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video_gen_physics_backup

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Video World — Training-Free Acceleration Benchmark

Benchmarking training-free acceleration strategies (Cache, Sparse Attention, Token Merging) across CtrlWorld and DreamGen/DreamDojo on robotic manipulation tasks.

export PATH="$HOME/VLA/doanh:$PATH" gsutil -m cp -r gs://openpi-assets/checkpoints/pi05_droid/* /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/checkpoints/pi05_droid CUDA_VISIBLE_DEVICES=0 bash scripts/run_single_arm_multiview_ctrlworld_pi.sh both

Ctrl world

export CUDA_VISIBLE_DEVICES=0 export PYTHONPATH="./models/Ctrl-World" python models/Ctrl-World/scripts/rollout_replay_traj.py
--dataset_root_path ./models/Ctrl-World/dataset_example
--dataset_meta_info_path ./models/Ctrl-World/dataset_meta_info
--dataset_names ./models/Ctrl-World/droid_subset
--svd_model_path ./checkpoints/stabilityai/stable-video-diffusion-img2vid
--clip_model_path ./checkpoints/openai/clip-vit-base-patch32
--ckpt_path ./checkpoints/ctrl_world/Ctrl-World/checkpoint-10000.pt

CUDA_VISIBLE_DEVICES=0 python scripts/rollout_key_board.py --dataset_root_path dataset_example --dataset_meta_info_path dataset_meta_info --dataset_names droid_subset --svd_model_path ${path to svd folder} --clip_model_path /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/checkpoints/openai/clip-vit-base-patch32 --ckpt_path ${path to ctrl-world ckpt} --task_type keyboard --keyboard lllrrr

Default

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/Ctrl-World" CUDA_VISIBLE_DEVICES=0 XLA_PYTHON_CLIENT_MEM_FRACTION=0.4 python models/Ctrl-World/scripts/rollout_interact_pi.py
--dataset_root_path ./models/Ctrl-World/dataset_example
--dataset_meta_info_path ./models/Ctrl-World/dataset_meta_info
--dataset_names ./models/Ctrl-World/droid_subset
--svd_model_path ./checkpoints/stabilityai/stable-video-diffusion-img2vid
--clip_model_path ./checkpoints/openai/clip-vit-base-patch32
--ckpt_path ./checkpoints/ctrl_world/Ctrl-World/checkpoint-10000.pt
--task_type pickplace

Use with Pisa

Use-svg v1

Use-svg v2

export VENV=/pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv export LD_LIBRARY_PATH=$VENV/lib/python3.10/site-packages/nvidia/nvjitlink/lib:$VENV/lib/python3.10/site-packages/nvidia/cusparse/lib:$VENV/lib/python3.10/site-packages/torch/lib:$LD_LIBRARY_PATH source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/Ctrl-World" CUDA_VISIBLE_DEVICES=0 XLA_PYTHON_CLIENT_MEM_FRACTION=0.4 python models/Ctrl-World/scripts/rollout_interact_pi.py
--dataset_root_path ./models/Ctrl-World/dataset_example
--dataset_meta_info_path ./models/Ctrl-World/dataset_meta_info
--dataset_names ./models/Ctrl-World/droid_subset
--svd_model_path ./checkpoints/stabilityai/stable-video-diffusion-img2vid
--clip_model_path ./checkpoints/openai/clip-vit-base-patch32
--ckpt_path ./checkpoints/ctrl_world/Ctrl-World/checkpoint-10000.pt
--task_type pickplace
--use_svg --svg_pattern SVG2 --svg_num_q_centroids 50 --svg_num_k_centroids 200

use with radial attention (padding)

use with radial attention (no padding)

dreamgen base

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 python -m scripts.prepare_batch_input_json
--dataset_path ./datasets/dream_gen_benchmark/gr1_object
--save_path ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_object
--output_path ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_object/batch_input.json

dreamgen base - one gpu

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}

torchrun -m models.dreamgen.examples.video2world
--model_size 14B
--resolution 480
--fps 16
--dit_path ./checkpoints/nvidia/Cosmos-Predict2-14B-Video2World/model-480p-16fps.pt
--batch_input_json /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/sampling_dataset/humanoid/singleview/input/PhysicalAI-Robotics-GR00T-GR1/dense/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}

