Name: Flat Color - Style
Rating: 5 (5313 reviews)
Author: motimalu

physical violence

weapon violence

wide hips

revealing clothes

thick thighs

downblouse

convenient censoring

huge breasts

pg-13

corpses

suggestive

oral invitation

pg13

sexy

sexual situations

male nudity

disturbing

male swimwear or underwear

female swimwear or underwear

partial nudity

undressed

female nudity

breasts out

exposed female nipple

breast out

lingerie

male underwear

hair over breasts

female swimwear

gigantic breasts

no panties

graphic violence or gore

covered nipples

huge butt

strapless leotard

sitting on face

emaciated bodies

one breast out

nsfw

female underwear

nude

graphic male nudity

adult toys

illustrated explicit nudity

nudity

graphic female nudity

hentai

futanari

porn

sexual intent

genitals

peeing

vore

oral

sexual activity

anal

blowjob

dildo riding

incest

hanging

hate symbols

nazi party

white supremacy

diapers

scat

self injury

hate speech

urine

extremist

child on child

latex clothing

swimwear

bukkake

fellatio

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

triple fellatio

autofellatio

fucked silly

cum on pussy

<h1 id="flat-color-style">Flat Color - Style</h1>Trained on images without visible lineart, flat colors, and little to no indication of depth.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 and workflow/training information.This is a small style LoRA I thought would be interesting to try with a v-pred model (noobai v-pred), for the reduced color bleeding and strong blacks in particular.The effect is quite nice and easy to evaluate in training, so I've extended the dataset with videos in following versions for text-to-video models like Wan and Hunyuan, and it is what I am generally using to test LoRA training on new models now.Recommended prompt structure:Positive prompt:<pre><code>flat color, no lineart, blending, negative space,
{{tags}}
masterpiece, best quality, very aesthetic, newest</code></pre>

Trained on <a target="_blank" rel="ugc" href="https://huggingface.co/Tongyi-MAI/Z-Image">Z Image Base </a>with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a>Same dataset/training settings as used for version <code>2.1 [z-image-turbo]</code>Training Config:<pre><code># dataset-zimage.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

[[directory]] # IMAGES
path = '/training_data/images'
num_repeats = 5
resolutions = [1024]

# config-zimage-base.toml

output_dir = '/mnt/d/zimage/training_output'
dataset = 'dataset-zimage.toml'

# training settings
epochs = 50
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1

# eval settings
eval_every_n_epochs = 1
#eval_every_n_steps = 100
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

# misc settings
save_every_n_epochs = 5
checkpoint_every_n_minutes = 120
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 8
steps_per_print = 1

[model]
type = 'z_image'
diffusion_model = '/diffusion_models/z_image_bf16.safetensors'
vae = '/models/vae/ae.safetensors'
text_encoders = [
 {path = '/models/text_encoders/qwen_3_4b.safetensors', type = 'lumina2'}
]
dtype = 'bfloat16'
#diffusion_model_dtype = 'float8'

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

[optimizer]
type = 'AdamW8bitKahan'
lr = 2e-5
betas = [0.9, 0.99]
weight_decay = 0.01</code></pre>

v2.1 [z-image-base]

Trained on <a target="_blank" rel="ugc" href="https://huggingface.co/Tongyi-MAI/Z-Image-Turbo">Z Image Turbo </a>with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a>Training Config:<pre><code># dataset-zimage.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

[[directory]] # IMAGES
path = '/mnt/d/huanvideo/training_data/images'
num_repeats = 5
resolutions = [1024]

# config-zimage-base.toml

output_dir = '/mnt/d/zimage/training_output'
dataset = 'dataset-zimage.toml'

# training settings
epochs = 50
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1

# eval settings
eval_every_n_epochs = 1
#eval_every_n_steps = 100
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

# misc settings
save_every_n_epochs = 5
checkpoint_every_n_minutes = 120
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 8
steps_per_print = 1

[model]
type = 'z_image'
diffusion_model = '/diffusion_models/z_image_bf16.safetensors'
vae = '/models/vae/ae.safetensors'
text_encoders = [
 {path = '/models/text_encoders/qwen_3_4b.safetensors', type = 'lumina2'}
]
# Use if training Z-Image-Turbo
merge_adapters = ['/models/loras/zimage_turbo_training_adapter_v2.safetensors']
dtype = 'bfloat16'
#diffusion_model_dtype = 'float8'

