FLUX.1

State-of-the-art image generation model from Black Forest Labs on CLORE.AI GPUs.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Why FLUX.1?

• Best quality - Superior to SDXL and Midjourney v5

• Text rendering - Actually readable text in images

• Prompt following - Excellent instruction adherence

• Fast variants - FLUX.1-schnell for quick generation

Model Variants

Model	Speed	Quality	VRAM	License
FLUX.1-schnell	Fast (4 steps)	Great	12GB+	Apache 2.0
FLUX.1-dev	Medium (20 steps)	Excellent	16GB+	Non-commercial
FLUX.1-pro	API only	Best	-	Commercial

Quick Deploy on CLORE.AI

Docker Image:

ghcr.io/huggingface/text-generation-inference:latest

Ports:

22/tcp
7860/http

For easiest deployment, use ComfyUI with FLUX nodes.

Installation Methods

Method 1: ComfyUI (Recommended)

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

# Download FLUX models
cd models/unet
wget https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/flux1-schnell.safetensors

# Download required components
cd ../clip
wget https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors
wget https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors

cd ../vae
wget https://huggingface.co/black-forest-labs/FLUX.1-schnell/resolve/main/ae.safetensors

# Run ComfyUI
python main.py --listen 0.0.0.0

Method 2: Diffusers

pip install diffusers transformers accelerate torch

python << 'PYEOF'
import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell",
    torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()

image = pipe(
    "A cat wearing a space suit on Mars",
    num_inference_steps=4,
    guidance_scale=0.0,
).images[0]

image.save("flux_output.png")
PYEOF

Method 3: Fooocus

Fooocus has built-in FLUX support:

git clone https://github.com/lllyasviel/Fooocus
cd Fooocus
pip install -r requirements.txt

# Download FLUX model to models/checkpoints/
python launch.py --listen

ComfyUI Workflow

FLUX.1-schnell (Fast)

Nodes needed:

1. Load Diffusion Model → flux1-schnell.safetensors

2. DualCLIPLoader → clip_l.safetensors + t5xxl_fp16.safetensors

3. CLIP Text Encode → your prompt

4. Empty SD3 Latent Image → set dimensions

5. KSampler → steps: 4, cfg: 1.0

6. VAE Decode → ae.safetensors

7. Save Image

FLUX.1-dev (Quality)

Same workflow but:

• Steps: 20-50

• CFG: 3.5

• Use guidance_scale in prompt

Python API

Basic Generation

import torch
from diffusers import FluxPipeline

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

# Generate
image = pipe(
    prompt="A serene Japanese garden with cherry blossoms",
    height=1024,
    width=1024,
    num_inference_steps=4,
    guidance_scale=0.0,
).images[0]

image.save("output.png")

With Memory Optimization

from diffusers import FluxPipeline
import torch

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell",
    torch_dtype=torch.bfloat16
)

# Enable optimizations
pipe.enable_model_cpu_offload()  # Saves ~10GB VRAM
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()

image = pipe(
    "Portrait of a cyberpunk samurai",
    height=1024,
    width=1024,
    num_inference_steps=4,
).images[0]

Batch Generation

prompts = [
    "A sunset over mountains",
    "A futuristic city at night",
    "An underwater coral reef",
]

images = pipe(
    prompts,
    height=1024,
    width=1024,
    num_inference_steps=4,
).images

for i, img in enumerate(images):
    img.save(f"output_{i}.png")

FLUX.1-dev (Higher Quality)

from diffusers import FluxPipeline
import torch

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

image = pipe(
    prompt="Hyperrealistic portrait of an elderly fisherman",
    height=1024,
    width=1024,
    num_inference_steps=50,
    guidance_scale=3.5,
).images[0]

Prompt Tips

FLUX excels at:

• Text in images: "A neon sign that says 'OPEN 24/7'"

• Complex scenes: "A busy Tokyo street at night with reflections"

• Specific styles: "Oil painting in the style of Monet"

