,46,Self-emp-inc,204928,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,40,United-States,True
-4,86,17,Not-Specified,237078,11th,7,Never-married,Not-Specified,Own-child,White,Female,0,0,35,United-States,True
-5,42,61,Private,147393,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,20,United-States,True
-6,89,27,Self-emp-not-inc,328119,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,30,Mexico,True
-7,147,41,Local-gov,174575,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,40,United-States,True
-8,56,37,Private,175759,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,40,United-States,False
-9,23,46,Self-emp-not-inc,199596,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,50,United-States,True
-10,17,46,State-gov,248895,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,40,United-States,False
-11,0,37,Private,126569,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,60,United-States,False
-12,28,43,Private,598606,9th,5,Separated,Handlers-cleaners,Unmarried,Black,Female,0,0,50,United-States,True
-13,189,58,Self-emp-not-inc,204816,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,60,United-States,False
-14,95,30,Private,113364,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,18,United-States,True
-15,60,26,Private,124068,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,20,United-States,True
-16,113,53,Self-emp-not-inc,297796,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,40,United-States,True
-17,59,33,Local-gov,251521,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,50,United-States,True
-18,132,37,Private,412296,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,35,Mexico,False
-19,181,27,Private,205440,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,40,United-States,True
+Age,Workclass,Fnlwgt,Education,Education-num,Marital-status,Occupation,Relationship,Race,Sex,Capital-gain,Capital-loss,Hours-per-week,Native-country,Income
+30,Private,149507,HS-grad,9,Married-civ-spouse,Sales,Husband,White,Male,0,0,42,United-States,True
+52,Private,72743,HS-grad,9,Married-spouse-absent,Adm-clerical,Not-in-family,White,Female,0,0,50,United-States,True
+44,Private,142968,HS-grad,9,Widowed,Other-service,Unmarried,White,Female,0,0,40,United-States,True
+46,Self-emp-inc,204928,Some-college,10,Married-civ-spouse,Sales,Husband,White,Male,0,0,40,United-States,True
+17,Not-Specified,237078,11th,7,Never-married,Not-Specified,Own-child,White,Female,0,0,35,United-States,True
+61,Private,147393,Prof-school,15,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,20,United-States,True
+27,Self-emp-not-inc,328119,9th,5,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,30,Mexico,True
+41,Local-gov,174575,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,40,United-States,True
+37,Private,175759,Bachelors,13,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,40,United-States,False
+46,Self-emp-not-inc,199596,Some-college,10,Divorced,Exec-managerial,Unmarried,White,Female,0,0,50,United-States,True
+46,State-gov,248895,Bachelors,13,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,40,United-States,False
+37,Private,126569,Masters,14,Married-civ-spouse,Tech-support,Husband,White,Male,0,0,60,United-States,False
+43,Private,598606,9th,5,Separated,Handlers-cleaners,Unmarried,Black,Female,0,0,50,United-States,True
+58,Self-emp-not-inc,204816,Some-college,10,Married-civ-spouse,Exec-managerial,Husband,White,Male,0,0,60,United-States,False
+30,Private,113364,Bachelors,13,Never-married,Sales,Own-child,White,Male,0,0,18,United-States,True
+26,Private,124068,Some-college,10,Never-married,Adm-clerical,Not-in-family,White,Female,0,0,20,United-States,True
+53,Self-emp-not-inc,297796,10th,6,Married-civ-spouse,Farming-fishing,Husband,White,Male,0,0,40,United-States,True
+33,Local-gov,251521,Bachelors,13,Never-married,Exec-managerial,Not-in-family,White,Female,0,0,50,United-States,True
+37,Private,412296,HS-grad,9,Married-civ-spouse,Craft-repair,Husband,White,Male,0,0,35,Mexico,False
+27,Private,205440,10th,6,Married-civ-spouse,Machine-op-inspct,Husband,White,Male,0,0,40,United-States,True
diff --git a/poetry.lock b/poetry.lock
index 862fd49..2594a1e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -12,6 +12,39 @@ files = [
     {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
 ]
 
+[[package]]
+name = "accelerate"
+version = "1.12.0"
+description = "Accelerate"
+optional = false
+python-versions = ">=3.10.0"
+groups = ["main"]
+files = [
+    {file = "accelerate-1.12.0-py3-none-any.whl", hash = "sha256:3e2091cd341423207e2f084a6654b1efcd250dc326f2a37d6dde446e07cabb11"},
+    {file = "accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6"},
+]
+
+[package.dependencies]
+huggingface_hub = ">=0.21.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = ">=0.4.3"
+torch = ">=2.0.0"
+
+[package.extras]
+deepspeed = ["deepspeed"]
+dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (==0.13.1)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+quality = ["ruff (==0.13.1)"]
+rich = ["rich"]
+sagemaker = ["sagemaker"]
+test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+test-fp8 = ["torchao"]
+test-prod = ["parameterized", "pytest (>=7.2.0)", "pytest-order", "pytest-subtests", "pytest-xdist"]
+test-trackers = ["comet-ml", "dvclive", "matplotlib", "swanlab[dashboard]", "tensorboard", "trackio", "wandb"]
+testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.4.4"
@@ -330,7 +363,7 @@ version = "3.0.0"
 description = "Annotate AST trees with source code positions"
 optional = false
 python-versions = ">=3.8"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
     {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
     {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
@@ -567,13 +600,64 @@ charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
+[[package]]
+name = "black"
+version = "25.11.0"
+description = "The uncompromising code formatter."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version == \"3.11\""
+files = [
+    {file = "black-25.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ec311e22458eec32a807f029b2646f661e6859c3f61bc6d9ffb67958779f392e"},
+    {file = "black-25.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1032639c90208c15711334d681de2e24821af0575573db2810b0763bcd62e0f0"},
+    {file = "black-25.11.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0c0f7c461df55cf32929b002335883946a4893d759f2df343389c4396f3b6b37"},
+    {file = "black-25.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:f9786c24d8e9bd5f20dc7a7f0cdd742644656987f6ea6947629306f937726c03"},
+    {file = "black-25.11.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:895571922a35434a9d8ca67ef926da6bc9ad464522a5fe0db99b394ef1c0675a"},
+    {file = "black-25.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:cb4f4b65d717062191bdec8e4a442539a8ea065e6af1c4f4d36f0cdb5f71e170"},
+    {file = "black-25.11.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d81a44cbc7e4f73a9d6ae449ec2317ad81512d1e7dce7d57f6333fd6259737bc"},
+    {file = "black-25.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:7eebd4744dfe92ef1ee349dc532defbf012a88b087bb7ddd688ff59a447b080e"},
+    {file = "black-25.11.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:80e7486ad3535636657aa180ad32a7d67d7c273a80e12f1b4bfa0823d54e8fac"},
+    {file = "black-25.11.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6cced12b747c4c76bc09b4db057c319d8545307266f41aaee665540bc0e04e96"},
+    {file = "black-25.11.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6cb2d54a39e0ef021d6c5eef442e10fd71fcb491be6413d083a320ee768329dd"},
+    {file = "black-25.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:ae263af2f496940438e5be1a0c1020e13b09154f3af4df0835ea7f9fe7bfa409"},
+    {file = "black-25.11.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0a1d40348b6621cc20d3d7530a5b8d67e9714906dfd7346338249ad9c6cedf2b"},
+    {file = "black-25.11.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:51c65d7d60bb25429ea2bf0731c32b2a2442eb4bd3b2afcb47830f0b13e58bfd"},
+    {file = "black-25.11.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:936c4dd07669269f40b497440159a221ee435e3fddcf668e0c05244a9be71993"},
+    {file = "black-25.11.0-cp313-cp313-win_amd64.whl", hash = "sha256:f42c0ea7f59994490f4dccd64e6b2dd49ac57c7c84f38b8faab50f8759db245c"},
+    {file = "black-25.11.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:35690a383f22dd3e468c85dc4b915217f87667ad9cce781d7b42678ce63c4170"},
+    {file = "black-25.11.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:dae49ef7369c6caa1a1833fd5efb7c3024bb7e4499bf64833f65ad27791b1545"},
+    {file = "black-25.11.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5bd4a22a0b37401c8e492e994bce79e614f91b14d9ea911f44f36e262195fdda"},
+    {file = "black-25.11.0-cp314-cp314-win_amd64.whl", hash = "sha256:aa211411e94fdf86519996b7f5f05e71ba34835d8f0c0f03c00a26271da02664"},
+    {file = "black-25.11.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a3bb5ce32daa9ff0605d73b6f19da0b0e6c1f8f2d75594db539fdfed722f2b06"},
+    {file = "black-25.11.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9815ccee1e55717fe9a4b924cae1646ef7f54e0f990da39a34fc7b264fcf80a2"},
+    {file = "black-25.11.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:92285c37b93a1698dcbc34581867b480f1ba3a7b92acf1fe0467b04d7a4da0dc"},
+    {file = "black-25.11.0-cp39-cp39-win_amd64.whl", hash = "sha256:43945853a31099c7c0ff8dface53b4de56c41294fa6783c0441a8b1d9bf668bc"},
+    {file = "black-25.11.0-py3-none-any.whl", hash = "sha256:e3f562da087791e96cefcd9dda058380a442ab322a02e222add53736451f604b"},
+    {file = "black-25.11.0.tar.gz", hash = "sha256:9a323ac32f5dc75ce7470501b887250be5005a01602e931a15e45593f70f6e08"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+packaging = ">=22.0"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+pytokens = ">=0.3.0"
+
+[package.extras]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.10)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
 [[package]]
 name = "bleach"
 version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
 optional = false
 python-versions = ">=3.9"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
     {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
     {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
@@ -744,6 +828,26 @@ six = "*"
 [package.extras]
 widget = ["ipython", "ipywidgets (>=7.0,<9.0)", "traitlets"]
 