14B Video2World 720p-16fps: ghi ra thư mục riêng (mặc định `720p_16fps/`), không đè `dense/`

(script tự clone batch_input.json và đổi `output_video`)

bash ./scripts/run_humanoid_gr1_video2world_720p16fps.sh

Tuỳ chọn: OUT_TAG=my_run để tách từng lần chạy

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/sampling_dataset/humanoid/singleview/input/PhysicalAI-Robotics-GR00T-GR1/dense/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/sampling_dataset/humanoid/singleview/input/PhysicalAI-Robotics-GR00T-GR1/dense/batch_input.json
--enable_prompt_refiner
--disable_guardrail
--num_gpus ${NUM_GPUS}

2B - 720P - prompt refinement

14B - 480B - no prompt refinement

dreamgen base - 4 gpu

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0,1,2,3 export NUM_GPUS=4 torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}

dreamgen - pisa

cd /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/methods/sparse_attention/piecewise-sparse-attention uv pip install -e .

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_env/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_pisa
--pisa_density 0.2
--pisa_block_size 64
--pisa_start_layer_idx 2 --> Total inference time: 414.5592 seconds

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_env/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_pisa
--pisa_density 0.05
--pisa_block_size 64
--pisa_start_layer_idx 0 --> Total inference time: 315.6579 seconds

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0,1 export NUM_GPUS=2 torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_pisa
--pisa_density 0.1
--pisa_block_size 64
--pisa_use_bias

dreamgen - radial

Radial is not supported in the default DreamDojo/.venv. It requires a separate env with flashinfer.

cd /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics bash ./scripts/bootstrap_radial_env.sh

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_object/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_radial
--radial_decay_factor 1.0
--radial_block_size 64
--radial_start_layer_idx 4
--radial_model_type hunyuan --> Total inference time: 397.3452 seconds

Quality-Focused Radial Configuration

To improve generation quality (at the cost of a slightly longer inference time), you can increase the start layer index and change the mask profile to wan which has a larger attention window:

bash

torchrun -m models.dreamgen.examples.video2world_gr00t \
  --model_size 14B \
  --gr00t_variant gr1 \
  --batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_object/batch_input.json \
  --disable_guardrail \
  --num_gpus ${NUM_GPUS} \
  --use_radial \
  --radial_decay_factor 1.0 \
  --radial_block_size 64 \
  --radial_start_layer_idx 8 \
  --radial_model_type wan

--> Total inference time: 418.5359 seconds

Tuning Radial Parameters

--radial_start_layer_idx (default: 4): The number of initial self-attention layers to compute exactly (no sparsity). Increasing this (e.g. to 8 or 12) is the most effective way to improve visual quality and object structure, as early layers are critical for forming the image.
--radial_model_type (default: hunyuan): The sparsity mask profile. Changing this to wan uses a larger attention window and sink, looking at more tokens and generally resulting in better quality.
--radial_decay_factor (default: 1.0): Controls how quickly the radial mask drops tokens.
--radial_block_size (default: 64): Block size for sparse attention. Only 64 and 128 are supported. 64 is typically the best balance.

dreamgen - svg

SVG1 and SVG2 have different environment requirements.

SVG1 can run in the default DreamDojo/.venv if that Torch build exposes torch.nn.attention.flex_attention.

SVG2 is not supported in the default DreamDojo/.venv. Use a separate env with Sparse-VideoGen, flashinfer, cuvs, and built SVG kernels.

