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README.md
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## Linear-Multi-step Solvers and NeuralSolvers
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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`.
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Compared to Henu/RK4, the linear-multi-step solver is more stable and faster.
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During some experiments, we supringly find that the linear-multi-step solver can achieve comparable results even with FlowTurbo.
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As they are distinct methods, so armed with Adams, we believe FlowTurbo can be more powerful.
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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**
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These solvers are really powerful and interesting. We place the related code in `./src/diffusion/flow_matching/ns_sampling.py`.
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| SiT-XL-R256 | Steps | NFE-CFG | Extra-Paramters | FID | IS | PR | Recall |
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| Heun | 8 | 16x2 | 0 | 3.68 | / | / | / |
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| Heun | 11 | 22x2 | 0 | 2.79 | / | / | / |
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| Heun | 15 | 30x2 | 0 | 2.42 | / | / | / |
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| Adam2 | 6 | 6x2 | 0 | 6.35 | 190 | 0.75 | 0.55 |
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| Adam2 | 8 | 8x2 | 0 | 4.16 | 212 | 0.78 | 0.56 |
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| Adam2 | 16 | 16x2 | 0 | 2.42 | 237 | 0.80 | 0.60 |
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| Adam4 | 16 | 16x2 | 0 | 2.27 | 243 | 0.80 | 0.60 |
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| FlowTurbo | 6 | (7+3)x2 | 30408704(29M) | 3.93 | 223.6 | 0.79 | 0.56 |
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| FlowTurbo | 8 | (8+2)x2 | 30408704(29M) | 3.63 | / | / | / |
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| FlowTurbo | 10 | (12+2)x2 | 30408704(29M) | 2.69 | / | / | / |
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| FlowTurbo | 15 | (17+3)x2 | 30408704(29M) | 2.22 | 248 | 0.81 | 0.60 |
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| NeuralSolver | 6 | 6x2 | 21 | 3.57 | 214 | 0.77 | 0.58 |
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| NeuralSolver | 7 | 7x2 | 28 | 2.78 | 229 | 0.79 | 0.60 |
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| NeuralSolver | 8 | 8x2 | 36 | 2.65 | 234 | 0.79 | 0.60 |
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| NeuralSolver | 10 | 10x2 | 55 | 2.40 | 238 | 0.79 | 0.60 |
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| NeuralSolver | 15 | 15x2 | 110 | 2.24 | 244 | 0.80 | 0.60 |
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## Citation
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```bibtex
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[//]: # ()
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[//]: # ()
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## Citation
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```bibtex
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