Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+---
+
+## [NeurIPS24] FlowDCN: Exploring DCN-like Architectures for Fast Image Generation with Arbitrary Resolution
+
+
+![caps](./figs/viscaption5.png)
+
+
+### [NEWS] [9.26] 💐💐 Our FlowDCN is accepted by NeurIPS 2024! 💐💐
+### [NEWS] [11.22] 🍺 Our FlowDCN models and code are now available in the official repo!
+
+## Pretrained Models
+Our Models consistently achieve state-of-the-art results on the sFID metrics compared to SiT/DiT.
+
+### Metrics 
+Our Models consistently has fewer parameters and GFLOPS compared to Transformer counterparts. 
+Our code also support LogNorm and VAR(Various Aspect Ratio Training)
+
+|    Model-iters     | Resolution |     Solver      | NFE-CFG | FID  | sFID | Params |Link|
+|:------------------:|:----------:|:---------------:|:-------:|:----:|:----:|:------:|:--:|
+|   FlowDCN-S-400k   |  256x256   |  EulerSDE-250   |  250x2  | 54.6 | 8.8  | 30.3M  |
+|   FlowDCN-B-400k   |  256x256   |  EulerSDE-250   |  250x2  | 28.5 | 6.09 |  120M  |
+| VAR-FlowDCN-B-400k |  256x256   |  EulerSDE-250   |  250x2  | 23.6 | 7.72 |  120M  |
+|   FlowDCN-L-400k   |  256x256   |  EulerSDE-250   |  250x2  | 13.8 | 4.69 |  421M  |
+|   FlowDCN-XL-2M    |  256x256   |  EulerODE-250   |  250x2  | 2.01 | 4.33 |  618M  |
+|   FlowDCN-XL-2M    |  256x256   |  EulerSDE-250   |  250x2  | 2.00 | 4.37 |  618M  |
+|   FlowDCN-XL-2M    |  256x256   | NeuralSolver-10 |  10x2   | 2.35 | 5.07 |  618M  |
+|  FlowDCN-XL-100k   |  512x512   |   EulerODE-50   |  50x2   | 2.76 | 5.29 |  618M  |
+|  FlowDCN-XL-100k   |  512x512   |  EulerSDE-250   |  250x2  | 2.44 | 4.53 |  618M  |
+|  FlowDCN-XL-100k   |  512x512   | NeuralSolver-10 |  10x2   | 2.77 | 4.68 |  618M  |
+
+### Visualizations
+
+![caps](./figs/vis_ode.png)
+
+### Various Resolution Extension
+| Models | 256x256 FID      | sFID  | IS    | 320x320 FID | sFID  | IS     | 224x448 FID | sFID  | IS     | 160x480 FID | sFID  | IS     |
+|------------------|-------|-------|-------------|-------|--------|-------------|-------|--------|-------------|-------|--------|-------|
+| DiT-B            | 44.83 | 8.49  | 32.05       | 95.47 | 108.68 | 18.38       | 109.1 | 110.71 | 14.00       | 143.8 | 122.81 | 8.93  |
+| with EI          | 44.83 | 8.49  | 32.05       | 81.48 | 62.25  | 20.97       | 133.2 | 72.53  | 11.11       | 160.4 | 93.91  | 7.30  |
+| with PI          | 44.83 | 8.49  | 32.05       | 72.47 | 54.02  | 24.15       | 133.4 | 70.29  | 11.73       | 156.5 | 93.80  | 7.80  |
+| FiT-B (+VAR)     | 36.36 | 11.08 | 40.69       | 61.35 | 30.71  | 31.01       | 44.67 | 24.09  | 37.1        | 56.81 | 22.07  | 25.25 |
+| with VisionYaRN  | 36.36 | 11.08 | 40.69       | 44.76 | 38.04  | 44.70       | 41.92 | 42.79  | 45.87       | 62.84 | 44.82  | 27.84 |
+| with VisionNTK   | 36.36 | 11.08 | 40.69       | 57.31 | 31.31  | 33.97       | 43.84 | 26.25  | 39.22       | 56.76 | 24.18  | 26.40 |
+| FlowDCN-B        | 28.5  | 6.09  | 51          | 34.4  | 27.2   | 52.2        | 71.7  | 62.0   | 23.7        | 211   | 111    | 5.83  |
+| FlowDCN-B (+VAR) | 23.6  | 7.72  | 62.8        | 29.1  | 15.8   | 69.5        | 31.4  | 17.0   | 62.4        | 44.7  | 17.8   | 35.8  |
+
+
+[//]: # ()
+[//]: # (![caps](./figs/var_fid.png))
+
+## Linear-Multi-step Solvers and NeuralSolvers
+We also provide a adams-like linear-multi-step solver for the recitified flow sampling. The related configs are named with `adam2` or `adam4`. The solver code are placed in `./src/diffusion/flow_matching/adam_sampling.py`.
