FLUX.2 Klein

FLUX.2 Klein — sub-second image generation on Clore.ai GPUs

FLUX.2 Klein by Black Forest Labs is the successor to FLUX.1, delivering the same image quality at 20–60× the speed. Where FLUX.1 took 10–30 seconds per image, FLUX.2 Klein generates in under 0.5 seconds on an RTX 4090. It's a 32B Diffusion Transformer (DiT) model with an Apache 2.0 license, and as of January 2026, it's even experimentally supported in Ollama.

Key Features

< 0.5 second generation: 20–60× faster than FLUX.1
32B DiT architecture: Same quality as FLUX.1 dev
Apache 2.0 license: Full commercial use
Ollama support: Experimental image generation via Ollama (Jan 2026)
ComfyUI compatible: Drop-in replacement for FLUX.1 workflows
LoRA + ControlNet: Community adapters available

Requirements

Component

Minimum

Recommended

GPU

RTX 3090 24GB

RTX 4090 24GB

VRAM

16GB (with offloading)

24GB

RAM

32GB

64GB

Disk

40GB

60GB

CUDA

12.8+

Recommended Clore.ai GPU: RTX 4090 24GB ($0.14–0.42/hr) — sub-second generation

Speed Comparison: FLUX.1 vs FLUX.2 Klein

GPU

FLUX.1 dev (20 steps)

FLUX.2 Klein

Speedup

RTX 3090

~25 sec

~1.2 sec

20×

RTX 4090

~12 sec

~0.4 sec

30×

RTX 5090

~8 sec

~0.25 sec

32×

H100

~5 sec

~0.15 sec

33×

Quick Start with diffusers

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.2-klein",
    torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Generate image in < 0.5 seconds!
image = pipe(
    prompt="a cyberpunk GPU mining rig in a neon-lit server room, photorealistic",
    height=1024,
    width=1024,
    num_inference_steps=4,  # Klein needs only 4 steps!
    guidance_scale=3.5,
).images[0]

image.save("output.png")

Memory-Efficient Mode (16GB GPUs)

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.2-klein",
    torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()  # Fits on 16GB
pipe.vae.enable_tiling()         # Saves ~2GB

image = pipe("a mountain landscape at sunset", num_inference_steps=4).images[0]

ComfyUI Workflow

FLUX.2 Klein works as a drop-in replacement in existing FLUX.1 ComfyUI workflows:

Download the FLUX.2 Klein checkpoint to ComfyUI/models/diffusion_models/
In your workflow, change the checkpoint node to point to FLUX.2 Klein
Reduce steps to 4 (instead of 20–50 for FLUX.1)
Set guidance scale to 3.0–4.0

# Download model for ComfyUI
cd ComfyUI/models/diffusion_models/
wget https://huggingface.co/black-forest-labs/FLUX.2-klein/resolve/main/flux2-klein.safetensors

Batch Generation

With sub-second generation, FLUX.2 Klein enables massive batch processing:

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.2-klein", torch_dtype=torch.bfloat16
).to("cuda")

prompts = [
    "a red sports car on a mountain road, cinematic",
    "a cozy coffee shop interior, warm lighting",
    "an astronaut floating above Earth, hyperrealistic",
    "a medieval castle in autumn, fantasy art",
    # ... add hundreds more
]

for i, prompt in enumerate(prompts):
    image = pipe(prompt, num_inference_steps=4, guidance_scale=3.5).images[0]
    image.save(f"batch_{i:04d}.png")
    print(f"Generated {i+1}/{len(prompts)}")

# On RTX 4090: ~100 images in under 1 minute!

LoRA Support

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.2-klein", torch_dtype=torch.bfloat16
).to("cuda")

# Load a LoRA trained on FLUX architecture
pipe.load_lora_weights("your-lora/flux2-style-lora", weight_name="lora.safetensors")
pipe.fuse_lora(lora_scale=0.8)

image = pipe("a portrait in the trained style", num_inference_steps=4).images[0]

Tips for Clore.ai Users

Batch processing king: At 0.4 sec/image, you can generate 10,000+ images per hour on RTX 4090
4 steps only: Don't use more — Klein is optimized for 4 steps (more doesn't improve quality)
Same LoRAs as FLUX.1: Most FLUX.1 LoRAs are compatible with Klein
ComfyUI drop-in: Just swap the checkpoint, change steps to 4
RTX 3090 is viable: 1.2 sec/image is still great at $0.07–0.21/hr

Troubleshooting

Issue

Solution

OOM on 24GB

Use enable_model_cpu_offload() + vae.enable_tiling()

Blurry images

Ensure num_inference_steps=4, not less. Check guidance_scale 3.0–4.0

Slow first generation

Normal — model loads on first call (~30s). Subsequent: sub-second

ComfyUI checkpoint error

Ensure you have the .safetensors file, not the diffusers format