---
license: mit
tags:
  - flow-matching
  - rectified-flow
  - diffusion
  - celeba
  - generative-model
---

# Flow Matching Checkpoints (CelebA-64)

Model checkpoints from **"From Diffusion to One-Step Generation: A Controlled Study of Flow Matching, Rectified Flow, and Inference Acceleration on CelebA-64"**.

**Code**: [github.com/kshitiz-1225/flowmatch-code](https://github.com/kshitiz-1225/flowmatch-code)

## Architecture

- **Model**: UNet (77.7M parameters)
- **Dataset**: CelebA-64×64
- **Framework**: PyTorch

## Checkpoint Index

### Teacher

| File | Description |
|---|---|
| `teacher/2rf_rfpp.pt` | 2-rectified-flow teacher (RFPP), used to generate all reflow pairs |

### Core Ablations (Reflow Factorial Study)

| Experiment | Config | Checkpoints | KID@1 (×1e-3) |
|---|---|---|---|
| `ablation_A_uniform_mse` | Uniform + MSE | final, 50k, 100k | **8.01** |
| `ablation_B_ushaped_mse` | U-shaped + MSE | final, 50k, 100k | 8.19 |
| `ablation_C_uniform_lpips_huber` | Uniform + LPIPS-Huber | final, 50k, 100k | ~7.76 |
| `ablation_D_ushaped_lpips_huber` | U-shaped + LPIPS-Huber | final, 50k, 100k | 8.21 |

### Extended Ablations

| Experiment | Config | Checkpoints |
|---|---|---|
| `ext_B2_beta2_mse` | Beta(2,2) + MSE | final, 50k, 100k |
| `ext_Bfix_rfpp_tdist_mse` | RFPP t-dist + MSE | final, 50k, 100k |
| `ext_Cext_uniform_lpips_huber_200k` | Uniform + LPIPS-Huber (200k iters) | final, 150k, 200k |
| `ext_D2_beta2_lpips_huber` | Beta(2,2) + LPIPS-Huber | final, 100k, 200k |
| `ext_Dext_ushaped_lpips_huber_200k` | U-shaped + LPIPS-Huber (200k) | final, 150k, 200k |
| `ext_Dfix_rfpp_tdist_lpips_huber` | RFPP t-dist + LPIPS-Huber | final, 100k, 200k |

### Acceleration Methods

| Experiment | Method | KID@1 (×1e-3) | KID@2 | KID@5 |
|---|---|---|---|---|
| `ext_I_ect` | Easy Consistency Tuning | **6.56** | 5.76 | 5.43 |
| `ext_J_consistency_fm` | Consistency Flow Matching | 6.75 | **5.30** | **4.71** |
| `ext_K_pcm` | Phased Consistency Model | 6.64 | 5.98 | 5.60 |
| `ext_M_meanflow` | MeanFlow | 9.02 | 6.49 | 5.53 |
| `ext_G_shortcut` | Shortcut conditioning | 415.5 | — | — |
| `ext_F_self_distill` | Self-distillation | 92.3 | — | — |
| `ext_L_gan_distill` | GAN distillation | ~20 | — | — |

### Timestep Distribution Variants

| Experiment | Description |
|---|---|
| `ext_H_adaptive_t` | Learned adaptive timestep schedule |
| `ext_H_uniformhist_lpips` | Uniform histogram + LPIPS |
| `ext_H_mixhist_lpips` | Mixed histogram + LPIPS |
| `ext_H_rfpphist_lpips` | RFPP-shaped histogram + LPIPS |
| `ext_H_adaptive_t_importance_weighted` | Importance-weighted adaptive |
| `ext_H_adaptive_t_objective_shaping` | Objective-shaped LPIPS |

### Distillation Variants

| Experiment | Description |
|---|---|
| `ext_F_preempt_s4` | Progressive distill, stride=4 |
| `ext_F_preempt_s8` | Progressive distill, stride=8 |
| `ext_F_preempt_s8_lp02` | Progressive distill, stride=8, LPIPS=0.2 |
| `ext_F_preempt_s16` | Progressive distill, stride=16 |

### Baseline

| Experiment | Description |
|---|---|
| `hw3_reflow_baseline` | Flow matching teacher → reflow baseline (uniform+MSE, 100k) |

## Usage

```python
import torch
from huggingface_hub import hf_hub_download

# Download a specific checkpoint
path = hf_hub_download(
    repo_id="loralover/xv7k-fm-weights-archive",
    filename="archive/ablation_A_uniform_mse/reflow_final.pt",
)
checkpoint = torch.load(path, map_location="cpu")
```

## Directory Structure

```
teacher/                    # 2-rectified-flow teacher
archive/<experiment>/       # From checkpoint archive (3 ckpts each)
logs/<experiment>/          # From training logs (1-3 ckpts each)
```

## Citation

See the [code repository](https://github.com/kshitiz-1225/flowmatch-code) for full details.