Do not install cuvs-cu12 into DreamDojo/.venv. That env currently uses a Torch CUDA 12.8 stack, and cuvs-cu12 upgrades parts of the CUDA userspace to 12.9, which breaks torch import with libcusparse.so.12 / libnvJitLink.so.12 symbol errors.

cd /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics bash ./scripts/bootstrap_svg_env.sh

dreamgen - svg1

SVG1 uses DreamGen's flex_attention + placement kernels path. It does not require flashinfer or cuvs, but it does require a Torch build with flex_attention.

torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_svg
--svg_variant svg1
--svg_start_layer_idx 4
--svg_dense_step_frac 0.1
--svg_sparsity 0.25
--svg_num_sampled_rows 64
--svg_sample_mse_max_row 3000

Speed-focused (max throughput, some quality loss):

torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_svg
--svg_variant svg1
--svg_start_layer_idx 4
--svg_dense_step_frac 0.05
--svg_sparsity 0.15
--svg_num_sampled_rows 16
--svg_sample_mse_max_row 1500

Quality-focused (only SVG on spatially-local layers):

--svg_start_layer_idx 20
--svg_dense_step_frac 0.1
--svg_sparsity 0.25
--svg_num_sampled_rows 64
--svg_sample_mse_max_row 3000

dreamgen - svg2

torchrun --master_port=29601 -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_svg
--svg_variant svg2
--svg_start_layer_idx 1
--svg_dense_step_frac 0.3
--svg_num_q_centroids 32
--svg_num_k_centroids 64
--svg_top_p_kmeans 0.9
--svg_min_kc_ratio 0.10
--svg_kmeans_iter_init 10
--svg_kmeans_iter_step 1 --> Total inference time: 473.8372 seconds

torchrun --master_port=29601 -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_svg
--svg_variant svg1
--svg_start_layer_idx 1
--svg_dense_step_frac 0.1
--svg_sparsity 0.15
--svg_num_sampled_rows 64
--svg_sample_mse_max_row 3000 --> Total inference time: 369.3853 seconds

torchrun --master_port=29601 -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_svg
--svg_variant svg1
--svg_start_layer_idx 4
--svg_dense_step_frac 0.4
--svg_sparsity 0.25
--svg_num_sampled_rows 64
--svg_sample_mse_max_row 3000

dreamdojo

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/DreamDojo" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 python -m cosmos_predict2._src.predict2.inference.inference_batch
--experiment "Stage-c_pt_4-reason_embeddings-v1p1-Index-26-Size-2B-Res-720-Fps-16-Note-T2V_high_sigma_loss_reweighted_1_1_rectified_flow_only_resume2"
--ckpt_path ./checkpoints/dreamdojo/2B_pretrain/iter_000140000/model
--batch_input ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.jsonl
--output_dir ./output/dream_gen_benchmark/output_base_2b
--resolution 480,640
--guidance 5
--num_output_frames 77
--num_steps 35
--use_neg_prompt

export VIRTUAL_ENV="$PWD/.venv" export PATH="$VIRTUAL_ENV/bin:$PATH" hash -r source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate export PYTHONPATH="./models/DreamDojo" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1 cd /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics

python -m models.DreamDojo.examples.action_conditioned
-o ./output/dreamdojo_hv_eval_2b_g1_gr1
--checkpoints-dir ./checkpoints/dreamdojo/2B_GR1_post-train/iter_000050000
--experiment dreamdojo_2b_480_640_gr1
--save-dir ./output/dreamdojo_results/dreamdojo_GR1_robot_2b_g1_gr1
--num-frames 49
--num-samples 65
--dataset-path "/pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/checkpoints/dreamdojo/datasets/PhysicalAI-Robotics-GR00T-Teleop-GR1/GR1_robot"
--data-split test
--deterministic-uniform-sampling
--checkpoint-interval 5000
--infinite

dreamdojo — humanoid singleview dense (full makovian + non_makovian)

Dense = default action-conditioned diffusion (no --use_svg / SiTo). Outputs default to sampling_dataset/dense/humanoid/output/singleview/dreamdojo/{makovian,non_makovian}/ (videos under .../<iter_*>/). The script runs repair_humanoid_singleview_split_meta.py first so meta/episodes.jsonl matches parquets when the split mixes DreamDojo-HV indices with GR1_robot (SKIP_META_REPAIR=1 to skip).

bash

cd /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics
bash ./scripts/run_humanoid_singleview_dreamdojo_dense.sh
# Tuỳ chọn: CHECKPOINTS_DIR=... SAVE_ROOT=/abs/path/to/out/root CUDA_VISIBLE_DEVICES=0 bash ...

