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README.md

@@ -1,242 +1,113 @@
 ---
 license: apache-2.0
+library_name: diffusers
+pipeline_tag: text-to-video
+base_model: robbyant/lingbot-video-moe-30b-a3b
+tags:
+- lingbot-video
+- sdnq
+- uint4
+- text-to-video
 ---
 
-# LingBot-Video
+# LingBot Video MoE 30B-A3B SDNQ UINT4 Static
 
-**🌐 [Project Page](https://technology.robbyant.com/lingbot-video)** | **🤗 [Hugging Face](https://huggingface.co/collections/robbyant/lingbot-video)** | **🤖 [ModelScope](https://www.modelscope.cn/collections/Robbyant/LingBot-Video)** | **📄 [Paper](https://github.com/Robbyant/lingbot-video/blob/main/paper.pdf)** | **⚖️ [License](LICENSE.txt)**
+This is a complete, loadable derivative of [robbyant/lingbot-video-moe-30b-a3b](https://huggingface.co/robbyant/lingbot-video-moe-30b-a3b) with the diffusion transformer stored using static SDNQ UINT4 weights. It is tied to source model revision `f2e538f64afe00cc4ae674db2aeb52e2945edfd5`, LingBot Video code `a2bb04b78edd848500dc27a26e035a95442ae186`, and SDNQ `d841c383ff7be38728d4df829e17af4f15d4fd66` (`v0.2.1-17-gd841c38`).
 
-We are excited to introduce **LingBot-Video**, the first open-source large-scale MoE (Mixture-of-Experts) video generation model dedicated to embodied intelligence. As a top-tier video model, LingBot-Video is designed to bridge the gap between video synthesis and physical world understanding.
+Text encoder, tokenizer/processor, scheduler, and VAE remain at their upstream precision. Convolutions and embeddings are not quantized. The recipe is `uint4-static-transformer-only-with-3d-expert-adapter`: `weights_dtype=uint4`, auto group size (`group_size=0`), no dynamic quantization, SVD, Hadamard transform, convolution quantization, or embedding quantization.
 
-## 🔥 Key Highlights
+## Coverage
 
-* **🚀 Efficient MoE Architecture**: Scaled from scratch; balanced between capacity and cost with **~3x** faster inference.
-* **📦 Data Engine**: Trained on massive web videos integrated with **70,000+ hours** of embodied data.
-* **⚖️ Multi Reward System**: Rewarded for **high aesthetics**, **physical rationality**, and **task completion**.
+Coverage is calculated from the original parameter inventory, not from model-file sizes.
 
-## 🔥 Latest News
+| Component | Total logical params | Quantized params | Original parameter bytes covered | Packed storage | Packed grouped experts |
+| --- | ---: | ---: | ---: | ---: | --- |
+| `transformer` | 30,084,506,176 | 99.8268% | 99.6550% | 17.42 GiB | 28,991,029,248 params / 16.88 GiB stored |
+| `refiner` | 30,084,506,176 | 99.8268% | 99.6550% | 17.42 GiB | 28,991,029,248 params / 16.88 GiB stored |
 
-- July 9, 2026: 🎉 We release the technical report, code, models, rewriters for LingBot-Video.
+Both the base transformer's and refiner's raw 3-D `w1`/`w2`/`w3` expert tensors are packed. They are not excluded from the reported coverage. Exact module-level coverage and unquantized tensors are in [`quantization_manifest.json`](quantization_manifest.json) and [`benchmark/coverage`](benchmark/coverage/).
 
-## 📦 Model Download
+## Reproducible base benchmark
 
-| Model Name | Components | Tasks | Download |
-| --- | --- | --- | --- |
-| ⚡ LingBot-Video-Dense | Dense (1.3B) | T2I, T2V, TI2V | 🤗 [Huggingface](https://huggingface.co/robbyant/lingbot-video-dense-1.3b)   🤖 [ModelScope](https://www.modelscope.cn/models/Robbyant/lingbot-video-dense-1.3b) |
-| 💪 LingBot-Video-MoE | MoE (30B-A3B) + Refiner | T2I, T2V, TI2V, Refinement | 🤗 [Huggingface](https://huggingface.co/robbyant/lingbot-video-moe-30b-a3b)   🤖 [ModelScope](https://www.modelscope.cn/models/Robbyant/lingbot-video-moe-30b-a3b) |
-| 📝 LingBot-Video-Rewriter-Base | Qwen3.6-27B official | Prompt rewriter (Expand) | 🤗 [Huggingface](https://huggingface.co/Qwen/Qwen3.6-27B)   🤖 [ModelScope](https://www.modelscope.cn/models/Qwen/Qwen3.6-27B) |
-| 📝 LingBot-Video-Rewriter-Adapter | Qwen3.6-27B LoRA | Prompt rewriter (Json) | 🤗 [Huggingface](https://huggingface.co/robbyant/lingbot-video-rewriter-lora)   🤖 [ModelScope](https://www.modelscope.cn/models/Robbyant/lingbot-video-rewriter-lora) |
+All five pairs use identical prompts, negative prompt, seeds 4201-4205, scheduler inputs, 832x480 dimensions, 73 frames, 24 fps, 40 steps, guidance 3.0, shift 3.0, `batch_cfg=False`, and `null_cond_clone_zero=False`. Resources were sampled every 250 ms from `/proc`, `psutil`, and `nvidia-smi`.
 
-## 🚀 Quick Start
+| Variant | Load (s) | Cold generation (s) | Hot mean (s) | Peak VRAM (MiB) | Peak Torch allocated (MiB) | Process RSS (GiB) | System RAM used (GiB) |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| Original BF16 | 50.29 | 140.90 | 139.63 | 126274 | 112470 | 3.00 | 86.52 |
+| SDNQ UINT4 | 43.81 | 159.77 | 158.62 | 42504 | 36068 | 3.08 | 129.39 |
 
-### 🛠️ Installation
+Observed base peak-VRAM reduction: **66.34%**. Timing and memory are measurements on the environment recorded in [`benchmark/environment`](benchmark/environment/), not universal performance claims.
 
-The root `requirements.txt` includes the recommended PyTorch build for LingBot-Video inference.
+Frame-aligned aggregate quality across the five pairs: MAE `0.160701`, RMSE `0.227453`, PSNR `13.561 dB`, SSIM `0.573403`, LPIPS-Alex `0.484469`.
 
-```bash
-git clone https://github.com/Robbyant/lingbot-video
-cd lingbot-video
-
-python -m venv .venv
-source .venv/bin/activate
-python -m pip install -U pip
-
-# Base requirements cover direct DiT inference and rewriter --backend transformers.
-pip install -r requirements.txt
-pip install -e .
-```
-
-> **💡 Rewriter deployment**: the bundled rewriter uses the single-process
-> `transformers` backend. For higher throughput, deploy the VLM yourself and call
-> it through an OpenAI-compatible API. Preserve the two-stage semantics: step 1
-> must use the base VLM without the rewriter LoRA, while step 2 must use the same
-> base VLM with the rewriter LoRA enabled. This can be implemented with two
-> endpoints, or with one server that can select the adapter per request. See
-> [vLLM](https://docs.vllm.ai) / [SGLang](https://docs.sglang.ai) official docs.
-
-Install the optional SGLang dependencies only when using SGLang Diffusion or the
-fused / FP8 MoE runtime:
+![Original versus SDNQ comparison](assets/comparison/base/original_vs_sdnq_overview.webp)
 
-```bash
-python -m pip install --no-deps -r requirements-sglang.txt
-```
-
-Recommended runtime versions:
+The complete contact sheets and side-by-side MP4s are under [`assets/comparison/base`](assets/comparison/base/). Quantized sample MP4s are under [`samples/base`](samples/base/). Raw per-prompt CSV/JSONL, resource samples, commands, ffprobe records, output sizes, and SHA-256 values are under [`benchmark`](benchmark/).
 
-| Package | Version |
-| --- | --- |
-| `Python` | `>=3.10` |
-| `torch` | `2.12.0.dev20260220+cu130` (recommended) |
-| `torchvision` | `0.26.0.dev20260220+cu130` (recommended) |
-| `transformers` | `5.8.1` |
-| `diffusers` | `0.39.0` |
-| `peft` | `0.19.1` |
-| `json_repair` | `>=0.30` |
-| `decord` | `>=0.6.0` |
-| `safetensors` | `>=0.4.5` |
+## Refiner A/B
 
-### 🎬 Inference
+The refiner pair used the same `502b10f841d96aa101e69421b20083aeb60427c054b42fb0db29c5a6e70824cf` initial latent generated from the same base MP4. Both outputs are 1920x1088, 73 frames at 24 fps, with 8 refiner steps and upstream-default `null_cond_clone_zero=True`.
 
-#### 🧭 Recommended Inference Workflow
+| Variant | Load (s) | Generation (s) | Peak VRAM (MiB) | Process RSS (GiB) | System RAM used (GiB) |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| Original BF16 | 139.01 | 478.60 | 179178 | 4.26 | 107.63 |
+| SDNQ UINT4 | 105.56 | 460.47 | 98232 | 3.85 | 105.73 |
 
-LingBot-Video DiT inference is designed to consume structured JSON captions,
-not casual natural-language prompts. The recommended public workflow is:
+Frame-aligned refiner quality: MAE `0.014650`, RMSE `0.034053`, PSNR `29.357 dB`, SSIM `0.954816`, LPIPS-Alex `0.063118`.
 
-1. Rewrite the user's plain prompt with
-   Prompt Rewriter.
-   For TI2V, pass the same first frame to the rewriter.
-2. Run Auto Negative by
-   default to prune the negative prompt for this specific caption.
-3. Run the unified inference runner with `--prompt_json` and select direct
-   diffusers or SGLang Diffusion through `--backend`.
+Visual inspection found the same strong pink/red color clipping and cyan/magenta speckling in both the original-BF16 and SDNQ refiner outputs. Their close frame metrics therefore demonstrate pairwise similarity, not natural-color reconstruction quality; treat this as a shared refiner quality failure in this recorded sample.
 
-Backend choices:
+![Refiner original versus SDNQ](assets/comparison/refiner/original_vs_sdnq_overview.webp)
 
-- `diffusers`: direct diffusers reference path.
-- `sglang`: SGLang Diffusion path. If the optional SGLang package is not
-  installed, it automatically falls back to direct diffusers and prints a
-  warning. Install `requirements-sglang.txt` to enable the SGLang runtime.
+Load the refiner by passing `transformer_subfolder="refiner"` to `load_pipeline`.
 
-For multi-GPU inference, add `--enable_fsdp_inference` to shard the base DiT and
-refiner DiT on GPU. This reduces GPU memory pressure after loading, but each
-rank still constructs the transformer on host memory before FSDP sharding; make
-sure the machine has enough system RAM for large MoE checkpoints.
-
-```bash
-# Model root (released Dense or MoE package) and rewriter weights.
-export MODEL_DIR="<path_to_lingbot-video-model>"
-export REWRITER_BASE_MODEL="<path_to_rewriter_base_vlm>"
-export REWRITER_ADAPTER="<path_to_rewriter_lora>"
-
-python rewriter/inference.py --backend transformers --mode t2v \
-  --prompt "<plain_user_prompt>" --duration 5 --output prompt.json
-
-# Recommended Auto Negative block. If skipped, remove --negative_prompt_json from
-# the DiT inference command.
-python rewriter/auto_negative.py --backend transformers --mode t2v \
-  --caption prompt.json --output negative.json
-
-export BACKEND=diffusers  # or: sglang
-
-python scripts/inference.py \
-  --backend "$BACKEND" \
-  --model_dir "$MODEL_DIR" \
-  --run_refiner \
-  --mode t2v \
-  --prompt_json prompt.json \
-  --negative_prompt_json negative.json \
-  --output "<output_dir>/base.mp4" \
-  --refiner_output "<output_dir>/refined.mp4" \
-  --height 480 \
-  --width 832 \
-  --fps 24 \
-  --steps 40 \
-  --refiner_steps 8 \
-  --guidance_scale 3 \
-  --refiner_guidance_scale 3 \
-  --shift 3 \
-  --refiner_shift 3 \
-  --transformer_dtype bf16 \
-  --text_encoder_dtype bf16 \
-  --vae_dtype fp32 \
-  --refiner_vae_dtype fp32 \
-  --reuse_condition_features
-```
+## Installation and load
 
-Ready-to-run scripts are provided for single-GPU and multi-GPU inference. Set
-your environment and model path first:
+Use the exact pinned dependencies shipped with the repository:
 
 ```bash
-source .venv/bin/activate
-export PYTHON_BIN=python
-export DENSE_MODEL_DIR="<path_to_lingbot-video-dense>"
-export MOE_MODEL_DIR="<path_to_lingbot-video-moe>"
+git clone https://huggingface.co/WaveCut/LingBot-Video-MoE-30B-A3B-SDNQ-uint4-static
+cd LingBot-Video-MoE-30B-A3B-SDNQ-uint4-static
+python -m pip install -r runtime-requirements.txt
 ```
 
-Single-GPU scripts use direct diffusers and batched CFG by default. They run
-base generation only.
+The tested MoE backend also needs the pinned SGLang userspace packages without replacing Torch:
 
 ```bash
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/single-gpu/run_dense_t2i.sh
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/single-gpu/run_dense_t2v.sh
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/single-gpu/run_dense_ti2v.sh
-
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/single-gpu/run_moe_t2i.sh
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/single-gpu/run_moe_t2v.sh
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/single-gpu/run_moe_ti2v.sh
+python -m pip install --no-deps sglang==0.5.13.post1 apache-tvm-ffi==0.1.9 tilelang==0.1.8 triton==3.6.0
 ```
 
-Multi-GPU no-refiner scripts use the same inference arguments as the single-GPU
-scripts, plus CP8 and FSDP:
-
-```bash
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/multi-gpus-no-refiner/run_dense_t2i_fsdp_cp8.sh
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/multi-gpus-no-refiner/run_dense_t2v_fsdp_cp8.sh
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/multi-gpus-no-refiner/run_dense_ti2v_fsdp_cp8.sh
+The repository includes the runtime adapter; no unmerged LingBot branch or local hidden file is needed:
 
