Name: Aesthetic Quality Modifiers - Masterpiece
Rating: 5 (9309 reviews)
Author: motimalu

physical violence

revealing clothes

weapon violence

pg-13

corpses

wide hips

convenient censoring

oral invitation

thick thighs

huge breasts

downblouse

suggestive

sexy

pg13

sexual situations

disturbing

male nudity

female swimwear or underwear

male swimwear or underwear

partial nudity

graphic violence or gore

emaciated bodies

exposed female nipple

female nudity

undressed

male underwear

female swimwear

female underwear

breasts out

strapless leotard

breast out

one breast out

gigantic breasts

huge butt

covered nipples

hair over breasts

no panties

sitting on face

nude

lingerie

nsfw

adult toys

nudity

graphic male nudity

illustrated explicit nudity

graphic female nudity

sexual intent

genitals

porn

futanari

hentai

peeing

blowjob

sexual activity

vore

anal

dildo riding

oral

incest

hanging

hate symbols

nazi party

white supremacy

self injury

extremist

hate speech

diapers

urine

scat

child on child

bukkake

fellatio

bikini

cumshot

implied fellatio

eat_cum

cumdrip

cum in pussy

cum on face

after fellatio

cum on hair

cum on body

cum on tongue

cum on hands

cum in mouth

<h1 id="aesthetic-quality-modifiers-masterpiece">Aesthetic Quality Modifiers - Masterpiece</h1>Training data is a subset of all my manually rated datasets with the quality/aesthetic modifiers, including only the <code>masterpiece</code> tagged images.Subset in the <a target="_blank" rel="ugc" href="https://civitai.com/collections/6456031">Aesthetic Quality Modifiers Collection</a>.ℹ️ LoRA work best when applied to the base models on which they are trained. Please read the About This Version on the appropriate base models, trigger usage, and workflow/training information.Generation Settings:Previews are generated in Forge with upscaling and adetailer.For Noobai V-Pred, a ComfyUI workflow reference with DynamicThresholding, Upscaling, and FaceDetailer can be found here: <a target="_blank" rel="ugc" href="https://civitai.com/posts/11457095">https://civitai.com/posts/11457095</a>

Trained on <a target="_blank" rel="ugc" href="https://civitai.com/models/2458426/anima-official">Anima Preview 2</a>Assume that any lora trained on the preview version won't work well on the final version.Recommended prompt structure:Positive prompt (quality tags at the start of prompt):<pre><code>masterpiece, best quality, very aesthetic, {{tags}}</code></pre>Slightly updated dataset of 96 images, trained at 1024 x 1024 and 1536 x 1024 resolutions, previews are mostly generated at 1536 x 1024 or 1024 x 1536 .Used <a target="_blank" rel="ugc" href="https://github.com/bluvoll/diffusion-pipe">diffusion-pipe</a> - fork by @bluvollConfig:<pre><code># Resolution settings.
resolutions = [[1280, 720], [1536, 1024]]

# Aspect ratio bucketing settings
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7

[[directory]] # IMAGES
# Path to the directory containing images and their corresponding caption files.
path = '/mnt/d/training_data/images'
num_repeats = 1
resolutions = [[1280, 720], [1536, 1024]]</code></pre><pre><code># Config for RTX 6000 Pro 96GB
# Run with NCCL_P2P_DISABLE="1" NCCL_IB_DISABLE="1" NCCL_CUMEM_ENABLE="0" deepspeed --num_gpus=1 train.py --deepspeed --config config-anima.toml

# Change these paths
output_dir = '/mnt/d/anima/training_output'
dataset = 'dataset-anima.toml'

# training settings
epochs = 50
micro_batch_size_per_gpu = 24
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1.0
warmup_steps = 150
train_llm_adapter = true

# eval settings
eval_every_n_epochs = 1
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

# misc settings
save_every_n_epochs = 1
checkpoint_every_n_epochs = 1
# checkpoint_every_n_minutes = 60
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 1
steps_per_print = 1

