Flux Realism Fine Detailed LLM Model

Introduction

Flux-Realism-FineDetailed is a text-to-image model designed to produce fine-detailed, realistic images. It utilizes LoRA (Low-Rank Adaptation) techniques and is part of the Flux.1-dev series. This model is hosted on Hugging Face and is suitable for generating high-quality, realistic images with detailed textures and lighting.

Architecture

The model is based on the FLUX.1-dev architecture, utilizing LoRA for improved realism in image generation. It employs a constant learning rate scheduler and AdamW optimizer, with specific parameters like noise offset and multires noise settings to enhance output quality.

Training

The training process involves:

Scheduler: Constant
Optimizer: AdamW
Network Dimensions: 64
Alpha: 32
Epochs: 15
Total Images: 27

Additional parameters include multires noise iterations and discount values. The model is labeled using natural language in English (florence2-en).

Guide: Running Locally

To run the model locally, follow these steps:

Setup Environment: Ensure you have Python and PyTorch installed.
Install Hugging Face Libraries: Use pip to install the required libraries.
```
pip install transformers diffusers torch
```

Load Model:

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/Flux-Realism-FineDetailed"
trigger_word = "Fine Detailed"
pipe.load_lora_weights(lora_repo)

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

Generate Images: Use the trigger word "Fine Detailed" to initiate image generation.

Cloud GPUs

For enhanced performance, consider using cloud solutions like AWS EC2 with GPU instances or Google Cloud's AI Platform.

License

Flux-Realism-FineDetailed is distributed under the creativeml-openrail-m license. Ensure to review the specific licensing terms on the Hugging Face model page.

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