+
+Compared to Henu/RK4, the linear-multi-step solver is more stable and faster.
+
+During some experiments, we supringly find that the linear-multi-step solver can achieve comparable results even with FlowTurbo.
+
+As they are distinct methods, so armed with Adams, we believe FlowTurbo can be more powerful.
+
+Also, We provide some magic solvers for the recitified flow sampling. These solvers are highly inspired by linear-multi-steps methods, and consists of just some **Magic Numbers**
+These solvers are really powerful and interesting. We place the related code in `./src/diffusion/flow_matching/ns_sampling.py`.
+
+| SiT-XL-R256 | Steps | NFE-CFG  | Extra-Paramters | FID  | IS    | PR   | Recall |
+|--|-------|----------|-----------------|------|-------|------|--------|
+| Heun | 8     | 16x2     | 0               | 3.68 | /     | /    | /      |
+| Heun | 11    | 22x2     | 0               | 2.79 | /     | /    | /      |
+| Heun | 15    | 30x2     | 0               | 2.42 | /     | /    | /      |
+| Adam2 | 6     | 6x2      | 0 | 6.35 | 190   | 0.75 | 0.55   |
+| Adam2 | 8     | 8x2      | 0 | 4.16 | 212   | 0.78 | 0.56   |
+| Adam2 | 16    | 16x2     | 0 | 2.42 | 237   | 0.80 | 0.60   |
+| Adam4 | 16    | 16x2     | 0 | 2.27 | 243   | 0.80 | 0.60   |
+| FlowTurbo | 6     | (7+3)x2  | 30408704(29M)   | 3.93 | 223.6 | 0.79 | 0.56   |
+| FlowTurbo | 8     | (8+2)x2  | 30408704(29M)   | 3.63 | /     | /    | /      |
+| FlowTurbo | 10    | (12+2)x2 | 30408704(29M)   | 2.69 | /     | /    | /      |
+| FlowTurbo | 15    | (17+3)x2 | 30408704(29M)   | 2.22 | 248   | 0.81 | 0.60   |
+| NeuralSolver | 6     | 6x2      | 21              | 3.57 | 214   | 0.77 | 0.58   |
+| NeuralSolver | 7     | 7x2      | 28              | 2.78 | 229   | 0.79 | 0.60   |
+| NeuralSolver | 8     | 8x2      | 36              | 2.65 | 234   | 0.79 | 0.60   |
+| NeuralSolver | 10    | 10x2     | 55              | 2.40 | 238   | 0.79 | 0.60   |
+| NeuralSolver | 15    | 15x2     | 110              | 2.24 | 244   | 0.80 | 0.60   |
+
+## Citation
+```bibtex
+@inproceedings{
+wang2024exploring,
+title={Exploring {DCN}-like architecture for fast image generation with arbitrary resolution},
+author={Shuai Wang and Zexian Li and Tianhui Song and Xubin Li and Tiezheng Ge and Bo Zheng and Limin Wang},
+booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
+year={2024},
+url={https://openreview.net/forum?id=e57B7BfA2B}
+}
+```