+[[package]]
+name = "category-encoders"
+version = "2.7.0"
+description = "A package for encoding categorical variables for machine learning"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "category_encoders-2.7.0-py3-none-any.whl", hash = "sha256:62c50d06c296cda69335ac3997456368a4d6eeabea0c15100db52235227c0758"},
+    {file = "category_encoders-2.7.0.tar.gz", hash = "sha256:ee49c550fde511b222bac40c9c8740a7afdbe2431eaafd2aa43d4cec0bf92374"},
+]
+
+[package.dependencies]
+numpy = ">=1.14.0"
+pandas = ">=1.0.5"
+patsy = ">=0.5.1"
+scikit-learn = ">=1.0.0,<1.6.0"
+scipy = ">=1.0.0"
+statsmodels = ">=0.9.0"
+
 [[package]]
 name = "certifi"
 version = "2024.12.14"
@@ -938,6 +1042,22 @@ files = [
     {file = "charset_normalizer-3.4.1.tar.gz", hash = "sha256:44251f18cd68a75b56585dd00dae26183e102cd5e0f9f1466e6df5da2ed64ea3"},
 ]
 
+[[package]]
+name = "choreographer"
+version = "1.2.1"
+description = "Devtools Protocol implementation for chrome."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "choreographer-1.2.1-py3-none-any.whl", hash = "sha256:9af5385effa3c204dbc337abf7ac74fd8908ced326a15645dc31dde75718c77e"},
+    {file = "choreographer-1.2.1.tar.gz", hash = "sha256:022afd72b1e9b0bcb950420b134e70055a294c791b6f36cfb47d89745b701b5f"},
+]
+
+[package.dependencies]
+logistro = ">=2.0.1"
+simplejson = ">=3.19.3"
+
 [[package]]
 name = "click"
 version = "8.1.8"
@@ -997,7 +1117,7 @@ files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
-markers = {main = "sys_platform == \"win32\" or platform_system == \"Windows\"", dev = "sys_platform == \"win32\""}
+markers = {dev = "sys_platform == \"win32\""}
 
 [[package]]
 name = "colorful"
@@ -1305,6 +1425,47 @@ files = [
     {file = "cython-3.0.11.tar.gz", hash = "sha256:7146dd2af8682b4ca61331851e6aebce9fe5158e75300343f80c07ca80b1faff"},
 ]
 
+[[package]]
+name = "datasets"
+version = "2.2.1"
+description = "HuggingFace community-driven open-source library of datasets"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "datasets-2.2.1-py3-none-any.whl", hash = "sha256:1938f3e99599422de50b9b54fe802aca854ed130382dab0b3820c821f7ae6d5e"},
+    {file = "datasets-2.2.1.tar.gz", hash = "sha256:d362717c4394589b516c8f397ff20a6fe720454aed877ab61d06f3bc05df9544"},
+]
+
+[package.dependencies]
+aiohttp = "*"
+dill = "*"
+fsspec = {version = ">=2021.05.0", extras = ["http"]}
+huggingface-hub = ">=0.1.0,<1.0.0"
+multiprocess = "*"
+numpy = ">=1.17"
+packaging = "*"
+pandas = "*"
+pyarrow = ">=6.0.0"
+requests = ">=2.19.0"
+responses = "<0.19"
+tqdm = ">=4.62.1"
+xxhash = "*"
+
+[package.extras]
+apache-beam = ["apache-beam (>=2.26.0)"]
+audio = ["librosa"]
+benchmarks = ["numpy (==1.18.5)", "tensorflow (==2.3.0)", "torch (==1.6.0)", "transformers (==3.0.2)"]
+dev = ["Pillow (>=6.2.1)", "Werkzeug (>=1.0.1)", "absl-py", "aiobotocore", "apache-beam (>=2.26.0)", "bert-score (>=0.3.6)", "black (>=22.0,<23.0)", "boto3", "botocore", "bs4", "conllu", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "flake8 (>=3.8.3)", "fsspec[s3]", "h5py", "importlib-resources ; python_version < \"3.7\"", "isort (>=5.0.0)", "jiwer", "langdetect", "librosa", "lxml", "lz4", "mauve-text", "moto[s3,server] (==2.0.4)", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "requests-file (>=1.5.1)", "rouge-score", "s3fs (==2021.08.1)", "sacrebleu", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "soundfile", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "torchaudio", "transformers", "zstandard"]
+docs = ["s3fs"]
+quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"]
+s3 = ["boto3", "botocore", "fsspec", "s3fs"]
+tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
+tests = ["Pillow (>=6.2.1)", "Werkzeug (>=1.0.1)", "absl-py", "aiobotocore", "apache-beam (>=2.26.0)", "bert-score (>=0.3.6)", "boto3", "botocore", "bs4", "conllu", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "fsspec[s3]", "h5py", "importlib-resources ; python_version < \"3.7\"", "jiwer", "langdetect", "librosa", "lxml", "lz4", "mauve-text", "moto[s3,server] (==2.0.4)", "mwparserfromhell", "nltk", "openpyxl", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "requests-file (>=1.5.1)", "rouge-score", "s3fs (==2021.08.1)", "sacrebleu", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "soundfile", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "torch", "torchaudio", "transformers", "zstandard"]
+torch = ["torch"]
+vision = ["Pillow (>=6.2.1)"]
+
 [[package]]
 name = "debugpy"
 version = "1.8.11"
@@ -1421,6 +1582,21 @@ files = [
     {file = "distlib-0.3.9.tar.gz", hash = "sha256:a60f20dea646b8a33f3e7772f74dc0b2d0772d2837ee1342a00645c81edf9403"},
 ]
 
+[[package]]
+name = "docker-pycreds"
+version = "0.4.0"
+description = "Python bindings for the docker credentials store API"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "docker-pycreds-0.4.0.tar.gz", hash = "sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4"},
+    {file = "docker_pycreds-0.4.0-py2.py3-none-any.whl", hash = "sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49"},
+]
+
+[package.dependencies]
+six = ">=1.4.0"
+
 [[package]]
 name = "einops"
 version = "0.8.0"
@@ -1433,13 +1609,25 @@ files = [
     {file = "einops-0.8.0.tar.gz", hash = "sha256:63486517fed345712a8385c100cb279108d9d47e6ae59099b07657e983deae85"},
 ]
 