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

[optimizer]
type = 'AdamW8bitKahan'
lr = 2e-5
betas = [0.9, 0.99]
weight_decay = 0.01</code></pre>

v2.1 [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>with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">diffusion-pipe</a>

v2.1 [klein-4b]

Trained on <a target="_blank" rel="ugc" href="https://huggingface.co/Qwen/Qwen-Image">Qwen Image</a>Using the default <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 lightx2v Lighting 4step LoRA:<a target="_blank" rel="ugc" href="https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-4steps-V1.0.safetensors">https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-4steps-V1.0.safetensors</a>

v2.0 [qwen]

Trained on <a target="_blank" rel="ugc" href="https://civitai.com/models/795765/illustrious-xl">Illustrious XL 0.1</a>With the updated dataset for v2

v2.0 [Illustrious]

[WAN 14B] LoRA<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 config as the v2.0 [hunyuan] version</li><li>Seems usable for both text-to-video and image-to-video workflows with Wan</li><li>Text to 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>* it is recommended to use the newer scaled 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_scaled.safetensors">wan2.1_t2v_14B_fp8_scaled.safetensors</a></li></ul></li><li>Image to Video previews generated with <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/wan/#image-to-video">ComfyUI_examples/wan/#image-to-video</a><ul><li>Using image generations from the IL/Noobai versions of this model card</li></ul></li></ul>

v2.0 [wan-t2v-14b]

v2 changelog:- large dataset update to better target <code>flat color</code> and <code>no lineart</code> styles and techniques, including <code>blending</code>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>With the kohya_ss dev branch, <code>v_paratemization</code> ,<code>zero_terminal_ssr</code> enabled, and noise offet set to 0.Previews are generated in Forge with DynamicThresholding (CFG-Fix) Integrated enabled, settings:<pre><code>dynthres_enabled: True, dynthres_mimic_scale: 5, 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>

v2.0 [noobai-v-pred-1]

[WAN 2.2 TI2V 5B] LoRA<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/Wan2.2-TI2V-5B">Wan2.2-TI2V-5B</a><ul><li>Experimental - first test for Wan 2.2 training</li><li>Image dataset only</li><li>Less effect at longer framerates</li></ul></li></ul><ul><li>Text to Video previews generated with <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/wan22/">ComfyUI_examples/wan22/#text-to-video</a><ul><li>Loading the LoRA with LoraLoaderModelOnly node</li></ul></li></ul>dataset.toml<pre><code># Resolution settings.
resolutions = [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 = 5
resolutions = [1024]</code></pre>config.toml<pre><code># Dataset config file.
output_dir = '/mnt/d/wan/training_output'
dataset = 'dataset.toml'

# Training settings
epochs = 50
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 4
gradient_clipping = 1.0
warmup_steps = 100
# blocks_to_swap=32

# eval settings
eval_every_n_epochs = 5
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

# misc settings
save_every_n_epochs = 5
checkpoint_every_n_minutes = 30
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 1
steps_per_print = 1
video_clip_mode = 'single_middle'

[model]
type = 'wan'
ckpt_path = '../Wan2.2-TI2V-5B' 
dtype = 'bfloat16'
# You can use fp8 for the transformer when training LoRA.
transformer_dtype = 'float8'
timestep_sample_method = 'logit_normal'

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

[optimizer]
type = 'adamw_optimi'
lr = 5e-5
betas = [0.9, 0.99]
weight_decay = 0.02
eps = 1e-8</code></pre>

v2.0 [wan-ti2v-5b]

v2.0 [wan-t2v-1.3b]

[HIDREAM] LoRA<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/HiDream-ai/HiDream-I1-Full">HiDream-I1-Full</a><ul><li>1480 steps on a 4090 over ~7hrs</li></ul></li><li>Previews generated with <a target="_blank" rel="ugc" href="https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-dev-workflow">ComfyUI_examples/hidream/#hidream-dev-workflow</a><ul><li>Loading the LoRA with LoraLoaderModelOnly node and using the HiDream DEV fp8 model: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/HiDream-I1_ComfyUI/blob/main/split_files/diffusion_models/hidream_i1_dev_fp8.safetensors">hidream_i1_dev_fp8.safetensors</a></li></ul></li></ul>config.toml<pre><code># Dataset config file.
output_dir = '/mnt/d/hidream/training_output'
dataset = 'dataset-hidream.toml'