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/multi-gpus-no-refiner/run_moe_t2i_fsdp_cp8.sh
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/multi-gpus-no-refiner/run_moe_t2v_fsdp_cp8.sh
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/multi-gpus-no-refiner/run_moe_ti2v_fsdp_cp8.sh
-```
+```python
+import sys
+from huggingface_hub import snapshot_download
 
-Multi-GPU refiner scripts use CP8 + FSDP + batched CFG by default. They also
-default to direct diffusers; set `BACKEND=sglang` externally when you want to
-exercise SGLang Diffusion. MoE multi-GPU T2V/TI2V scripts additionally run the
-refiner.
+root = snapshot_download("WaveCut/LingBot-Video-MoE-30B-A3B-SDNQ-uint4-static")
+sys.path.insert(0, root)
 
-```bash
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/multi-gpus/run_dense_t2i_fsdp_cp8.sh
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/multi-gpus/run_dense_t2v_fsdp_cp8.sh
-MODEL_DIR="$DENSE_MODEL_DIR" ./scripts/multi-gpus/run_dense_ti2v_fsdp_cp8.sh
+from lingbot_sdnq_runtime import load_pipeline
 
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/multi-gpus/run_moe_t2i_fsdp_cp8.sh
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/multi-gpus/run_moe_t2v_refiner_fsdp_cp8.sh
-MODEL_DIR="$MOE_MODEL_DIR" ./scripts/multi-gpus/run_moe_ti2v_refiner_fsdp_cp8.sh
+pipe = load_pipeline(root, device="cuda")
+# For the MoE refiner: load_pipeline(root, transformer_subfolder="refiner", device="cuda")
 ```
 
-All scripts accept the same environment overrides, such as `PROMPT_JSON`,
-`IMAGE`, `OUT_DIR`, `HEIGHT`, `WIDTH`, `STEPS`, `GUIDANCE_SCALE`, `SHIFT`,
-`SEED`, `FPS`, `BACKEND`, and `PYTHON_BIN`. Refiner scripts also accept
-`REFINER_HEIGHT`, `REFINER_WIDTH`, `REFINER_STEPS`,
-`REFINER_GUIDANCE_SCALE`, `REFINER_SHIFT`, `REFINER_T_THRESH`, and
-`REFINER_SIGMA_TAIL_STEPS`. MoE scripts default to grouped expert execution
-(`LINGBOT_MOE_EXPERT_BACKEND=grouped_mm`).
-
-See English Docs or 中文文档 for the
-detailed prompt rewrite, auto-negative, TI2V, base-only/refiner, distributed
-SGLang, and speed-first FP8 workflows.
+See [`prompts.json`](prompts.json) for the exact A/B inputs and [`benchmark/summary.json`](benchmark/summary.json) for portable metrics. Recorded consumer/offload smoke artifacts: `benchmark/smokes/moe-refiner-sdnq-standard.json`, `benchmark/smokes/moe-refiner-sdnq-standard.mp4`, `benchmark/smokes/moe-sdnq-model.json`, `benchmark/smokes/moe-sdnq-model.mp4`, `benchmark/smokes/moe-sdnq-sequential.json`, `benchmark/smokes/moe-sdnq-sequential.mp4`, `benchmark/smokes/moe-sdnq-standard.json`, `benchmark/smokes/moe-sdnq-standard.mp4`.
 
-## 📊 Benchmarks
+## Runtime behavior and limitations
 
-### 🏛️ Public Benchmark
+- Generic SDNQ Linear layers use eager BF16 dequantization followed by `F.linear` in the tested Torch 2.8/CUDA 12.8 environment because the current SDNQ Triton quantized-matmul path is incompatible there.
+- Packed MoE experts are dequantized for each expert call and executed by the pinned SGLang Triton fused-MoE path. The adapter does not keep a persistent BF16 expert-weight cache.
+- Static UINT4 materially changes generated pixels. Inspect the published matrices and per-prompt metrics before choosing this derivative for quality-sensitive work.
+- The MoE SDNQ factory prompt is a severe framing/adherence regression: the valid 832x480 MP4 contains a smaller portrait-like factory view centered on a white canvas (MAE `0.338658`). Treat that sample as a quality failure, not as a successful match to the original.
+- Peak residency and speed depend strongly on resolution, frame count, attention backend, offload mode, and GPU. The numbers above describe the exact recorded B200 run only.
+- The Apache-2.0 upstream license is retained. Users remain responsible for evaluating generated content for their application.
 
-As of July 9th, 2026, LingBot-Video ranks top in [RBench Leaderboard](https://huggingface.co/spaces/DAGroup-PKU/RBench-Leaderboard).
+## Evidence map
 
-| Models | Open-source | Avg. | Manip. | Spatial | Multi-entity | Long-hor. | Reasoning | Single arm | Dual arm | Quadruped | Humanoid |
-| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **LingBot-Video (Ours)** | ✅ | **0.620** | **0.578** | <u>0.643</u> | 0.444 | **0.634** | <u>0.505</u> | 0.636 | 0.639 | **0.758** | 0.689 |
-| Cosmos3 Super | ✅ | 0.581 | 0.487 | 0.642 | 0.444 | <u>0.591</u> | 0.395 | 0.615 | 0.623 | <u>0.739</u> | <u>0.691</u> |
-| LongCat-Video | ✅ | 0.437 | 0.372 | 0.310 | 0.220 | 0.384 | 0.186 | 0.586 | 0.576 | 0.681 | 0.621 |
-| Wan 2.2 A14B | ✅ | 0.507 | 0.381 | 0.454 | 0.373 | 0.501 | 0.330 | 0.608 | 0.582 | 0.690 | 0.648 |
-| HunyuanVideo 1.5 | ✅ | 0.460 | 0.442 | 0.316 | 0.312 | 0.438 | 0.364 | 0.513 | 0.526 | 0.634 | 0.595 |
-| Wan 2.6 | ❌ | <u>0.607</u> | 0.546 | **0.656** | <u>0.479</u> | 0.514 | **0.531** | **0.666** | **0.681** | 0.723 | 0.667 |
-| Seedance 1.5 pro | ❌ | 0.584 | <u>0.577</u> | 0.495 | **0.484** | 0.570 | 0.470 | <u>0.648</u> | <u>0.641</u> | 0.680 | **0.692** |
-| Veo 3 | ❌ | 0.563 | 0.521 | 0.508 | 0.430 | 0.530 | 0.504 | 0.634 | 0.610 | 0.689 | 0.637 |
-
-
-*Note: **Bold** indicates the best performance, and <u>underline</u> indicates the second best.*
-
-## ⚖️ License
-This project is licensed under the Apache 2.0 License. Please refer to the [LICENSE file](LICENSE) for the full text, including details on rights and restrictions.
-
-## 📚 Citation
-If you find this work useful for your research, please cite our paper:
-
-```bibtex
-@article{lingbot-video,
-  title = {Scaling Mixture-of-Experts Video Pretraining for Embodied Intelligence},
-  author = {Shuailei Ma and Jiaqi Liao and Xinyang Wang and Jingjing Wang and Chaoran Feng and Zijing Hu and Chong Bao and Zichen Xi and Yuqi Gan and Weisen Wang and Yanhong Zeng and Qin Zhao and Zifan Shi and Wei Wu and Hao Ouyang and Qiuyu Wang and Shangzhan Zhang and Jiahao Shao and Yipengjing Sun and Liangxiao Hu and Lunke Pan and Nan Xue and Kecheng Zheng and Yinghao Xu and Xing Zhu and Yujun Shen and Ka Leong Cheng},
-  journal={arXiv preprint arXiv:2607.xxxxx},
-  year = {2026}
-}
-```
+- [`quantization_manifest.json`](quantization_manifest.json): recipe, revisions, per-component and expert coverage.
+- [`prompts.json`](prompts.json): exact structured prompts, negative prompt, seeds, and generation settings.
+- [`benchmark/summary.json`](benchmark/summary.json): portable aggregate benchmark record.
+- [`benchmark/base`](benchmark/base/): unmodified original and SDNQ raw metrics and resource samples.
+- [`benchmark/comparison`](benchmark/comparison/): frame-aligned MAE/RMSE/PSNR/SSIM/LPIPS records.
+- [`SHA256SUMS`](SHA256SUMS): hashes for all published files, including model shards.

SHA256SUMS

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benchmark/audits/numerical-smoke.json

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benchmark/audits/source-audit.json

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benchmark/audits/video-encoding-audit.json

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benchmark/audits/video-encoding-repair.json

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benchmark/base/original/command.json

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+}

benchmark/base/original/metrics.csv

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benchmark/base/original/metrics.jsonl

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benchmark/base/sdnq/metrics.csv

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benchmark/base/sdnq/summary.json

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+      "raw_frames_sha256": "7a51833e767e2dffec08ef8a47ab57a5541ad34cdedc30553362369cd287f2c9",
+      "raw_frames_size_bytes": 87459968,
+      "sample_count": 555,
+      "sample_interval_seconds": 0.25,
+      "seconds_per_frame": 2.1726942936019102,
+      "seed": 4204,
+      "shift": 3.0,
+      "torch_allocated_after_mb": 27094.3193359375,
+      "torch_peak_allocated_mb": 36067.27880859375,
+      "torch_peak_reserved_mb": 40756.0,
+      "torch_reserved_after_mb": 40756.0,
+      "variant": "sdnq",
+      "width": 832
+    },
+    {
+      "batch_cfg": false,
+      "cold_generation": false,
+      "ffprobe": {
+        "programs": [],
+        "streams": [
+          {
+            "codec_name": "h264",
+            "duration": "3.041667",
+            "height": 480,
+            "nb_frames": "73",
+            "pix_fmt": "yuv420p",
+            "r_frame_rate": "24/1",
+            "width": 832
+          }
+        ]
+      },
+      "fps": 24,
+      "generation_seconds": 158.87487718299963,
+      "guidance_scale": 3.0,
+      "height": 480,
+      "model_key": "moe",
+      "negative_prompt": "{\"universal_negative\": {\"visual_quality\": [\"low quality\", \"worst quality\", \"blurry\", \"pixelated\", \"jpeg artifacts\", \"low resolution\", \"unstable color\", \"color flicker\", \"underexposed\", \"overexposed\", \"invisible subject\", \"subject hidden in darkness\"], \"artistic_style\": [\"painting\", \"illustration\", \"drawing\", \"cartoon\", \"3d render\", \"cgi\", \"sketch\", \"digital art\"], \"composition_and_content\": [\"text\", \"watermark\", \"signature\", \"logo\", \"subtitles\", \"pillarboxed\", \"side bars\", \"portrait image in landscape frame\"], \"temporal_and_motion_stability\": [\"flickering\", \"jittery\", \"motion blur\", \"temporal inconsistency\", \"warping\", \"morphing\", \"incoherent motion\", \"unnatural movement\", \"static object with sudden jump\", \"frame-to-frame inconsistency\"], \"material_and_structure\": [\"plastic-like glass\", \"unrealistic texture\", \"deformed bottle\", \"liquid freezing improperly\", \"distorted reflections\"]}}",
+      "null_cond_clone_zero": false,
+      "num_frames": 73,
+      "num_inference_steps": 40,
+      "nvidia_smi_after": {
+        "memory.total": "183359",
+        "memory.used": "41878",
+        "name": "NVIDIA B200",
+        "power.draw": "260.35",
+        "temperature.gpu": "43",
+        "timestamp": "2026/07/10 08:54:25.766",
+        "utilization.gpu": "0"
+      },
+      "output_file": "results/benchmark/moe/sdnq/videos/05_factory_battery_cells.mp4",
+      "output_sha256": "7b6909a2aa49397f574338560cce4a3e47bb1930e55304f897523fa436fe7333",
+      "output_size_bytes": 138406,
+      "peak_disk_used_bytes": 183981420544.0,
+      "peak_gpu_memory_used_mb": 41878.0,
+      "peak_gpu_power_watts": 981.13,
+      "peak_gpu_utilization_percent": 100.0,
+      "peak_process_rss_bytes": 3307651072.0,
+      "peak_system_used_bytes": 87309594624.0,
+      "prompt": "{\"comprehensive_description\":{\"scene_content_description\":\"A clean high-tech factory line assembling transparent solid-state battery cells. Two white robotic arms move with precise synchronized motion over a brushed steel conveyor. One arm lowers a translucent rectangular cell into a copper test fixture while the other arm scans it with a blue light bar. Tiny reflections slide across glass safety panels, and status lights pulse softly. The scene should look like realistic industrial automation footage, not CGI, with crisp metal surfaces and controlled motion.\",\"camera_movement_description\":\"The camera is locked off in a medium-wide three-quarter view with a subtle mechanical vibration, emphasizing repeatable robotic motion.\"},\"camera_info\":{\"color\":\"White, brushed steel, copper, blue inspection light\",\"frame_size\":\"Medium wide\",\"shot_type_angle\":\"Three-quarter eye-level industrial view\",\"lens_size\":\"Medium lens\",\"composition\":\"Two robotic arms framing the battery cell at center\",\"lighting\":\"Even overhead factory lighting with blue scanner highlight\",\"lighting_type\":\"Artificial industrial lighting\"},\"world_knowledge\":[],\"prominent_elements\":[{\"name\":\"left robotic arm\",\"description\":\"A white six-axis robotic arm with a vacuum gripper holding a transparent rectangular battery cell.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.7s]\",\"action\":\"moves downward from upper left with the transparent cell held steady\"},{\"timestamp\":\"[0.7s - 1.4s]\",\"action\":\"places the cell into a copper test fixture\"},{\"timestamp\":\"[1.4s - 2.0s]\",\"action\":\"releases the cell and retracts slightly upward\"}],\"location\":\"left side moving toward center\",\"relative_size\":\"large\",\"shape_and_color\":\"white articulated segments with black joints\",\"texture\":\"smooth painted metal and rubber vacuum cups\",\"appearance_details\":\"visible cable routing, small green status LED\",\"relationship\":\"positions the battery cell for testing\",\"orientation\":\"angled downward toward the center fixture\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"},{\"name\":\"right robotic scanner arm\",\"description\":\"A second white robotic arm carrying a rectangular blue inspection light bar.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.9s]\",\"action\":\"waits above the copper fixture with blue scanner light dim\"},{\"timestamp\":\"[0.9s - 1.6s]\",\"action\":\"sweeps the blue light bar across the transparent cell\"},{\"timestamp\":\"[1.6s - 2.0s]\",\"action\":\"pauses as the scanner light pulses once\"}],\"location\":\"right side above the conveyor\",\"relative_size\":\"large\",\"shape_and_color\":\"white arm with glowing blue rectangular scanner\",\"texture\":\"smooth metal casing and glass scanner cover\",\"appearance_details\":\"blue light strip, black joints, compact sensor module\",\"relationship\":\"inspects the cell after placement\",\"orientation\":\"angled left toward the battery cell\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"},{\"name\":\"transparent battery cell and copper fixture\",\"description\":\"A clear rectangular solid-state cell seated into a copper test fixture on a steel conveyor.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.7s]\",\"action\":\"moves with the left arm above the fixture\"},{\"timestamp\":\"[0.7s - 1.4s]\",\"action\":\"is lowered precisely into the fixture\"},{\"timestamp\":\"[1.4s - 2.0s]\",\"action\":\"remains stationary while blue inspection light passes over it\"}],\"location\":\"center of the frame on the conveyor\",\"relative_size\":\"medium\",\"shape_and_color\":\"transparent rectangle with faint internal layers, copper fixture\",\"texture\":\"clear glass-like cell, brushed copper, polished steel\",\"appearance_details\":\"thin internal laminate layers, small alignment pins, clean conveyor surface\",\"relationship\":\"object being assembled and inspected\",\"orientation\":\"horizontal in the fixture\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"}]}",
+      "prompt_file": "05_factory_battery_cells.json",
+      "prompt_id": "05_factory_battery_cells",
+      "raw_frames_file": "results/benchmark/moe/sdnq/raw_frames/05_factory_battery_cells.npy",
+      "raw_frames_sha256": "2a8b445ef18edfe1789e3d4b779b9a8eb290055605f24b4e7cef890f927a3e5e",
+      "raw_frames_size_bytes": 87459968,
+      "sample_count": 556,
+      "sample_interval_seconds": 0.25,
+      "seconds_per_frame": 2.176368180589036,
+      "seed": 4205,
+      "shift": 3.0,
+      "torch_allocated_after_mb": 27094.3193359375,
+      "torch_peak_allocated_mb": 36067.6962890625,
+      "torch_peak_reserved_mb": 40756.0,
+      "torch_reserved_after_mb": 40756.0,
+      "variant": "sdnq",
+      "width": 832
+    }
+  ],
+  "sdnq_revision": "d841c383ff7be38728d4df829e17af4f15d4fd66",
+  "settings": {
+    "batch_cfg": false,
+    "fps": 24,
+    "guidance_scale": 3.0,
+    "height": 480,
