Deploy FLUX.2
ImageFLUX.2 is Black Forest Labs' 32B parameter model with multi-image editing capabilities. The Dev FP8 variant runs on RTX 4090/5090, while the full model requires 48GB+ VRAM for maximum quality.
Deploy FLUX.2 in minutes
Starting at $0.66/hr on dedicated GPU
Available Variants (3)
| Model | GPU | VRAM | Price | Action |
|---|---|---|---|---|
FLUX.2 Dev FP8 Dev FP8 (Balanced) | RTX A6000 | 48 GB | $0.72/hr | Deploy |
FLUX.2 Dev Full Dev Full (64GB) | A100 80GB PCIe | 80 GB | $1.85/hr | Deploy |
FLUX.2 Dev Q4 GGUF Dev Q4 (Low VRAM) | L4 | 24 GB | $0.66/hr | Deploy |
Prices include the service fee. Charges follow actual running time.
Requirements
ModelPilot assigns 24–80GB cloud GPUs across the listed variants. Actual local VRAM requirements vary with model variant, precision, quantization, resolution, and workflow settings.
On ModelPilot, deploy on a dedicated cloud GPU (up to 80GB VRAM) starting at $0.66/hr with no setup required.
Compare FLUX.2
Source-backed GPU, VRAM, and cost comparisons for nearby deployment choices.
Qwen Image 2512 (Latest) vs FLUX.2 Dev Full (64GB)
Compare FLUX.2 Dev Full (64GB) against Qwen Image 2512 (Latest) by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion XL
Compare FLUX.2 Dev Full (64GB) against Stable Diffusion XL by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion 3.5 Large
Compare FLUX.2 Dev Full (64GB) against Stable Diffusion 3.5 Large by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion 3.5 Medium
Compare FLUX.2 Dev Full (64GB) against Stable Diffusion 3.5 Medium by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion 3.5 Turbo
Compare FLUX.2 Dev Full (64GB) against Stable Diffusion 3.5 Turbo by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Stable Diffusion 1.5 (Legacy)
Compare FLUX.2 Dev Full (64GB) against Stable Diffusion 1.5 (Legacy) by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs HiDream I1 Dev FP8 (Recommended)
Compare FLUX.2 Dev Full (64GB) against HiDream I1 Dev FP8 (Recommended) by GPU tier, VRAM, and base hourly cost.
FLUX.2 Dev Full (64GB) vs Flux Dev (Quality)
Compare FLUX.2 Dev Full (64GB) against Flux Dev (Quality) by GPU tier, VRAM, and base hourly cost.
Use Cases
- ✓Multi-image editing workflows
- ✓Professional image generation
- ✓Fine-grained image control
- ✓Production-quality outputs
Related Models
Known Issues & Fixes
Real failures from running FLUX.2 and similar pipelines, with the mechanism behind each one.
Over-caution that fp8 degrades quality, so users force bf16 and double VRAM/download for no gain
Cause: fp8_e4m3fn_scaled checkpoints carry per-block scale factors that preserve dynamic range, matching bf16 quality for many modern diffusion/video models at ~half VRAM. Plain (unscaled) fp8_e4m3fn CAN visibly degrade — that's why the scaled repacks exist.
Fix: Prefer the 'scaled' fp8 variant; don't reflexively pick bf16. Do NOT generalize the claim to unscaled fp8.
A v2 workflow fails to load its encoder, or a user reuses v1 encoders and gets nothing
Cause: FLUX.2 dropped FLUX.1's dual T5-XXL+CLIP_L for a single Mistral-3-Small encoder (18GB fp8 / 36GB bf16) — and the smaller Klein 4B/9B variants use a Qwen-3 encoder instead. The encoder stack isn't stable across versions or even size tiers of one release.
Fix: Match encoder files to the exact variant (mistral*3_small_flux2 for FLUX.2 dev; qwen_3*\* for Klein). Don't reuse the previous version's encoders.
User expects an anime specialist but gets photorealism, or mis-sizes VRAM
Cause: Z-Image Turbo is a photoreal-leaning generalist, not an anime model; Z-Anime (SeeSee21 fine-tune) is a separate community model on the identical architecture and loads via the same zimage deploy path. Encoder architecture is Qwen-3-4B, VAE = Flux's ae.safetensors (335MB). fp8 diffusion 6.15GB (fits 12GB), bf16 12.3GB; GGUF Q3-Q8 for low VRAM.
Fix: A Turbo base and its fine-tunes share architecture + VAE (and the Qwen-3-4B encoder architecture — a fine-tune may ship its own tuned encoder weights, e.g. Z-Anime's qwen_3_4b-engineer-v4-fp8), so one zimage deploy path serves both.
A .gguf model downloaded fine but doesn't show in the Unet/loader dropdown
Cause: ComfyUI-GGUF's UnetLoaderGGUF reads a dedicated 'unet_gguf' folder key (fed from both unet and diffusion_models). models/unet is the reliably-covered location; the equivalent .safetensors goes in models/diffusion_models.
Fix: Route .gguf diffusion weights to models/unet (canonical, always covered). A GGUF in diffusion_models may not appear depending on ComfyUI/GGUF version — don't rely on it.
Subject renders with an unexpected color cast — e.g. a white marble statue goes green-bronze/metallic
Cause: A relight LoRA above strength 0 imposes a relighting color shift matching the driving scene instead of preserving source color (shipped default is 0.0; Kijai's enabled default is 1.0).
Fix: Zero the relight/style LoRA when you want source color preserved. A tinted/off-color output means suspect an auxiliary LoRA near strength 1.0.
A workflow that rendered perfectly once produces a BLACK VOID / garbage on a later redeploy — identical JSON, config, and GPU
Cause: Unpinned by default: models pull from floating HF /resolve/main (branch HEAD), custom nodes are git-clone-latest (no SHA), Docker tags are reused, and only ~95% file-size is validated, not content hash. An upstream re-upload or newer node code silently changed bytes/behavior behind unchanged names.
Fix: Pin weights to a commit/revision, nodes to a SHA, images to a digest. Verify a pinned HF URL with `curl -sI -r 0-0 -A 'Mozilla/5.0' -L '<url>'` — MUST include -L (HF /resolve/ 302-redirects to a CDN; without -L you see 302 not 206).
Hitting a specific error message? Browse ComfyUI error fixes.
Frequently Asked Questions
How much GPU memory is allocated for FLUX.2?
The listed ModelPilot variants use 24–80GB cloud GPUs. Local memory needs vary with the variant, precision, quantization, and workflow settings.
How much does it cost to run FLUX.2?
Starting at $0.66/hr on a dedicated GPU. Charges are calculated from actual running time, with auto-stop when credits run out.
How long does FLUX.2 take to deploy?
Most deployments complete in 10–20 minutes including model download and environment setup.
Can I run FLUX.2 on my local GPU?
It depends on the selected variant, precision, quantization, and workflow settings. Compare the variants below with your available VRAM; the table shows ModelPilot's cloud GPU allocation, not a universal local minimum.
Ready to deploy FLUX.2?
Pick your GPU and have it running in minutes. No infrastructure setup required.