# Training settings
epochs = 50
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 4
gradient_clipping = 1.0
warmup_steps = 100
blocks_to_swap = 20

# eval settings
eval_every_n_epochs = 5
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

# misc settings
save_every_n_epochs = 10
checkpoint_every_n_minutes = 30
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 1
steps_per_print = 1
video_clip_mode = 'single_beginning'

[model]
type = 'hidream'
diffusers_path = '../hidream-full'
llama3_path = '../llama-3.1'
llama3_4bit = true
dtype = 'bfloat16'
transformer_dtype = 'nf4'
max_llama3_sequence_length = 256
flux_shift = true

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

[optimizer]
type = 'adamw_optimi'
lr = 5e-5
betas = [0.9, 0.99]
weight_decay = 0.02
eps = 1e-8</code></pre>dataset.toml<pre><code># Resolution settings.
resolutions = [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/huanvideo/training_data/images'
num_repeats = 5
resolutions = [1024]</code></pre>

v2.0 [hidream]

Trained with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">https://github.com/tdrussell/diffusion-pipe</a>Training data consists of:<ul><li>42 images as a combination of<ul><li>Images used from other versions this model card</li><li>Images extracted as keyframes from several videos</li></ul></li><li>19 short video clips ~40 frames each</li></ul>Training configs:dataset.toml<pre><code># 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/huanvideo/training_data/images'
num_repeats = 5
resolutions = [1024]
frame_buckets = [1] # Use 1 frame for images.


[[directory]] # VIDEOS
# Path to the directory containing videos and their corresponding caption files.
path = '/mnt/d/huanvideo/training_data/videos'
num_repeats = 5
resolutions = [256] # Set video resolution to 256 (e.g., 244p).
frame_buckets = [33, 49, 81] # Define frame buckets for videos.</code></pre>config.toml<pre><code># Dataset config file.
output_dir = '/mnt/d/huanvideo/training_output'
dataset = 'dataset.toml'

# Training settings
epochs = 50
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 4
gradient_clipping = 1.0
warmup_steps = 100

# eval settings
eval_every_n_epochs = 5
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1

# misc settings
save_every_n_epochs = 15
checkpoint_every_n_minutes = 30
activation_checkpointing = true
partition_method = 'parameters'
save_dtype = 'bfloat16'
caching_batch_size = 1
steps_per_print = 1
video_clip_mode = 'single_middle'

[model]
type = 'hunyuan-video'

transformer_path = '/mnt/d/huanvideo/models/diffusion_models/hunyuan_video_720_cfgdistill_fp8_e4m3fn.safetensors'
vae_path = '/mnt/d/huanvideo/models/vae/hunyuan_video_vae_bf16.safetensors'
llm_path = '/mnt/d/huanvideo/models/llm'
clip_path = '/mnt/d/huanvideo/models/clip'

dtype = 'bfloat16'
transformer_dtype = 'float8'
timestep_sample_method = 'logit_normal'

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

[optimizer]
type = 'adamw_optimi'
lr = 5e-5
betas = [0.9, 0.99]
weight_decay = 0.02
eps = 1e-8</code></pre>

v2.0 [hunyuan]

Trained as a small experiment with <a target="_blank" rel="ugc" href="https://github.com/tdrussell/diffusion-pipe">https://github.com/tdrussell/diffusion-pipe</a>Gives a charming flat color style to Hunyuan t2v generations

v1.0 [hunyuan]

Trained on <a target="_blank" rel="ugc" href="https://civitai.com/models/795765/illustrious-xl">Illustrious XL 0.1</a>

v1.0 [Illustrious]

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>With the kohya_ss dev branch, <code>v_paratemization</code> ,<code>zero_terminal_ssr</code> enabled, and noise offet set to 0.

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

Flat Color - Style

motimalu-profile-vid.webp