[model]
type = 'anima'

transformer_path = '/mnt/c/models/diffusion_models/anima-preview2.safetensors'
vae_path = '/mnt/c/models/vae/qwen_image_vae.safetensors'
qwen_path = '../qwen0.6/Qwen3-0.6B/'
dtype = 'bfloat16'
timestep_sample_method = 'logit_normal'
sigmoid_scale = 1.0
shift = 3.0

# Caption Processing Options
cache_text_embeddings = false
# NOTE: Requires cache_text_embeddings = false to work!
# For cached embeddings, use cache_shuffle_num in your dataset config instead.
shuffle_tags = true
tag_delimiter = ', '
keep_first_n_tags = 5
shuffle_keep_first_n = 5
tag_dropout_percent = 0.3
protected_tags_file = './protected_tags.txt'

nl_shuffle_sentences = false
nl_keep_first_sentence = true

# 'tags' 'nl' 'mixed'
caption_mode = 'mixed'

debug_caption_processing = false
debug_caption_interval = 1000

llm_adapter_lr = 6e-5 # 1e-5 base

[adapter]
type = 'lora'
rank = 32
dtype = 'bfloat16'

# AdamW from the optimi library is a good default since it automatically uses Kahan summation when training bfloat16 weights.
[optimizer]
type = 'adamw_optimi'
lr = 3.5e-4 # 2e-5 base
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-7 # 1e-8 base</code></pre>

v4.1 [anima-preview-2]

Trained on <a target="_blank" rel="ugc" href="https://huggingface.co/circlestone-labs/Anima">Anima Preview</a>Assume that any lora trained on the preview version won't work well on the final version.Recommended prompt structure:Positive prompt (quality tags at the start of prompt):<pre><code>masterpiece, best quality, very aesthetic, {{tags}}</code></pre>Updated dataset and reduced to 78 images, adjusted captions for Anima with a mix of NL and tags.Used <a target="_blank" rel="ugc" href="https://github.com/bluvoll/diffusion-pipe">diffusion-pipe</a> - fork by @bluvollConfig:<pre><code># dataset-anima.toml
# Resolution settings.
resolutions = [1024]

# Aspect ratio bucketing settings
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7

# config-anima.toml
[[directory]] # IMAGES
# Path to the directory containing images and their corresponding caption files.
path = '/mnt/d/huanvideo/training_data/images'
num_repeats = 1
resolutions = [1024]

# Change these paths
output_dir = '/mnt/d/anima/training_output'
dataset = 'dataset-anima.toml'

# training settings
epochs = 50
micro_batch_size_per_gpu = 6
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1.0
warmup_steps = 100

# eval settings
eval_every_n_epochs = 1
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

# misc settings
save_every_n_epochs = 1
checkpoint_every_n_minutes = 120
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 1
steps_per_print = 1


[model]
type = 'anima'

transformer_path = '/mnt/c/models/diffusion_models/anima-preview.safetensors'
vae_path = '/mnt/c/models/vae/qwen_image_vae.safetensors'
qwen_path = '../qwen0.6/Qwen3-0.6B/'
dtype = 'bfloat16'
timestep_sample_method = 'logit_normal'
sigmoid_scale = 1.0
shift = 3.0

# Caption Processing Options
cache_text_embeddings = false
# NOTE: Requires cache_text_embeddings = false to work!
# For cached embeddings, use cache_shuffle_num in your dataset config instead.
shuffle_tags = true
tag_delimiter = ', '
keep_first_n_tags = 5
shuffle_keep_first_n = 5
tag_dropout_percent = 0.10
protected_tags_file = './protected_tags.txt'

nl_shuffle_sentences = true
nl_keep_first_sentence = false

# 'tags' 'nl' 'mixed'
caption_mode = 'mixed'

debug_caption_processing = true
debug_caption_interval = 100

[adapter]
type = 'lora'
rank = 32
dtype = 'bfloat16'

