by Black Forest Labs
Distilled version of Flux.1 Dev optimized for speed. Only 4 steps needed (vs 28 for Dev). Same architecture but ~7x faster generation. Apache 2.0 licensed.
VRAM requirements, GPU fit, and setup notes for Flux.1 Schnell, including 8GB/12GB fit guidance where relevant. Recommended runtimes: ComfyUI and Diffusers support. Best download size: ~6.8 GB at Q4_0.
Your hardware
Detecting...
Measured quality metrics for Flux.1 Schnell 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)
Compare which GPUs can run Flux.1 Schnell at different precisions. FP8 uses less memory than FP16 when available, and the grade shows how comfortably each GPU handles the workload.
| 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● |
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 18.6 GB | S | F | F | B |
| 768×768 | 18.8 GB | S | F | F | B |
| 1024×1024 | 19.2 GB | S | F | F | B |
| 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 |
| Resolution | VRAM Required | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 | 12.3 GB | S | B | F | S |
| 768×768 | 12.5 GB | S | B | F | S |
| 1024×1024 | 12.9 GB | S | B | F | S |
GGUF Q4 available
Quantized GGUF format for lower VRAM and smaller downloads -- reduces download from 23.8 GB to 6.8 GB
from diffusers import FluxPipeline
import torch
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell",
torch_dtype=torch.float16
)
pipe.to("cuda")
image = pipe(
prompt="your prompt here",
num_inference_steps=4,
height=1024,
width=1024,
).images[0]
image.save("output.png")Get started
Setup instructions for running Flux.1 Schnell 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 Schnell
Drag & drop into ComfyUI or use File → Import
VRAM allocation at 1024×1024 on RTX 4090 24GB (24 GB)
Time per image at 1024×1024, 28 steps, FP16.
Download Flux.1 Schnell in the precision that matches your GPU. Lower precision usually means less VRAM pressure, while higher precision keeps more quality.
| Format | Precision | Size | Provider | |
|---|---|---|---|---|
| Official Weights | ||||
| safetensorsRecommended | FP16 | 23.8 GB | official | Download |
| Community Conversions | ||||
| ggufCommunity | Q2_K | 4.0 GB | community-gguf | Download |
| ggufCommunity | Q3_K_S | 5.2 GB | community-gguf | Download |
| ggufCommunity | Q4_0 | 6.8 GB | community-gguf | Download |
| ggufCommunity | Q4_K_S | 6.8 GB | community-gguf | Download |
| ggufCommunity | Q5_0 | 8.3 GB | community-gguf | Download |
| ggufCommunity | Q5_K_S | 8.3 GB | community-gguf | Download |
| ggufCommunity | Q6_K | 9.8 GB | community-gguf | Download |
| ggufCommunity | Q8_0 | 12.7 GB | community-gguf | Download |
Few LoRAs available. Most Flux LoRAs are trained for Flux.1 Dev and don't work well with Schnell's distilled pipeline.
Frequently asked questions
Flux.1 Schnell (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.
Yes, Flux.1 Schnell 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.
Flux.1 Schnell 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.
Flux.1 Schnell can run on the RTX 4090 with sequential offloading enabled, though generation will be slower than native fit.
There are currently no known ControlNet adapters for Flux.1 Schnell. Check Hugging Face and Civitai for community-contributed adapters.
Few LoRAs available. Most Flux LoRAs are trained for Flux.1 Dev and don't work well with Schnell's distilled pipeline. The LoRA ecosystem for Flux.1 Schnell is rated as "minimal". Each LoRA adds roughly 0.3 GB of extra VRAM.
On a reference GPU (RTX 4090 24GB), Flux.1 Schnell generates a 1024×1024 image in approximately ~3.5s at FP16 with 28 inference steps. Faster GPUs with higher memory bandwidth will produce images more quickly.
See also