ByteDanceCryofm V2
Cryofm V 2 is a flow-based generative foundation model for cryo-EM density maps developed by ByteDance, exceling at learning general priors of high-quality cryo-EM densities and adaptable for downstream tasks such as density map enhancement. Its 3D UNet architecture enables stable generation and flexible adaptation, with a notable technical trait being its use of positional encoding for time embedding.
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Frequently asked questions about Cryofm V2
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
ByteDance has not published a context window for Cryofm V2, so the catalog shows it as not available rather than estimating one.
Yes. Cryofm V2 is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Cryofm V2 accepts Image and produces Image. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Cryofm V2 is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Cryofm V2 is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
ByteDance has not published a parameter count for Cryofm V2, so the catalog cannot estimate its memory footprint. The capacity planner can size it from a parameter count you supply.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 14, 2026
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