+[[package]]
+name = "et-xmlfile"
+version = "2.0.0"
+description = "An implementation of lxml.xmlfile for the standard library"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "et_xmlfile-2.0.0-py3-none-any.whl", hash = "sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa"},
+    {file = "et_xmlfile-2.0.0.tar.gz", hash = "sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54"},
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
-groups = ["dev"]
+groups = ["main", "dev"]
 markers = "python_version == \"3.10\""
 files = [
     {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
@@ -1455,7 +1643,7 @@ version = "2.1.0"
 description = "Get the currently executing AST node of a frame, and other information"
 optional = false
 python-versions = ">=3.8"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
     {file = "executing-2.1.0-py2.py3-none-any.whl", hash = "sha256:8d63781349375b5ebccc3142f4b30350c0cd9c79f921cde38be2be4637e98eaf"},
     {file = "executing-2.1.0.tar.gz", hash = "sha256:8ea27ddd260da8150fa5a708269c4a10e76161e2496ec3e587da9e3c0fe4b9ab"},
@@ -1802,6 +1990,9 @@ files = [
     {file = "fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f"},
 ]
 
+[package.dependencies]
+aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
+
 [package.extras]
 abfs = ["adlfs"]
 adl = ["adlfs"]
@@ -1854,6 +2045,40 @@ files = [
     {file = "gast-0.6.0.tar.gz", hash = "sha256:88fc5300d32c7ac6ca7b515310862f71e6fdf2c029bbec7c66c0f5dd47b6b1fb"},
 ]
 
+[[package]]
+name = "gitdb"
+version = "4.0.12"
+description = "Git Object Database"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "gitdb-4.0.12-py3-none-any.whl", hash = "sha256:67073e15955400952c6565cc3e707c554a4eea2e428946f7a4c162fab9bd9bcf"},
+    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
+]
+
+[package.dependencies]
+smmap = ">=3.0.1,<6"
+
+[[package]]
+name = "gitpython"
+version = "3.1.45"
+description = "GitPython is a Python library used to interact with Git repositories"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
+    {file = "gitpython-3.1.45-py3-none-any.whl", hash = "sha256:8908cb2e02fb3b93b7eb0f2827125cb699869470432cc885f019b8fd0fccff77"},
+    {file = "gitpython-3.1.45.tar.gz", hash = "sha256:85b0ee964ceddf211c41b9f27a49086010a190fd8132a24e21f362a4b36a791c"},
+]
+
+[package.dependencies]
+gitdb = ">=4.0.1,<5"
+
+[package.extras]
+doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
+test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock ; python_version < \"3.8\"", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions ; python_version < \"3.11\""]
+
 [[package]]
 name = "google-api-core"
 version = "2.24.0"
@@ -2186,6 +2411,24 @@ dev = ["black", "nose", "pre-commit", "pytest"]
 mongotrials = ["pymongo"]
 sparktrials = ["pyspark"]
 
+[[package]]
+name = "icecream"
+version = "2.1.8"
+description = "Never use print() to debug again: inspect variables, expressions, and program execution with a single, simple function call."
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "icecream-2.1.8-py3-none-any.whl", hash = "sha256:10b1c39dcb54cb28eb487bac56c35dbf9c2b2f406d24340e1a615c3f17274852"},
+    {file = "icecream-2.1.8.tar.gz", hash = "sha256:37269bbc62b02f0d85bfaf3a0eb4df272c967fad059f7ddcdaee5303ea2b2a62"},
+]
+
+[package.dependencies]
+asttokens = ">=2.0.1"
+colorama = ">=0.3.9"
+executing = ">=2.1.0"
+pygments = ">=2.2.0"
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -2201,6 +2444,46 @@ files = [
 [package.extras]
 all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
+[[package]]
+name = "imbalanced-learn"
+version = "0.14.0"
+description = "Toolbox for imbalanced dataset in machine learning"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "imbalanced_learn-0.14.0-py3-none-any.whl", hash = "sha256:8a8700c02ca185e113064815513f990fbf84eb4e7701f1d4e944ce67fb259a60"},
+    {file = "imbalanced_learn-0.14.0.tar.gz", hash = "sha256:22b9ba6dbd681a9ec613cd6e08c21d39639fb5ccbf2a3c991f9c36415b70522c"},
+]
+
+[package.dependencies]
+joblib = ">=1.2.0,<2"
+numpy = ">=1.25.2,<3"
+scikit-learn = ">=1.4.2,<2"
+scipy = ">=1.11.4,<2"
+threadpoolctl = ">=2.0.0,<4"
+
+[package.extras]
+dev = ["ipykernel", "ipython", "jupyterlab"]
+docs = ["matplotlib (>=3.7.3,<4)", "memory_profiler (>=0.61.0,<1)", "numpydoc (>=1.5.0,<2)", "pandas (>=2.0.3,<3)", "pydata-sphinx-theme (>=0.15.4,<1)", "seaborn (>=0.12.2,<1)", "sphinx (>=8.0.2,<9)", "sphinx-copybutton (>=0.5.2,<1)", "sphinx-design (>=0.6.1,<1)", "sphinx-gallery (>=0.13.0,<1)", "sphinxcontrib-bibtex (>=2.6.3,<3)", "tensorflow (>=2.16.1,<3)"]
+keras = ["keras (>=3.3.3,<4)"]
+linters = ["black (==23.3.0)", "pre-commit", "ruff (==0.4.8)"]
+optional = ["pandas (>=2.0.3,<3)"]
+tensorflow = ["tensorflow (>=2.16.1,<3)"]
+tests = ["packaging (>=23.2,<25)", "pytest (>=7.2.2,<9)", "pytest-cov (>=4.1.0,<6)", "pytest-xdist (>=3.5.0,<4)"]
+
+[[package]]
+name = "iniconfig"
+version = "2.3.0"
+description = "brain-dead simple config-ini parsing"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12"},
+    {file = "iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730"},
+]
+
 [[package]]
 name = "ipykernel"
 version = "6.29.5"
@@ -2717,6 +3000,100 @@ files = [
     {file = "jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed"},
 ]
 