+    "null_cond_clone_zero": false,
+    "num_frames": 73,
+    "num_inference_steps": 40,
+    "shift": 3.0,
+    "width": 832
+  },
+  "source_repo_id": "robbyant/lingbot-video-moe-30b-a3b",
+  "source_revision": "f2e538f64afe00cc4ae674db2aeb52e2945edfd5",
+  "variant": "sdnq"
+}

benchmark/environment/environment.json

@@ -0,0 +1,14 @@
+{
+  "accelerate": "1.14.0",
+  "cuda": "12.8",
+  "cuda_available": true,
+  "cuda_device": "NVIDIA B200",
+  "diffusers": "0.39.0",
+  "huggingface_hub": "1.23.0",
+  "platform": "Linux-6.8.0-124-generic-x86_64-with-glibc2.39",
+  "python": "3.12.3",
+  "safetensors": "0.8.0",
+  "sdnq_file": "/workspace/lingbot-video-sdnq/sources/sdnq/src/sdnq/__init__.py",
+  "torch": "2.8.0+cu128",
+  "transformers": "5.8.1"
+}

benchmark/environment/nvidia-smi-q.txt

@@ -0,0 +1,258 @@
+
+==============NVSMI LOG==============
+
+Timestamp                                              : Fri Jul 10 07:54:56 2026
+Driver Version                                         : 595.71.05
+CUDA Version                                           : 13.2
+
+Attached GPUs                                          : 1
+GPU 00000000:17:00.0
+    Product Name                                       : NVIDIA B200
+    Product Brand                                      : NVIDIA
+    Product Architecture                               : Blackwell
+    Display Mode                                       : Requested functionality has been deprecated
+    Display Attached                                   : Yes
+    Display Active                                     : Disabled
+    Persistence Mode                                   : Enabled
+    Addressing Mode                                    : None
+    MIG Mode
+        Current                                        : Disabled
+        Pending                                        : Disabled
+    Accounting Mode                                    : Disabled
+    Accounting Mode Buffer Size                        : 4000
+    Driver Model
+        Current                                        : N/A
+        Pending                                        : N/A
+    Serial Number                                      : 1650526070674
+    GPU UUID                                           : GPU-894b3998-1968-8750-364f-2fd6553c0678
+    GPU PDI                                            : 0x584812bd84a56e07
+    Minor Number                                       : 0
+    VBIOS Version                                      : 97.00.E4.00.11
+    MultiGPU Board                                     : No
+    Board ID                                           : 0x1700
+    Board Part Number                                  : 692-2G525-0220-500
+    GPU Part Number                                    : 2901-886-A1
+    FRU Part Number                                    : N/A
+    Platform Info
+        Chassis Serial Number                          : 
+        Slot Number                                    : N/A
+        Tray Index                                     : N/A
+        Host ID                                        : 1
+        Peer Type                                      : Switch Connected
+        Module Id                                      : 6
+        GPU Fabric GUID                                : 0x78375a0003e23aac
+    Inforom Version
+        Image Version                                  : G525.0220.00.03
+        OEM Object                                     : 2.1
+        ECC Object                                     : 7.16
+        Power Management Object                        : N/A
+    Inforom BBX Object Flush
+        Latest Timestamp                               : 2026/07/09 21:45:51.022
+        Latest Duration                                : 59141 us
+    GPU Operation Mode
+        Current                                        : N/A
+        Pending                                        : N/A
+    GPU C2C Mode                                       : Disabled
+    GPU Virtualization Mode
+        Virtualization Mode                            : None
+        Host VGPU Mode                                 : N/A
+        vGPU Heterogeneous Mode                        : N/A
+    GPU Recovery Action                                : None
+    GSP Firmware Version                               : 595.71.05
+    IBMNPU
+        Relaxed Ordering Mode                          : N/A
+    PCI
+        Bus                                            : 0x17
+        Device                                         : 0x00
+        Domain                                         : 0x0000
+        Base Classcode                                 : 0x3
+        Sub Classcode                                  : 0x2
+        Device Id                                      : 0x290110DE
+        Bus Id                                         : 00000000:17:00.0
+        Sub System Id                                  : 0x199910DE
+        GPU Link Info
+            PCIe Generation
+                Max                                    : 5
+                Current                                : 5
+                Device Current                         : 5
+                Device Max                             : 5
+                Host Max                               : 5
+            Link Width
+                Max                                    : 16x
+                Current                                : 16x
+        Bridge Chip
+            Type                                       : N/A
+            Firmware                                   : N/A
+        Replays Since Reset                            : 0
+        Replay Number Rollovers                        : 0
+        Tx Throughput                                  : 814 KB/s
+        Rx Throughput                                  : 553 KB/s
+        Atomic Caps Outbound                           : FETCHADD_32 FETCHADD_64 SWAP_32 SWAP_64 CAS_32 CAS_64 
+        Atomic Caps Inbound                            : FETCHADD_32 FETCHADD_64 SWAP_32 SWAP_64 CAS_32 CAS_64 
+    Fan Speed                                          : N/A
+    Performance State                                  : P0
+    Clocks Event Reasons
+        Idle                                           : Not Active
+        Applications Clocks Setting                    : Not Active
+        SW Power Cap                                   : Not Active
+        HW Slowdown                                    : Not Active
+            HW Thermal Slowdown                        : Not Active
+            HW Power Brake Slowdown                    : Not Active
+        Sync Boost                                     : Not Active
+        SW Thermal Slowdown                            : Not Active
+        Display Clock Setting                          : Not Active
+    Clocks Event Reasons Counters
+        SW Power Capping                               : 35245214504 us
+        Sync Boost                                     : 0 us
+        SW Thermal Slowdown                            : 0 us
+        HW Thermal Slowdown                            : 0 us
+        HW Power Braking                               : 0 us
+    Sparse Operation Mode                              : N/A
+    FB Memory Usage
+        Total                                          : 183359 MiB
+        Reserved                                       : 735 MiB
+        Used                                           : 0 MiB
+        Free                                           : 182625 MiB
+    BAR1 Memory Usage
+        Total                                          : 262144 MiB
+        Used                                           : 0 MiB
+        Free                                           : 262144 MiB
+    Conf Compute Protected Memory Usage
+        Total                                          : 0 MiB
+        Used                                           : 0 MiB
+        Free                                           : 0 MiB
+    Compute Mode                                       : Default
+    Utilization
+        GPU                                            : 0 %
+        Memory                                         : 0 %
+        Encoder                                        : 0 %
+        Decoder                                        : 0 %
+        JPEG                                           : 0 %
+        OFA                                            : 0 %
+    Encoder Stats
+        Active Sessions                                : 0
+        Average FPS                                    : 0
+        Average Latency                                : 0
+    FBC Stats
+        Active Sessions                                : 0
+        Average FPS                                    : 0
+        Average Latency                                : 0
+    DRAM Encryption Mode
+        Current                                        : N/A
+        Pending                                        : N/A
+    ECC Mode
+        Current                                        : Enabled
+        Pending                                        : Enabled
+    ECC Errors
+        Volatile
+            SRAM Correctable                           : 0
+            SRAM Uncorrectable Parity                  : 0
+            SRAM Uncorrectable SEC-DED                 : 0
+            DRAM Correctable                           : 0
+            DRAM Uncorrectable                         : 0
+        Aggregate
+            SRAM Correctable                           : 0
+            SRAM Uncorrectable Parity                  : 0
+            SRAM Uncorrectable SEC-DED                 : 0
+            DRAM Correctable                           : 0
+            DRAM Uncorrectable                         : 0
+            SRAM Threshold Exceeded                    : No
+        Aggregate Uncorrectable SRAM Sources
+            SRAM L2                                    : 0
+            SRAM SM                                    : 0
+            SRAM Microcontroller                       : 0
+            SRAM PCIE                                  : 0
+            SRAM Other                                 : 0
+        Channel Repair Pending                         : No
+        TPC Repair Pending                             : No
+        Unrepairable Memory                            : No
+    Retired Pages
+        Single Bit ECC                                 : N/A
+        Double Bit ECC                                 : N/A
+        Pending Page Blacklist                         : N/A
+    Remapped Rows
+        Correctable Error                              : 0
+        Inactive Correctable Error                     : 0
+        Uncorrectable Error                            : 0
+        Inactive Uncorrectable Error                   : 0
+        Pending                                        : No
+        Remapping Failure Occurred                     : No
+        Bank Remap Availability Histogram
+            Max                                        : 3840 bank(s)
+            High                                       : 0 bank(s)
+            Partial                                    : 0 bank(s)
+            Low                                        : 0 bank(s)
+            None                                       : 0 bank(s)
+    Temperature
+        GPU Current Temp                               : 31 C
+        GPU T.Limit Temp                               : 57 C
+        GPU Shutdown T.Limit Temp                      : -5 C
+        GPU Slowdown T.Limit Temp                      : -3 C
+        GPU Max Operating T.Limit Temp                 : 0 C
+        GPU Target Temperature                         : N/A
+        Memory Current Temp                            : 31 C
+        Memory Max Operating T.Limit Temp              : 0 C
+    GPU Power Readings
+        Average Power Draw                             : 248.19 W
+        Instantaneous Power Draw                       : 248.11 W
+        Current Power Limit                            : 1000.00 W
+        Requested Power Limit                          : 1000.00 W
+        Default Power Limit                            : 1000.00 W
+        Min Power Limit                                : 200.00 W
+        Max Power Limit                                : 1000.00 W
+    GPU Memory Power Readings 
+        Average Power Draw                             : 24.90 W
+        Instantaneous Power Draw                       : N/A
+    Module Power Readings
+        Average Power Draw                             : N/A
+        Instantaneous Power Draw                       : N/A
+        Current Power Limit                            : N/A
+        Requested Power Limit                          : N/A
+        Default Power Limit                            : N/A
+        Min Power Limit                                : N/A
+        Max Power Limit                                : N/A
+    Power Smoothing                                    : Insufficient Permissions
+    Workload Power Profiles
+        Requested Profiles                             : N/A
+        Enforced Profiles                              : N/A
+    EDPp Multiplier                                    : 100.00%
+    Clocks
+        Graphics                                       : 1965 MHz
+        SM                                             : 1965 MHz
+        Memory                                         : 3996 MHz
+        Video                                          : 1860 MHz
+    Applications Clocks
+        Graphics                                       : Requested functionality has been deprecated
+        Memory                                         : Requested functionality has been deprecated
+    Default Applications Clocks
+        Graphics                                       : Requested functionality has been deprecated
+        Memory                                         : Requested functionality has been deprecated
+    Deferred Clocks
+        Memory                                         : N/A
+    Max Clocks
+        Graphics                                       : 1965 MHz
+        SM                                             : 1965 MHz
+        Memory                                         : 3996 MHz
+        Video                                          : 1965 MHz
+    Max Customer Boost Clocks
+        Graphics                                       : 1965 MHz
+    Clock Policy
+        Auto Boost                                     : N/A
+        Auto Boost Default                             : N/A
+    Fabric
+        State                                          : Completed
+        Status                                         : Success
+        CliqueId                                       : 0
+        ClusterUUID                                    : 00000000-0000-0000-0000-000000000000
+        Health
+            Summary                                    : Healthy
+            Bandwidth                                  : N/A
+            Route Recovery in progress                 : N/A
+            Route Unhealthy                            : N/A
+            Access Timeout Recovery                    : False
+            Incorrect Configuration                    : N/A
+            Partition Assigned                         : N/A
+    Processes                                          : None
+    Capabilities
+        EGM                                            : disabled
