Flux LoRA Collections
Collection
Flux THE LoRA
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64 items
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Updated
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21
The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.
prithivMLmods/Flux-Realism-FineDetailed
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 | 28 & 3400 |
Epoch | 15 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 27
Here's a table summarizing the relevant information about the Flux-Super-Realism-LoRA
model on Hugging Face:
Feature | Details |
---|---|
Model Name | Flux-Super-Realism-LoRA |
Repository | strangerzonehf/Flux-Super-Realism-LoRA |
Author | strangerzonehf |
Description | Super-realism LoRA model designed to produce high-quality, hyper-realistic images using LoRA fine-tuning techniques. This model can generate lifelike textures, lighting, and intricate details. |
Model Type | LoRA (Low-Rank Adaptation for Transformers) |
Use Cases | - Photorealistic image generation - High-fidelity art - Texture detailing and enhancement |
Primary Language | Not applicable (model is image-based) |
Base Model | Model used as the foundation for LoRA fine-tuning (may vary per implementation) |
License | Refer to Hugging Face model page for specific licensing information. |
Tags | super-realism, LoRA, high-fidelity, hyper-realistic |
Usage | This model is typically used with tools like Hugging Face's Diffusers or other libraries supporting LoRA fine-tuning for enhanced realism in image generation. |
Pipeline | Use in StableDiffusionPipeline or compatible image generation pipelines. |
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)
Trigger words: You should use
Fine Detailed
to trigger the image generation.
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Base model
black-forest-labs/FLUX.1-dev