+[[package]]
+name = "kaggle"
+version = "1.7.4.5"
+description = "Access Kaggle resources anywhere"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+markers = "python_version == \"3.10\""
+files = [
+    {file = "kaggle-1.7.4.5-py3-none-any.whl", hash = "sha256:9732afb1c073f14acc7e49dfab98456f887d10b735bcd6348bc1340e92393882"},
+    {file = "kaggle-1.7.4.5.tar.gz", hash = "sha256:1d9821bd6a6a1470741c76d26495a18475b5a7bfe0c80b19191254b2735d41dd"},
+]
+
+[package.dependencies]
+bleach = "*"
+certifi = ">=14.05.14"
+charset-normalizer = "*"
+idna = "*"
+protobuf = "*"
+python-dateutil = ">=2.5.3"
+python-slugify = "*"
+requests = "*"
+setuptools = ">=21.0.0"
+six = ">=1.10"
+text-unidecode = "*"
+tqdm = "*"
+urllib3 = ">=1.15.1"
+webencodings = "*"
+
+[[package]]
+name = "kaggle"
+version = "1.8.2"
+description = "Access Kaggle resources anywhere"
+optional = false
+python-versions = ">=3.11"
+groups = ["main"]
+markers = "python_version == \"3.11\""
+files = [
+    {file = "kaggle-1.8.2-py3-none-any.whl", hash = "sha256:5cbe52d0d225ce69222afd02121e781f614534dd8633370a5124d7de29596a66"},
+    {file = "kaggle-1.8.2.tar.gz", hash = "sha256:d3945c32ce6f81046592d14f6d233ee1aec92769682fce4b05b3409e086db5f7"},
+]
+
+[package.dependencies]
+black = ">=24.10.0"
+bleach = "*"
+kagglesdk = "*"
+mypy = ">=1.15.0"
+protobuf = "*"
+python-dateutil = "*"
+python-slugify = "*"
+requests = "*"
+setuptools = ">=21.0.0"
+six = ">=1.10"
+tqdm = "*"
+types-requests = "*"
+types-tqdm = "*"
+urllib3 = ">=1.15.1"
+
+[[package]]
+name = "kagglesdk"
+version = "0.1.13"
+description = "Bindings to access kaggle's external-facing APIs"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version == \"3.11\""
+files = [
+    {file = "kagglesdk-0.1.13-py3-none-any.whl", hash = "sha256:76a96b6bd0379482b3e53cd8ecfa7d181e6cf2b43c105bee32798cd06cf93eab"},
+    {file = "kagglesdk-0.1.13.tar.gz", hash = "sha256:5cecab69ab0d242b6a760f146fc77e0810b34b46e90d3e4104e4a1ba37bcd39f"},
+]
+
+[package.dependencies]
+protobuf = "*"
+requests = "*"
+
+[[package]]
+name = "kaleido"
+version = "1.2.0"
+description = "Plotly graph export library"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "kaleido-1.2.0-py3-none-any.whl", hash = "sha256:c27ed82b51df6b923d0e656feac221343a0dbcd2fb9bc7e6b1db97f61e9a1513"},
+    {file = "kaleido-1.2.0.tar.gz", hash = "sha256:fa621a14423e8effa2895a2526be00af0cf21655be1b74b7e382c171d12e71ef"},
+]
+
+[package.dependencies]
+choreographer = ">=1.1.1"
+logistro = ">=1.0.8"
+orjson = ">=3.10.15"
+packaging = "*"
+pytest-timeout = ">=2.4.0"
+
 [[package]]
 name = "keras"
 version = "3.8.0"
@@ -2966,6 +3343,18 @@ files = [
     {file = "llvmlite-0.43.0.tar.gz", hash = "sha256:ae2b5b5c3ef67354824fb75517c8db5fbe93bc02cd9671f3c62271626bc041d5"},
 ]
 
+[[package]]
+name = "logistro"
+version = "2.0.1"
+description = "Simple wrapper over logging for a couple basic features"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "logistro-2.0.1-py3-none-any.whl", hash = "sha256:06ffa127b9fb4ac8b1972ae6b2a9d7fde57598bf5939cd708f43ec5bba2d31eb"},
+    {file = "logistro-2.0.1.tar.gz", hash = "sha256:8446affc82bab2577eb02bfcbcae196ae03129287557287b6a070f70c1985047"},
+]
+
 [[package]]
 name = "marisa-trie"
 version = "1.2.1"
@@ -3359,6 +3748,25 @@ files = [
 [package.dependencies]
 typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
 
+[[package]]
+name = "ml-collections"
+version = "1.1.0"
+description = "ML Collections is a library of Python collections designed for ML usecases."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "ml_collections-1.1.0-py3-none-any.whl", hash = "sha256:23b6fa4772aac1ae745a96044b925a5746145a70734f087eaca6626e92c05cbc"},
+    {file = "ml_collections-1.1.0.tar.gz", hash = "sha256:0ac1ac6511b9f1566863e0bb0afad0c64e906ea278ad3f4d2144a55322671f6f"},
+]
+
+[package.dependencies]
+absl-py = "*"
+PyYAML = "*"
+
+[package.extras]
+dev = ["pyink", "pylint (>=2.6.0)", "pytest", "pytest-xdist"]
+
 [[package]]
 name = "ml-dtypes"
 version = "0.3.2"
@@ -3592,6 +4000,35 @@ files = [
 [package.dependencies]
 typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.11\""}
 
+[[package]]
+name = "multiprocess"
+version = "0.70.17"
+description = "better multiprocessing and multithreading in Python"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "multiprocess-0.70.17-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7ddb24e5bcdb64e90ec5543a1f05a39463068b6d3b804aa3f2a4e16ec28562d6"},
+    {file = "multiprocess-0.70.17-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:d729f55198a3579f6879766a6d9b72b42d4b320c0dcb7844afb774d75b573c62"},
+    {file = "multiprocess-0.70.17-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:c2c82d0375baed8d8dd0d8c38eb87c5ae9c471f8e384ad203a36f095ee860f67"},
+    {file = "multiprocess-0.70.17-pp38-pypy38_pp73-macosx_10_9_arm64.whl", hash = "sha256:a22a6b1a482b80eab53078418bb0f7025e4f7d93cc8e1f36481477a023884861"},
+    {file = "multiprocess-0.70.17-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:349525099a0c9ac5936f0488b5ee73199098dac3ac899d81d326d238f9fd3ccd"},
+    {file = "multiprocess-0.70.17-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:27b8409c02b5dd89d336107c101dfbd1530a2cd4fd425fc27dcb7adb6e0b47bf"},
+    {file = "multiprocess-0.70.17-pp39-pypy39_pp73-macosx_10_13_arm64.whl", hash = "sha256:2ea0939b0f4760a16a548942c65c76ff5afd81fbf1083c56ae75e21faf92e426"},
+    {file = "multiprocess-0.70.17-pp39-pypy39_pp73-macosx_10_13_x86_64.whl", hash = "sha256:2b12e081df87ab755190e227341b2c3b17ee6587e9c82fecddcbe6aa812cd7f7"},
+    {file = "multiprocess-0.70.17-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:a0f01cd9d079af7a8296f521dc03859d1a414d14c1e2b6e676ef789333421c95"},
+    {file = "multiprocess-0.70.17-py310-none-any.whl", hash = "sha256:38357ca266b51a2e22841b755d9a91e4bb7b937979a54d411677111716c32744"},
+    {file = "multiprocess-0.70.17-py311-none-any.whl", hash = "sha256:2884701445d0177aec5bd5f6ee0df296773e4fb65b11903b94c613fb46cfb7d1"},
+    {file = "multiprocess-0.70.17-py312-none-any.whl", hash = "sha256:2818af14c52446b9617d1b0755fa70ca2f77c28b25ed97bdaa2c69a22c47b46c"},
+    {file = "multiprocess-0.70.17-py313-none-any.whl", hash = "sha256:20c28ca19079a6c879258103a6d60b94d4ffe2d9da07dda93fb1c8bc6243f522"},
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+]
+
+[package.dependencies]
+dill = ">=0.3.9"
+
 [[package]]
 name = "murmurhash"
 version = "1.0.11"
@@ -3634,7 +4071,7 @@ version = "1.15.0"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.9"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
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@@ -3669,6 +4106,7 @@ files = [
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 ]
+markers = {main = "python_version == \"3.11\""}
 
 [package.dependencies]
 mypy_extensions = ">=1.0.0"
@@ -3688,11 +4126,12 @@ version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
 optional = false
 python-versions = ">=3.5"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
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 ]
+markers = {main = "python_version == \"3.11\""}
 
 [[package]]
 name = "namex"
@@ -4138,6 +4577,21 @@ files = [
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 ]
 
+[[package]]
+name = "openpyxl"
+version = "3.1.5"
+description = "A Python library to read/write Excel 2010 xlsx/xlsm files"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
+et-xmlfile = "*"
+
 [[package]]
 name = "opt-einsum"
 version = "3.4.0"
@@ -4260,6 +4714,103 @@ numpy = ["numpy"]
 test = ["pytest", "pytest-cov", "pytest-xdist"]
 torch = ["torch"]
 
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+]
+
 [[package]]
 name = "overrides"
 version = "7.7.0"
@@ -4398,6 +4949,67 @@ files = [
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["docopt", "pytest"]
 
