ModelsLab/Flux1-Dev FP8&NF4&GGUF 6 Steps, Hybrid 4 Steps : SVDQuant-Int4-Flux.1-Dev : LORA Models - FinesseV2P

flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora

Text to Image Community ModelFree for Premium Users LLMs.txt

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

API Playground API Documentation

API Endpoint URL

Base URL for all API requests to this endpoint.

https://modelslab.com/api/v6/images/text2img

API Authentication

Authentication requires a valid API key included in the request. Generate and manage your API keys from your developer dashboard. Include the key in the key parameter for all API requests.

Integration Examples

Production-ready code samples for API integration

{
  "prompt": "R3alisticF, hauntingly beautiful oriental necromancer, long flowing brown hair, bangs, darkly tanned skin, earrings, bone necklaces, dark eyeshadow, red lips, vibrant, front-laced transparent, filmy silk blouse, cleavage, holding skull, in a sandstone room lit by candles, High Detail, Perfect Composition, high contrast, silhouetted, chiascuro",
  "model_id": "fluxdev",
  "lora_model": "flux1devfp8nf4gguf6stepshybrid4stepssvdquantint4flux1devloramodels-finessev2pf16lora",
  "width": "1024",
  "height": "1024",
  "negative_prompt": "(worst quality:2), (low quality:2), (normal quality:2), (jpeg artifacts), (blurry), (duplicate), (morbid), (mutilated), (out of frame), (extra limbs), (bad anatomy), (disfigured), (deformed), (cross-eye), (glitch), (oversaturated), (overexposed), (underexposed), (bad proportions), (bad hands), (bad feet), (cloned face), (long neck), (missing arms), (missing legs), (extra fingers), (fused fingers), (poorly drawn hands), (poorly drawn face), (mutation), (deformed eyes), watermark, text, logo, signature, grainy, tiling, censored, nsfw, ugly, blurry eyes, noisy image, bad lighting, unnatural skin, asymmetry",
  "num_inference_steps": "31",
  "scheduler": "DPMSolverMultistepScheduler",
  "guidance_scale": "7.5",
  "enhance_prompt": false,
  "key": "YOUR_API_KEY"
}

SDKs

Official SDKs

Production-ready SDKs and client libraries for all major programming languages

JavaScript

Python

PHP

API Parameters

Technical specifications for API request parameters.

Field Name	Parameter	Technical Description
prompt	`prompt`	prompt help in image generation
Model	`model_id`	Enter model_id that can help in image generation
lora Model	`lora_model`	No description available
Width	`width`	width of the image
Height	`height`	height of the image
Negative Prompt	`negative_prompt`	Negative prompt help in avoid things that you do not want in image
Steps	`num_inference_steps`	Number of inference steps
Scheduler	`scheduler`	Sampling scheduler
Guidance Scale	`guidance_scale`	How closely to follow the prompt (1-10)
enhance_prompt	`enhance_prompt`	Automatically enhance the prompt