dreamdojo svg 1

python -m models.DreamDojo.examples.action_conditioned
-o ./output/dreamdojo_svg1_eval
--checkpoints-dir ./checkpoints/dreamdojo/2B_GR1_post-train/iter_000050000
--experiment dreamdojo_2b_480_640_gr1
--save-dir ./output/dreamdojo_results/svg1_results
--num-frames 49
--num-samples 65
--dataset-path "./checkpoints/dreamdojo/datasets/PhysicalAI-Robotics-GR00T-Teleop-GR1/GR1_robot"
--data-split test
--deterministic-uniform-sampling
--use_svg
--svg_variant svg1

dreadojo svg2

python -m models.DreamDojo.examples.action_conditioned
-o ./output/dreamdojo_svg2_eval
--checkpoints-dir ./checkpoints/dreamdojo/2B_GR1_post-train/iter_000050000
--experiment dreamdojo_2b_480_640_gr1
--save-dir ./output/dreamdojo_results/svg2_results
--num-frames 49
--num-samples 65
--dataset-path "./checkpoints/dreamdojo/datasets/PhysicalAI-Robotics-GR00T-Teleop-GR1/GR1_robot"
--data-split test
--deterministic-uniform-sampling
--use_svg
--svg_start_layer_idx 2
--svg_variant svg2

cd /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics python -m models.DreamDojo.examples.action_conditioned
-o ./output/dreamdojo_svg2_eval
--checkpoints-dir ./checkpoints/dreamdojo/2B_GR1_post-train/iter_000050000
--experiment dreamdojo_2b_480_640_gr1
--save-dir ./output/dreamdojo_results/svg2_results
--num-frames 49
--num-samples 65
--dataset-path "./checkpoints/dreamdojo/datasets/PhysicalAI-Robotics-GR00T-Teleop-GR1/GR1_robot"
--data-split test
--deterministic-uniform-sampling
--use_svg
--svg_variant svg2
--svg_start_layer_idx 18
--svg_num_q_centroids 8
--svg_num_k_centroids 16
--svg_top_p_kmeans 0.3
--svg_kmeans_iter_init 2
--svg_kmeans_iter_step 1

dreamgen - sito

SiTo is inference-only, self-attention only, and single-GPU only in DreamGen v1.

bash

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate
export PYTHONPATH="./models/dreamgen"
export CUDA_VISIBLE_DEVICES=0
export NUM_GPUS=1

torchrun -m models.dreamgen.examples.video2world_gr00t \
  --model_size 14B \
  --gr00t_variant gr1 \
  --batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json \
  --disable_guardrail \
  --num_gpus ${NUM_GPUS} \
  --use_sito \
  --sito_start_layer_idx 6 \
  --sito_keep_last_n_dense 2

dreamdojo - sito

SiTo uses post-patchify per-frame token grouping. For the common (4, 60, 80) latent grid, the DiT sequence is 4 * 30 * 40 = 4800 tokens.

bash

python -m models.DreamDojo.examples.action_conditioned \
  -o ./output/dreamdojo_sito_eval \
  --checkpoints-dir ./checkpoints/dreamdojo/2B_GR1_post-train/iter_000050000 \
  --experiment dreamdojo_2b_480_640_gr1 \
  --save-dir ./output/dreamdojo_results/sito_results \
  --num-frames 49 \
  --num-samples 65 \
  --dataset-path "./checkpoints/dreamdojo/datasets/PhysicalAI-Robotics-GR00T-Teleop-GR1/GR1_robot" \
  --data-split test \
  --deterministic-uniform-sampling \
  --use_sito \
  --sito_start_layer_idx 4 \
  --sito_keep_last_n_dense 2

ctrl-world - sito

SiTo is applied only on large spatial self-attention layers in Ctrl-World.