+[[package]]
+name = "pathspec"
+version = "0.12.1"
+description = "Utility library for gitignore style pattern matching of file paths."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version == \"3.11\""
+files = [
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+]
+
+[[package]]
+name = "patsy"
+version = "1.0.2"
+description = "A Python package for describing statistical models and for building design matrices."
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
+numpy = ">=1.4"
+
+[package.extras]
+test = ["pytest", "pytest-cov", "scipy"]
+
+[[package]]
+name = "peft"
+version = "0.9.0"
+description = "Parameter-Efficient Fine-Tuning (PEFT)"
+optional = false
+python-versions = ">=3.8.0"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
+accelerate = ">=0.21.0"
+huggingface-hub = ">=0.17.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+psutil = "*"
+pyyaml = "*"
+safetensors = "*"
+torch = ">=1.13.0"
+tqdm = "*"
+transformers = "*"
+
+[package.extras]
+dev = ["black", "hf-doc-builder", "ruff (>=0.2.1,<0.3.0)"]
+docs-specific = ["black", "hf-doc-builder"]
+quality = ["black", "hf-doc-builder", "ruff (>=0.2.1,<0.3.0)"]
+test = ["black", "datasets", "diffusers (<0.21.0)", "hf-doc-builder", "parameterized", "pytest", "pytest-cov", "pytest-xdist", "ruff (>=0.2.1,<0.3.0)", "scipy"]
+
 [[package]]
 name = "pexpect"
 version = "4.9.0"
@@ -4548,6 +5160,22 @@ files = [
 packaging = "*"
 tenacity = ">=6.2.0"
 
+[[package]]
+name = "pluggy"
+version = "1.6.0"
+description = "plugin and hook calling mechanisms for python"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+]
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+[package.extras]
+dev = ["pre-commit", "tox"]
+testing = ["coverage", "pytest", "pytest-benchmark"]
+
 [[package]]
 name = "pomegranate"
 version = "0.15.0"
@@ -4571,6 +5199,24 @@ scipy = ">=0.17.0"
 gpu = ["cupy"]
 plotting = ["matplotlib", "pygraphviz"]
 
+[[package]]
+name = "prdc"
+version = "0.2"
+description = "Compute precision, recall, density, and coverage metrics for two sets of vectors."
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
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+numpy = "*"
+scikit-learn = "*"
+scipy = "*"
+
 [[package]]
 name = "preshed"
 version = "3.0.9"
@@ -5180,6 +5826,45 @@ files = [
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
+[[package]]
+name = "pytest"
+version = "9.0.1"
+description = "pytest: simple powerful testing with Python"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
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+exceptiongroup = {version = ">=1", markers = "python_version < \"3.11\""}
+iniconfig = ">=1.0.1"
+packaging = ">=22"
+pluggy = ">=1.5,<2"
+pygments = ">=2.7.2"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "requests", "setuptools", "xmlschema"]
+
+[[package]]
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+version = "2.4.0"
+description = "pytest plugin to abort hanging tests"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
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+]
+
+[package.dependencies]
+pytest = ">=7.0.0"
+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
@@ -5210,6 +5895,40 @@ files = [
 [package.extras]
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+[[package]]
+name = "python-slugify"
+version = "8.0.4"
+description = "A Python slugify application that also handles Unicode"
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
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+[package.dependencies]
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+
+[package.extras]
+unidecode = ["Unidecode (>=1.1.1)"]
+
+[[package]]
+name = "pytokens"
+version = "0.3.0"
+description = "A Fast, spec compliant Python 3.14+ tokenizer that runs on older Pythons."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "python_version == \"3.11\""
+files = [
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+
 [[package]]
 name = "pytz"
 version = "2024.2"
@@ -5576,6 +6295,131 @@ files = [
 attrs = ">=22.2.0"
 rpds-py = ">=0.7.0"
 
+[[package]]
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 name = "requests"
 version = "2.32.3"
@@ -5598,6 +6442,25 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "responses"
+version = "0.18.0"
+description = "A utility library for mocking out the `requests` Python library."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
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+urllib3 = ">=1.25.10"
+
+[package.extras]
+tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=4.6)", "pytest-cov", "pytest-localserver", "types-mock", "types-requests"]
+
 [[package]]
 name = "rfc3339-validator"
 version = "0.1.4"
@@ -6079,6 +6942,179 @@ nativelib = ["pyobjc-framework-Cocoa ; sys_platform == \"darwin\"", "pywin32 ; s
 objc = ["pyobjc-framework-Cocoa ; sys_platform == \"darwin\""]
 win32 = ["pywin32 ; sys_platform == \"win32\""]
 
+[[package]]
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+version = "2.46.0"
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+python-versions = ">=3.6"
+groups = ["main"]
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+
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+anthropic = ["anthropic (>=0.16)"]
+arq = ["arq (>=0.23)"]
+asyncpg = ["asyncpg (>=0.23)"]
+beam = ["apache-beam (>=2.12)"]
+bottle = ["bottle (>=0.12.13)"]
+celery = ["celery (>=3)"]
+celery-redbeat = ["celery-redbeat (>=2)"]
+chalice = ["chalice (>=1.16.0)"]
+clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
+django = ["django (>=1.8)"]
+falcon = ["falcon (>=1.4)"]
+fastapi = ["fastapi (>=0.79.0)"]
+flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
+google-genai = ["google-genai (>=1.29.0)"]
+grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
+http2 = ["httpcore[http2] (==1.*)"]
+httpx = ["httpx (>=0.16.0)"]
+huey = ["huey (>=2)"]
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 webhdfs = ["requests"]
 zst = ["zstandard"]
 
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+
 [[package]]
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 version = "1.13.3"
@@ -6573,6 +7793,18 @@ docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
 typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
+[[package]]
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+description = "The most basic Text::Unidecode port"
+optional = false
+python-versions = "*"
+groups = ["main"]
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 name = "textual"
 version = "1.0.0"
@@ -6696,47 +7928,62 @@ webencodings = ">=0.4"
 doc = ["sphinx", "sphinx_rtd_theme"]
 test = ["pytest", "ruff"]
 
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+groups = ["main"]
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+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+tiktoken = ["blobfile", "tiktoken"]
+timm = ["timm (<=1.0.11)"]
+tokenizers = ["tokenizers (>=0.21,<0.22)"]
+torch = ["accelerate (>=0.26.0)", "torch (>=2.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.26.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch (>=2.0)", "tqdm (>=4.27)"]
+video = ["av"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
 [[package]]
 name = "triton"
 version = "2.2.0"
@@ -6955,6 +8274,22 @@ files = [
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 ]
 
+[[package]]
+name = "types-requests"
+version = "2.32.4.20250913"
+description = "Typing stubs for requests"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version == \"3.11\""
+files = [
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+]
+
+[package.dependencies]
+urllib3 = ">=2"
+
 [[package]]
 name = "types-tabulate"
 version = "0.9.0.20241207"
@@ -6967,6 +8302,22 @@ files = [
     {file = "types_tabulate-0.9.0.20241207.tar.gz", hash = "sha256:ac1ac174750c0a385dfd248edc6279fa328aaf4ea317915ab879a2ec47833230"},
 ]
 
+[[package]]
+name = "types-tqdm"
+version = "4.67.0.20250809"
+description = "Typing stubs for tqdm"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+markers = "python_version == \"3.11\""
+files = [
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+
+[package.dependencies]
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+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -7061,6 +8412,49 @@ platformdirs = ">=3.9.1,<5"
 docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
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+[[package]]
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+version = "0.17.3"
+description = "A CLI and library for interacting with the Weights & Biases API."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
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+platformdirs = "*"
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+psutil = ">=5.0.0"
+pyyaml = "*"
+requests = ">=2.0.0,<3"
+sentry-sdk = ">=1.0.0"
+setproctitle = "*"
+setuptools = "*"
+
+[package.extras]
+aws = ["boto3"]
+azure = ["azure-identity", "azure-storage-blob"]
+gcp = ["google-cloud-storage"]
+importers = ["filelock", "mlflow", "polars", "rich", "tenacity"]
+kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
+launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "typing-extensions"]
+media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
+models = ["cloudpickle"]
+perf = ["orjson"]
+sweeps = ["sweeps (>=0.2.0)"]
+workspaces = ["wandb-workspaces"]
+
 [[package]]
 name = "wasabi"
 version = "1.1.3"
@@ -7129,7 +8523,7 @@ version = "0.5.1"
 description = "Character encoding aliases for legacy web content"
 optional = false
 python-versions = "*"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
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@@ -7302,6 +8696,173 @@ plotting = ["graphviz", "matplotlib"]
 pyspark = ["cloudpickle", "pyspark", "scikit-learn"]
 scikit-learn = ["scikit-learn"]
 
