We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| newbeeer/pfgmpp | canonical | 1 of 1 |
| copy not recorded | — | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| edm_sampler | Ran | newbeeer/pfgmpp/generate.py pointer only (licence: NOASSERTION) · get_code("1a497dc67152411a") |
| parse_int_list | Ran | this paper's copy was not recorded; identical code first harvested from newbeeer/stf pointer only · get_code("cacd4f6ec202d9b4") |
| ablation_sampler | Not yet run | this paper's copy was not recorded; identical code first harvested from newbeeer/stf pointer only · get_code("daa5186ef7b993ca") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce a new family of physics-inspired generative models termed PFGM++ that unifies diffusion models and Poisson Flow Generative Models (PFGM). These models realize generative trajectories for $N$ dimensional data by embedding paths in $N{+}D$ dimensional space while still controlling the progression with a simple scalar norm of the $D$ additional variables. The new models reduce to PFGM when $D{=}1$ and to diffusion models when $D{\to}\infty$. The flexibility of choosing $D$ allows us to trade off robustness against rigidity as increasing $D$ results in more concentrated coupling between the data and the additional variable norms. We dispense with the biased large batch field targets used in PFGM and instead provide an unbiased perturbation-based objective similar to diffusion models. To explore different choices of $D$, we provide a direct alignment method for transferring well-tuned hyperparameters from diffusion models ($D{\to} \infty$) to any finite $D$ values. Our experiments show that models with finite $D$ can be superior to previous state-of-the-art diffusion models on CIFAR-10/FFHQ $64{\times}64$ datasets, with FID scores of $1.91/2.43$ when $D{=}2048/128$. In class-conditional setting, $D{=}2048$ yields current state-of-the-art FID of $1.74$ on CIFAR-10. In addition, we demonstrate that models with smaller $D$ exhibit improved robustness against modeling errors. Code is available at https://github.com/Newbeeer/pfgmpp
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.04265")
get_code_for_paper("2302.04265")
have("2302.04265")
Connect an agent — have() is free.