---
title: Flux1 Dev FP8&NF4&GGUF 6 Steps, Hybrid 4 Steps : SVDQuant Int4 Flux.1 Dev : LORA Models FinesseV2P API | ModelsLab | Flux AI Model | Model ID: flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora | Flux API for Developers
description: Generate stunning images with Flux1 Dev FP8&NF4&GGUF 6 Steps, Hybrid 4 Steps : SVDQuant Int4 Flux.1 Dev : LORA Models   FinesseV2P Flux API ModelsLab. Create unlimited high-quality images using our advanced AI image generator. Model ID: flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora
url: https://modelslab.com/models/modelslab/flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora
canonical: https://modelslab.com/models/modelslab/flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora
type: product
component: Playground/Endpoint/Index
generated_at: 2026-01-20T13:02:47.868947Z
---

# Flux1 Dev FP8&NF4&GGUF 6 Steps, Hybrid 4 Steps : SVDQuant Int4 Flux.1 Dev : LORA Models FinesseV2P API | ModelsLab | Flux AI Model | Model ID: flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora | Flux API for Developers

> Generate stunning images with Flux1 Dev FP8&NF4&GGUF 6 Steps, Hybrid 4 Steps : SVDQuant Int4 Flux.1 Dev : LORA Models   FinesseV2P Flux API ModelsLab. Create unlimited high-quality images using our advanced AI image generator. Model ID: flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora

## Model

### [Flux1-Dev FP8&NF4&GGUF 6 steps, Hybrid 4 steps : SVDQuant-int4-Flux.1-Dev : LORA models - FinesseV2P](https://modelslab.com/models/modelslab/flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora)

**Model ID:** flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora
**Category:** Flux
**Type:** Lora
**Provider:** Modelslab

How to generate images:

Base Model + Finesse LORA: For more customization, combine the base model (Flux1-Dev-FP8) with the FinesseV2 LORA. Think of the LORA as a special ingredient that gives the images a unique style.

Why use the LORA?

Save space: The LORA is much smaller than the full model. So you don´t have to repeat and reapeat downloading checkpoints or unets. The specialiced information added to a base model only needs to stay in a smaller LORA

More flexibility: Experiment with different styles by combining the LORA with other base models (Flux1-dev-fp8 checkpoints). If you use ComfyUi you can make a unet, just use two nodes, LoadCheckpoint to load flux1-de-fp8 and ModelSave to save the unet, just link only the model points from both

Something you can´t do with checkpoints and unets, you can play with the strength of the lora. For some features you can enhance the image, e.g, make a woman more curvy (1.2 is enough)

Using the Flux1.Dev base model you prefer wheather checkpoint or unet and the Finesse LORA together saves you space in disk and makes it easier to experiment with different styles. It's like having a modular system where you can customize your cake with different toppings

This is an attempt to distribute a modification of a basic model in the LORA format instead of a full trained or merged model. Every time we download a trained model, for each model we download we download again: the basic model, the VAE, Clip-L and T5, in total about 17Gb and if you you use unet the penalty in each download is 11 GB. If you believe that GGUF is a solution, the penalty is only reduced by half (5Gb). That is to say that if we download "n" models based on FluxDevfp8 checkpoint , we have a redundancy of n x 17Gb. SSD vendors are very happy and grateful. Using a distribution based on LORAs, you just download your prefered base model Flux1.Dev, with the included or not VAE, Clip-L and T5 only once and then the specific LORA.

The base model to make the sample images was:

https://huggingface.co/lllyasviel/flux1_dev/resolve/main/flux1-dev-fp8.safetensors

If you want an image in 6-8 steps download Bytedance also include in the prompt (strength 0.125),

https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-FLUX.1-dev-8steps-lora.safetensors

For those who like GGUF, there are several cuantizations versions with 6-8 steps accelerator included

https://huggingface.co/mhnakif/flux-hyp8/tree/main

Tags: photorealistic, base model, realistic, efficiency, low vram, low-end gpu

## Endpoint

### Text to Image Community Model

**Endpoint:** `v6/images/text2img`
**Provider:** Modelslab
**Category:** Image

Generate photorealistic and imaginative images from text prompts with advanced detail, inpainting, and image-to-image translation features, ideal for creatives and marketers.

Per image generation will cost 0.0047$ 
For premium plan image generation will cost 0.00$ i.e Free.

## API Docs

- [API Docs](https://modelslab.com/models/modelslab/flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora/llms.txt)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-01-20*