Flux-Microworld-NFT-LoRA Open-source Model - Freely Generate Miniature World NFT-style Images

Flux Microworld NFT LoRA

Developed by strangerzonehf

A LoRA model fine-tuned based on the FLUX.1-dev base model, specifically designed for generating Microworld NFT-style images.

Image Generation Open Source License:Openrail #Miniature Scene Generation #High Contrast White Background #LoRA Adapted Diffusion Model

Downloads 30

Release Time : 12/8/2024

Model Overview

This model utilizes LoRA (Low-Rank Adaptation) technology to fine-tune the FLUX.1-dev base model, focusing on generating high-quality Microworld NFT-style images with support for various scene descriptions.

Model Features

Microworld NFT Style

Optimized specifically for generating Microworld NFT-style images, capable of producing miniature model images with sharp contrasts and intricate details.

LoRA Technology

Employs LoRA (Low-Rank Adaptation) technology for fine-tuning the base model, reducing computational resource requirements while maintaining model performance.

Multi-Scene Support

Supports generating miniature model images for various scenes, including mountains, forests, street markets, and rural cottages.

Model Capabilities

Text-to-Image Generation

Microworld NFT-Style Image Generation

Multi-Scene Image Generation

Use Cases

Digital Art

Microworld NFT Creation

Generate Microworld NFT-style images for digital art creation and NFT distribution.

High-quality miniature model images with sharp contrasts and intricate details.

Game Design

Game Scene Design

Generate miniature scenes for use in game design and development.

Diverse miniature scene images suitable for game backgrounds or prop design.

🚀 Flux-Microworld-NFT-LoRA

This is a LoRA model for text-to-image generation, which can generate various Microworld NFT images based on specific prompts.

🚀 Quick Start

Prerequisites

Before using this model, make sure you have installed the necessary libraries. You can set up the environment as follows:

import torch
from pipelines import DiffusionPipeline

base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)

lora_repo = "strangerzonehf/Flux-Microworld-NFT-LoRA"
trigger_word = "Microworld NFT"  
pipe.load_lora_weights(lora_repo)

device = torch.device("cuda")
pipe.to(device)

Image Generation

You should use the trigger word Microworld NFT to generate relevant images.

✨ Features

Text-to-Image Generation: Generate high - quality Microworld NFT images based on text prompts.
Flexible Configuration: Support adjustment of multiple image processing parameters to meet different generation requirements.

📦 Installation

The installation process mainly involves setting up the Python environment and loading the model. The specific steps are as follows:

import torch
from pipelines import DiffusionPipeline

base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)

lora_repo = "strangerzonehf/Flux-Microworld-NFT-LoRA"
trigger_word = "Microworld NFT"  
pipe.load_lora_weights(lora_repo)

device = torch.device("cuda")
pipe.to(device)

💻 Usage Examples

Basic Usage

# Assume you have completed the setup steps above
prompt = "Microworld NFT, a miniature model of a snow - covered mountain with a red pickup truck parked on the left side of the mountain. The model is set against a stark white backdrop, creating a stark contrast to the scene."
image = pipe(prompt).images[0]
image.save("generated_image.png")

📚 Documentation

Image Processing Parameters

Parameter	Value	Parameter	Value
LR Scheduler	constant	Noise Offset	0.03
Optimizer	AdamW	Multires Noise Discount	0.1
Network Dim	64	Multires Noise Iterations	10
Network Alpha	32	Repeat & Steps	20 & 2460
Epoch	12	Save Every N Epochs	1

Best Dimensions & Inference

Dimensions	Aspect Ratio	Recommendation
1280 x 832	3:2	Best
1024 x 1024	1:1	Default

Inference Range

Recommended Inference Steps: 30–35

Trigger words

You should use Microworld NFT to trigger the image generation.

📄 License

This model is released under the creativeml-openrail-m license.

Download model

Weights for this model are available in Safetensors format. Download them in the Files & versions tab.

Labeling: florence2 - en(natural language & English)

Total Images Used for Training: 18 [ HD ]

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