anima/rank64: epoch 2 LoRA (mega-liminal-anima-r64-e02, 277 MB)

anima/rank64/adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": null,
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "kasa_config": null,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.21.0",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "adaln_modulation_cross_attn.1",
+    "adaln_modulation_mlp.1",
+    "adaln_modulation_mlp.2",
+    "o_proj",
+    "layer1",
+    "adaln_modulation_self_attn.1",
+    "q_proj",
+    "adaln_modulation_cross_attn.2",
+    "adaln_modulation_self_attn.2",
+    "layer2",
+    "output_proj",
+    "k_proj",
+    "mlp.2",
+    "mlp.0"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}

anima/rank64/mega-liminal-anima-r64-e02.safetensors

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+size 277206000

anima/rank64/training/liminal_dataset.toml

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+# liminal_dataset.toml — the liminal Anima LoRA (2026-09-25). One [[directory]] per class folder; num_repeats from the
+# anima-trainer's dampened_repeats (alpha 0.5, max 8x per image, effective <= 1.25 x top) so sparse classes contribute
+# without memorization. The '3d' folder is omitted entirely (all 38 were Garry's Mod / Source-engine screenshots).
+# Captions: 'liminal, <class>, <Qwen3.5-9B description>' — tag-order sensitive model -> never shuffled.
+# samples per epoch: 2789
+resolutions = [1024]           # Anima's ~1 MP sweet spot; 60% of the kept images are >= 1 MP, 7% are 0.26-0.45 MP
+enable_ar_bucket = true
+min_ar = 0.5
+max_ar = 2.0
+num_ar_buckets = 7
+frame_buckets = [1]
+shuffle_caption = false
+
+[[directory]]   # mega liminal: 870 images x 1 = 870 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, mega liminal'
+caption_extension = '.txt'
+num_repeats = 1
+
+[[directory]]   # landscape: 766 images x 1 = 766 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, landscape'
+caption_extension = '.txt'
+num_repeats = 1
+
+[[directory]]   # suburban: 111 images x 3 = 333 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, suburban'
+caption_extension = '.txt'
+num_repeats = 3
+
+[[directory]]   # simulacrum: 36 images x 5 = 180 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, simulacrum'
+caption_extension = '.txt'
+num_repeats = 5
+
+[[directory]]   # vanishing point: 21 images x 6 = 126 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, vanishing point'
+caption_extension = '.txt'
+num_repeats = 6
+
+[[directory]]   # empty mall: 20 images x 7 = 140 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, empty mall'
+caption_extension = '.txt'
+num_repeats = 7
+
+[[directory]]   # hand sourced: 18 images x 7 = 126 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, hand sourced'
+caption_extension = '.txt'
+num_repeats = 7
+
+[[directory]]   # parking garage: 13 images x 8 = 104 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, parking garage'
+caption_extension = '.txt'
+num_repeats = 8
+
+[[directory]]   # cityscape: 10 images x 8 = 80 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, cityscape'
+caption_extension = '.txt'
+num_repeats = 8
+
+[[directory]]   # movie theatre: 8 images x 8 = 64 samples/epoch
+path = '/root/liminal/data/liminal/1_liminal, movie theatre'
+caption_extension = '.txt'
+num_repeats = 8

anima/rank64/training/liminal_lora.toml

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+# liminal_lora.toml — diffusion-pipe (Phil's fork, main 812449a) config for the liminal Anima LoRA (2026-09-25).
+# Based on the anima-trainer template (geolip_anima_trainer/templates/anima_lora.toml) with the house riders:
+# plain Adam (never AdamW) with fp32 master weights (the fork's bf16_master_weights), llm_adapter_lr = 0 (Anima's text adapter
+# frozen), bf16 throughout, no fp8. Two RTX 5090s, data-parallel: deepspeed --num_gpus=2, pipeline_stages = 1.
+output_dir = '/root/liminal/runs/liminal_lora'
+dataset = '/root/liminal/configs/liminal_dataset.toml'
+
+epochs = 16                          # 2789 samples/epoch at effective batch 8 -> ~349 steps/epoch; snapshots every 2 epochs
+micro_batch_size_per_gpu = 4
+pipeline_stages = 1
+gradient_accumulation_steps = 1
+gradient_clipping = 1.0
+warmup_steps = 100
+seed = 42
+
+save_every_n_epochs = 2
+checkpoint_every_n_minutes = 30      # full training state for --resume_from_checkpoint (pruned to the newest 2)
+save_dtype = 'bfloat16'
+
+activation_checkpointing = true      # 32 GB cards: OFF needs ~89 GB at 1024^2 micro-batch 4
+compile = false
+partition_method = 'parameters'
+caching_batch_size = 8
+map_num_proc = 32
+steps_per_print = 10
+blocks_to_swap = 0
+bf16_master_weights = true           # the fork's MasterWeightsAdam: fp32 masters inside plain Adam (needs dtype bf16 + type adam)
+
+[model]
+type = 'anima'
+transformer_path = '/root/liminal/models/anima/anima-base-v1.0.safetensors'
+vae_path = '/root/liminal/models/anima/qwen_image_vae.safetensors'
+llm_path = '/root/liminal/models/anima/qwen_3_06b_base.safetensors'
+dtype = 'bfloat16'
+llm_adapter_lr = 0
+
+[adapter]
+type = 'lora'
+rank = 64                            # multi-class style LoRA (ten classes) -> the template's multi-concept rank
+dtype = 'bfloat16'
+
+[optimizer]
+type = 'adam'
+lr = 2e-5                            # the template's Anima starting point ("Anima likes LOW lr")
+betas = [0.9, 0.99]
+weight_decay = 0.0
+eps = 1e-8

anima/rank64/training/runs/events.out.tfevents.1790381639.30ed19890bfc.67782.0

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