Name: Flat Color - Style
Rating: 5 (4734 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="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]

[WAN 1.3B] 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-1.3B">Wan2.1-T2V-1.3B</a><ul><li>Increased video training resolution to 512</li><li>Lowered video FPS to 16</li><li>Updated frame_buckets to match video frame counts</li></ul></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 fp16 1.3B <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_1.3B_fp16.safetensors">wan2.1_t2v_1.3B_fp16.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>dataset.toml<pre><code># Resolution settings.
resolutions = [512]

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

# Frame buckets (1 is for images)
frame_buckets = [1]

[[directory]] # IMAGES
# Path to the directory containing images and their corresponding caption files.
path = '/mnt/d/huanvideo/training_data/images'
num_repeats = 5
resolutions = [720]
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 = [512] # Set video resolution
frame_buckets = [28, 31, 32, 36, 42, 43, 48, 50, 53]</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

# 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 = '../Wan2.1-T2V-1.3B'
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-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

Type	LoRA
Stats	323 10
Reviews	Very Positive (52)
Published	Jan 28, 2026
Base Model	ZImageBase
Training	Steps: 6,500 Epochs: 50
Trigger Words	flat color no lineart
Hash	AutoV2 8CBC55449E