FLUX Local Setup: Run AI Image Generation (2026 Guide)

What is FLUX?

FLUX is a 12-billion parameter text-to-image model from Black Forest Labs—the same team that created Stable Diffusion. Released in 2024, FLUX represents the next generation of open image generation.

Why FLUX Over Stable Diffusion?

Company Background

Black Forest Labs secured:

• $300M funding at $3.25B valuation (2025)
• $140M Meta partnership
• NVIDIA Blackwell integration
• Adobe Photoshop integration

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FLUX Model Variants

FLUX.1 Family (12B Parameters)

FLUX.1 [schnell] ("fast" in German):

• Generates in just 1-4 steps via adversarial distillation
• Free for commercial use (Apache 2.0)
• Best for rapid prototyping

FLUX.1 [dev]:

• Guidance-distilled from pro
• Best quality for local use
• Requires commercial license for business use

FLUX.2 Family (32B Parameters)

Released November 2025 with major improvements:

• Multi-reference support (up to 10 images)
• 4-megapixel editing
• Complex typography and infographics
• Couples Mistral-3 24B vision-language model

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Hardware Requirements

VRAM by Precision

Recommended GPUs

High-End (Full Models):

Mid-Range (Quantized):

Budget:

Apple Silicon

Note: 2-4x slower than NVIDIA. Use Draw Things or Stability Matrix for best Mac support.

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ComfyUI Setup

Step 1: Install ComfyUI

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt

Step 2: Download Required Files

Text Encoders (models/clip/):

VAE (models/vae/):

UNET Model (models/unet/):

Step 3: For GGUF Models (Low VRAM)

1. Open ComfyUI Manager
2. Install "ComfyUI-GGUF" node
3. Restart ComfyUI
4. Use GGUF-specific workflow

Step 4: Run ComfyUI

# Standard
python main.py

# Low VRAM (8-12GB)
python main.py --lowvram

# Very Low VRAM (6-8GB)
python main.py --lowvram --cpu-text-encoder

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Forge WebUI Setup

Note: Automatic1111 does NOT support FLUX. Use Forge instead.

Installation

1. Download Forge one-click package (CUDA 12.1 + PyTorch 2.3.1)
2. Extract and run update.bat
3. Run run.bat

Model Download

Download flux1-dev-bnb-nf4 from Hugging Face:

https://huggingface.co/lllyasviel/flux1-dev-bnb-nf4/tree/main

Place in: stable-diffusion-webui-forge/models/Stable-diffusion/

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Python/Diffusers Setup

import torch
from diffusers import FluxPipeline

# Load model
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16
)

# Memory optimization
pipe.enable_model_cpu_offload()

# Generate image
image = pipe(
    "A photorealistic portrait of a woman, golden hour lighting, "
    "shot on Fujifilm X-T5, 35mm f/1.4",
    num_inference_steps=28,
    guidance_scale=3.5
).images[0]

image.save("output.png")

4-bit Quantization (Low VRAM)

from diffusers import FluxPipeline, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=True)

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    quantization_config=quantization_config,
    device_map="cpu"
)
pipe.enable_model_cpu_offload()

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Prompting Guide

Prompt Structure

Subject + Action + Style + Context

Example Prompts

Photorealistic:

A weathered fisherman with deep wrinkles, wearing a yellow raincoat,
standing on a wooden dock at golden hour, dramatic rim lighting,
shot on Fujifilm X-T5, 35mm f/1.4

Artistic:

A bioluminescent forest with crystalline trees, ethereal mist
rising from an obsidian lake, otherworldly atmosphere,
hyper-detailed fantasy illustration

Typography:

A neon sign reading "OPEN 24 HOURS" in pink and blue,
mounted on a brick wall, rain-slicked street reflections,
night photography, shallow depth of field

Prompting Do's and Don'ts

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Recommended Settings

FLUX.1 [dev]

FLUX.1 [schnell]

Speed LoRAs

Use HyperFlux or FluxTurbo LoRAs to reduce dev from 25 steps to 4-9:

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Memory Optimization

ComfyUI Launch Flags

python main.py \
  --lowvram \
  --cpu-text-encoder \
  --preview-method none \
  --disable-xformers

Flag Reference

General Tips

1. Close background apps (browsers, Discord)
2. Reduce resolution for testing (768x768)
3. Keep batch size at 1
4. Use GGUF Q5 - 95%+ quality at 1/4 memory
5. Restart ComfyUI between model changes

VRAM Rule of Thumb

GGUF file size ≈ VRAM usage

• Q8: ~12-13GB file = ~12-13GB VRAM
• Q5: ~6-8GB file = ~6-8GB VRAM
• Q4: ~4-6GB file = ~4-6GB VRAM

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FLUX ControlNets

Available Tools

Download

Full models from Hugging Face:

• flux1-canny-dev.safetensors
• flux1-depth-dev.safetensors

LoRA versions for lower VRAM:

• flux1-canny-dev-lora.safetensors
• flux1-depth-dev-lora.safetensors

Redux requires sigclip_vision encoder in models/clip_vision/.

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Performance Benchmarks

Generation Speed

Quality vs Speed Trade-off

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Key Takeaways

1. FLUX.1 schnell is Apache 2.0 - Free for commercial use
2. 8GB GPUs work with GGUF Q4/Q5 quantization
3. RTX 4090 generates in 10-18 seconds at full quality
4. Natural language prompting - No weights or negatives
5. Use FP8 T5 encoder to save 5GB VRAM
6. Apple Silicon is 2-4x slower but works with MPS
7. FLUX.2 requires 54-90GB VRAM - Most stay on FLUX.1

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Next Steps

1. Check VRAM requirements for your GPU
2. Compare with RTX 5090 for upgrades
3. Learn quantization techniques
4. Explore local AI tools for LLMs
5. Set up RAG for text-based AI

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FLUX represents the cutting edge of open-source image generation, delivering Midjourney-level quality that runs on consumer hardware. Whether you're using a high-end RTX 4090 for instant generation or an 8GB GPU with quantized models, FLUX enables professional-quality AI art creation without cloud dependencies or API costs.