by Black Forest Labs
Lightweight 4B variant of FLUX.2 for efficient generation. Distilled from FLUX.2-dev for faster inference on consumer GPUs. Apache 2.0 licensed — the most accessible Flux model for commercial use.
VRAM requirements, GPU fit, and setup notes for Flux.2 Klein 4B, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Diffusers support. Best download size: ~4.0 GB at FP8.
Your hardware
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Measured quality metrics for Flux.2 Klein 4B outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
Compare which GPUs can run Flux.2 Klein 4B at different precisions. FP8 uses less memory than FP16 when available, and the grade shows how comfortably each GPU handles the workload.
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 9.8 GB | S● | A● | D● | S● |
| 768×768 | 9.9 GB | S● | A● | D● | S● |
| 1024×1024 | 10.0 GB | S● | A● | D● | S● |
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 10.1 GB | S | A | D | S |
| 768×768 | 10.3 GB | S | A | D | S |
| 1024×1024 | 10.6 GB | S | A | D | S |
Turbo / LCM distillation
Use distilled scheduler at 4-8 steps for faster iteration
from diffusers import FluxPipeline
import torch
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.2-klein-4B",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=20,
guidance_scale=3.5,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running Flux.2 Klein 4B locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyVRAM allocation at 1024×1024 on RTX 4090 24GB (24 GB)
Time per image at 1024×1024, 28 steps, FP16.
Download Flux.2 Klein 4B in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
Limited LoRA availability. Some FLUX.2 LoRAs may be compatible.
Frequently asked questions
Flux.2 Klein 4B (4B parameters) requires approximately 10.0 GB of VRAM at FP16 precision for standard 1024×1024 image generation. If you want a lighter setup, lower precisions like FP8 can reduce memory use when available.
Flux.2 Klein 4B usually needs more than 8GB for comfortable local use. Check the VRAM table above for the exact resolution and precision trade-off.
Flux.2 Klein 4B is marked for ComfyUI and Diffusers support in our catalog, so those are the runtimes we recommend first for local setup. If your workflow uses another front end, check the model's available formats and workflow notes above before downloading.
Yes, the RTX 4090 (24 GB VRAM) can run Flux.2 Klein 4B comfortably at FP16. Expected generation time is around ~1.2s per image at 1024×1024.
There are currently no known ControlNet adapters for Flux.2 Klein 4B. Check Hugging Face and Civitai for community-contributed adapters.
Limited LoRA availability. Some FLUX.2 LoRAs may be compatible. The LoRA ecosystem for Flux.2 Klein 4B is rated as "minimal". Each LoRA adds roughly 0.2 GB of extra VRAM.
On a reference GPU (RTX 4090 24GB), Flux.2 Klein 4B generates a 1024×1024 image in approximately ~1.2s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
See also