About this version Trained on Anima Preview 2 Assume that any lora trained on the preview version won't work well on the final version. Recommended prompt structure: Positive prompt (quality tags at the start of prompt): masterpiece, best quality, very aesthetic, {{tags}} Slightly update dataset of 96 images, trained at 1024 x 1024 and 1536 x 1024 resolutions, previews are mostly generated at 1536 x 1024 or 1024 x 1536 . Used diffusion-pipe - fork by @bluvoll Config: # Resolution settings. resolutions = [[1280, 720], [1536, 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 = 1 resolutions = [[1280, 720], [1536, 1024]] # Config for RTX 6000 Pro 96GB # Run with NCCL_P2P_DISABLE="1" NCCL_IB_DISABLE="1" NCCL_CUMEM_ENABLE="0" deepspeed --num_gpus=1 train.py --deepspeed --config config-anima.toml # Change these paths output_dir = '/mnt/d/anima/training_output' dataset = 'dataset-anima.toml' # training settings epochs = 50 micro_batch_size_per_gpu = 24 pipeline_stages = 1 gradient_accumulation_steps = 1 gradient_clipping = 1.0 warmup_steps = 150 train_llm_adapter = true # eval settings eval_every_n_epochs = 1 eval_before_first_step = true eval_micro_batch_size_per_gpu = 1 eval_gradient_accumulation_steps = 1 # misc settings save_every_n_epochs = 1 checkpoint_every_n_epochs = 1 # checkpoint_every_n_minutes = 60 activation_checkpointing = true partition_method = 'parameters' save_dtype = 'bfloat16' caching_batch_size = 1 steps_per_print = 1 [model] type = 'anima' transformer_path = '/mnt/c/models/diffusion_models/anima-preview2.safetensors' vae_path = '/mnt/c/models/vae/qwen_image_vae.safetensors' qwen_path = '../qwen0.6/Qwen3-0.6B/' dtype = 'bfloat16' timestep_sample_method = 'logit_normal' sigmoid_scale = 1.0 shift = 3.0 # Caption Processing Options cache_text_embeddings = false # NOTE: Requires cache_text_embeddings = false to work! # For cached embeddings, use cache_shuffle_num in your dataset config instead. shuffle_tags = true tag_delimiter = ', ' keep_first_n_tags = 5 shuffle_keep_first_n = 5 tag_dropout_percent = 0.3 protected_tags_file = './protected_tags.txt' nl_shuffle_sentences = false nl_keep_first_sentence = true # 'tags' 'nl' 'mixed' caption_mode = 'mixed' debug_caption_processing = false debug_caption_interval = 1000 llm_adapter_lr = 6e-5 # 1e-5 base [adapter] type = 'lora' rank = 32 dtype = 'bfloat16' # AdamW from the optimi library is a good default since it automatically uses Kahan summation when training bfloat16 weights. [optimizer] type = 'adamw_optimi' lr = 3.5e-4 # 2e-5 base betas = [0.9, 0.99] weight_decay = 0.01 eps = 1e-7 # 1e-8 base