+

benchmark/environment/pip-freeze.txt

@@ -0,0 +1,170 @@
+accelerate==1.14.0
+annotated-doc==0.0.4
+anyio==4.11.0
+argon2-cffi==25.1.0
+argon2-cffi-bindings==25.1.0
+arrow==1.3.0
+asttokens==3.0.0
+async-lru==2.0.5
+attrs==25.4.0
+babel==2.17.0
+beautifulsoup4==4.14.2
+bleach==6.2.0
+blinker==1.7.0
+certifi==2025.10.5
+cffi==2.0.0
+charset-normalizer==3.4.3
+click==8.4.2
+comm==0.2.3
+cryptography==41.0.7
+dbus-python==1.3.2
+debugpy==1.8.17
+decorator==5.2.1
+decord==0.6.0
+defusedxml==0.7.1
+diffusers==0.39.0
+distlib==0.4.0
+distro==1.9.0
+executing==2.2.1
+fastjsonschema==2.21.2
+filelock==3.20.0
+fqdn==1.5.1
+fsspec==2024.6.1
+h11==0.16.0
+hf-xet==1.5.1
+hf_transfer==0.1.9
+httpcore==1.0.9
+httplib2==0.20.4
+httpx==0.28.1
+huggingface_hub==1.23.0
+idna==3.10
+ImageIO==2.37.3
+imageio-ffmpeg==0.6.0
+importlib_metadata==9.0.0
+ipykernel==6.30.1
+ipython==9.6.0
+ipython_pygments_lexers==1.1.1
+ipywidgets==8.1.7
+isoduration==20.11.0
+jedi==0.19.2
+Jinja2==3.1.6
+json5==0.12.1
+json_repair==0.61.2
+jsonpointer==3.0.0
+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+jupyter-archive==3.4.0
+jupyter-events==0.12.0
+jupyter-lsp==2.3.0
+jupyter_client==8.6.3
+jupyter_core==5.8.1
+jupyter_server==2.17.0
+jupyter_server_terminals==0.5.3
+jupyterlab==4.4.9
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.27.3
+jupyterlab_widgets==3.0.15
+lark==1.3.0
+launchpadlib==1.11.0
+lazr.restfulclient==0.14.6
+lazr.uri==1.0.6
+lazy-loader==0.5
+-e git+https://github.com/Robbyant/lingbot-video.git@a2bb04b78edd848500dc27a26e035a95442ae186#egg=lingbot_video
+lpips==0.1.4
+markdown-it-py==4.2.0
+MarkupSafe==3.0.3
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mistune==3.1.4
+mpmath==1.3.0
+nbclient==0.10.2
+nbconvert==7.16.6
+nbformat==5.10.4
+nest-asyncio==1.6.0
+networkx==3.3
+notebook==7.4.2
+notebook_shim==0.2.4
+numpy==2.1.2
+nvidia-cublas-cu12==12.8.4.1
+nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cudnn-cu12==9.10.2.21
+nvidia-cufft-cu12==11.3.3.83
+nvidia-cufile-cu12==1.13.1.3
+nvidia-curand-cu12==10.3.9.90
+nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusparse-cu12==12.5.8.93
+nvidia-cusparselt-cu12==0.7.1
+nvidia-nccl-cu12==2.27.3
+nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvtx-cu12==12.8.90
+oauthlib==3.2.2
+packaging==25.0
+pandocfilters==1.5.1
+parso==0.8.5
+peft==0.19.1
+pexpect==4.9.0
+pillow==12.3.0
+platformdirs==4.5.0
+prometheus_client==0.23.1
+prompt_toolkit==3.0.52
+psutil==7.2.2
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pycparser==2.23
+Pygments==2.19.2
+PyGObject==3.48.2
+PyJWT==2.7.0
+pyparsing==3.1.1
+python-apt==2.7.7+ubuntu5
+python-dateutil==2.9.0.post0
+python-json-logger==4.0.0
+PyYAML==6.0.3
+pyzmq==27.1.0
+referencing==0.36.2
+regex==2026.6.28
+requests==2.34.2
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rfc3987-syntax==1.1.0
+rich==15.0.0
+rpds-py==0.27.1
+safetensors==0.8.0
+scikit-image==0.26.0
+scipy==1.18.0
+-e git+https://github.com/Disty0/sdnq.git@d841c383ff7be38728d4df829e17af4f15d4fd66#egg=sdnq
+Send2Trash==1.8.3
+setuptools==83.0.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+soupsieve==2.8
+stack-data==0.6.3
+sympy==1.13.3
+terminado==0.18.1
+tifffile==2026.6.1
+tinycss2==1.4.0
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benchmark/paired.metrics.csv

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benchmark/paired.metrics.jsonl

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benchmark/prompts/01_arctic_research_drone.json

@@ -0,0 +1,95 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "A cinematic dusk shot of an Arctic research outpost on a wind-polished ice shelf under green aurora ribbons. A compact orange rover with a roof-mounted lidar mast drives slowly between blue instrument crates and slender weather antennas. Fine snow dust trails behind the tires, and warm light glows from the station windows against the cold cyan landscape. The atmosphere is realistic, high-detail documentary science footage with crisp snow texture, vapor in the air, and physically plausible shadows.",
+      "camera_movement_description": "The camera begins as a low, smooth drone tracking shot behind the rover, then arcs gently left to reveal the glowing station and aurora-filled sky while keeping the rover dominant in frame."
+    },
+    "camera_info": {
+      "color": "Cold cyan with warm orange accents",
+      "frame_size": "Wide",
+      "shot_type_angle": "Low angle tracking shot",
+      "lens_size": "Wide lens",
+      "composition": "Rover in lower center, research station in the background, aurora above",
+      "lighting": "Soft twilight with window glow",
+      "lighting_type": "Natural dusk and practical interior lights"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "orange autonomous research rover",
+        "description": "A rugged six-wheeled scientific rover with orange body panels, black tires, a lidar mast, and small blinking status lights.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 0.7s]",
+            "action": "drives forward slowly across packed snow, tires compressing the surface"
+          },
+          {
+            "timestamp": "[0.7s - 1.5s]",
+            "action": "turns slightly left as the lidar mast rotates"
+          },
+          {
+            "timestamp": "[1.5s - 2.0s]",
+            "action": "continues toward the lit station while fine snow trails behind"
+          }
+        ],
+        "location": "lower center of the frame",
+        "relative_size": "dominant",
+        "shape_and_color": "low rectangular orange body with black wheels and grey sensor mast",
+        "texture": "matte painted metal, rubber tires, frost on edges",
+        "appearance_details": "roof lidar, small antennas, narrow headlights, compact cargo rack",
+        "relationship": "the main moving subject, traveling toward the research outpost",
+        "orientation": "moving away from the camera and slightly left",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "Arctic research station",
+        "description": "A modular polar station made of connected white containers with warm yellow light in the windows.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 2.0s]",
+            "action": "remains stationary as wind-blown snow passes in front of it"
+          }
+        ],
+        "location": "middle background",
+        "relative_size": "large",
+        "shape_and_color": "rectangular white modules with dark seams and glowing windows",
+        "texture": "frosted metal panels and glass",
+        "appearance_details": "small stairs, railings, antennas, blue equipment crates nearby",
+        "relationship": "destination of the rover",
+        "orientation": "angled three-quarter view",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "aurora ribbons",
+        "description": "Green aurora curtains stretching across the darkening polar sky.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 2.0s]",
+            "action": "shimmer slowly and drift laterally across the sky"
+          }
+        ],
+        "location": "upper half of the frame",
+        "relative_size": "large",
+        "shape_and_color": "soft green luminous ribbons",
+        "texture": "translucent atmospheric glow",
+        "appearance_details": "layered folds and faint stars behind",
+        "relationship": "dominates the sky above the station",
+        "orientation": "horizontal sweeping arcs",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": true,
+        "number_of_objects": "many luminous bands"
+      }
+    ]
+  },
+  "duration": 2.0
+}

benchmark/prompts/02_macro_pastry_glaze.json

@@ -0,0 +1,73 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "An extreme macro food cinematography shot in a professional pastry kitchen. A glossy dark chocolate dome dessert sits on a rotating metal turntable. A thin stream of amber caramel glaze pours from above, flowing over the curved surface in slow ribbons and revealing tiny reflections from softbox lights. Crushed pistachio dust and edible gold flakes sit around the base on a matte black plate. The look is photorealistic, tactile, high-speed macro video with shallow depth of field and no text.",
+      "camera_movement_description": "The camera holds a close macro angle with a subtle push-in while the dessert rotates clockwise on the turntable."
+    },
+    "camera_info": {
+      "color": "Deep brown, amber, green pistachio, gold highlights",
+      "frame_size": "Extreme close up",
+      "shot_type_angle": "Slight high angle",
+      "lens_size": "Macro lens",
+      "composition": "Dessert centered with glaze entering from top",
+      "lighting": "Large softbox reflections",
+      "lighting_type": "Controlled studio kitchen lighting"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "chocolate dome dessert",
+        "description": "A small mirror-glazed chocolate dome on a black plate, surrounded by pistachio dust and gold flakes.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 2.0s]",
+            "action": "rotates slowly clockwise on a metal turntable"
+          }
+        ],
+        "location": "center of the frame",
+        "relative_size": "dominant",
+        "shape_and_color": "hemispherical dark chocolate dome on round black plate",
+        "texture": "mirror-gloss surface with smooth curved reflections",
+        "appearance_details": "tiny gold flakes, green pistachio crumbs, clean pastry plating",
+        "relationship": "receives the caramel glaze",
+        "orientation": "upright on the turntable",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "caramel glaze stream",
+        "description": "A viscous amber caramel stream pouring from above and spreading over the dessert.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 0.8s]",
+            "action": "falls as a narrow glossy stream onto the crown of the dome"
+          },
+          {
+            "timestamp": "[0.8s - 1.6s]",
+            "action": "forms slow ribbons that slide down the curved chocolate surface"
+          },
+          {
+            "timestamp": "[1.6s - 2.0s]",
+            "action": "collects at the base in a thin shiny ring"
+          }
+        ],
+        "location": "entering from top center and flowing over the dessert",
+        "relative_size": "medium",
+        "shape_and_color": "thin amber liquid ribbons",
+        "texture": "viscous, glossy, translucent caramel",
+        "appearance_details": "bright highlights and tiny bubbles",
+        "relationship": "coats the chocolate dome",
+        "orientation": "vertical stream becoming downward trails",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      }
+    ]
+  },
+  "duration": 2.0
+}

benchmark/prompts/03_rain_market_lanterns.json

@@ -0,0 +1,99 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "A lively rainy night market in a narrow old-city street, filmed like naturalistic cinema. Red paper lanterns hang from wooden stalls, steam rises from bowls of soup, and wet cobblestones mirror orange and teal reflections. A vendor in a dark apron slides a steaming bowl across a counter to a traveler in a blue raincoat. Umbrellas pass in the foreground, partially occluding the scene for a moment. The mood is warm, humid, detailed, and grounded, with no readable signs or subtitles.",
+      "camera_movement_description": "The camera performs a slow lateral dolly from left to right at counter height, with foreground umbrellas briefly crossing the lens."
+    },
+    "camera_info": {
+      "color": "Warm lantern orange, teal rain reflections, red accents",
+      "frame_size": "Medium wide",
+      "shot_type_angle": "Eye level",
+      "lens_size": "Medium lens",
+      "composition": "Vendor stall centered, traveler on right, passing umbrellas in foreground",
+      "lighting": "Lantern light mixed with rain reflections",
+      "lighting_type": "Night practical lighting"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "food vendor",
+        "description": "A middle-aged street vendor wearing a dark apron and working behind a steaming wooden food stall.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 0.8s]",
+            "action": "lifts a white ceramic bowl from a steaming pot"
+          },
+          {
+            "timestamp": "[0.8s - 1.6s]",
+            "action": "slides the bowl carefully across the counter"
+          },
+          {
+            "timestamp": "[1.6s - 2.0s]",
+            "action": "wipes condensation from the counter with a cloth"
+          }
+        ],
+        "location": "center-left behind the stall counter",
+        "relative_size": "large",
+        "shape_and_color": "human figure in dark apron under warm lantern light",
+        "texture": "wet fabric, skin highlights, wood grain around the stall",
+        "appearance_details": "rolled sleeves, focused expression, steam around hands",
+        "relationship": "serves food to the traveler",
+        "orientation": "facing right toward the customer",
+        "pose": "leaning slightly forward over the counter",
+        "expression": "focused and calm",
+        "clothing": "dark apron over a muted shirt",
+        "gender": "",
+        "skin_tone_and_texture": "natural skin with rain-lit highlights"
+      },
+      {
+        "name": "traveler in blue raincoat",
+        "description": "A traveler wearing a blue hooded raincoat, waiting at the stall with wet sleeves and a folded umbrella.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 1.2s]",
+            "action": "waits with hands near the counter, watching the bowl"
+          },
+          {
+            "timestamp": "[1.2s - 2.0s]",
+            "action": "reaches forward to receive the steaming bowl"
+          }
+        ],
+        "location": "right side of the frame",
+        "relative_size": "medium",
+        "shape_and_color": "blue hooded raincoat silhouette",
+        "texture": "water beads on waterproof fabric",
+        "appearance_details": "hood up, folded umbrella tucked under one arm",
+        "relationship": "customer receiving food from the vendor",
+        "orientation": "facing left toward the stall",
+        "pose": "standing close to the counter",
+        "expression": "expectant and tired",
+        "clothing": "blue raincoat",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "rain and lantern reflections",
+        "description": "Falling rain, steam, and glowing reflections on wet cobblestones.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 2.0s]",
+            "action": "rain falls continuously while reflections ripple on the street"
+          }
+        ],
+        "location": "foreground and background throughout the frame",
+        "relative_size": "large",
+        "shape_and_color": "thin rain streaks, orange and teal reflected pools",
+        "texture": "wet stone, vapor, glossy water",
+        "appearance_details": "soft steam plumes, umbrellas passing close to lens",
+        "relationship": "sets the atmosphere around the market",
+        "orientation": "vertical rainfall and horizontal street reflections",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": true,
+        "number_of_objects": "many raindrops and reflections"
+      }
+    ]
+  },
+  "duration": 2.0
+}

benchmark/prompts/04_underwater_manta.json