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+version = "2.0.2"
+description = "Library for developers to extract data from Microsoft Excel (tm) .xls spreadsheet files"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+groups = ["main"]
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+test = ["pytest", "pytest-cov"]
+
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+    {file = "xxhash-3.6.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:881b47fc47e051b37d94d13e7455131054b56749b91b508b0907eb07900d1c13"},
+    {file = "xxhash-3.6.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c6dc31591899f5e5666f04cc2e529e69b4072827085c1ef15294d91a004bc1bd"},
+    {file = "xxhash-3.6.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:15e0dac10eb9309508bfc41f7f9deaa7755c69e35af835db9cb10751adebc35d"},
+    {file = "xxhash-3.6.0.tar.gz", hash = "sha256:f0162a78b13a0d7617b2845b90c763339d1f1d82bb04a4b07f4ab535cc5e05d6"},
+]
+
 [[package]]
 name = "yarl"
 version = "1.18.3"
@@ -7402,4 +8963,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.12"
-content-hash = "c8a88a21ee8b7ccb836249142e376e22d69c57daf24ff56218a9e03067b95b3f"
+content-hash = "761530503dfb430b7f704b0dde15067a39a1da3d9fbd236be825b667b5b05d90"
diff --git a/pyproject.toml b/pyproject.toml
index 364ccdf..20498ff 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,6 +33,26 @@ torch = "2.2.2"
 dill = "^0.3.9"
 tabulate = "^0.9.0"
 types-tabulate = "^0.9.0.20241207"
+# from tabdiff
+numpy = ">=1.26.4,<2.1"
+scipy = ">=1.15.0,<2.0"
+icecream = ">=2.1.8,<3.0"
+xlrd = ">=2.0.2,<3.0"
+tomli-w = ">=1.2.0,<2.0"
+tomli = ">=2.0.1,<3.0"
+category_encoders = ">=2.7.0,<3.0"
+imbalanced-learn = ">=0.14.0,<0.15"
+kaggle = ">=1.7.4.5,<2.0"
+transformers = ">=4.50.3,<5.0"
+datasets = ">=2.2.1,<3.0"
+peft = ">=0.9.0,<1.0"
+ml_collections = ">=1.1.0,<2.0"
+prdc = ">=0.2,<0.3"
+rdt = ">=1.13.2,<2.0"
+openpyxl = ">=3.1.5,<4.0"
+xgboost = ">=2.1.3,<3.0"
+wandb = ">=0.17.3,<1.0"
+kaleido = ">=1.2.0,<2.0"
 
 [tool.poetry.group.dev.dependencies]
 jupyter = "^1.0.0"
@@ -77,6 +97,9 @@ ignore = [
     "FIX002", # allow TODO without author
     "ERA001", # allow comment code examples
 ]
+"synthius/TabDiff/**" = [
+    "ALL",  # ignore all rules for everything in TabDiff
+]
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"
diff --git a/synthius/TabDiff b/synthius/TabDiff
new file mode 160000
index 0000000..09b4977
--- /dev/null
+++ b/synthius/TabDiff
@@ -0,0 +1 @@
+Subproject commit 09b497721a43693affbafc7bb5f4594dc5f99aca
diff --git a/synthius/api/generator.py b/synthius/api/generator.py
index a7116ba..493a148 100644
--- a/synthius/api/generator.py
+++ b/synthius/api/generator.py
@@ -14,6 +14,7 @@
     SDVTVAESynthesizer,
     Synthesizer,
     SynthesizerGaussianMultivariate,
+    TabDiffSynthesizer,
 )
 
 
@@ -115,6 +116,7 @@ def _generate(  # noqa: PLR0913
             SDVTVAESynthesizer(),
             SynthesizerGaussianMultivariate(results_path=synth_dir),
             ARFSynthesizer(id_column=id_column),
+            TabDiffSynthesizer(id_column=id_column),
         ]
 
     # Run each model
diff --git a/synthius/data/__init__.py b/synthius/data/__init__.py
index ee5e5f3..e7faecb 100644
--- a/synthius/data/__init__.py
+++ b/synthius/data/__init__.py
@@ -1,5 +1,6 @@
 from .continuous_transformer import ContinuousDataTransformer
 from .data_imputer import DataImputationPreprocessor
+from .dataset_torch import TorchDataset
 from .encoder import CategoricalEncoder, NumericalLabelEncoder
 from .uniform_encoder import UniformDataEncoder
 
@@ -8,5 +9,6 @@
     "ContinuousDataTransformer",
     "DataImputationPreprocessor",
     "NumericalLabelEncoder",
+    "TorchDataset",
     "UniformDataEncoder",
 ]
diff --git a/synthius/data/data_imputer.py b/synthius/data/data_imputer.py
index 99910d6..00a128e 100644
--- a/synthius/data/data_imputer.py
+++ b/synthius/data/data_imputer.py
@@ -78,6 +78,7 @@ def __init__(self: DataImputationPreprocessor, data: pd.DataFrame, id_column: st
         self.decimal_places: dict[str, int] = {}
         self.fill_values: dict[str, float] = {}
         self.id_column_index: int | None = None
+        self.original_column_order = data.columns.to_list()
 
         self.random_generator = np.random.default_rng()
 
@@ -87,6 +88,8 @@ def __init__(self: DataImputationPreprocessor, data: pd.DataFrame, id_column: st
                 self.data = self.data.drop(self.id_column, axis=1)
             else:
                 logger.warning("The ID column %s does not exist in the dataset.", self.id_column)
+        self.cat_cols: list = self.data.select_dtypes(include=["object", "bool"]).columns.to_list()
+        self.num_cols: list = self.data.select_dtypes(include=["int64", "float64"]).columns.to_list()
 
     def get_decimal_places(self: DataImputationPreprocessor, series: pd.Series) -> int:
         """Calculate the number of decimal places in a given series.
@@ -135,11 +138,13 @@ def fit_transform(self: DataImputationPreprocessor) -> pd.DataFrame:
 
         return processed_data
 
-    def inverse_transform(self: DataImputationPreprocessor, processed_data: pd.DataFrame) -> pd.DataFrame:
+    def inverse_transform(self: DataImputationPreprocessor, processed_data: pd.DataFrame, multiply_categories: bool = True) -> pd.DataFrame:  # noqa: FBT001, FBT002
         """Reverse the transformation by scaling back and reintroducing missing values.
 
         Args:
             processed_data (pd.DataFrame): The transformed DataFrame to inverse transform.
+            multiply_categories (bool, optional): Whether to multiply the categorical columns by their cardinality.
+                (Note: In TabDiff this is implicitly handled, and we want to avoid it here).
 
