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----
-license: mit
-language:
-  - en
-tags:
-  - education
-  - pytorch
-  - flow-matching
-  - diffusion
-  - esm2
-  - protein-generation
-  - guidance
-  - mnist
-  - image-generation
----
-# CIS 6270 Course Code
-![CIS6270_Unified_Generative_Dynamics (1)](https://cdn-uploads.huggingface.co/production/uploads/64cd5b3f0494187a9e8b7c69/myMO3iMdYkquG6D5mhU4S.png)
-This repository contains code examples for CIS 6270, organized by lecture.
-We will develop the implementations alongside the course material, which connect
-the mathematical definitions, training objectives, and sampling equations to
-the executable PyTorch code herein. Each lecture directory contains its examples, data,
-and implementation notes.
-## Lecture index
-| Lecture | Topics | Code and documentation |
-| --- | --- | --- |
-| 2 | Unconditional MNIST image generation with flow matching and a simple U-Net | [Guide](lecture_2/README.md) · [Script](lecture_2/flow_matching_unet_lecture.py) · [Saved checkpoint](https://huggingface.co/ChatterjeeLab/CIS6270/resolve/main/lecture_2/flow_unet_mnist.pt?download=true) |
-| 3 | Flow matching, diffusion, and guidance for ESM-2 residue embeddings | [Guide](lecture_3/README.md) · [Flow matching](lecture_3/esm2_flow_guidance.py) · [Diffusion](lecture_3/esm2_diffusion_guidance.py) |
-| 4 | Discrete diffusion, masked and uniform corruption, block generation, and guidance | [Guide](lecture_4/README.md) · [Training and generation](lecture_4/run.py) · [Slide code map](lecture_4/SLIDE_CODE_MAP.md) |
-| 5 | Discrete flow matching, Dirichlet and Fisher paths, Gumbel-Softmax, rectification, and multi-objective generation | [Guide](lecture_5/README.md) · [Training and generation](lecture_5/run.py) · [Slide code map](lecture_5/SLIDE_CODE_MAP.md) |
-| 6 | Continuous, latent, categorical, posterior, expanding, and strong stochastic flow maps | [Guide](lecture_6/README.md) · [Training and generation](lecture_6/run.py) · [Slide code map](lecture_6/SLIDE_CODE_MAP.md) |
-| 7 | Optimal transport, Sinkhorn, continuous and discrete Schrödinger bridges, matching, reward tilting, branching, and interacting control | [Guide](lecture_7/README.md) · [Training and generation](lecture_7/run.py) · [Slide code map](lecture_7/SLIDE_CODE_MAP.md) |
-## Installation
-Use Python 3.11, or another compatible Python version at least 3.10, in a new
-virtual environment. The shared `requirements.txt` pins PyTorch 2.9.1,
-TorchVision 0.24.1, and Transformers 4.57.6. Lecture 2 uses PyTorch and
-TorchVision; Lecture 3 also uses Transformers. Lectures 4 and 5 use PyTorch,
-NumPy, and SciPy. Lecture 6 uses PyTorch and NumPy. Lecture 7 uses PyTorch, NumPy, and SciPy. These lecture folders also provide minimal requirements.
-```bash
-git clone https://huggingface.co/ChatterjeeLab/CIS6270
-cd CIS6270
-git lfs pull
-python3 -m venv .venv
-source .venv/bin/activate
-python -m pip install --upgrade pip
-python -m pip install -r requirements.txt
-```
-On Windows PowerShell, create the environment with `python -m venv .venv`
-and activate it with `.venv\Scripts\Activate.ps1`.
-The saved checkpoint uses Git LFS. If Git LFS is unavailable, download
-[flow_unet_mnist.pt](https://huggingface.co/ChatterjeeLab/CIS6270/resolve/main/lecture_2/flow_unet_mnist.pt?download=true)
-directly and place it in `lecture_2/`. Training from scratch does not require
-the saved checkpoint.