bash

source /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/models/DreamDojo/.venv/bin/activate
export PYTHONPATH="./models/Ctrl-World"
CUDA_VISIBLE_DEVICES=0 XLA_PYTHON_CLIENT_MEM_FRACTION=0.4 python models/Ctrl-World/scripts/rollout_interact_pi.py \
  --dataset_root_path ./models/Ctrl-World/dataset_example \
  --dataset_meta_info_path ./models/Ctrl-World/dataset_meta_info \
  --dataset_names ./models/Ctrl-World/droid_subset \
  --svd_model_path ./checkpoints/stabilityai/stable-video-diffusion-img2vid \
  --clip_model_path ./checkpoints/openai/clip-vit-base-patch32 \
  --ckpt_path ./checkpoints/ctrl_world/Ctrl-World/checkpoint-10000.pt \
  --task_type pickplace \
  --use_sito \
  --sito_max_downsample_ratio 4

itm

ITM is inference-only, CFG-driven, and mutually exclusive with SiTo, PISA, Radial, SVG, and NATTEN.

DreamGen:

bash

export PYTHONPATH="./models/dreamgen"
export CUDA_VISIBLE_DEVICES=0
export NUM_GPUS=1

torchrun -m models.dreamgen.examples.video2world_gr00t \
  --model_size 14B \
  --gr00t_variant gr1 \
  --batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json \
  --disable_guardrail \
  --num_gpus ${NUM_GPUS} \
  --use_itm \
  --itm_start_layer_idx 6 \
  --itm_keep_last_n_dense 2 \
  --itm_prune_from_step 1 \
  --itm_merge_from_step 2

DreamDojo:

bash

python -m models.DreamDojo.examples.action_conditioned \
  -o ./output/dreamdojo_itm_eval \
  --checkpoints-dir ./checkpoints/dreamdojo/2B_GR1_post-train/iter_000050000 \
  --experiment dreamdojo_2b_480_640_gr1 \
  --save-dir ./output/dreamdojo_results/itm_results \
  --num-frames 49 \
  --num-samples 65 \
  --dataset-path "./checkpoints/dreamdojo/datasets/PhysicalAI-Robotics-GR00T-Teleop-GR1/GR1_robot" \
  --data-split test \
  --deterministic-uniform-sampling \
  --guidance 1.5 \
  --use_itm \
  --itm_start_layer_idx 4 \
  --itm_keep_last_n_dense 2

Ctrl-World:

bash

export PYTHONPATH="./models/Ctrl-World"
CUDA_VISIBLE_DEVICES=0 XLA_PYTHON_CLIENT_MEM_FRACTION=0.4 python models/Ctrl-World/scripts/rollout_interact_pi.py \
  --dataset_root_path ./models/Ctrl-World/dataset_example \
  --dataset_meta_info_path ./models/Ctrl-World/dataset_meta_info \
  --dataset_names ./models/Ctrl-World/droid_subset \
  --svd_model_path ./checkpoints/stabilityai/stable-video-diffusion-img2vid \
  --clip_model_path ./checkpoints/openai/clip-vit-base-patch32 \
  --ckpt_path ./checkpoints/ctrl_world/Ctrl-World/checkpoint-10000.pt \
  --task_type pickplace \
  --use_itm \
  --itm_max_downsample_ratio 4

Sparse Attention Benchmark Scripts (SiTo & ITM)

These scripts run inference with sparse attention methods and save results to sampling_dataset/{sito,itm}/ for the webpage demo.

Humanoid Singleview

bash

# DreamDojo + SiTo (2B, action-conditioned)
bash scripts/run_humanoid_singleview_dreamdojo_sito.sh

# DreamDojo + ITM (2B, action-conditioned)
bash scripts/run_humanoid_singleview_dreamdojo_itm.sh

# DreamGen + SiTo (14B, video2world)
bash scripts/run_humanoid_singleview_dreamgen_sito.sh

# DreamGen + ITM (14B, video2world)
bash scripts/run_humanoid_singleview_dreamgen_itm.sh

Ctrl-World + SiTo / ITM (Single-Arm Multiview)

bash

# Ctrl-World + SiTo
bash scripts/run_single_arm_multiview_ctrlworld_sito.sh [makovian|non_makovian|both]

# Ctrl-World + ITM
bash scripts/run_single_arm_multiview_ctrlworld_itm.sh [makovian|non_makovian|both]

Single-Arm Singleview (DreamDojo)

bash

# DreamDojo + SiTo
bash scripts/run_single_arm_singleview_dreamdojo_sito.sh

# DreamDojo + ITM
bash scripts/run_single_arm_singleview_dreamdojo_itm.sh

Collecting Videos for Webpage

After inference, the videos are automatically served by the webpage via symlink:

bash

# webpage/videos -> ../sampling_dataset (already linked)
# Videos accessible at: webpage/videos/{sito,itm}/{embodiment}/output/...

