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	12.0+	12.1+

Recommended Clore.ai GPU: RTX 4090 24GB (~$0.5–2/day) — 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:

1. Download the FLUX.2 Klein checkpoint to ComfyUI/models/diffusion_models/

2. In your workflow, change the checkpoint node to point to FLUX.2 Klein

3. Reduce steps to 4 (instead of 20–50 for FLUX.1)

4. 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 $0.3/day

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

Further Reading

• FLUX.1 Guide — original FLUX guide with LoRA and ControlNet details

• ComfyUI Guide — ComfyUI setup and workflows

• Black Forest Labs Blog

• HuggingFace Model