# AdamW from the optimi library is a good default since it automatically uses Kahan summation when training bfloat16 weights.
[optimizer]
type = 'adamw_optimi'
lr = 5e-5
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-8</code></pre>

v4.0 [anima-preview]

Trained on <a target="_blank" rel="ugc" href="https://huggingface.co/Tongyi-MAI/Z-Image-Turbo">Z Image Turbo</a>With a much smaller dataset of 78 images to prevent degradation at higher step counts

v3.2 [z-image-turbo]

Trained on <a target="_blank" rel="ugc" href="https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B">FLUX.2-klein-base-4B</a>

v3.1 [klein-4b]

Trained on <a target="_blank" rel="ugc" href="https://civitai.com/models/795765/illustrious-xl">Illustrious v0.1</a>Included newly rated images.Dataset size: 332 images.Recommended prompt structure:Positive prompt (quality tags at the end of prompt):<pre><code>{{tags}}
masterpiece, best quality, very aesthetic</code></pre>

v3.0 [illustrious]

Trained on <a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image">Qwen Image</a>Using <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe/blob/4a61bcfd82eb8d54a807ac7a15f548fab73ae5e1/examples/qwen_image_24gb_vram.toml">diffusion-pipe qwen config</a>Dataset resolution of 640Previews generated with the <a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-4steps-V1.0.safetensors">lightx2v Lightning 4step LoRA</a>

v3.0 [qwen]

Trained on <a target="_blank" rel="ugc" href="https://civitai.com/models/833294/noobai-xl-nai-xl?modelVersionId=1190596">NoobAI-XL (NAI-XL) V-Pred 1.0-Version</a>Recommended prompt structure:Positive prompt (quality tags at the end of prompt):<pre><code>{{tags}}
masterpiece, best quality, very aesthetic</code></pre>Included many newly rated images and small training/tagging updates:<ul><li>change<code>min_snr_gamma</code> from 5 to 0<ul><li>should produce closer lighting/blacks to base model</li></ul></li><li>change <code>very awa</code> tag back to <code>very aesthetic</code><ul><li>reverted this change from previous version v1.0</li></ul></li><li>tag <code>nsfw</code> <code>explicit</code> with imgutils anime_rating</li></ul>Previews are generated in Forge with DynamicThresholding (CFG-Fix) Integrated enabled, settings:<pre><code>dynthres_enabled: True, dynthres_mimic_scale: 7, dynthres_threshold_percentile: 1, dynthres_mimic_mode: Half Cosine Down, dynthres_mimic_scale_min: 1, dynthres_cfg_mode: Half Cosine Down, dynthres_cfg_scale_min: 3, dynthres_sched_val: 1, dynthres_separate_feature_channels: enable, dynthres_scaling_startpoint: ZERO, dynthres_variability_measure: STD, dynthres_interpolate_phi: 1</code></pre>A ComfyUI workflow reference with DynamicThresholding, Upscaling, and FaceDetailer can be found here: <a target="_blank" rel="ugc" href="https://civitai.com/posts/11457095">https://civitai.com/posts/11457095</a>

v2.3 [noobai-v-pred-1]