@@ -0,0 +1,95 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "A tranquil underwater wildlife scene in clear tropical water. A large manta ray glides above a colorful coral garden while a scuba diver hovers several meters behind, holding a small camera rig. Sunbeams ripple through the surface, schools of tiny silver fish split and rejoin around the manta, and suspended particles drift slowly. The image should feel like realistic underwater documentary footage with natural motion, soft blue-green color, and no fantasy elements.",
+      "camera_movement_description": "The camera tracks smoothly beside the manta ray from a slightly lower angle, drifting forward with the current."
+    },
+    "camera_info": {
+      "color": "Blue-green water with coral reds and yellows",
+      "frame_size": "Wide",
+      "shot_type_angle": "Slight low angle",
+      "lens_size": "Wide underwater lens",
+      "composition": "Manta ray crossing the upper center, coral below, diver in background",
+      "lighting": "Dappled sunlight",
+      "lighting_type": "Natural underwater daylight"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "manta ray",
+        "description": "A large manta ray with broad triangular fins, dark top surface, and pale underside.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 0.8s]",
+            "action": "glides from left to right with slow wing-like fin movement"
+          },
+          {
+            "timestamp": "[0.8s - 1.5s]",
+            "action": "tilts slightly upward as sunbeams cross its back"
+          },
+          {
+            "timestamp": "[1.5s - 2.0s]",
+            "action": "continues forward while small fish scatter around it"
+          }
+        ],
+        "location": "upper center moving toward right",
+        "relative_size": "dominant",
+        "shape_and_color": "wide diamond-like silhouette, dark grey top, pale underside",
+        "texture": "smooth skin with subtle mottling",
+        "appearance_details": "cephalic fins near mouth, long tail trailing behind",
+        "relationship": "main wildlife subject of the video",
+        "orientation": "moving left to right and slightly upward",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "scuba diver",
+        "description": "A diver in black wetsuit and fins, carrying a small underwater camera rig.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 2.0s]",
+            "action": "hovers calmly in the background, exhaling small bubble streams"
+          }
+        ],
+        "location": "mid-background behind the manta ray",
+        "relative_size": "small",
+        "shape_and_color": "human figure in black wetsuit with silver air tank",
+        "texture": "neoprene suit, metal tank, glass mask",
+        "appearance_details": "fins, mask, regulator, compact camera lights",
+        "relationship": "observes the manta ray without touching it",
+        "orientation": "facing the manta ray",
+        "pose": "horizontal hover",
+        "expression": "",
+        "clothing": "black scuba gear",
+        "gender": "",
+        "skin_tone_and_texture": ""
+      },
+      {
+        "name": "coral reef and fish",
+        "description": "A colorful coral garden with small silver fish schooling above it.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 2.0s]",
+            "action": "fish swirl and split around the manta while coral remains fixed"
+          }
+        ],
+        "location": "lower half of the frame",
+        "relative_size": "large",
+        "shape_and_color": "branching corals in red, yellow, and muted purple; silver fish clusters",
+        "texture": "rough coral, shimmering fish scales",
+        "appearance_details": "sand patches, sea fans, drifting particles",
+        "relationship": "environment below the manta ray",
+        "orientation": "reef spreads horizontally across the bottom",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": true,
+        "number_of_objects": "many fish and coral structures"
+      }
+    ]
+  },
+  "duration": 2.0
+}

benchmark/prompts/05_factory_battery_cells.json

@@ -0,0 +1,111 @@
+{
+  "caption": {
+    "comprehensive_description": {
+      "scene_content_description": "A clean high-tech factory line assembling transparent solid-state battery cells. Two white robotic arms move with precise synchronized motion over a brushed steel conveyor. One arm lowers a translucent rectangular cell into a copper test fixture while the other arm scans it with a blue light bar. Tiny reflections slide across glass safety panels, and status lights pulse softly. The scene should look like realistic industrial automation footage, not CGI, with crisp metal surfaces and controlled motion.",
+      "camera_movement_description": "The camera is locked off in a medium-wide three-quarter view with a subtle mechanical vibration, emphasizing repeatable robotic motion."
+    },
+    "camera_info": {
+      "color": "White, brushed steel, copper, blue inspection light",
+      "frame_size": "Medium wide",
+      "shot_type_angle": "Three-quarter eye-level industrial view",
+      "lens_size": "Medium lens",
+      "composition": "Two robotic arms framing the battery cell at center",
+      "lighting": "Even overhead factory lighting with blue scanner highlight",
+      "lighting_type": "Artificial industrial lighting"
+    },
+    "world_knowledge": [],
+    "prominent_elements": [
+      {
+        "name": "left robotic arm",
+        "description": "A white six-axis robotic arm with a vacuum gripper holding a transparent rectangular battery cell.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 0.7s]",
+            "action": "moves downward from upper left with the transparent cell held steady"
+          },
+          {
+            "timestamp": "[0.7s - 1.4s]",
+            "action": "places the cell into a copper test fixture"
+          },
+          {
+            "timestamp": "[1.4s - 2.0s]",
+            "action": "releases the cell and retracts slightly upward"
+          }
+        ],
+        "location": "left side moving toward center",
+        "relative_size": "large",
+        "shape_and_color": "white articulated segments with black joints",
+        "texture": "smooth painted metal and rubber vacuum cups",
+        "appearance_details": "visible cable routing, small green status LED",
+        "relationship": "positions the battery cell for testing",
+        "orientation": "angled downward toward the center fixture",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "right robotic scanner arm",
+        "description": "A second white robotic arm carrying a rectangular blue inspection light bar.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 0.9s]",
+            "action": "waits above the copper fixture with blue scanner light dim"
+          },
+          {
+            "timestamp": "[0.9s - 1.6s]",
+            "action": "sweeps the blue light bar across the transparent cell"
+          },
+          {
+            "timestamp": "[1.6s - 2.0s]",
+            "action": "pauses as the scanner light pulses once"
+          }
+        ],
+        "location": "right side above the conveyor",
+        "relative_size": "large",
+        "shape_and_color": "white arm with glowing blue rectangular scanner",
+        "texture": "smooth metal casing and glass scanner cover",
+        "appearance_details": "blue light strip, black joints, compact sensor module",
+        "relationship": "inspects the cell after placement",
+        "orientation": "angled left toward the battery cell",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      },
+      {
+        "name": "transparent battery cell and copper fixture",
+        "description": "A clear rectangular solid-state cell seated into a copper test fixture on a steel conveyor.",
+        "actions": [
+          {
+            "timestamp": "[0.0s - 0.7s]",
+            "action": "moves with the left arm above the fixture"
+          },
+          {
+            "timestamp": "[0.7s - 1.4s]",
+            "action": "is lowered precisely into the fixture"
+          },
+          {
+            "timestamp": "[1.4s - 2.0s]",
+            "action": "remains stationary while blue inspection light passes over it"
+          }
+        ],
+        "location": "center of the frame on the conveyor",
+        "relative_size": "medium",
+        "shape_and_color": "transparent rectangle with faint internal layers, copper fixture",
+        "texture": "clear glass-like cell, brushed copper, polished steel",
+        "appearance_details": "thin internal laminate layers, small alignment pins, clean conveyor surface",
+        "relationship": "object being assembled and inspected",
+        "orientation": "horizontal in the fixture",
+        "pose": "",
+        "expression": "",
+        "clothing": "",
+        "is_cluster": false,
+        "number_of_objects": ""
+      }
+    ]
+  },
+  "duration": 2.0
+}

benchmark/refiner.original.metrics.csv

@@ -0,0 +1,2 @@
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+results/benchmark/moe/original/videos/01_arctic_research_drone.mp4,e84547fdbd70c1f118435c0b0dee74ccb719a64f1c51e2d7b1260f2185f3dad9,False,"{'programs': [], 'streams': [{'codec_name': 'h264', 'duration': '3.041667', 'height': 1088, 'nb_frames': '73', 'pix_fmt': 'yuv420p', 'r_frame_rate': '24/1', 'width': 1920}]}",24,478.6046418759506,3.0,1088,moe_refiner,"{""universal_negative"": {""visual_quality"": [""low quality"", ""worst quality"", ""blurry"", ""pixelated"", ""jpeg artifacts"", ""low resolution"", ""unstable color"", ""color flicker"", ""underexposed"", ""overexposed"", ""invisible subject"", ""subject hidden in darkness""], ""artistic_style"": [""painting"", ""illustration"", ""drawing"", ""cartoon"", ""3d render"", ""cgi"", ""sketch"", ""digital art""], ""composition_and_content"": [""text"", ""watermark"", ""signature"", ""logo"", ""subtitles"", ""pillarboxed"", ""side bars"", ""portrait image in landscape frame""], ""temporal_and_motion_stability"": [""flickering"", ""jittery"", ""motion blur"", ""temporal inconsistency"", ""warping"", ""morphing"", ""incoherent motion"", ""unnatural movement"", ""static object with sudden jump"", ""frame-to-frame inconsistency""], ""material_and_structure"": [""plastic-like glass"", ""unrealistic texture"", ""deformed bottle"", ""liquid freezing improperly"", ""distorted reflections""]}}",True,73,8,"{'memory.total': '183359', 'memory.used': '179178', 'name': 'NVIDIA B200', 'power.draw': '252.18', 'temperature.gpu': '35', 'timestamp': '2026/07/10 09:42:11.197', 'utilization.gpu': '0'}",results/benchmark/moe_refiner/original/videos/01_arctic_research_drone.mp4,b3bdfa0eaa7f4a3b9cbc3627f15f144b2416778f5160e3242bb2fc1ff284a7de,1953653,232971304960.0,179178.0,965.67,100.0,4571336704.0,115570741248.0,01_arctic_research_drone,results/benchmark/moe_refiner/original/raw_frames/01_arctic_research_drone.npy,36469b4d213a6e5eeaaa3ad49005fbb6a9c7417c598f1d251b7e0fedc1ed514a,1096,0.25,4201,results/benchmark/moe/refiner_shared/01_arctic_research_drone.safetensors,502b10f841d96aa101e69421b20083aeb60427c054b42fb0db29c5a6e70824cf,3.0,2,0.85,103627.8291015625,150526.1474609375,178056.0,178056.0,original,1920

benchmark/refiner.original.metrics.jsonl

@@ -0,0 +1 @@
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benchmark/refiner.paired.metrics.csv

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benchmark/refiner.paired.metrics.jsonl

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benchmark/refiner.sdnq.metrics.csv

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benchmark/refiner.sdnq.metrics.jsonl

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benchmark/sdnq.metrics.csv

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benchmark/sdnq.metrics.jsonl

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benchmark/shared/01_arctic_research_drone.meta.json

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benchmark/smokes/moe-refiner-sdnq-standard.json

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+{
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benchmark/smokes/moe-sdnq-model.json

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benchmark/smokes/moe-sdnq-standard.json

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benchmark/summary.json

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+        "paired_video_sha256": "603c8ca01e33b3cb2649b73efa97d6d116d0d5791ee8327d9c87e13d578d5d71",
+        "prompt_id": "01_arctic_research_drone",
+        "psnr_db": 29.356930044470634,
+        "rmse": 0.03405285254120827,
+        "seed": 4201,
+        "ssim_mean": 0.9548161111465873,
+        "ssim_min": 0.9171391129493713,
+        "ssim_per_frame": [
+          0.9661064147949219,
+          0.9666761755943298,
+          0.9663284420967102,
+          0.9695181846618652,
+          0.9697737097740173,
+          0.9728912711143494,
+          0.9697763323783875,
+          0.9725530743598938,
+          0.9697721600532532,
+          0.9728060364723206,
+          0.9706771969795227,
+          0.9732361435890198,
+          0.9718213081359863,
+          0.9754683375358582,
+          0.9743171334266663,
+          0.9752790331840515,
+          0.9729723930358887,
+          0.9752058982849121,
+          0.9695265889167786,
+          0.970918595790863,
+          0.9685561060905457,
+          0.9747555255889893,
+          0.9721786379814148,
+          0.9732691645622253,
+          0.9707412719726562,
+          0.973728358745575,
+          0.9704788327217102,
+          0.971571147441864,
+          0.9674180150032043,
+          0.9711597561836243,
+          0.9675024151802063,
+          0.9691000580787659,
+          0.9649383425712585,
+          0.9690258502960205,
+          0.9678574204444885,
+          0.969526469707489,
+          0.9640713334083557,
+          0.9640881419181824,
+          0.9616990089416504,
+          0.9643573760986328,
+          0.9572374224662781,
+          0.9586830735206604,
+          0.9585137367248535,
+          0.9593629837036133,
+          0.9536706805229187,
+          0.9532110095024109,
+          0.9491230845451355,
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+          0.9403883814811707,
+          0.9420010447502136,
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+          0.9386496543884277,
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+          0.9251346588134766,
+          0.9279537200927734,
+          0.9244167804718018,
+          0.9291841983795166,
+          0.9192748069763184
+        ]
+      }
+    ],
+    "ssim_mean": 0.9548161111465873
+  },
+  "schema_version": 1,
+  "sdnq_revision": "d841c383ff7be38728d4df829e17af4f15d4fd66",
+  "smoke_evidence": [
+    "benchmark/smokes/moe-refiner-sdnq-standard.json",
+    "benchmark/smokes/moe-refiner-sdnq-standard.mp4",
+    "benchmark/smokes/moe-sdnq-model.json",
+    "benchmark/smokes/moe-sdnq-model.mp4",
+    "benchmark/smokes/moe-sdnq-sequential.json",
+    "benchmark/smokes/moe-sdnq-sequential.mp4",
+    "benchmark/smokes/moe-sdnq-standard.json",
+    "benchmark/smokes/moe-sdnq-standard.mp4"
+  ],
+  "source_repo_id": "robbyant/lingbot-video-moe-30b-a3b",
+  "source_revision": "f2e538f64afe00cc4ae674db2aeb52e2945edfd5",
+  "target_repo_id": "WaveCut/LingBot-Video-MoE-30B-A3B-SDNQ-uint4-static"
+}

lingbot_sdnq_runtime/__init__.py

@@ -353,10 +353,28 @@ def install_runtime_patch() -> None:
         ensure_sglang_moe_ready,
         sglang_fused_experts,
     )
+    from lingbot_video import pipeline_lingbot_video as pipeline_module
 
     original_grouped = LingBotVideoSparseMoeBlock._run_grouped_experts