         Returns:
             pd.DataFrame: The original DataFrame with imputed values and reintroduced missing values.
@@ -156,8 +161,9 @@ def inverse_transform(self: DataImputationPreprocessor, processed_data: pd.DataF
                 if col in self.float_cols:
                     original_data[col] = original_data[col].round(self.decimal_places[col])
             elif col not in self.bool_cols:
-                original_data[col] = (original_data[col] * (self.label_encoders[col].classes_.size - 1)).round().astype(int)
-                original_data[col] = self.label_encoders[col].inverse_transform(original_data[col])
+                if multiply_categories:  # in TabDiff this is implicitly handled
+                    original_data[col] = (original_data[col] * (self.label_encoders[col].classes_.size - 1)).round()
+                original_data[col] = self.label_encoders[col].inverse_transform(original_data[col].astype(int))
 
         # Convert boolean columns back to booleans
         bool_cols = [col for col in self.bool_cols if col in original_data.columns and col != self.id_column]
@@ -181,4 +187,4 @@ def inverse_transform(self: DataImputationPreprocessor, processed_data: pd.DataF
             random_ids = [f"ID-{rng.integers(10000000, 99999999)}" for _ in range(len(original_data))]
             original_data.insert(self.id_column_index, self.id_column, random_ids)
 
-        return original_data
+        return original_data[self.original_column_order]
diff --git a/synthius/data/dataset_torch.py b/synthius/data/dataset_torch.py
new file mode 100644
index 0000000..33c5ebb
--- /dev/null
+++ b/synthius/data/dataset_torch.py
@@ -0,0 +1,26 @@
+import numpy as np
+import pandas as pd
+from torch.utils.data import Dataset
+
+
+class TorchDataset(Dataset):
+    """A torch Dataset designed for TabDiff."""
+
+    def __init__(self, data: pd.DataFrame, d_numerical: int, categories: list[int]) -> None:
+        """Initialize the torch dataset.
+
+        Args:
+            data (pd.DataFrame): The data as dataframe.
+            d_numerical (int): The number of numerical features in `data`.
+            categories(list[int]): A list with the unique number of categories
+                for every categorical features in `data`.
+        """
+        self.X = data.to_numpy()
+        self.d_numerical = d_numerical
+        self.categories = categories
+
+    def __getitem__(self, index: int) -> np.ndarray:  # noqa: D105
+        return self.X[index]
+
+    def __len__(self) -> int:  # noqa: D105
+        return self.X.shape[0]
diff --git a/synthius/model/__init__.py b/synthius/model/__init__.py
index fa72ca3..fd51cc7 100644
--- a/synthius/model/__init__.py
+++ b/synthius/model/__init__.py
@@ -8,6 +8,7 @@
     SDVTVAESynthesizer,
 )
 from .synthesizer import Synthesizer
+from .tabdiff import TabDiffSynthesizer
 from .wgan import WGAN, WGANSynthesizer
 
