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You can find detailed information about the versions in these articles:<ul><li><a target="_blank" rel="ugc" href="https://civitai.com/articles/18990">[Wan 2.1 T2V-14B]</a></li><li><a target="_blank" rel="ugc" href="https://civitai.com/articles/18999">[Wan 2.2 TI2V-5B]</a></li><li><a target="_blank" rel="ugc" href="https://civitai.com/articles/23124">[Wan 2.2 T2V-A14B]</a></li><li><a target="_blank" rel="ugc" href="https://civitai.com/articles/30259">[LTX-2.3]</a></li><li>(coming soon) [Minimax H3]</li></ul>

minimax H3

LTX-2.3

Training Detailsdataset.toml<pre><code>resolutions = [[192, 341] ,[480, 853], [600, 1067]]
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7
frame_buckets = [ 1, 16, 33]
[[directory]]
path = "/home/user/beauty_of_rain_dataset/videos"
num_repeats = 1</code></pre>train.toml<pre><code>output_dir = "/home/user/beauty_of_rain/14B"
dataset = "/home/user/beauty_of_rain_dataset_14B.toml"
epochs = 60
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1
warmup_steps = 10
eval_every_n_epochs = 1
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1
save_every_n_epochs = 3
activation_checkpointing = 'unsloth'
partition_method = "parameters"
save_dtype = "bfloat16"
caching_batch_size = 1
steps_per_print = 10
video_clip_mode = "multiple_overlapping"
blocks_to_swap = 32

[model]
type = "wan"
ckpt_path = "/home/user/Wan2.1-T2V-14B"
dtype = "bfloat16"
transformer_dtype = "float8"
timestep_sample_method = "logit_normal"

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

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

wan 2.1 t2v-14B

dataset.toml<pre><code>resolutions = [ [ 342, 192,], [ 854, 480,], [1067, 600]]
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7
frame_buckets = [ 1, 16, 30, 33]
[[directory]]
path = "/home/beauty_of_rain_dataset/videos"
num_repeats = 2</code></pre>train.toml<pre><code>output_dir = "/home/beauty_of_rain_dataset/A14B"
dataset = "/home/dataset_v000_854_480_A14B.toml"
epochs = 100
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1
warmup_steps = 10
eval_every_n_epochs = 1
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1
save_every_n_epochs = 6
activation_checkpointing = true
partition_method = "parameters"
save_dtype = "bfloat16"
caching_batch_size = 1
steps_per_print = 10
video_clip_mode = "single_beginning"
blocks_to_swap = 32

[model]
type = "wan"
ckpt_path = "/home/Wan2.2-T2V-A14B"
dtype = "bfloat16"
transformer_dtype = "float8"
timestep_sample_method = "logit_normal"
transformer_path = "/home/Wan2.2-T2V-A14B/high_noise_model"
min_t = 0.875
max_t = 1

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

[optimizer]
type = "adamw_optimi"
lr = 7e-5
betas = [ 0.9, 0.99,]
weight_decay = 0.01
eps = 1e-8</code></pre>

wan 2.2 t2v-A14B HIGH

dataset.toml<pre><code>resolutions = [ [ 342, 192,], [ 854, 480,], [1067, 600]]
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7
frame_buckets = [ 1, 16, 30, 33]
[[directory]]
path = "/home/beauty_of_rain_dataset/videos"
num_repeats = 2</code></pre>train.toml<pre><code>output_dir = "/home/beauty_of_rain_dataset/A14B"
dataset = "/home/dataset_v000_854_480_A14B.toml"
epochs = 100
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1
warmup_steps = 10
eval_every_n_epochs = 1
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1
save_every_n_epochs = 6
activation_checkpointing = true
partition_method = "parameters"
save_dtype = "bfloat16"
caching_batch_size = 1
steps_per_print = 10
video_clip_mode = "single_beginning"
blocks_to_swap = 32

[model]
type = "wan"
ckpt_path = "/home/Wan2.2-T2V-A14B"
dtype = "bfloat16"
transformer_dtype = "float8"
timestep_sample_method = "logit_normal"
transformer_path = "/home/Wan2.2-T2V-A14B/low_noise_model"
min_t = 0
max_t = 0.875

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

[optimizer]
type = "adamw_optimi"
lr = 5e-5
betas = [ 0.9, 0.99,]
weight_decay = 0.01
eps = 1e-8</code></pre>

wan 2.2 t2v-A14B LOW

Training Detailsdataset.toml<pre><code>resolutions = [[ 1280, 704]]
enable_ar_bucket = true
min_ar = 0.5
max_ar = 2.0
num_ar_buckets = 7
ar_buckets = [[1280, 704]]
frame_buckets = [ 1, 24, 46, 81,]
[[directory]]
path = "/home/user/beauty_of_rain_dataset/videos"
num_repeats = 4</code></pre>train.toml<pre><code>output_dir = "/home/user/beauty_of_rain_dataset/5B"
dataset = "/home/user/beauty_of_rain_5B.toml"
epochs = 120
micro_batch_size_per_gpu = 1
pipeline_stages = 1
gradient_accumulation_steps = 1
gradient_clipping = 1
warmup_steps = 100
eval_every_n_epochs = 1
eval_before_first_step = true
eval_micro_batch_size_per_gpu = 1
eval_gradient_accumulation_steps = 1
save_every_n_epochs = 12
activation_checkpointing = true
partition_method = "parameters"
save_dtype = "bfloat16"
caching_batch_size = 1
steps_per_print = 10
video_clip_mode = "single_beginning"

[model]
type = "wan"
ckpt_path = "/home/user/Wan2.2-TI2V-5B"
dtype = "bfloat16"
transformer_dtype = "float8"
timestep_sample_method = "logit_normal"

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

[optimizer]
type = "adamw_optimi"
lr = 8e-5
betas = [ 0.9, 0.99,]
weight_decay = 0.01
eps = 1e-8</code></pre>

wan 2.2 ti2v-5B

Beauty of rain

vlcsnap-2025-05-18-20h54m31s079.png

wide hips

downblouse

pg-13

suggestive

oral invitation

pg13

sexy

huge breasts

thick thighs

physical violence

weapon violence

revealing clothes

convenient censoring

corpses

male nudity

female swimwear or underwear

female nudity

breast out

male underwear

hair over breasts

female swimwear

gigantic breasts

graphic violence or gore

emaciated bodies

nude

lingerie

sexual situations

disturbing

male swimwear or underwear

partial nudity

undressed

breasts out

exposed female nipple

no panties

covered nipples

huge butt

strapless leotard

sitting on face

one breast out

female underwear

nsfw

graphic female nudity

hentai

porn

genitals

graphic male nudity

adult toys

illustrated explicit nudity

nudity

futanari

sexual intent

peeing

sexual activity

incest

vore

oral

anal

blowjob

dildo riding

hanging

hate symbols

nazi party

scat

extremist

child on child

white supremacy

diapers

self injury

hate speech

urine

latex clothing

swimwear

bukkake

fellatio

cumshot

implied fellatio

eat_cum

cumdrip

cum in pussy

cum on face

after fellatio

cum on hair

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