Golden-Dust-Flux-LoRA Open-Source Image Generation Model - Free Generation of High-Quality Images with Golden Particle Effects

Golden Dust Flux LoRA

Developed by prithivMLmods

This is a LoRA fine-tuned text-to-image diffusion model, specializing in generating high-quality images with golden particle effects.

Image Generation Open Source License:Openrail #Golden particle effects #LoRA fine-tuned diffusion model #High saturation colors

Downloads 832

Release Time : 11/6/2024

Model Overview

The model is optimized through LoRA fine-tuning technology on the base diffusion model, capable of generating visual content with golden dust effects based on text descriptions, particularly suitable for creating images with festive atmospheres or artistic appeal.

Model Features

Golden particle effects

The model excels at generating images with golden dust or light spot effects, adding a festive or artistic atmosphere to the scene.

LoRA fine-tuning technology

Utilizes LoRA (Low-Rank Adaptation) technology for efficient fine-tuning of the base diffusion model, enhancing specific style generation capabilities while keeping the model lightweight.

High-quality close-up generation

Particularly suitable for generating detailed images such as facial close-ups and food close-ups.

Model Capabilities

Text-to-image generation

Artistic style image generation

Special effects image generation

Use Cases

Artistic creation

Festive theme images

Generate images with golden particle effects for a festive atmosphere

As shown in the example, a close-up of a golden retriever with orange light spots around its neck fur.

Food photography

Generate artistic food close-up images

As shown in the example, a close-up of a cheeseburger with sesame seeds on the bun having a sparkling effect.

Portrait photography

Artistic portraits

Generate portrait close-ups with golden glow effects

As shown in the example, a close-up of a woman's face adorned with golden highlights.

🚀 Golden-Dust-Flux-LoRA

This is a text-to-image LoRA model that can generate various images triggered by the keyword "Golden Dust". It's still in the training phase and may have some performance issues.

🚀 Quick Start

Setting Up

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 = "prithivMLmods/Golden-Dust-Flux-LoRA"
trigger_word = "Golden Dust"  
pipe.load_lora_weights(lora_repo)

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

✨ Features

Text-to-Image Generation: Generate images based on text prompts.
Trigger Word: Use the trigger word "Golden Dust" to generate specific images.

📦 Installation

The weights for this model are available in Safetensors format. You can download them in the Files & versions tab.

💻 Usage Examples

Basic Usage

You should use Golden Dust to trigger the image generation. For example, you can use the following code after setting up the model:

# Assume you have set up the pipe as above
prompt = "Golden Dust, A beautiful landscape"
image = pipe(prompt).images[0]
image.save("generated_image.png")

📚 Documentation

Model description

prithivMLmods/Golden-Dust-Flux-LoRA

Property	Details
Base Model	black-forest-labs/FLUX.1-dev
Instance Prompt	Golden Dust
License	creativeml-openrail-m
LR Scheduler	constant
Noise Offset	0.03
Optimizer	AdamW
Multires Noise Discount	0.1
Network Dim	64
Network Alpha	32
Epoch	15
Save Every N Epochs	1
Multires Noise Iterations	10
Repeat & Steps	25 & 2500
Labeling	florence2 - en(natural language & English)
Total Images Used for Training	17 [Hi - RES]

Gallery

Hosted Here🧨: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC

Other Sample Image

prithivMLmods/Golden-Dust-Flux-LoRA

Best Dimensions

1024 x 1024 (Default)

Data source

https://freeflo.ai/

Trigger words

You should use Golden Dust to trigger the image generation.

📄 License

The model is licensed under creativeml-openrail-m.

⚠️ Important Note

The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.

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