     original_loop = LingBotVideoSparseMoeBlock._run_experts_for_loop
     original_sglang = LingBotVideoSparseMoeBlock._run_sglang_triton_experts
+    original_module_device = pipeline_module._module_device
+
+    def accelerate_aware_module_device(module: nn.Module) -> torch.device:
+        # Accelerate CPU-offload hooks keep parameters on CPU between calls.
+        # LingBot prepares VAE tensors before the hook runs, so use the hook's
+        # execution device when present instead of the current parameter device.
+        for candidate in module.modules():
+            hook = getattr(candidate, "_hf_hook", None)
+            hooks = (hook, *getattr(hook, "hooks", ())) if hook is not None else ()
+            for nested_hook in hooks:
+                execution_device = getattr(nested_hook, "execution_device", None)
+                if execution_device is None:
+                    continue
+                if isinstance(execution_device, int):
+                    return torch.device("cuda", execution_device)
+                return torch.device(execution_device)
+        return original_module_device(module)
 
     def packed_aware_block_forward(
         self,
@@ -497,6 +515,7 @@ def install_runtime_patch() -> None:
     LingBotVideoSparseMoeBlock._run_grouped_experts = packed_grouped
     LingBotVideoSparseMoeBlock._run_sglang_triton_experts = packed_sglang
     LingBotVideoBlock.forward = packed_aware_block_forward
+    pipeline_module._module_device = accelerate_aware_module_device
     _PATCHED = True
 
 

prompts.json

@@ -0,0 +1,536 @@
+{
+  "base_settings": {
+    "batch_cfg": false,
+    "fps": 24,
+    "guidance_scale": 3.0,
+    "height": 480,
+    "null_cond_clone_zero": false,
+    "num_frames": 73,
+    "num_inference_steps": 40,
+    "shift": 3.0,
+    "width": 832
+  },
+  "negative_prompt": "{\"universal_negative\": {\"visual_quality\": [\"low quality\", \"worst quality\", \"blurry\", \"pixelated\", \"jpeg artifacts\", \"low resolution\", \"unstable color\", \"color flicker\", \"underexposed\", \"overexposed\", \"invisible subject\", \"subject hidden in darkness\"], \"artistic_style\": [\"painting\", \"illustration\", \"drawing\", \"cartoon\", \"3d render\", \"cgi\", \"sketch\", \"digital art\"], \"composition_and_content\": [\"text\", \"watermark\", \"signature\", \"logo\", \"subtitles\", \"pillarboxed\", \"side bars\", \"portrait image in landscape frame\"], \"temporal_and_motion_stability\": [\"flickering\", \"jittery\", \"motion blur\", \"temporal inconsistency\", \"warping\", \"morphing\", \"incoherent motion\", \"unnatural movement\", \"static object with sudden jump\", \"frame-to-frame inconsistency\"], \"material_and_structure\": [\"plastic-like glass\", \"unrealistic texture\", \"deformed bottle\", \"liquid freezing improperly\", \"distorted reflections\"]}}",
+  "prompts": [
+    {
+      "prompt_id": "01_arctic_research_drone",
+      "rendered_prompt": "{\"comprehensive_description\":{\"scene_content_description\":\"A cinematic dusk shot of an Arctic research outpost on a wind-polished ice shelf under green aurora ribbons. A compact orange rover with a roof-mounted lidar mast drives slowly between blue instrument crates and slender weather antennas. Fine snow dust trails behind the tires, and warm light glows from the station windows against the cold cyan landscape. The atmosphere is realistic, high-detail documentary science footage with crisp snow texture, vapor in the air, and physically plausible shadows.\",\"camera_movement_description\":\"The camera begins as a low, smooth drone tracking shot behind the rover, then arcs gently left to reveal the glowing station and aurora-filled sky while keeping the rover dominant in frame.\"},\"camera_info\":{\"color\":\"Cold cyan with warm orange accents\",\"frame_size\":\"Wide\",\"shot_type_angle\":\"Low angle tracking shot\",\"lens_size\":\"Wide lens\",\"composition\":\"Rover in lower center, research station in the background, aurora above\",\"lighting\":\"Soft twilight with window glow\",\"lighting_type\":\"Natural dusk and practical interior lights\"},\"world_knowledge\":[],\"prominent_elements\":[{\"name\":\"orange autonomous research rover\",\"description\":\"A rugged six-wheeled scientific rover with orange body panels, black tires, a lidar mast, and small blinking status lights.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.7s]\",\"action\":\"drives forward slowly across packed snow, tires compressing the surface\"},{\"timestamp\":\"[0.7s - 1.5s]\",\"action\":\"turns slightly left as the lidar mast rotates\"},{\"timestamp\":\"[1.5s - 2.0s]\",\"action\":\"continues toward the lit station while fine snow trails behind\"}],\"location\":\"lower center of the frame\",\"relative_size\":\"dominant\",\"shape_and_color\":\"low rectangular orange body with black wheels and grey sensor mast\",\"texture\":\"matte painted metal, rubber tires, frost on edges\",\"appearance_details\":\"roof lidar, small antennas, narrow headlights, compact cargo rack\",\"relationship\":\"the main moving subject, traveling toward the research outpost\",\"orientation\":\"moving away from the camera and slightly left\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"},{\"name\":\"Arctic research station\",\"description\":\"A modular polar station made of connected white containers with warm yellow light in the windows.\",\"actions\":[{\"timestamp\":\"[0.0s - 2.0s]\",\"action\":\"remains stationary as wind-blown snow passes in front of it\"}],\"location\":\"middle background\",\"relative_size\":\"large\",\"shape_and_color\":\"rectangular white modules with dark seams and glowing windows\",\"texture\":\"frosted metal panels and glass\",\"appearance_details\":\"small stairs, railings, antennas, blue equipment crates nearby\",\"relationship\":\"destination of the rover\",\"orientation\":\"angled three-quarter view\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"},{\"name\":\"aurora ribbons\",\"description\":\"Green aurora curtains stretching across the darkening polar sky.\",\"actions\":[{\"timestamp\":\"[0.0s - 2.0s]\",\"action\":\"shimmer slowly and drift laterally across the sky\"}],\"location\":\"upper half of the frame\",\"relative_size\":\"large\",\"shape_and_color\":\"soft green luminous ribbons\",\"texture\":\"translucent atmospheric glow\",\"appearance_details\":\"layered folds and faint stars behind\",\"relationship\":\"dominates the sky above the station\",\"orientation\":\"horizontal sweeping arcs\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":true,\"number_of_objects\":\"many luminous bands\"}]}",
+      "seed": 4201,
+      "structured_input": {
+        "caption": {
+          "camera_info": {
+            "color": "Cold cyan with warm orange accents",
+            "composition": "Rover in lower center, research station in the background, aurora above",
+            "frame_size": "Wide",
+            "lens_size": "Wide lens",
+            "lighting": "Soft twilight with window glow",
+            "lighting_type": "Natural dusk and practical interior lights",
+            "shot_type_angle": "Low angle tracking shot"
+          },
+          "comprehensive_description": {
+            "camera_movement_description": "The camera begins as a low, smooth drone tracking shot behind the rover, then arcs gently left to reveal the glowing station and aurora-filled sky while keeping the rover dominant in frame.",
+            "scene_content_description": "A cinematic dusk shot of an Arctic research outpost on a wind-polished ice shelf under green aurora ribbons. A compact orange rover with a roof-mounted lidar mast drives slowly between blue instrument crates and slender weather antennas. Fine snow dust trails behind the tires, and warm light glows from the station windows against the cold cyan landscape. The atmosphere is realistic, high-detail documentary science footage with crisp snow texture, vapor in the air, and physically plausible shadows."
+          },
+          "prominent_elements": [
+            {
+              "actions": [
+                {
+                  "action": "drives forward slowly across packed snow, tires compressing the surface",
+                  "timestamp": "[0.0s - 0.7s]"
+                },
+                {
+                  "action": "turns slightly left as the lidar mast rotates",
+                  "timestamp": "[0.7s - 1.5s]"
+                },
+                {
+                  "action": "continues toward the lit station while fine snow trails behind",
+                  "timestamp": "[1.5s - 2.0s]"
+                }
+              ],
+              "appearance_details": "roof lidar, small antennas, narrow headlights, compact cargo rack",
+              "clothing": "",
+              "description": "A rugged six-wheeled scientific rover with orange body panels, black tires, a lidar mast, and small blinking status lights.",
+              "expression": "",
+              "is_cluster": false,
+              "location": "lower center of the frame",
+              "name": "orange autonomous research rover",
+              "number_of_objects": "",
+              "orientation": "moving away from the camera and slightly left",
+              "pose": "",
+              "relationship": "the main moving subject, traveling toward the research outpost",
+              "relative_size": "dominant",
+              "shape_and_color": "low rectangular orange body with black wheels and grey sensor mast",
+              "texture": "matte painted metal, rubber tires, frost on edges"
+            },
+            {
+              "actions": [
+                {
+                  "action": "remains stationary as wind-blown snow passes in front of it",
+                  "timestamp": "[0.0s - 2.0s]"
+                }
+              ],
+              "appearance_details": "small stairs, railings, antennas, blue equipment crates nearby",
+              "clothing": "",
+              "description": "A modular polar station made of connected white containers with warm yellow light in the windows.",
+              "expression": "",
+              "is_cluster": false,
+              "location": "middle background",
+              "name": "Arctic research station",
+              "number_of_objects": "",
+              "orientation": "angled three-quarter view",
+              "pose": "",
+              "relationship": "destination of the rover",
+              "relative_size": "large",
+              "shape_and_color": "rectangular white modules with dark seams and glowing windows",
+              "texture": "frosted metal panels and glass"
+            },
+            {
+              "actions": [
+                {
+                  "action": "shimmer slowly and drift laterally across the sky",
+                  "timestamp": "[0.0s - 2.0s]"
+                }
+              ],
+              "appearance_details": "layered folds and faint stars behind",
+              "clothing": "",
+              "description": "Green aurora curtains stretching across the darkening polar sky.",
+              "expression": "",
+              "is_cluster": true,
+              "location": "upper half of the frame",
+              "name": "aurora ribbons",
+              "number_of_objects": "many luminous bands",
+              "orientation": "horizontal sweeping arcs",
+              "pose": "",
+              "relationship": "dominates the sky above the station",
+              "relative_size": "large",
+              "shape_and_color": "soft green luminous ribbons",
+              "texture": "translucent atmospheric glow"
+            }
+          ],
+          "world_knowledge": []
+        },
+        "duration": 2.0
+      }
+    },
+    {
+      "prompt_id": "02_macro_pastry_glaze",
+      "rendered_prompt": "{\"comprehensive_description\":{\"scene_content_description\":\"An extreme macro food cinematography shot in a professional pastry kitchen. A glossy dark chocolate dome dessert sits on a rotating metal turntable. A thin stream of amber caramel glaze pours from above, flowing over the curved surface in slow ribbons and revealing tiny reflections from softbox lights. Crushed pistachio dust and edible gold flakes sit around the base on a matte black plate. The look is photorealistic, tactile, high-speed macro video with shallow depth of field and no text.\",\"camera_movement_description\":\"The camera holds a close macro angle with a subtle push-in while the dessert rotates clockwise on the turntable.\"},\"camera_info\":{\"color\":\"Deep brown, amber, green pistachio, gold highlights\",\"frame_size\":\"Extreme close up\",\"shot_type_angle\":\"Slight high angle\",\"lens_size\":\"Macro lens\",\"composition\":\"Dessert centered with glaze entering from top\",\"lighting\":\"Large softbox reflections\",\"lighting_type\":\"Controlled studio kitchen lighting\"},\"world_knowledge\":[],\"prominent_elements\":[{\"name\":\"chocolate dome dessert\",\"description\":\"A small mirror-glazed chocolate dome on a black plate, surrounded by pistachio dust and gold flakes.\",\"actions\":[{\"timestamp\":\"[0.0s - 2.0s]\",\"action\":\"rotates slowly clockwise on a metal turntable\"}],\"location\":\"center of the frame\",\"relative_size\":\"dominant\",\"shape_and_color\":\"hemispherical dark chocolate dome on round black plate\",\"texture\":\"mirror-gloss surface with smooth curved reflections\",\"appearance_details\":\"tiny gold flakes, green pistachio crumbs, clean pastry plating\",\"relationship\":\"receives the caramel glaze\",\"orientation\":\"upright on the turntable\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"},{\"name\":\"caramel glaze stream\",\"description\":\"A viscous amber caramel stream pouring from above and spreading over the dessert.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.8s]\",\"action\":\"falls as a narrow glossy stream onto the crown of the dome\"},{\"timestamp\":\"[0.8s - 1.6s]\",\"action\":\"forms slow ribbons that slide down the curved chocolate surface\"},{\"timestamp\":\"[1.6s - 2.0s]\",\"action\":\"collects at the base in a thin shiny ring\"}],\"location\":\"entering from top center and flowing over the dessert\",\"relative_size\":\"medium\",\"shape_and_color\":\"thin amber liquid ribbons\",\"texture\":\"viscous, glossy, translucent caramel\",\"appearance_details\":\"bright highlights and tiny bubbles\",\"relationship\":\"coats the chocolate dome\",\"orientation\":\"vertical stream becoming downward trails\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"}]}",
+      "seed": 4202,
+      "structured_input": {
+        "caption": {
+          "camera_info": {
+            "color": "Deep brown, amber, green pistachio, gold highlights",
+            "composition": "Dessert centered with glaze entering from top",
+            "frame_size": "Extreme close up",
+            "lens_size": "Macro lens",
+            "lighting": "Large softbox reflections",
+            "lighting_type": "Controlled studio kitchen lighting",
+            "shot_type_angle": "Slight high angle"
+          },
+          "comprehensive_description": {
+            "camera_movement_description": "The camera holds a close macro angle with a subtle push-in while the dessert rotates clockwise on the turntable.",