-## Lecture 2: MNIST flow matching
-Lecture 2 contains one self-contained, commented script. It downloads MNIST,
-loads batches of normalized images, defines a small U-Net velocity network,
-trains it with conditional flow matching, and generates images from Gaussian
-noise using Euler integration.
-From the repository root:
-```bash
-python lecture_2/flow_matching_unet_lecture.py
-```
-The default run trains for 20 epochs and writes `samples.png`, `trajectory.png`,
-and a new `flow_unet_mnist.pt` to `flow_matching_outputs/`. The script selects
-CUDA, Apple MPS, or CPU according to availability. Its numbered `# %%` sections
-match the lecture walkthrough.
-The bundled [checkpoint](lecture_2/flow_unet_mnist.pt) has **5 completed epochs**
-on all 60,000 MNIST training images. The [Lecture 2 guide](lecture_2/README.md)
-includes a short example for loading it and generating images without training,
-along with selected generated digits and their noise-to-image trajectories.
-## Lecture 3: Flow and diffusion guidance
-From the repository root:
-```bash
-python lecture_3/esm2_flow_guidance.py --epochs 200 --samples 8
-python lecture_3/esm2_diffusion_guidance.py --epochs 200 --samples 8
-```
-Both scripts train on ESM-2 residue embeddings and compare classifier-free
-guidance, single-objective reward steering, and scalarized multi-objective
-steering. We use the same property definitions, normalization, and final
-constrained decoder to compare the flow and diffusion implementations.
-The first run downloads the public ESM-2 checkpoint. Each script then writes
-generated sequences and model parameters to its own output directory.
-The [lecture guide](lecture_3/README.md) describes the data format, training
-and sampling settings, property calculations, normalization, and residue-count
-constraint, with commands for using a custom dataset.
-## Lecture 4 - Discrete diffusion
-Train small DNA denoisers and generate sequences with MDLM, UDLM, block diffusion,
-classifier-free guidance, exact and gradient-based classifier guidance, and a
-PepTune-style search. The [guide](lecture_4/README.md) includes each method's
-command and mathematical assumptions. The [code map](lecture_4/SLIDE_CODE_MAP.md)
-links the slide walkthroughs to their functions.
-From the repository root, run the complete MDLM example.
-```bash
-python lecture_4/run.py --method mdlm --data lecture_4/data/dna_train.tsv --out lecture_4/outputs/mdlm
-python lecture_4/run.py --method mdlm --mode sample --out lecture_4/outputs/mdlm
-```
-The script trains, saves a checkpoint, and writes generated DNA and loss logs.
-The bundled data are synthetic, and the guidance objectives are explicit toy
-properties. No pretrained model or external dataset is required.
-## Lecture 5 - Discrete flow matching
-Start with Gat et al.'s discrete flow matching, then run Dirichlet, Fisher,
-Gumbel-Softmax, rectified flow, ReDi, MOG-DFM, and AReUReDi examples. Each method
-has a complete training and generation command in the [guide](lecture_5/README.md).
-```bash
-python lecture_5/run.py --method gat --data lecture_5/data/dna_train.tsv --out lecture_5/outputs/gat
-python lecture_5/run.py --method gat --mode sample --out lecture_5/outputs/gat
-```
-Both folders include numerical examples, mathematical tests, and saved results
-from seeded CPU runs. The guides explain finite endpoint approximations and
-classroom simplifications for each method.
-## Lecture 6 - Flow Maps
-Learn finite-time motion after the local flows from Lecture 5. The new folder
-contains 16 complete examples covering flow-map matching and self-distillation,
-consistency, Shortcut, MeanFlow, learned latent representations, Flow Map
-Language Models, Categorical and Discrete Flow Maps, Diamond Maps, Meta Flow
-Maps, Expanding Flow Maps, and Strong Stochastic Flow Maps.