# Or explicitly check what's available:
python scripts/collect_sparse_attention_videos.py --dry-run

python -m models.dreamgen.scripts.prepare_batch_input_json
--dataset_path /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/datasets/dream_gen_benchmark/gr1_behavior
--save_path /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/output/dreamgen_lvg/2b_behaviour
--output_path /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/output/dreamgen_lvg/2b_behaviour/batch_input.json

export PYTHONPATH="./models/dreamgen" export NUM_GPUS=2

torchrun --nproc_per_node=${NUM_GPUS} -m models.dreamgen.examples.video2world_lvg
--model_size 2B
--batch_input_json /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/output/dreamgen_lvg/2b_behaviour/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}

torchrun -m models.dreamgen.examples.video2world_gr00t_lvg
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--num_chunks 3
--num_conditional_frames 1

dreamgen - multiview

export PYTHONPATH="./models/dreamgen" export CUDA_VISIBLE_DEVICES=0 export NUM_GPUS=1

torchrun -m models.dreamgen.examples.video2world_gr00t
--model_size 14B
--gr00t_variant gr1
--batch_input_json ./output/dream_gen_benchmark/cosmos_predict2_14b_gr1_behavior/batch_input.json
--disable_guardrail
--num_gpus ${NUM_GPUS}
--use_itm
--itm_start_layer_idx 6
--itm_keep_last_n_dense 2
--itm_prune_from_step 1
--itm_merge_from_step 2

TMP_OUT=/pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/tmp
NUM_GPUS=1
BATCH_JSON=./sampling_dataset/dense/single_arm/input/multiview/dreamgen/makovian/batch_input.json
bash scripts/run_single_arm_multiview_video2world_2b_tmp.sh

Successfully saved prompt file to: /pfss/mlde/workspaces/mlde_wsp_IAS_SAMMerge/VLA/doanh/video_world/video_gen_physics/sampling_dataset/humanoid/multiview/non_makovian/dreamgen/Airbot_MMK2_Airbot_MMK2_storage_mango_pomegranate/episode_000002.txt

doanh25032004/video_gen_physics_backup

Video World — Training-Free Acceleration Benchmark

Ctrl world

Default

Use with Pisa

Use-svg v1

Use-svg v2

use with radial attention (padding)

use with radial attention (no padding)

use with radial attention (no padding)

dreamgen base

dreamgen base - one gpu

14B Video2World 720p-16fps: ghi ra thư mục riêng (mặc định 720p_16fps/), không đè dense/

(script tự clone batch_input.json và đổi output_video)

Tuỳ chọn: OUT_TAG=my_run để tách từng lần chạy

dreamgen base - 4 gpu

dreamgen - pisa

dreamgen - radial

Quality-Focused Radial Configuration

Tuning Radial Parameters

dreamgen - svg

dreamgen - svg1

dreamgen - svg2

dreamdojo

dreamdojo — humanoid singleview dense (full makovian + non_makovian)

dreamdojo svg 1

dreadojo svg2

dreamgen - sito

dreamdojo - sito

ctrl-world - sito

itm

Sparse Attention Benchmark Scripts (SiTo & ITM)

Humanoid Singleview

Ctrl-World + SiTo / ITM (Single-Arm Multiview)

Single-Arm Singleview (DreamDojo)

Collecting Videos for Webpage

dreamgen - multiview

doanh25032004/
video_gen_physics_backup

14B Video2World 720p-16fps: ghi ra thư mục riêng (mặc định `720p_16fps/`), không đè `dense/`

(script tự clone batch_input.json và đổi `output_video`)