The 32B FLUX.2 dev transformer with the fal Turbo LoRA merged into the weights — one GGUF file per quant tier, 12 steps, ≈85 s per 1024² image on an RTX 5090.

What this repo is: true merged turbo checkpoints (base ⊕ Turbo LoRA at full precision, then quantized), plus the Ministral text encoder and VAE they need — weights only, not a retrain. No runtime LoRA required: load the transformer and generate. The Q4-class file uses a mixed-precision recipe that exists for a measured reason — see the gotchas.

Samples

prompt: a red apple on a rustic wooden table beside a window, soft daylight, fine skin texture and wood grain, studio photograph, sharp focus — 1024×1024, 12 steps, guidance 2.5, seed 12345	prompt: portrait of an older fisherman with a weathered face, natural window light, shallow depth of field, photorealistic — 1024×1024, 12 steps, guidance 2.5, seed 99
_{prompt: `a red apple on a rustic wooden table beside a window, soft daylight, fine skin texture and wood grain, studio photograph, sharp focus` — 1024×1024, 12 steps, guidance 2.5, seed 12345}	_{prompt: `portrait of an older fisherman with a weathered face, natural window light, shallow depth of field, photorealistic` — 1024×1024, 12 steps, guidance 2.5, seed 99}
prompt: a rain-slicked Tokyo street at night, neon signs reflecting in puddles, cinematic, 35mm photograph — 1024×1024, 12 steps, guidance 2.5, seed 1234
_{prompt: `a rain-slicked Tokyo street at night, neon signs reflecting in puddles, cinematic, 35mm photograph` — 1024×1024, 12 steps, guidance 2.5, seed 1234}

img2img

One source (generated by this same build), three prompts, identical settings — the difference you see is the prompt.

source: alpine lake at golden hour	oil render	watercolor render	sketch render
_{source — txt2img output of this build (`a dramatic mountain landscape at golden hour, snow-capped peaks, alpine lake reflection, photorealistic, 35mm photograph`, seed 42)}	_{img2img prompt: `An oil painting of a dramatic mountain landscape at sunset, thick impasto brushstrokes` — 1024×1024, 12 steps, guidance 3.5, strength 0.85, seed 42}	_{img2img prompt: `A watercolor painting of a mountain valley at dawn, soft wet-on-wet washes` — 1024×1024, 12 steps, guidance 3.5, strength 0.85, seed 42}	_{img2img prompt: `A detailed pencil sketch of a mountain landscape in warm sepia and sanguine tones` — 1024×1024, 12 steps, guidance 3.5, strength 0.85, seed 42}

strength 0.7 (the production default) preserves more of the source and reads as a lighter restyle; 0.85 is the value for a genuine change of medium. At either value the composition — peaks, valley, lake, reflection — survives.

Recommended settings

Values these weights are run with in practice.

Parameter	Production value	Meaning
`width`	`1024`	Output width in pixels
`height`	`1024`	Output height in pixels
`steps`	`12`	Denoising steps (dynamic-shifted schedule)
`guidance`	`2.5`	Embedded guidance (txt2img)
`guidance` (img2img)	`3.5`	Stronger prompt adherence for restyling
`strength`	`0.7`	img2img denoise strength (`0.85` for style transfer)

Supported modes: txt2img, img2img

Notes and gotchas

Why the Q4-class file is "mixed": a uniform Q4_K bake of merged turbo weights is broken. The merged turbo delta averages ≈0.065% of weight magnitude; Q4_K's per-element quantization step on these tensors is ≈3–7%. Quantizing the merged weights uniformly at Q4_K shreds the delta into a structured-noise residue — a fine crackle/debris texture across flat image regions (walls, skin, sky). Uniform Q6_K is clean but does not fit a 32 GB card at 1024². flux2-dev-turbo-Q4_K_M-mixed.gguf therefore keeps the Q4_K_M bulk but holds the artifact-carrying tensors — single-block linear2, the attention out-projections, and every modulation/embedding/conditioning tensor — at Q6_K/Q8_0. Verified equivalent to an exact-delta (runtime-LoRA) reference in same-seed A/B tests; 21.0 GB, ≈22.6 GB VRAM peak at 1024².
12 steps on the standard dynamic schedule, not 8 turbo sigmas. With the published 8-step sigma schedule this Q4-class build ghosts (double exposure); 12-step dynamic is the verified preset. Executed img2img steps are steps × strength — schedule more if you lower strength.
The 14B Ministral text encoder runs on CPU in the reference setup — moving a GGML-loaded encoder to CPU after encode does not free its VRAM, so it lives on CPU permanently. Prompt encode costs a few seconds.
Distilled weights are quantization-sensitive: a uniform Q3_K_M bake of dev+turbo produced pure noise mosaics, while the base model at Q3_K_M is fine. Do not requantize these files downward.

Layout

Components ship as separate files: the merged transformer GGUF (pick one quant tier), the text encoder GGUF (+ tokenizer under split/text_encoders/config/), and the VAE. Any GGUF-capable Flux2 loader that accepts explicit per-component paths can consume this directly — no LoRA handling required.

A generic diffusers snippet is deliberately omitted. This is a split layout with a substituted encoder, so an upstream example will not load it unmodified.

Files

File	Size	Role
`split/gguf/flux2-dev-turbo-Q4_K_M-mixed.gguf`	21.02 GB	merged turbo transformer, mixed-precision Q4-class — fits 32 GB at 1024²; the tier these settings were measured on
`split/gguf/flux2-dev-turbo-Q6_K.gguf`	26.73 GB	merged turbo transformer, uniform Q6_K (needs >32 GB at 1024²)
`split/gguf/flux2-dev-turbo-Q8_0.gguf`	34.47 GB	merged turbo transformer, uniform Q8_0 (server/RunPod tier)
`split/text_encoders/Ministral-3-14B-Instruct-2512-Q3_K_S.gguf`	6.07 GB	text encoder
`split/text_encoders/mmproj-Ministral-3-14B-Instruct-2512-F16.gguf`	878 MB	multimodal projector (vision path, optional)
`split/vae/diffusion_pytorch_model.safetensors`	336 MB	VAE
`split/text_encoders/config/`	≈34 MB	tokenizer + encoder configs

Provenance

Upstream base model: black-forest-labs/FLUX.2-dev (upstream license: FLUX.2-dev Non-Commercial License)
Turbo LoRA: fal/FLUX.2-dev-Turbo, merged at full precision before quantization
This build: base ⊕ LoRA merged in float32 from a Q6_K dequant of the base, written to an F16 master, then quantized per tier (mixed-precision recipe for the Q4-class file). Weights are not retrained.
License: left as unknown in this repo's metadata. Refer to the upstream licenses for redistribution and commercial-use terms — the FLUX.2-dev base is non-commercial.

ChrisColeTech/flux2-dev-turbo-GGUF

FLUX.2 dev Turbo — merged GGUF