• Detailed descriptions: Long, detailed prompts work well

Example Prompts

# Photorealistic
A professional photograph of a golden retriever puppy playing in autumn leaves, 
shallow depth of field, warm afternoon light, Canon EOS R5

# Artistic
An impressionist painting of a Parisian cafe in the rain, 
oil on canvas, visible brushstrokes, warm colors

# Text rendering
A vintage movie poster with the title "COSMIC VOYAGE" in bold retro letters,
1960s sci-fi aesthetic, astronaut illustration

# Complex scene
A cozy library interior with floor-to-ceiling bookshelves, 
a leather armchair by a fireplace, rain visible through a window,
warm lamp light, photorealistic

Memory Optimization

For 12GB VRAM (RTX 3060)

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell",
    torch_dtype=torch.float16  # Use fp16 instead of bf16
)
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()

# Generate at lower resolution
image = pipe(prompt, height=768, width=768, num_inference_steps=4).images[0]

For 8GB VRAM

Use quantized version or ComfyUI with GGUF:

# In ComfyUI, install GGUF nodes
cd custom_nodes
git clone https://github.com/city96/ComfyUI-GGUF

# Download quantized model
wget https://huggingface.co/city96/FLUX.1-schnell-gguf/resolve/main/flux1-schnell-Q4_K_S.gguf

Performance Comparison

Model	Steps	Time (4090)	Quality
FLUX.1-schnell	4	~3 sec	Great
FLUX.1-dev	20	~12 sec	Excellent
FLUX.1-dev	50	~30 sec	Best
SDXL	30	~8 sec	Good

GPU Requirements

Setup	Minimum	Recommended
FLUX.1-schnell	12GB	16GB+
FLUX.1-dev	16GB	24GB+
With CPU offload	8GB	12GB+
Quantized (GGUF)	6GB	8GB+

GPU Presets

RTX 3060 12GB (Budget)

# Use quantized model
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell",
    torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()
pipe.enable_vae_tiling()

# Settings:
# - schnell only (dev may OOM)
# - 512x512 to 768x768
# - 4 steps
# - Batch size 1

RTX 3090 24GB (Optimal)

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.enable_vae_tiling()

# Settings:
# - schnell: 1024x1024, batch 2
# - dev: 1024x1024, batch 1
# - 20-30 steps for dev
# - Enable VAE tiling for high-res

RTX 4090 24GB (Performance)

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.float16
)
pipe.to("cuda")

# Settings:
# - schnell: 1024x1024, batch 4
# - dev: 1024x1024, batch 2
# - 30-50 steps for best quality
# - Can do 1536x1536 with tiling

A100 40GB/80GB (Production)

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

# Settings:
# - schnell: 1024x1024, batch 8+
# - dev: 1024x1024, batch 4
# - 50 steps for maximum quality
# - 2048x2048 possible

Cost Estimate

GPU	Hourly	Images/Hour
RTX 3060 12GB	~$0.03	~200 (schnell)
RTX 3090 24GB	~$0.06	~600 (schnell)
RTX 4090 24GB	~$0.10	~1000 (schnell)
A100 40GB	~$0.17	~1500 (schnell)

Troubleshooting

Out of Memory

# Use CPU offload
pipe.enable_model_cpu_offload()

# Or sequential CPU offload (slower but less VRAM)
pipe.enable_sequential_cpu_offload()

# Reduce resolution
height=768, width=768

Slow Generation

• Use FLUX.1-schnell (4 steps)

• Enable torch.compile: pipe.unet = torch.compile(pipe.unet)

• Use fp16 instead of bf16 on older GPUs

Poor Quality

• Use more steps (FLUX-dev: 30-50)

• Increase guidance_scale (3.0-4.0 for dev)

• Write more detailed prompts

Next Steps

• ComfyUI - Best interface for FLUX

• Fooocus - Simple alternative

• ControlNet - Guided generation

• Kohya Training - Train FLUX LoRAs