+            "scene_content_description": "An extreme macro food cinematography shot in a professional pastry kitchen. A glossy dark chocolate dome dessert sits on a rotating metal turntable. A thin stream of amber caramel glaze pours from above, flowing over the curved surface in slow ribbons and revealing tiny reflections from softbox lights. Crushed pistachio dust and edible gold flakes sit around the base on a matte black plate. The look is photorealistic, tactile, high-speed macro video with shallow depth of field and no text."
+          },
+          "prominent_elements": [
+            {
+              "actions": [
+                {
+                  "action": "rotates slowly clockwise on a metal turntable",
+                  "timestamp": "[0.0s - 2.0s]"
+                }
+              ],
+              "appearance_details": "tiny gold flakes, green pistachio crumbs, clean pastry plating",
+              "clothing": "",
+              "description": "A small mirror-glazed chocolate dome on a black plate, surrounded by pistachio dust and gold flakes.",
+              "expression": "",
+              "is_cluster": false,
+              "location": "center of the frame",
+              "name": "chocolate dome dessert",
+              "number_of_objects": "",
+              "orientation": "upright on the turntable",
+              "pose": "",
+              "relationship": "receives the caramel glaze",
+              "relative_size": "dominant",
+              "shape_and_color": "hemispherical dark chocolate dome on round black plate",
+              "texture": "mirror-gloss surface with smooth curved reflections"
+            },
+            {
+              "actions": [
+                {
+                  "action": "falls as a narrow glossy stream onto the crown of the dome",
+                  "timestamp": "[0.0s - 0.8s]"
+                },
+                {
+                  "action": "forms slow ribbons that slide down the curved chocolate surface",
+                  "timestamp": "[0.8s - 1.6s]"
+                },
+                {
+                  "action": "collects at the base in a thin shiny ring",
+                  "timestamp": "[1.6s - 2.0s]"
+                }
+              ],
+              "appearance_details": "bright highlights and tiny bubbles",
+              "clothing": "",
+              "description": "A viscous amber caramel stream pouring from above and spreading over the dessert.",
+              "expression": "",
+              "is_cluster": false,
+              "location": "entering from top center and flowing over the dessert",
+              "name": "caramel glaze stream",
+              "number_of_objects": "",
+              "orientation": "vertical stream becoming downward trails",
+              "pose": "",
+              "relationship": "coats the chocolate dome",
+              "relative_size": "medium",
+              "shape_and_color": "thin amber liquid ribbons",
+              "texture": "viscous, glossy, translucent caramel"
+            }
+          ],
+          "world_knowledge": []
+        },
+        "duration": 2.0
+      }
+    },
+    {
+      "prompt_id": "03_rain_market_lanterns",
+      "rendered_prompt": "{\"comprehensive_description\":{\"scene_content_description\":\"A lively rainy night market in a narrow old-city street, filmed like naturalistic cinema. Red paper lanterns hang from wooden stalls, steam rises from bowls of soup, and wet cobblestones mirror orange and teal reflections. A vendor in a dark apron slides a steaming bowl across a counter to a traveler in a blue raincoat. Umbrellas pass in the foreground, partially occluding the scene for a moment. The mood is warm, humid, detailed, and grounded, with no readable signs or subtitles.\",\"camera_movement_description\":\"The camera performs a slow lateral dolly from left to right at counter height, with foreground umbrellas briefly crossing the lens.\"},\"camera_info\":{\"color\":\"Warm lantern orange, teal rain reflections, red accents\",\"frame_size\":\"Medium wide\",\"shot_type_angle\":\"Eye level\",\"lens_size\":\"Medium lens\",\"composition\":\"Vendor stall centered, traveler on right, passing umbrellas in foreground\",\"lighting\":\"Lantern light mixed with rain reflections\",\"lighting_type\":\"Night practical lighting\"},\"world_knowledge\":[],\"prominent_elements\":[{\"name\":\"food vendor\",\"description\":\"A middle-aged street vendor wearing a dark apron and working behind a steaming wooden food stall.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.8s]\",\"action\":\"lifts a white ceramic bowl from a steaming pot\"},{\"timestamp\":\"[0.8s - 1.6s]\",\"action\":\"slides the bowl carefully across the counter\"},{\"timestamp\":\"[1.6s - 2.0s]\",\"action\":\"wipes condensation from the counter with a cloth\"}],\"location\":\"center-left behind the stall counter\",\"relative_size\":\"large\",\"shape_and_color\":\"human figure in dark apron under warm lantern light\",\"texture\":\"wet fabric, skin highlights, wood grain around the stall\",\"appearance_details\":\"rolled sleeves, focused expression, steam around hands\",\"relationship\":\"serves food to the traveler\",\"orientation\":\"facing right toward the customer\",\"pose\":\"leaning slightly forward over the counter\",\"expression\":\"focused and calm\",\"clothing\":\"dark apron over a muted shirt\",\"gender\":\"\",\"skin_tone_and_texture\":\"natural skin with rain-lit highlights\"},{\"name\":\"traveler in blue raincoat\",\"description\":\"A traveler wearing a blue hooded raincoat, waiting at the stall with wet sleeves and a folded umbrella.\",\"actions\":[{\"timestamp\":\"[0.0s - 1.2s]\",\"action\":\"waits with hands near the counter, watching the bowl\"},{\"timestamp\":\"[1.2s - 2.0s]\",\"action\":\"reaches forward to receive the steaming bowl\"}],\"location\":\"right side of the frame\",\"relative_size\":\"medium\",\"shape_and_color\":\"blue hooded raincoat silhouette\",\"texture\":\"water beads on waterproof fabric\",\"appearance_details\":\"hood up, folded umbrella tucked under one arm\",\"relationship\":\"customer receiving food from the vendor\",\"orientation\":\"facing left toward the stall\",\"pose\":\"standing close to the counter\",\"expression\":\"expectant and tired\",\"clothing\":\"blue raincoat\",\"gender\":\"\",\"skin_tone_and_texture\":\"\"},{\"name\":\"rain and lantern reflections\",\"description\":\"Falling rain, steam, and glowing reflections on wet cobblestones.\",\"actions\":[{\"timestamp\":\"[0.0s - 2.0s]\",\"action\":\"rain falls continuously while reflections ripple on the street\"}],\"location\":\"foreground and background throughout the frame\",\"relative_size\":\"large\",\"shape_and_color\":\"thin rain streaks, orange and teal reflected pools\",\"texture\":\"wet stone, vapor, glossy water\",\"appearance_details\":\"soft steam plumes, umbrellas passing close to lens\",\"relationship\":\"sets the atmosphere around the market\",\"orientation\":\"vertical rainfall and horizontal street reflections\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":true,\"number_of_objects\":\"many raindrops and reflections\"}]}",
+      "seed": 4203,
+      "structured_input": {
+        "caption": {
+          "camera_info": {
+            "color": "Warm lantern orange, teal rain reflections, red accents",
+            "composition": "Vendor stall centered, traveler on right, passing umbrellas in foreground",
+            "frame_size": "Medium wide",
+            "lens_size": "Medium lens",
+            "lighting": "Lantern light mixed with rain reflections",
+            "lighting_type": "Night practical lighting",
+            "shot_type_angle": "Eye level"
+          },
+          "comprehensive_description": {
+            "camera_movement_description": "The camera performs a slow lateral dolly from left to right at counter height, with foreground umbrellas briefly crossing the lens.",
+            "scene_content_description": "A lively rainy night market in a narrow old-city street, filmed like naturalistic cinema. Red paper lanterns hang from wooden stalls, steam rises from bowls of soup, and wet cobblestones mirror orange and teal reflections. A vendor in a dark apron slides a steaming bowl across a counter to a traveler in a blue raincoat. Umbrellas pass in the foreground, partially occluding the scene for a moment. The mood is warm, humid, detailed, and grounded, with no readable signs or subtitles."
+          },
+          "prominent_elements": [
+            {
+              "actions": [
+                {
+                  "action": "lifts a white ceramic bowl from a steaming pot",
+                  "timestamp": "[0.0s - 0.8s]"
+                },
+                {
+                  "action": "slides the bowl carefully across the counter",
+                  "timestamp": "[0.8s - 1.6s]"
+                },
+                {
+                  "action": "wipes condensation from the counter with a cloth",
+                  "timestamp": "[1.6s - 2.0s]"
+                }
+              ],
+              "appearance_details": "rolled sleeves, focused expression, steam around hands",
+              "clothing": "dark apron over a muted shirt",
+              "description": "A middle-aged street vendor wearing a dark apron and working behind a steaming wooden food stall.",
+              "expression": "focused and calm",
+              "gender": "",
+              "location": "center-left behind the stall counter",
+              "name": "food vendor",
+              "orientation": "facing right toward the customer",
+              "pose": "leaning slightly forward over the counter",
+              "relationship": "serves food to the traveler",
+              "relative_size": "large",
+              "shape_and_color": "human figure in dark apron under warm lantern light",
+              "skin_tone_and_texture": "natural skin with rain-lit highlights",
+              "texture": "wet fabric, skin highlights, wood grain around the stall"
+            },
+            {
+              "actions": [
+                {
+                  "action": "waits with hands near the counter, watching the bowl",
+                  "timestamp": "[0.0s - 1.2s]"
+                },
+                {
+                  "action": "reaches forward to receive the steaming bowl",
+                  "timestamp": "[1.2s - 2.0s]"
+                }
+              ],
+              "appearance_details": "hood up, folded umbrella tucked under one arm",
+              "clothing": "blue raincoat",
+              "description": "A traveler wearing a blue hooded raincoat, waiting at the stall with wet sleeves and a folded umbrella.",
+              "expression": "expectant and tired",
+              "gender": "",
+              "location": "right side of the frame",
+              "name": "traveler in blue raincoat",
+              "orientation": "facing left toward the stall",
+              "pose": "standing close to the counter",
+              "relationship": "customer receiving food from the vendor",
+              "relative_size": "medium",
+              "shape_and_color": "blue hooded raincoat silhouette",
+              "skin_tone_and_texture": "",
+              "texture": "water beads on waterproof fabric"
+            },
+            {
+              "actions": [
+                {
+                  "action": "rain falls continuously while reflections ripple on the street",
+                  "timestamp": "[0.0s - 2.0s]"
+                }
+              ],
+              "appearance_details": "soft steam plumes, umbrellas passing close to lens",
+              "clothing": "",
+              "description": "Falling rain, steam, and glowing reflections on wet cobblestones.",
+              "expression": "",
+              "is_cluster": true,
+              "location": "foreground and background throughout the frame",
+              "name": "rain and lantern reflections",
+              "number_of_objects": "many raindrops and reflections",
+              "orientation": "vertical rainfall and horizontal street reflections",
+              "pose": "",
+              "relationship": "sets the atmosphere around the market",
+              "relative_size": "large",
+              "shape_and_color": "thin rain streaks, orange and teal reflected pools",
+              "texture": "wet stone, vapor, glossy water"
+            }
+          ],
+          "world_knowledge": []
+        },
+        "duration": 2.0
+      }
+    },
+    {
+      "prompt_id": "04_underwater_manta",
+      "rendered_prompt": "{\"comprehensive_description\":{\"scene_content_description\":\"A tranquil underwater wildlife scene in clear tropical water. A large manta ray glides above a colorful coral garden while a scuba diver hovers several meters behind, holding a small camera rig. Sunbeams ripple through the surface, schools of tiny silver fish split and rejoin around the manta, and suspended particles drift slowly. The image should feel like realistic underwater documentary footage with natural motion, soft blue-green color, and no fantasy elements.\",\"camera_movement_description\":\"The camera tracks smoothly beside the manta ray from a slightly lower angle, drifting forward with the current.\"},\"camera_info\":{\"color\":\"Blue-green water with coral reds and yellows\",\"frame_size\":\"Wide\",\"shot_type_angle\":\"Slight low angle\",\"lens_size\":\"Wide underwater lens\",\"composition\":\"Manta ray crossing the upper center, coral below, diver in background\",\"lighting\":\"Dappled sunlight\",\"lighting_type\":\"Natural underwater daylight\"},\"world_knowledge\":[],\"prominent_elements\":[{\"name\":\"manta ray\",\"description\":\"A large manta ray with broad triangular fins, dark top surface, and pale underside.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.8s]\",\"action\":\"glides from left to right with slow wing-like fin movement\"},{\"timestamp\":\"[0.8s - 1.5s]\",\"action\":\"tilts slightly upward as sunbeams cross its back\"},{\"timestamp\":\"[1.5s - 2.0s]\",\"action\":\"continues forward while small fish scatter around it\"}],\"location\":\"upper center moving toward right\",\"relative_size\":\"dominant\",\"shape_and_color\":\"wide diamond-like silhouette, dark grey top, pale underside\",\"texture\":\"smooth skin with subtle mottling\",\"appearance_details\":\"cephalic fins near mouth, long tail trailing behind\",\"relationship\":\"main wildlife subject of the video\",\"orientation\":\"moving left to right and slightly upward\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"},{\"name\":\"scuba diver\",\"description\":\"A diver in black wetsuit and fins, carrying a small underwater camera rig.\",\"actions\":[{\"timestamp\":\"[0.0s - 2.0s]\",\"action\":\"hovers calmly in the background, exhaling small bubble streams\"}],\"location\":\"mid-background behind the manta ray\",\"relative_size\":\"small\",\"shape_and_color\":\"human figure in black wetsuit with silver air tank\",\"texture\":\"neoprene suit, metal tank, glass mask\",\"appearance_details\":\"fins, mask, regulator, compact camera lights\",\"relationship\":\"observes the manta ray without touching it\",\"orientation\":\"facing the manta ray\",\"pose\":\"horizontal hover\",\"expression\":\"\",\"clothing\":\"black scuba gear\",\"gender\":\"\",\"skin_tone_and_texture\":\"\"},{\"name\":\"coral reef and fish\",\"description\":\"A colorful coral garden with small silver fish schooling above it.\",\"actions\":[{\"timestamp\":\"[0.0s - 2.0s]\",\"action\":\"fish swirl and split around the manta while coral remains fixed\"}],\"location\":\"lower half of the frame\",\"relative_size\":\"large\",\"shape_and_color\":\"branching corals in red, yellow, and muted purple; silver fish clusters\",\"texture\":\"rough coral, shimmering fish scales\",\"appearance_details\":\"sand patches, sea fans, drifting particles\",\"relationship\":\"environment below the manta ray\",\"orientation\":\"reef spreads horizontally across the bottom\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":true,\"number_of_objects\":\"many fish and coral structures\"}]}",
