Flux.1 Dev
Frontierby Black Forest Labs
State-of-the-art text-to-image model from Black Forest Labs. Excels at photorealism, text rendering, and prompt adherence. 12B parameter DiT architecture with dual text encoders: T5-XXL (4.7B) and CLIP-L (0.12B).
VRAM requirements, GPU fit, and setup notes for Flux.1 Dev, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Diffusers support. Best download size: ~6.8 GB at Q4_0.
- Best-in-class text rendering in images
- Excellent prompt adherence
- 12B DiT architecture with flow matching
- Requires T5-XXL (4.7B) + CLIP-L text encoders
Your hardware
Detecting...
Image Quality Benchmarks
Measured quality metrics for Flux.1 Dev outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
Text-image alignment accuracy (higher is better)
VRAM Requirements by Resolution and Precision
Compare which GPUs can run Flux.1 Dev at different precisions. FP8 uses less memory than FP16 when available, and the grade shows how comfortably each GPU handles the workload.
FP16 (full precision)
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 23.6 GB | B● | F● | F● | F● |
| 768×768 | 23.8 GB | B● | F● | F● | F● |
| 1024×1024 | 24.0 GB | B● | F● | F● | F● |
FP8 (~40% less VRAM)
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 12.6 GB | S● | B● | F● | S● |
| 768×768 | 12.8 GB | S● | B● | F● | S● |
| 1024×1024 | 13.0 GB | S● | B● | F● | S● |
Q8_0
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 12.6 GB | S● | B● | F● | S● |
| 768×768 | 12.8 GB | S● | B● | F● | S● |
| 1024×1024 | 13.0 GB | S● | B● | F● | S● |
Q5_K_S
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 13.8 GB | S | D | F | A |
| 768×768 | 14.1 GB | S | D | F | A |
| 1024×1024 | 14.5 GB | S | D | F | A |
Q4_0
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 6.7 GB | S● | S● | A● | S● |
| 768×768 | 6.8 GB | S● | S● | A● | S● |
| 1024×1024 | 7.0 GB | S● | S● | A● | S● |
Optimization Tips
GGUF Q4 available
Quantized GGUF format for lower VRAM and smaller downloads -- reduces download from 23.8 GB to 6.8 GB
Turbo / LCM distillation
Use distilled scheduler at 4-8 steps for faster iteration
ControlNets available
Add guided generation with 3 adapters (+3.6 GB VRAM each)
Run with Python
from diffusers import FluxPipeline
import torch
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=28,
guidance_scale=3.5,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running Flux.1 Dev locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/ (or ComfyUI/models/unet/ for GGUF)3. Launch ComfyUI
python main.pyComfyUI Workflow
Basic txt2img workflow for Flux.1 Dev
Drag & drop into ComfyUI or use File → Import
Memory Breakdown
VRAM allocation at 1024×1024 on RTX 4090 24GB (24 GB)
Estimated Generation Time
Time per image at 1024×1024, 28 steps, FP16.
Sample Outputs
Available Formats, Downloads & Setup
Download Flux.1 Dev in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
| Formato | Precisión | Tamaño | Proveedor | |
|---|---|---|---|---|
| Pesos oficiales | ||||
| safetensorsRecomendado | FP16 | 23.8 GB | official | Descargar |
| safetensors | FP8 | 11.9 GB | official | Descargar |
| Conversiones de la comunidad | ||||
| ggufComunidad | Q2_K | 4.0 GB | community-gguf | Descargar |
| ggufComunidad | Q3_K_S | 5.2 GB | community-gguf | Descargar |
| ggufComunidad | Q4_0 | 6.8 GB | community-gguf | Descargar |
| ggufComunidad | Q4_K_S | 6.8 GB | community-gguf | Descargar |
| ggufComunidad | Q5_0 | 8.3 GB | community-gguf | Descargar |
| ggufComunidad | Q5_K_S | 8.3 GB | community-gguf | Descargar |
| ggufComunidad | Q6_K | 9.9 GB | community-gguf | Descargar |
| ggufComunidad | Q8_0 | 12.7 GB | community-gguf | Descargar |
ControlNet Support
3 ControlNets available for Flux.1 Dev. ControlNets add guided image generation (edges, depth, pose) at the cost of extra VRAM.
Canny Edge
+3.6 GB VRAMExtract edges from reference image to guide composition and structure. Best for architectural and product photography.
Depth Map
+3.6 GB VRAMUse depth estimation to maintain 3D spatial relationships. Great for scenes with foreground/background separation.
Union (Multi-Control)
+3.6 GB VRAMSingle model that handles canny, depth, pose, tile, and blur conditions. Most versatile option for Flux.
LoRA Ecosystem
Growing EcosystemGrowing ecosystem with hundreds of LoRAs on CivitAI and HuggingFace. Flux LoRAs are typically smaller than SDXL LoRAs due to the DiT architecture.
Approximately 500 LoRAs available on CivitAI. Each LoRA adds ~0.3 GB VRAM.
Popular LoRAs for Flux.1 Dev
Related Workflows
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Frequently asked questions
FAQ — Flux.1 Dev VRAM, Runtimes & Fit
How much VRAM does Flux.1 Dev need?
Flux.1 Dev (12B parameters) requires approximately 24.0 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.
Can I run Flux.1 Dev on an 8GB GPU?
Yes, Flux.1 Dev can fit on some 8GB GPUs at ~6.8 GB at Q4_0. Check the VRAM table above for the exact resolution and precision trade-off.
Does Flux.1 Dev work in ComfyUI and Diffusers?
Flux.1 Dev 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.
Can I run Flux.1 Dev on RTX 4090?
Flux.1 Dev can run on the RTX 4090 with sequential offloading enabled, though generation will be slower than native fit.
Does Flux.1 Dev support ControlNet?
Yes, Flux.1 Dev has 3 ControlNet adapters available: Canny Edge, Depth Map, Union (Multi-Control). Each ControlNet adds roughly 3.6 GB of extra VRAM.
Does Flux.1 Dev have LoRA support?
Growing ecosystem with hundreds of LoRAs on CivitAI and HuggingFace. Flux LoRAs are typically smaller than SDXL LoRAs due to the DiT architecture. The LoRA ecosystem for Flux.1 Dev is rated as "moderate". There are approximately 500 LoRAs available on Civitai. Each LoRA adds roughly 0.3 GB of extra VRAM.
How fast is Flux.1 Dev?
On a reference GPU (RTX 4090 24GB), Flux.1 Dev generates a 1024×1024 image in approximately ~18s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
About Flux.1 Dev
See also