Instructions to use Bedovyy/arcaillous-nbxl-v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Bedovyy/arcaillous-nbxl-v10 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Bedovyy/arcaillous-nbxl-v10", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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---
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license: other
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license_name: fair-ai-public-license-1.0-sd
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license_link: https://freedevproject.org/faipl-1.0-sd/
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language:
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- en
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library_name: diffusers
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pipeline_tag: text-to-image
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base_model: Laxhar/noobai-XL-1.0
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---
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license: other
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license_name: fair-ai-public-license-1.0-sd
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license_link: https://freedevproject.org/faipl-1.0-sd/
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language:
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- en
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library_name: diffusers
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pipeline_tag: text-to-image
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base_model: Laxhar/noobai-XL-1.0
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datasets:
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- pls2000/pixiv20161029_20241026_monthly_rank_1_50
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- pls2000/aiart_channel_nai3_geachu
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- cagliostrolab/860k-ordered-tags
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---
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## Training
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Trained in 2steps, `Lion8bit` for quick training and `Lion` for detail.
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- Tool: kohya-ss/sd-scripts
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- GPUs: 2x RTX3090
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### arcaillous-nbxl-v10b.safetensors
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```
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NCCL_P2P_DISABLE=1 NCCL_IB_DISABLE=1 accelerate launch --num_cpu_threads_per_process 8 sdxl_train.py $@ \
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--pretrained_model_name_or_path="/ai/data/sd/models/Stable-diffusion/noobaiXLNAIXL_epsilonPred10Version.safetensors" \
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--dataset_config="arca_nbxl.toml" \
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--output_dir="results/ckpt" --output_name="arcaillous-nbxl-v10b" \
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--save_model_as="safetensors" \
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--train_batch_size 4 --gradient_accumulation_steps 64 \
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--learning_rate=1e-5 --optimizer_type="Lion8bit" \
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--lr_scheduler="constant_with_warmup" --lr_warmup_steps 100 --optimizer_args "weight_decay=0.01" "betas=0.9,0.95" --min_snr_gamma 5 \
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--sdpa \
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--no_half_vae \
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--cache_latents --cache_latents_to_disk \
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--gradient_checkpointing \
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--full_bf16 --mixed_precision="bf16" --save_precision="fp16" \
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--ddp_timeout=10000000 \
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--max_train_epochs 4 --save_every_n_epochs 1 \
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--log_with wandb --log_tracker_name kohya-ss --wandb_run_name "arca_nbxl_`date +%y%m%d-%H%M`" --logging_dir wandb
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```
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### arcaillous-nbxl-v10.safetensors
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```
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NCCL_P2P_DISABLE=1 NCCL_IB_DISABLE=1 accelerate launch --num_cpu_threads_per_process 8 sdxl_train.py $@ \
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--pretrained_model_name_or_path="/ai/train/ckpt/arcaillous-nbxl-v10b.safetensors" \
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--dataset_config="arca_nbxl.toml" \
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--output_dir="results/ckpt" --output_name="arcaillous-nbxl-v10" \
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--save_model_as="safetensors" \
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--train_batch_size 1 --gradient_accumulation_steps 256 \
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--learning_rate=1e-5 --optimizer_type="Lion" \
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--lr_scheduler="constant_with_warmup" --lr_warmup_steps 100 --optimizer_args "weight_decay=0.01" "betas=0.9,0.95" \
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--min_snr_gamma 5 --ip_noise_gamma 0.05 --debiased_estimation_loss \
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--xformers \
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--no_half_vae \
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--cache_latents --cache_latents_to_disk \
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--gradient_checkpointing \
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--full_bf16 --mixed_precision="bf16" --save_precision="fp16" \
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--ddp_timeout=10000000 \
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--max_train_epochs 8 --save_every_n_epochs 1 --save_every_n_steps 200 \
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--log_with wandb --log_tracker_name kohya-ss --wandb_run_name "arca_nbxl_`date +%y%m%d-%H%M`" --logging_dir wandb
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```
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### arca_nbxl.toml
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```
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[general]
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shuffle_caption = true
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caption_tag_dropout_rate = 0.2
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keep_tokens_separator = "|||"
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caption_extension = ".txt"
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[[datasets]]
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enable_bucket = true
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min_bucket_reso = 512
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max_bucket_reso = 4096
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resolution = 1024
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[[datasets.subsets]]
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image_dir = "/ai/data/sd/datasets/danbooru-gs"
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num_repeats = 1
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[[datasets.subsets]]
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image_dir = "/storage/pls2000_pixiv20161029_20241026_monthly_rank_1_50/to_train"
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num_repeats = 1
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[[datasets.subsets]]
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image_dir = "/storage/aichan/to_train"
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num_repeats = 1
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```
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