Save a price and reuse it. Let a price change on its own, by time, by purchase count, or by how much it has earned. We're also proposing to retire donation goals. Tell us where we've got it wrong.

Proposal: Price Tags and Price Steps

We're a Day-0 launch partner for MiniMax H3, a next-gen open-weights multimodal video model - and it's already in the generator. No waitlist. Open weights land in a few days.

MiniMax H3 is live: open-weights video, generate now

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 rel="ugc" href="https://civitai.com/articles/30259">[LTX-2.3]</a></li></ul>

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

physical violence

weapon violence

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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

male underwear

hair over breasts

female swimwear

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no panties

graphic violence or gore

covered nipples

huge butt

strapless leotard

sitting on face

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one breast out

female underwear

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nsfw

lingerie

graphic male nudity

adult toys

illustrated explicit nudity

nudity

graphic female nudity

hentai

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porn

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genitals

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sexual activity

anal

blowjob

dildo riding

incest

hanging

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child on child

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swimwear

bukkake

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