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wan_t2v_tgst1k_epoch110/adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k",
+    "o",
+    "ffn.2",
+    "ffn.0",
+    "v",
+    "q"
+  ],
+  "task_type": null,
+  "use_dora": false,
+  "use_rslora": false
+}

wan_t2v_tgst1k_epoch110/adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:72622a518f8c0a2ff70fc15048bc15fb3d4353344343d0833b844d7d4dbb9954
+size 306807976

wan_t2v_tgst1k_epoch110/wan_t2v_config.toml

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+# Output path for training runs. Each training run makes a new directory in here.
+output_dir = '/workspace/output_models/run_2_t2v'
+
+# Dataset config file.
+dataset = 'examples/wan_t2v_dataset.toml'
+
+# training settings
+
+# I usually set this to a really high value because I don't know how long I want to train.
+epochs = 1000
+# Batch size of a single forward/backward pass for one GPU.
+micro_batch_size_per_gpu = 1
+# Pipeline parallelism degree. A single instance of the model is divided across this many GPUs.
+pipeline_stages = 1
+# Number of micro-batches sent through the pipeline for each training step.
+# If pipeline_stages > 1, a higher GAS means better GPU utilization due to smaller pipeline bubbles (where GPUs aren't overlapping computation).
+gradient_accumulation_steps = 4
+# Grad norm clipping.
+gradient_clipping = 1.0
+# Learning rate warmup.
+warmup_steps = 100
+
+# eval settings
+
+eval_every_n_epochs = 1
+eval_before_first_step = true
+# Might want to set these lower for eval so that less images get dropped (eval dataset size is usually much smaller than training set).
+# Each size bucket of images/videos is rounded down to the nearest multiple of the global batch size, so higher global batch size means
+# more dropped images. Usually doesn't matter for training but the eval set is much smaller so it can matter.
+eval_micro_batch_size_per_gpu = 1
+eval_gradient_accumulation_steps = 1
+
+# misc settings
+
+# Probably want to set this a bit higher if you have a smaller dataset so you don't end up with a million saved models.
+save_every_n_epochs = 2
+checkpoint_every_n_minutes = 20
+# Always set to true unless you have a huge amount of VRAM.
+activation_checkpointing = true
+# Controls how Deepspeed decides how to divide layers across GPUs. Probably don't change this.
+partition_method = 'parameters'
+# dtype for saving the LoRA or model, if different from training dtype
+save_dtype = 'bfloat16'
+# Batch size for caching latents and text embeddings. Increasing can lead to higher GPU utilization during caching phase but uses more memory.
+caching_batch_size = 1
+# How often deepspeed logs to console.
+steps_per_print = 1
+# How to extract video clips for training from a single input video file.
+# The video file is first assigned to one of the configured frame buckets, but then we must extract one or more clips of exactly the right
+# number of frames for that bucket.
+# single_beginning: one clip starting at the beginning of the video
+# single_middle: one clip from the middle of the video (cutting off the start and end equally)
+# multiple_overlapping: extract the minimum number of clips to cover the full range of the video. They might overlap some.
+# default is single_beginning
+video_clip_mode = 'single_beginning'
+
+[model]
+type = 'wan'
+ckpt_path = '/workspace/models/wan21t2v'
+dtype = 'bfloat16'
+# You can use fp8 for the transformer when training LoRA.
+#transformer_dtype = 'float8'
+timestep_sample_method = 'logit_normal'
+
+# For models that support full fine tuning, simply delete or comment out the [adapter] table to FFT.
+[adapter]
+type = 'lora'
+rank = 32
+# Dtype for the LoRA weights you are training.
+dtype = 'bfloat16'
+# You can initialize the lora weights from a previously trained lora.
+#init_from_existing = '/data/diffusion_pipe_training_runs/something/epoch50'
+
+[optimizer]
+# AdamW from the optimi library is a good default since it automatically uses Kahan summation when training bfloat16 weights.
+# Look at train.py for other options. You could also easily edit the file and add your own.
+type = 'adamw_optimi'
+lr = 5e-5
+betas = [0.9, 0.99]
+weight_decay = 0.01
+eps = 1e-8