Model reference · open weights
flux.1-lite-alpha is an open-weight image model from Freepik. flux.1-lite-8B-alpha (BF16) weighs 26.3 GB; the smallest configuration that runs it is RTX 4090 24 GB.
Summary of the Freepik/flux.1-lite-8B-alpha model card, 2026-10-01
What it is
| Released by | Freepik |
|---|---|
| Released | 2024-10-17 |
| Parameters | 8.2B |
| VRAM | 26.3 GB for the weights |
What it runs on
| Card | One image | Memory |
|---|---|---|
| RTX 3060 12 GB … RTX 4060 Ti 16 GB | does not fit | |
| RTX 3090 24 GB | tight (encoders offloaded) | 23.4 GB |
| RTX 4090 24 GB | tight (encoders offloaded) | 23.4 GB |
| RTX 5090 32 GB | fits (encoders offloaded) | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
We are thrilled to announce the alpha release of Flux.1 Lite, an 8B parameter transformer model distilled from the FLUX.1-dev model. This version uses 7 GB less RAM and runs 23% faster while maintaining the same precision (bfloat16) as the original model.
Flux.1 Lite is ready to unleash your creativity! For the best results, we strongly recommend using a guidance_scale of 3.5 and setting n_steps between 22 and 30.
import torch
from diffusers import FluxPipeline
base_model_id = "Freepik/flux.1-lite-8B-alpha"
torch_dtype = torch.bfloat16
device = "cuda"
# Load the pipe
model_id = "Freepik/flux.1-lite-8B-alpha"
pipe = FluxPipeline.from_pretrained(
model_id, torch_dtype=torch_dtype
).to(device)
# Inference
prompt = "A close-up image of a green alien with fluorescent skin in the middle of a dark purple forest"
guidance_scale = 3.5 # Keep guidance_scale at 3.5
n_steps = 28
seed = 11
with torch.inference_mode():
image = pipe(
prompt=prompt,
generator=torch.Generator(device="cpu").manual_seed(seed),
num_inference_steps=n_steps,
guidance_scale=guidance_scale,
height=1024,
width=1024,
).images[0]
image.save("output.png")
Inspired by Ostris findings, we analyzed the mean squared error (MSE) between the input and output of each block to quantify their contribution to the final result, revealing significant variability.
As Ostris pointed out, not all blocks contribute equally. While skipping just one of the early MMDiT or late DiT blocks can significantly impact model performance, skipping any single block in between does not have a significant impact over the final image quality.
Stay tuned! Our goal is to distill FLUX.1-dev further until it can run smoothly on 24 GB consumer-grade GPU cards, maintaining its original precision (bfloat16), and running even faster, making high-quality AI models accessible to everyone.
We've also crafted a ComfyUI workflow to make using Flux.1 Lite even more seamless! Find it in comfy/flux.1-lite_workflow.json.
The safetensors checkpoint is available here: flux.1-lite-8B-alpha.safetensors
You can also test the model on Flux.1 Lite HF space thanks to TheAwakenOne
Our AI generator is now powered by Flux.1 Lite!
If you find our work helpful, please cite it!
@article{flux1-lite,
title={Flux.1 Lite: Distilling Flux1.dev for Efficient Text-to-Image Generation},
author={Daniel Verdú, Javier Martín},
email={dverdu@freepik.com, javier.martin@freepik.com},
year={2024},
}
The FLUX.1 [dev] Model is licensed by Black Forest Labs. Inc. under the FLUX.1 [dev] Non-Commercial License. Copyright Black Forest Labs. Inc.
Our model weights are released under the FLUX.1 [dev] Non-Commercial License.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Running it yourself
Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel and load the workflow from the model's card on Hugging Face; choose this model's file in its loader.
# on your rented machine: pip install diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("Freepik/flux.1-lite-8B-alpha", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(prompt="a red bicycle on a cobbled street").images[0]
image.save("/workspace/out.png")# on your rented machine (the ssh line is on its page in the console)
# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }
# the model (15.2 GB)
get Freepik/flux.1-lite-8B-alpha flux.1-lite-8B-alpha.safetensors diffusion_models
start-comfyui# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST and PORT are your machine's, from its page in the console
ssh -L 8188:localhost:8188 dev@HOST -p PORT