 __all__ = [
@@ -20,9 +21,9 @@
     "SDVCTGANSynthesizer",
     "SDVCopulaGANSynthesizer",
     "SDVGaussianCopulaSynthesizer",
-    "SDVSynthesizer",
     "SDVTVAESynthesizer",
     "Synthesizer",
     "SynthesizerGaussianMultivariate",
+    "TabDiffSynthesizer",
     "WGANSynthesizer",
 ]
diff --git a/synthius/model/tabdiff.py b/synthius/model/tabdiff.py
new file mode 100644
index 0000000..a6bacaf
--- /dev/null
+++ b/synthius/model/tabdiff.py
@@ -0,0 +1,318 @@
+import logging
+from dataclasses import asdict, dataclass
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import tomli
+import torch
+from torch.utils.data import DataLoader
+
+from synthius.data import DataImputationPreprocessor, TorchDataset
+from synthius.model import Synthesizer
+from synthius.TabDiff.src import get_categories
+from synthius.TabDiff.tabdiff.models.unified_ctime_diffusion import UnifiedCtimeDiffusion
+from synthius.TabDiff.tabdiff.modules.main_modules import Model, UniModMLP
+from synthius.TabDiff.tabdiff.trainer import Trainer
+
+logging.basicConfig(level=logging.INFO, format="%(message)s")
+logger = logging.getLogger(__name__)
+
+TABDIFF_BASE_PATH = Path(__file__).parent / "../TabDiff"
+CONFIG_PATH = TABDIFF_BASE_PATH / "tabdiff/configs/tabdiff_configs.toml"
+
+
+@dataclass
+class DataConfig:  # noqa: D101
+    dequant_dist: str
+    int_dequant_factor: int
+
+
+@dataclass
+class UniModMLPParams:  # noqa: D101
+    num_layers: int
+    d_token: int
+    n_head: int
+    factor: int
+    bias: bool
+    dim_t: int
+    use_mlp: bool
+    d_numerical: int
+    categories: list[int]
+
+
+@dataclass
+class SamplerParams:  # noqa: D101
+    stochastic_sampler: bool
+    second_order_correction: bool
+
+
+@dataclass
+class EDMParams:  # noqa: D101
+    precond: bool
+    sigma_data: float
+    net_conditioning: str
+
+
+@dataclass
+class NoiseDistParams:  # noqa: D101
+    P_mean: float
+    P_std: float
+
+
+@dataclass
+class NoiseScheduleParams:  # noqa: D101
+    sigma_min: float
+    sigma_max: float
+    rho: float
+    eps_max: float
+    eps_min: float
+    rho_init: float
+    rho_offset: float
+    k_init: float
+    k_offset: float
+
+
+@dataclass
+class DiffusionParams:  # noqa: D101
+    num_timesteps: int
+    scheduler: str
+    cat_scheduler: str
+    noise_dist: str
+    sampler_params: SamplerParams
+    edm_params: EDMParams
+    noise_dist_params: NoiseDistParams
+    noise_schedule_params: NoiseScheduleParams
+
+
+@dataclass
+class TrainMain:  # noqa: D101
+    steps: int
+    lr: float
+    weight_decay: float
+    ema_decay: float
+    batch_size: int
+    check_val_every: int
+    lr_scheduler: str
+    factor: float
+    reduce_lr_patience: int
+    closs_weight_schedule: str
+    c_lambda: float
+    d_lambda: float
+    num_workers: int
+
+
+@dataclass
+class TrainConfig:  # noqa: D101
+    main: TrainMain
+
+
+@dataclass
+class SampleConfig:  # noqa: D101
+    batch_size: int
+
+
+@dataclass
+class TabDiffConfig:  # noqa: D101
+    data: DataConfig
+    unimodmlp_params: UniModMLPParams
+    diffusion_params: DiffusionParams
+    train: TrainConfig
+    sample: SampleConfig
+    model_save_path: str | Path
+    result_save_path: str | Path
+
+
+def load_config(path: Path) -> TabDiffConfig:
+    """Loads the config from `path`.
+
+    Parameters:
+        path: Path
+            The path to the config.
+
+    Returns:
+            TabDiffConfig
+                The config loaded as TabDiffConfig.
+    """
+    with path.open("rb") as f:
+        logger.warning("!!Loading the model config %s with the provided parameters in it!!", str(path))
+        toml_data = tomli.load(f)
+        # Build the dataclasses from the TOML
+        data_cfg = DataConfig(**toml_data["data"])
+
+        unimodmlp_params = UniModMLPParams(**toml_data["unimodmlp_params"], d_numerical=0, categories=[])
+
+        diffusion_params = DiffusionParams(
+            num_timesteps=toml_data["diffusion_params"]["num_timesteps"],
+            scheduler=toml_data["diffusion_params"]["scheduler"],
+            cat_scheduler=toml_data["diffusion_params"]["cat_scheduler"],
+            noise_dist=toml_data["diffusion_params"]["noise_dist"],
+            sampler_params=SamplerParams(**toml_data["diffusion_params"]["sampler_params"]),
+            edm_params=EDMParams(**toml_data["diffusion_params"]["edm_params"]),
+            noise_dist_params=NoiseDistParams(**toml_data["diffusion_params"]["noise_dist_params"]),
+            noise_schedule_params=NoiseScheduleParams(**toml_data["diffusion_params"]["noise_schedule_params"]),
+        )
+
+        train_config = TrainConfig(main=TrainMain(**toml_data["train"]["main"]))
+        sample_config = SampleConfig(**toml_data["sample"])
+
+        return TabDiffConfig(
+            data=data_cfg,
+            unimodmlp_params=unimodmlp_params,
+            diffusion_params=diffusion_params,
+            train=train_config,
+            sample=sample_config,
+            model_save_path="",
+            result_save_path="",
+        )
+
+
+class TabDiffSynthesizer(Synthesizer):
+    """Tabular data synthesizer using a TabDiff::UnifiedCtimeDiffusion model."""
+
+    def __init__(self, id_column: str | None = None, gpu: int = -1) -> None:
+        """Initialize the TabDiffSynthesizer."""
+        self.exp_name = "learnable_schedule"
+        self.id_column = id_column
+        if gpu != -1 and torch.cuda.is_available():
+            self.device = f"cuda:{gpu}"
+        else:
+            self.device = "cpu"
+
+        self.model: Trainer
+        self.preprocessor: DataImputationPreprocessor
+        self.name = "TabDiffSynthesizer"
+        self.raw_config: TabDiffConfig = load_config(CONFIG_PATH)
+        self.data_cols: list
+        self.categories: np.ndarray
+
+    def _update_config(self, data: pd.DataFrame) -> None:
+        """Updates self.raw_config with metadata from `data`.
+
+        Following the template config from TabDiff, we need to update it with metadata derived
+        from the categorical features from `data`, a well as the number of numerical columns in `data`.
+
+        Parameters:
+            data : pd.DataFrame
+                Tabular dataset used for metadata extraction (originally used for the model training).
+        """
+        self.categories = np.array(get_categories(data[self.preprocessor.cat_cols].to_numpy()))
+        d_numerical = len(self.preprocessor.float_cols) + len(self.preprocessor.int_cols)
+        self.raw_config.unimodmlp_params.d_numerical = d_numerical
+        self.raw_config.unimodmlp_params.categories = (self.categories + 1).tolist()
+
+        self.raw_config.model_save_path = TABDIFF_BASE_PATH / "tabdiff" / f"ckpt/{self.exp_name}"
+        self.raw_config.result_save_path = TABDIFF_BASE_PATH / "tabdiff" / f"result/{self.exp_name}"
+
+        logger.info("TabDiff: Model save path %s", self.raw_config.model_save_path)
+        logger.info("TabDiff: Result path %s", self.raw_config.result_save_path)
+        self.raw_config.model_save_path.mkdir(parents=True, exist_ok=True)
+        self.raw_config.result_save_path.mkdir(parents=True, exist_ok=True)
+
+    def _generate_processor(self, data: pd.DataFrame) -> None:
+        """Generates DataImputationPreprocessor from `data` and self.id_column.
+
+        Parameters:
+            data : pd.DataFrame
+                Tabular dataset used for creating self.preprocessor (originally used for the model training).
+        """
+        self.preprocessor = DataImputationPreprocessor(data, self.id_column)
+        self.data_cols = self.preprocessor.num_cols + self.preprocessor.cat_cols
+        self._update_config(data)
+
+    def _generate_torch_dataset(self) -> tuple[TorchDataset, DataLoader]:
+        """Generates TorchDataset and DataLoader for TabDiff from self.preprocessor.
+
+        Returns:
+            tuple[TorchDataset, DataLoader]: The dataset and dataloader for training TabDiff::UnifiedCtimeDiffusion.
+        """
+        preprocessed_data = self.preprocessor.fit_transform()
+        dataset = TorchDataset(
+            preprocessed_data[self.data_cols], d_numerical=self.raw_config.unimodmlp_params.d_numerical, categories=self.raw_config.unimodmlp_params.categories
+        )
+        train_loader = DataLoader(
+            dataset,
+            batch_size=self.raw_config.train.main.batch_size,
+            shuffle=True,
+            num_workers=self.raw_config.train.main.num_workers,
+        )
+        return dataset, train_loader
+
+    def _create_model(self, train_loader: DataLoader, train_dataset: TorchDataset) -> Trainer:
+        """Creates the model for training (TabDiff::Trainer).
+
+        First creates a UniModMLP then the TabDiff diffusion model - UnifiedCtimeDiffusion,
+        finally, wraps everything in the Trainer wrapper class from TabDiff.
+
+        Parameters:
+            train_loader: DataLoader
+                The torch DataLoader for creating TabDiff::UnifiedCtimeDiffusion.
+            train_dataset: TorchDataset
+                The dataset TorchDataset for creating TabDiff::UnifiedCtimeDiffusion.
+
+        Returns:
+            Trainer
+                The generated Trainer from TabDiff.
+        """
+        backbone = UniModMLP(**asdict(self.raw_config.unimodmlp_params))
+        model = Model(backbone, **asdict(self.raw_config.diffusion_params.edm_params))
+        model.to(self.device)
+
+        diffusion = UnifiedCtimeDiffusion(
+            num_classes=self.categories,
+            num_numerical_features=self.raw_config.unimodmlp_params.d_numerical,
+            denoise_fn=model,
+            y_only_model=None,
+            **asdict(self.raw_config.diffusion_params),
+            device=self.device,
+        )
+        diffusion.to(self.device)
+        diffusion.train()
+
+        sample_batch_size = self.raw_config.sample.batch_size
+        return Trainer(
+            diffusion=diffusion,
+            train_iter=train_loader,
+            dataset=train_dataset,
+            test_dataset=None,
+            metrics=None,
+            logger=None,
+            **asdict(self.raw_config.train.main),
+            sample_batch_size=sample_batch_size,
+            num_samples_to_generate=0,
+            model_save_path=self.raw_config.model_save_path,
+            result_save_path=self.raw_config.result_save_path,
+            device=self.device,
+            ckpt_path=None,
+            y_only=False,
+            id_col=self.id_column,
+        )
+
+    def fit(self, train_data: pd.DataFrame) -> None:
+        """Fit the TabDiff::UnifiedCtimeDiffusion model to training data.
+
+        Parameters:
+            train_data : pd.DataFrame
+                Tabular dataset to train the TabDiff::UnifiedCtimeDiffusion model.
+        """
+        self._generate_processor(train_data)
+        dataset, data_loader = self._generate_torch_dataset()
+        self.model = self._create_model(train_dataset=dataset, train_loader=data_loader)
+        self.model.run_loop()
+
+    def generate(self, total_samples: int, conditions: list | None = None) -> pd.DataFrame:  # noqa: ARG002
+        """Generate synthetic samples from the fitted TabDiff::UnifiedCtimeDiffusion model.
+
+        Parameters:
+            total_samples : int
+                Number of synthetic rows to generate.
+            conditions : list | None, optional
+                Currently ignored; included for compatibility with the Synthesizer protocol.
+
+        Returns:
+            pd.DataFrame
+                Synthetic samples as a DataFrame with preprocessing reversed.
+        """
+        samples = self.model.sample(total_samples)
+        data = pd.DataFrame(samples, columns=self.data_cols)
+        return self.preprocessor.inverse_transform(data, multiply_categories=False)  # tabdiff does this on its own
diff --git a/synthius/model/wgan.py b/synthius/model/wgan.py
index 3784311..36e1e3b 100644
--- a/synthius/model/wgan.py
+++ b/synthius/model/wgan.py
@@ -281,8 +281,9 @@ def gradient_penalty(self: WGAN, real_data: tf.Tensor, fake_data: tf.Tensor) ->
 class WGANSynthesizer(Synthesizer):
     """Tabular data synthesizer using a WGAN (Wasserstein GAN) model."""
 
-    def __init__(self) -> None:
+    def __init__(self, id_column: str | None = None) -> None:
         """Initialize the WGANSynthesizer."""
+        self.id_column = id_column
         self.model: WGAN
         self.preprocessor: DataImputationPreprocessor
         self.processed_data: pd.DataFrame
@@ -296,7 +297,7 @@ def fit(self, train_data: pd.DataFrame) -> None:
             train_data : pd.DataFrame
                 Tabular dataset to train the WGAN model.
         """
-        self.preprocessor = DataImputationPreprocessor(train_data)
+        self.preprocessor = DataImputationPreprocessor(train_data, self.id_column)
         self.processed_data = self.preprocessor.fit_transform()
         n_features = self.processed_data.shape[1]