-```bash
-python lecture_6/run.py --method self-distill --out lecture_6/outputs/self-distill
-python lecture_6/run.py --mode sample --out lecture_6/outputs/self-distill --sample-steps 1
-python lecture_6/run_all.py --quick
-```
-The [Lecture 6 guide](lecture_6/README.md) includes all method commands,
-training objectives, data formats, and numerical assumptions. The
-[slide code map](lecture_6/SLIDE_CODE_MAP.md) links implementations to the
-[Flow Maps presentation](https://docs.google.com/presentation/d/1wfLAazqYveMoyvdy0y5ILeionp7eUjcPgxu-5QFlWwI/edit).
-[Source notes](lecture_6/SOURCES.md) identify the exact papers and inspected
-author-code revisions, including differences between paper pseudocode and
-released implementations. [Verified examples](lecture_6/verified_examples/README.md)
-contain actual training logs, generated samples, and checkpoint-reload checks.
-## Lecture 7 - Optimal Transport and Schrödinger Bridges
-The final lecture includes 17 complete numerical and learned examples: OT,
-Sinkhorn, finite and continuous-time bridges, DSB, DSBM, SF2M, DDSBM, CSBM,
-TR2-D2, BranchSBM, and EntangledSBM. Each runner saves its solved distribution
-or learned parameters and can reload them to generate samples.
-```bash
-python lecture_7/run.py --method sf2m --out lecture_7/outputs/sf2m
-python lecture_7/run.py --mode sample --out lecture_7/outputs/sf2m
-python lecture_7/run_all.py --quick
-```
-The [guide](lecture_7/README.md), [mathematical notes](lecture_7/MATHEMATICS.md),
-[slide code map](lecture_7/SLIDE_CODE_MAP.md), and [verified examples](lecture_7/verified_examples/README.md)
-follow the earlier lecture structure. The synthetic demonstrations distinguish
-exact calculations, finite discretizations, and learned approximations.
-Lectures [6](lecture_6/EQUATION_READINGS.md) and [7](lecture_7/EQUATION_READINGS.md)
-also include spoken readings of their equations before the intuition.
-## Repository organization
-| Location | Contents |
-| --- | --- |
-| Repository root | Course index, installation requirements, and license |
-| [`lecture_2/`](https://huggingface.co/ChatterjeeLab/CIS6270/tree/main/lecture_2) | One MNIST flow-matching script, guide, trained checkpoint, and selected example images |
-| [`lecture_3/`](https://huggingface.co/ChatterjeeLab/CIS6270/tree/main/lecture_3) | ESM-2 flow and diffusion guidance scripts, sequence data, guide, and mathematical notes |
-| [`lecture_4/`](https://huggingface.co/ChatterjeeLab/CIS6270/tree/main/lecture_4) | Seven discrete diffusion and guidance examples, synthetic DNA, slide code map, and verified outputs |
-| [`lecture_5/`](https://huggingface.co/ChatterjeeLab/CIS6270/tree/main/lecture_5) | Eight discrete and simplex flow examples, synthetic DNA, slide code map, and verified outputs |
-| [`lecture_6/`](https://huggingface.co/ChatterjeeLab/CIS6270/tree/main/lecture_6) | Sixteen flow-map implementations, text data, mathematical notes, slide links, and verified outputs |
-| [`lecture_7/`](https://huggingface.co/ChatterjeeLab/CIS6270/tree/main/lecture_7) | Seventeen OT and bridge examples, derivations, equation readings, slide links, tests, and verified outputs |
-Installation instructions and the lecture index are maintained at the
-repository root. Lecture-specific commands, mathematical explanations, and
-references accompany the corresponding code.
-## License
-The repository code is distributed under the
-[MIT License](https://huggingface.co/ChatterjeeLab/CIS6270/blob/main/LICENSE).
-ESM-2 weights are downloaded from their original repository under the
-corresponding distribution terms.