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forkjoin-ai/flux-1-schnell
flux-1-schnell is a text-to-image model from forkjoin-ai. Use it when you need an image from a text prompt. The card lists the license as apache-2.0.
Apache-2.0 text-to-image model from Black Forest Labs, mirrored in the sovereign KNOT format for the Aether edge-inference runtime on Edgework.ai.
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Updated Jun 9, 2026
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From the Hugging Face model README
Apache-2.0 text-to-image model from Black Forest Labs, mirrored in the sovereign KNOT format for the Aether edge-inference runtime on Edgework.ai.
| Property | Value |
|---|---|
| Base model | black-forest-labs/FLUX.1-schnell |
| Parameters | ~12B |
| Architecture | Rectified-flow diffusion transformer (DiT) |
| Task | Text-to-image |
| Steps | Timestep-distilled — high quality in 1–4 steps |
| License | apache-2.0 |
This repository mirrors the FLUX.1-schnell weights as a single .knot
container (plus *.knot.manifest.json and *.knot.admission.json sidecars). It
is a format mirror of the upstream model under its original Apache-2.0 license —
not a new or fine-tuned model.
The KNOT carries the FLUX component tensors used by the runtime: the flow
transformer (transformer), the autoencoder (vae), and the text encoders
(text_encoder, text_encoder_2). The manifest sidecar records the tensor
inventory; the admission sidecar records the artifact's quality-gate status.
The artifact is shape-admitted (weights load and produce finite, correctly shaped tensors). Full prompt-faithful, visible-image generation through the runtime is tracked separately in the project's admission ledger — treat this repo as the canonical weight mirror, not a turnkey image endpoint.
Published by forkjoin-ai · Managed by @buley. We mirror production weights in the KNOT format for distributed edge inference via the Aether runtime.
.knot (sovereign format)This repo ships flux-1-schnell.knot — the model weights in the KNOT container that the Aether distributed-inference runtime loads natively (the GGUF, when present, sits right beside it). A KNOT is a single self-describing file with a JSON table-of-contents, so any single tensor is one HTTP Range request — ideal for streaming weights to edge nodes.
| GGUF | KNOT | |
|---|---|---|
| Container | format-specific header | single file, JSON table-of-contents |
| Per-tensor fetch | whole-file oriented | one tensor = one Range request |
| Ecosystem | broad (llama.cpp, …) | Aether / Gnosis runtime |
huggingface-cli download forkjoin-ai/flux-1-schnell flux-1-schnell.knot --local-dir ./knots
Full format spec: KNOT_FORMAT.md. Inspect the header with bun run open-source/bitwise/scripts/dump-knot.ts flux-1-schnell.knot.