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

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