[WAN 14B] LoRA (experimental)<ul><li>Trained with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a> on <a target="_blank" rel="ugc" href="https://huggingface.co/Wan-AI/Wan2.1-T2V-14B">Wan2.1-T2V-14B</a> with the same (image-only) dataset as v2.3 [noobai v-pred]<ul><li>Currently curating a video dataset</li></ul></li><li>Video previews generated with <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/wan/#text-to-video">ComfyUI_examples/wan/#text-to-video</a><ul><li>Loading the LoRA with LoraLoaderModelOnly node and using the fp8 14B: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_14B_fp8_e4m3fn.safetensors">wan2.1_t2v_14B_fp8_e4m3fn.safetensors</a></li><li>Higher quality previews use the full fp16 14b: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_14B_fp16.safetensors">wan2.1_t2v_14B_fp16.safetensors</a></li><li>Recommend following prompting guide for movement to avoid still images/jitter: <a target="_blank" rel="ugc" href="https://www.comfyonline.app/blog/wan2-1-prompt-guide">https://www.comfyonline.app/blog/wan2-1-prompt-guide</a></li></ul></li><li>Image previews generated with modified <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/wan/#text-to-video">ComfyUI_examples/wan/#text-to-video</a><ul><li>Setting the frame length to 1</li><li>Adding Upscaling</li></ul></li><li>Better results with text-to-image than text-to-video for this version (due to training on images only)</li></ul>

v2.0-alpha [wan-t2v-14b]

[WAN 1.3B] LoRA (experimental)<ul><li>Trained with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a> on <a target="_blank" rel="ugc" href="https://huggingface.co/Wan-AI/Wan2.1-T2V-14B">Wan2.1-T2V-14B</a> with the same (image-only) dataset as v2.0-alpha [WAN 14b]<ul><li>Currently curating a video dataset</li></ul></li><li>Video previews generated with <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/wan/#text-to-video">ComfyUI_examples/wan/#text-to-video</a><ul><li>Loading the LoRA with LoraLoaderModelOnly node</li><li>Recommend following prompting guide for movement to avoid still images/jitter: <a target="_blank" rel="ugc" href="https://www.comfyonline.app/blog/wan2-1-prompt-guide">https://www.comfyonline.app/blog/wan2-1-prompt-guide</a></li></ul></li></ul>

v2.0-alpha [wan-t2v-1.3b]

Trained on <a target="_blank" rel="ugc" href="https://civitai.com/models/833294/noobai-xl-nai-xl?modelVersionId=1190596">NoobAI-XL (NAI-XL) V-Pred 1.0-Version</a>Recommended prompt structure:Positive prompt (quality tags at the end of prompt):<pre><code>{{tags}}
masterpiece, best quality, very aesthetic</code></pre>With the kohya_ss dev branch, <code>v_paratemization</code> ,<code>zero_terminal_ssr</code> enabled, and noise offet set to 0.Included some newly rated images and small updates to match the noobai tagging:<ul><li><code>by {artist}</code> -&gt; <code>artist:{artist}</code></li><li><code>very aesthetic</code> -&gt; <code>very awa</code></li></ul>Previews are generated in Forge with DynamicThresholding (CFG-Fix) Integrated enabled, settings:<pre><code>dynthres_enabled: True, dynthres_mimic_scale: 7, dynthres_threshold_percentile: 1, dynthres_mimic_mode: Half Cosine Down, dynthres_mimic_scale_min: 1, dynthres_cfg_mode: Half Cosine Down, dynthres_cfg_scale_min: 3, dynthres_sched_val: 1, dynthres_separate_feature_channels: enable, dynthres_scaling_startpoint: ZERO, dynthres_variability_measure: STD, dynthres_interpolate_phi: 1</code></pre>

v1.0 [noobai-v-pred-1]

Trained on NoobAI-XL (NAI-XL) <a target="_blank" rel="ugc" href="https://civitai.com/models/833294?modelVersionId=1022833">Epsilon-pred 1.0-Version</a>Recommended prompt structure:Positive prompt (quality tags at the end of prompt):<pre><code>{{tags}}
masterpiece, best quality, very aesthetic</code></pre>

Type	LoRA
Stats	8,549 1.3M 227.3K
Reviews	Overwhelmingly Positive (1,642)
Published	Jan 11, 2025
Base Model	NoobAI
Training	Steps: 1,620 Epochs: 30
Usage Tips	Clip Skip: 2 Strength: 1
Trigger Words	masterpiece, best quality, very awa, absurdres
Hash	AutoV2 FEEA184C0A