Luminia v3 is good at reasoning to enhance Stable Diffusion prompt from short summary description, may output NSFW content.
LoRa is include and Quants: exllamav2 2.4bpw-h6, 4.25bpw-h6, 8.0bpw-h8 | GGUF Q4_K_M, IQ4_NL |
Prompt template: Alpaca
Output example tested In text-generation-webui
| Input | base llama-2-chat | QLoRa |
|---|---|---|
| [question]: Create stable diffusion metadata based on the given english description. Luminia \n### Input:\n favorites and popular SFW |
Answer: Luminia, a mystical world of wonder and magic 🧝♀️✨ A place where technology and nature seamlessly blend together ... |
Answer! < lora:Luminari-10:0.8> Luminari, 1girl, solo, blonde hair, long hair, blue eyes, (black dress), looking at viewer, night sky, starry sky, constellation, smile, upper body, outdoors, forest, moon, tree, mountain, light particle .... |
Output prompt from QLoRa to A1111/SD-WebUI:
Full Prompt
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Create stable diffusion metadata based on the given english description. Luminia
### Input:
favorites and popular SFW
### Response:
"Luminia" can be any short description, more info on my SD dataset here.
Training Details
Click to see details
Model Description
Train by: Nekochu, Model type: Llama, Finetuned from model Llama-2-13b-chat
Continue from the base of LoRA Luminia-13B-v2-QLora
Know issue: [issue]
Trainer
hiyouga/LLaMA-Efficient-Tuning
Hardware: QLoRA training OS Windows, Python 3.10.8, CUDA 12.1 on 24GB VRAM.
Training hyperparameters
The following hyperparameters were used during training:
- num_epochs: 1.0
- finetuning_type: lora
- quantization_bit: 4
- stage: sft
- learning_rate: 5e-05
- cutoff_len: 4096
- num_train_epochs: 3.0
- max_samples: 100000
- warmup_steps: 0
- train_batch_size: 1
- distributed_type: single-GPU
- num_devices: 1
- warmup_steps: 0
- rope_scaling: linear
- lora_rank: 32
- lora_target: all
- lora_dropout: 0.15
- bnb_4bit_compute_dtype: bfloat16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
training_loss:
Framework versions
- PEFT 0.9.0
- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.14.5
- Tokenizers 0.15.0
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