Inference Recipes

Bash-level inference scripts mirroring train/ — one script per memory row, all calling inference/unified_inference.py.

Usage

export WAN_BASE_MODEL=/path/to/Wan2.1-T2V-1.3B

# Single memory type
CKPT=./ckpts/context_k1/epoch-0.safetensors \
  bash inference/memory_baselines_basic/run_infer_context_k1.sh

# With custom prompt and context image
CKPT=./ckpts/context_k1/epoch-0.safetensors \
PROMPT="A toy bear on a table" \
CONTEXT_IMAGE=assets/opendomain_revisit/1774363417.png \
  bash inference/memory_baselines_basic/run_infer_context_k1.sh

# All memory baselines (needs CKPT_DIR with per-row folders)
CKPT_DIR=./ckpts bash inference/memory_baselines_basic/run_infer_all.sh

# Dynamic SpatialVID row
CKPT=/path/to/retrained_dynamic_spatial_mem/epoch-0.safetensors \
  bash inference/dynamic_spatialvid/run_infer_dyn_spatial_mem.sh

Environment Variables

Variable	Default	Description
`CKPT`	(required)	Path to `.safetensors` checkpoint
`WAN_BASE_MODEL`	(required)	Wan 2.1 base model directory
`PROMPT`	Generic game scene prompt	Text prompt
`CONTEXT_IMAGE`	(none)	First-frame context image path
`ACTION_PATH`	`env/action_rotation_left_45.json`	Camera trajectory JSON
`SEED`	`0`	Random seed
`HEIGHT` / `WIDTH`	`352` / `640`	Resolution
`NUM_FRAMES`	`81`	Frames per chunk
`NUM_INFERENCE_STEPS`	`50`	Denoising steps
`SIGMA_SHIFT`	`15.0` (memory baselines) / `5.0` (context learning)	Timestep shift
`INFER_OUTPUT_ROOT`	`inference_outputs/`	Output directory

Script Mapping

Memory Baselines (`inference/memory_baselines_basic/`)

Inference script	`--memory_type`	Training script
`run_infer_no_memory.sh`	`no_memory`	`run_ablation_no_memory_baseline_two_chunk.sh`
`run_infer_framepack_weight.sh`	`framepack_weight`	`run_ablation_framepack_weight_two_chunk.sh`
`run_infer_framepack_len_r2.sh`	`framepack_len_r2`	`run_ablation_framepack_len_r2_two_chunk.sh`
`run_infer_framepack_len_r4.sh`	`framepack_len_r4`	`run_ablation_framepack_len_r4_two_chunk.sh`
`run_infer_framepack_hybrid_r2.sh`	`framepack_hybrid_r2`	`run_ablation_framepack_hybrid_r2_weight_two_chunk.sh`
`run_infer_framepack_hybrid_r4.sh`	`framepack_hybrid_r4`	`run_ablation_framepack_hybrid_r4_weight_two_chunk.sh`
`run_infer_spatial_mem.sh`	`spatial_mem`	`run_spatial_memory_baseline.sh`
`run_infer_spatial_concat_text.sh`	`spatial_concat_text`	`run_ablation_spatial_concat_text_two_chunk.sh`
`run_infer_spatial_inject_none.sh`	`spatial_inject_none`	`run_ablation_spatial_inject_none_two_chunk.sh`
`run_infer_spatial_cross_attn_readout.sh`	`spatial_cross_attn_readout`	`run_ablation_spatial_cross_attn_readout_two_chunk.sh`
`run_infer_videossm_hybrid.sh`	`videossm_hybrid`	`run_videossm_hybrid_baseline.sh`
`run_infer_block_wise_ssm.sh`	`block_wise_ssm`	`run_ablation_block_wise_ssm_two_chunk.sh`

Context Learning (`inference/context_learning/`)

Inference script	`--memory_type`	Training script
`run_infer_ctx1.sh`	`context_k1`	`run_pre_qkv_ctx1.sh`
`run_infer_ctx5.sh`	`context_k5`	`run_pre_qkv_ctx5.sh`
`run_infer_ctx20.sh`	`context_k20`	`run_pre_qkv_ctx20.sh`

Dynamic SpatialVID (`inference/dynamic_spatialvid/`)

Dynamic wrappers mirror the six dynamic training rows in train/dynamic_spatialvid/. They are intended for qualitative replay and demo generation; dynamic evaluation scripts are TODO.