open-image-preferences-v1-flux-dev-lora Open Source Model - Freely Assist in Generating Exquisite Images from Text

Open Image Preferences V1 Flux Dev Lora

Developed by data-is-better-together

DreamBooth LoRA weights based on FLUX.1-schnell for text-to-image generation tasks

Image Generation Open Source License:Other #Multi-style Art Generation #High-resolution Image Synthesis #LoRA Fine-tuning Adaptation

Downloads 94

Release Time : 12/4/2024

Model Overview

This is a DreamBooth LoRA model based on black-forest-labs/FLUX.1-schnell, trained using the diffusers library, primarily for text-to-image generation tasks.

Model Features

DreamBooth LoRA Weights

LoRA weights trained using DreamBooth technology with the Flux diffusers trainer

Diverse Trigger Words Support

Supports various trigger words such as cinematic, photographic, anime, manga, etc., to generate images in different styles

High-quality Image Generation

Capable of generating high-resolution, sharply focused images with rich textures and dynamic effects

Model Capabilities

Text-to-Image Generation

Stylized Image Generation

High-resolution Image Generation

Use Cases

Creative Design

Anime-style Image Generation

Generate anime-style images based on text descriptions

Example images such as bustling manga streets, orange poppies, etc.

Art Style Transformation

Convert text descriptions into images with specific art styles

Example images such as watercolor-style Venice canals, oil painting-style flowers, etc.

Educational Demonstration

Scientific Diagram Generation

Generate labeled scientific illustrations

Example images such as anatomical diagrams of armored armadillos

🚀 Flux DreamBooth LoRA - data-is-better-together/image-preferences-flux-dev-lora

These DreamBooth LoRA weights are designed to enhance the image generation capabilities of black-forest-labs/FLUX.1-schnell, offering diverse styles and high - quality outputs.

🚀 Quick Start

Model Information

Property	Details
Base Model	black-forest-labs/FLUX.1-dev
Library Name	diffusers
License	other
Tags	text-to-image, diffusers-training, diffusers, lora, flux, flux-diffusers, template:sd-lora
Datasets	data-is-better-together/image-preferences-results-binarized, data-is-better-together/open-image-preferences-v1-results

Gallery

✨ Features

These are davidberenstein1957/image-preferences-flux-schnell-lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-schnell.
The weights were trained using DreamBooth with the Flux diffusers trainer.
LoRA for the text encoder was not enabled (False).

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

You should use ["Cinematic", "Photographic", "Anime", "Manga", "Digital art", "Pixel art", "Fantasy art", "Neonpunk", "3D Model", "Painting", "Animation", "Illustration"] to trigger the image generation.

from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('davidberenstein1957/image-preferences-flux-dev-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('["Cinematic", "Photographic", "Anime", "Manga", "Digital art", "Pixel art", "Fantasy art", "Neonpunk", "3D Model", "Painting", "Animation", "Illustration"]').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

Download Model

Download the *.safetensors LoRA in the Files & versions tab.

📚 Documentation

Intended Uses & Limitations

How to use

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]

📄 License

Please adhere to the licensing terms as described here.

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