+      "seed": 4204,
+      "structured_input": {
+        "caption": {
+          "camera_info": {
+            "color": "Blue-green water with coral reds and yellows",
+            "composition": "Manta ray crossing the upper center, coral below, diver in background",
+            "frame_size": "Wide",
+            "lens_size": "Wide underwater lens",
+            "lighting": "Dappled sunlight",
+            "lighting_type": "Natural underwater daylight",
+            "shot_type_angle": "Slight low angle"
+          },
+          "comprehensive_description": {
+            "camera_movement_description": "The camera tracks smoothly beside the manta ray from a slightly lower angle, drifting forward with the current.",
+            "scene_content_description": "A tranquil underwater wildlife scene in clear tropical water. A large manta ray glides above a colorful coral garden while a scuba diver hovers several meters behind, holding a small camera rig. Sunbeams ripple through the surface, schools of tiny silver fish split and rejoin around the manta, and suspended particles drift slowly. The image should feel like realistic underwater documentary footage with natural motion, soft blue-green color, and no fantasy elements."
+          },
+          "prominent_elements": [
+            {
+              "actions": [
+                {
+                  "action": "glides from left to right with slow wing-like fin movement",
+                  "timestamp": "[0.0s - 0.8s]"
+                },
+                {
+                  "action": "tilts slightly upward as sunbeams cross its back",
+                  "timestamp": "[0.8s - 1.5s]"
+                },
+                {
+                  "action": "continues forward while small fish scatter around it",
+                  "timestamp": "[1.5s - 2.0s]"
+                }
+              ],
+              "appearance_details": "cephalic fins near mouth, long tail trailing behind",
+              "clothing": "",
+              "description": "A large manta ray with broad triangular fins, dark top surface, and pale underside.",
+              "expression": "",
+              "is_cluster": false,
+              "location": "upper center moving toward right",
+              "name": "manta ray",
+              "number_of_objects": "",
+              "orientation": "moving left to right and slightly upward",
+              "pose": "",
+              "relationship": "main wildlife subject of the video",
+              "relative_size": "dominant",
+              "shape_and_color": "wide diamond-like silhouette, dark grey top, pale underside",
+              "texture": "smooth skin with subtle mottling"
+            },
+            {
+              "actions": [
+                {
+                  "action": "hovers calmly in the background, exhaling small bubble streams",
+                  "timestamp": "[0.0s - 2.0s]"
+                }
+              ],
+              "appearance_details": "fins, mask, regulator, compact camera lights",
+              "clothing": "black scuba gear",
+              "description": "A diver in black wetsuit and fins, carrying a small underwater camera rig.",
+              "expression": "",
+              "gender": "",
+              "location": "mid-background behind the manta ray",
+              "name": "scuba diver",
+              "orientation": "facing the manta ray",
+              "pose": "horizontal hover",
+              "relationship": "observes the manta ray without touching it",
+              "relative_size": "small",
+              "shape_and_color": "human figure in black wetsuit with silver air tank",
+              "skin_tone_and_texture": "",
+              "texture": "neoprene suit, metal tank, glass mask"
+            },
+            {
+              "actions": [
+                {
+                  "action": "fish swirl and split around the manta while coral remains fixed",
+                  "timestamp": "[0.0s - 2.0s]"
+                }
+              ],
+              "appearance_details": "sand patches, sea fans, drifting particles",
+              "clothing": "",
+              "description": "A colorful coral garden with small silver fish schooling above it.",
+              "expression": "",
+              "is_cluster": true,
+              "location": "lower half of the frame",
+              "name": "coral reef and fish",
+              "number_of_objects": "many fish and coral structures",
+              "orientation": "reef spreads horizontally across the bottom",
+              "pose": "",
+              "relationship": "environment below the manta ray",
+              "relative_size": "large",
+              "shape_and_color": "branching corals in red, yellow, and muted purple; silver fish clusters",
+              "texture": "rough coral, shimmering fish scales"
+            }
+          ],
+          "world_knowledge": []
+        },
+        "duration": 2.0
+      }
+    },
+    {
+      "prompt_id": "05_factory_battery_cells",
+      "rendered_prompt": "{\"comprehensive_description\":{\"scene_content_description\":\"A clean high-tech factory line assembling transparent solid-state battery cells. Two white robotic arms move with precise synchronized motion over a brushed steel conveyor. One arm lowers a translucent rectangular cell into a copper test fixture while the other arm scans it with a blue light bar. Tiny reflections slide across glass safety panels, and status lights pulse softly. The scene should look like realistic industrial automation footage, not CGI, with crisp metal surfaces and controlled motion.\",\"camera_movement_description\":\"The camera is locked off in a medium-wide three-quarter view with a subtle mechanical vibration, emphasizing repeatable robotic motion.\"},\"camera_info\":{\"color\":\"White, brushed steel, copper, blue inspection light\",\"frame_size\":\"Medium wide\",\"shot_type_angle\":\"Three-quarter eye-level industrial view\",\"lens_size\":\"Medium lens\",\"composition\":\"Two robotic arms framing the battery cell at center\",\"lighting\":\"Even overhead factory lighting with blue scanner highlight\",\"lighting_type\":\"Artificial industrial lighting\"},\"world_knowledge\":[],\"prominent_elements\":[{\"name\":\"left robotic arm\",\"description\":\"A white six-axis robotic arm with a vacuum gripper holding a transparent rectangular battery cell.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.7s]\",\"action\":\"moves downward from upper left with the transparent cell held steady\"},{\"timestamp\":\"[0.7s - 1.4s]\",\"action\":\"places the cell into a copper test fixture\"},{\"timestamp\":\"[1.4s - 2.0s]\",\"action\":\"releases the cell and retracts slightly upward\"}],\"location\":\"left side moving toward center\",\"relative_size\":\"large\",\"shape_and_color\":\"white articulated segments with black joints\",\"texture\":\"smooth painted metal and rubber vacuum cups\",\"appearance_details\":\"visible cable routing, small green status LED\",\"relationship\":\"positions the battery cell for testing\",\"orientation\":\"angled downward toward the center fixture\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"},{\"name\":\"right robotic scanner arm\",\"description\":\"A second white robotic arm carrying a rectangular blue inspection light bar.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.9s]\",\"action\":\"waits above the copper fixture with blue scanner light dim\"},{\"timestamp\":\"[0.9s - 1.6s]\",\"action\":\"sweeps the blue light bar across the transparent cell\"},{\"timestamp\":\"[1.6s - 2.0s]\",\"action\":\"pauses as the scanner light pulses once\"}],\"location\":\"right side above the conveyor\",\"relative_size\":\"large\",\"shape_and_color\":\"white arm with glowing blue rectangular scanner\",\"texture\":\"smooth metal casing and glass scanner cover\",\"appearance_details\":\"blue light strip, black joints, compact sensor module\",\"relationship\":\"inspects the cell after placement\",\"orientation\":\"angled left toward the battery cell\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"},{\"name\":\"transparent battery cell and copper fixture\",\"description\":\"A clear rectangular solid-state cell seated into a copper test fixture on a steel conveyor.\",\"actions\":[{\"timestamp\":\"[0.0s - 0.7s]\",\"action\":\"moves with the left arm above the fixture\"},{\"timestamp\":\"[0.7s - 1.4s]\",\"action\":\"is lowered precisely into the fixture\"},{\"timestamp\":\"[1.4s - 2.0s]\",\"action\":\"remains stationary while blue inspection light passes over it\"}],\"location\":\"center of the frame on the conveyor\",\"relative_size\":\"medium\",\"shape_and_color\":\"transparent rectangle with faint internal layers, copper fixture\",\"texture\":\"clear glass-like cell, brushed copper, polished steel\",\"appearance_details\":\"thin internal laminate layers, small alignment pins, clean conveyor surface\",\"relationship\":\"object being assembled and inspected\",\"orientation\":\"horizontal in the fixture\",\"pose\":\"\",\"expression\":\"\",\"clothing\":\"\",\"is_cluster\":false,\"number_of_objects\":\"\"}]}",
+      "seed": 4205,
+      "structured_input": {
+        "caption": {
+          "camera_info": {
+            "color": "White, brushed steel, copper, blue inspection light",
+            "composition": "Two robotic arms framing the battery cell at center",
+            "frame_size": "Medium wide",
+            "lens_size": "Medium lens",
+            "lighting": "Even overhead factory lighting with blue scanner highlight",
+            "lighting_type": "Artificial industrial lighting",
+            "shot_type_angle": "Three-quarter eye-level industrial view"
+          },
+          "comprehensive_description": {
+            "camera_movement_description": "The camera is locked off in a medium-wide three-quarter view with a subtle mechanical vibration, emphasizing repeatable robotic motion.",
+            "scene_content_description": "A clean high-tech factory line assembling transparent solid-state battery cells. Two white robotic arms move with precise synchronized motion over a brushed steel conveyor. One arm lowers a translucent rectangular cell into a copper test fixture while the other arm scans it with a blue light bar. Tiny reflections slide across glass safety panels, and status lights pulse softly. The scene should look like realistic industrial automation footage, not CGI, with crisp metal surfaces and controlled motion."
+          },
+          "prominent_elements": [
+            {
+              "actions": [
+                {
+                  "action": "moves downward from upper left with the transparent cell held steady",
+                  "timestamp": "[0.0s - 0.7s]"
+                },
+                {
+                  "action": "places the cell into a copper test fixture",
+                  "timestamp": "[0.7s - 1.4s]"
+                },
+                {
+                  "action": "releases the cell and retracts slightly upward",
+                  "timestamp": "[1.4s - 2.0s]"
+                }
+              ],
+              "appearance_details": "visible cable routing, small green status LED",
+              "clothing": "",
+              "description": "A white six-axis robotic arm with a vacuum gripper holding a transparent rectangular battery cell.",
+              "expression": "",
+              "is_cluster": false,
+              "location": "left side moving toward center",
+              "name": "left robotic arm",
+              "number_of_objects": "",
+              "orientation": "angled downward toward the center fixture",
+              "pose": "",
+              "relationship": "positions the battery cell for testing",
+              "relative_size": "large",
+              "shape_and_color": "white articulated segments with black joints",
+              "texture": "smooth painted metal and rubber vacuum cups"
+            },
+            {
+              "actions": [
+                {
+                  "action": "waits above the copper fixture with blue scanner light dim",
+                  "timestamp": "[0.0s - 0.9s]"
+                },
+                {
+                  "action": "sweeps the blue light bar across the transparent cell",
+                  "timestamp": "[0.9s - 1.6s]"
+                },
+                {
+                  "action": "pauses as the scanner light pulses once",
+                  "timestamp": "[1.6s - 2.0s]"
+                }
+              ],
+              "appearance_details": "blue light strip, black joints, compact sensor module",
+              "clothing": "",
+              "description": "A second white robotic arm carrying a rectangular blue inspection light bar.",
+              "expression": "",
+              "is_cluster": false,
+              "location": "right side above the conveyor",
+              "name": "right robotic scanner arm",
+              "number_of_objects": "",
+              "orientation": "angled left toward the battery cell",
+              "pose": "",
+              "relationship": "inspects the cell after placement",
+              "relative_size": "large",
+              "shape_and_color": "white arm with glowing blue rectangular scanner",
+              "texture": "smooth metal casing and glass scanner cover"
+            },
+            {
+              "actions": [
+                {
+                  "action": "moves with the left arm above the fixture",
+                  "timestamp": "[0.0s - 0.7s]"
+                },
+                {
+                  "action": "is lowered precisely into the fixture",
+                  "timestamp": "[0.7s - 1.4s]"
+                },
+                {
+                  "action": "remains stationary while blue inspection light passes over it",
+                  "timestamp": "[1.4s - 2.0s]"
+                }
+              ],
+              "appearance_details": "thin internal laminate layers, small alignment pins, clean conveyor surface",
+              "clothing": "",
+              "description": "A clear rectangular solid-state cell seated into a copper test fixture on a steel conveyor.",
+              "expression": "",
+              "is_cluster": false,
+              "location": "center of the frame on the conveyor",
+              "name": "transparent battery cell and copper fixture",
+              "number_of_objects": "",
+              "orientation": "horizontal in the fixture",
+              "pose": "",
+              "relationship": "object being assembled and inspected",
+              "relative_size": "medium",
+              "shape_and_color": "transparent rectangle with faint internal layers, copper fixture",
+              "texture": "clear glass-like cell, brushed copper, polished steel"
+            }
+          ],
+          "world_knowledge": []
+        },
+        "duration": 2.0
+      }
+    }
+  ],
+  "refiner": {
+    "base_video_sha256": "e84547fdbd70c1f118435c0b0dee74ccb719a64f1c51e2d7b1260f2185f3dad9",
+    "prompt_id": "01_arctic_research_drone",
+    "seed": 4201,
+    "settings": {
+      "batch_cfg": false,
+      "fps": 24,
+      "guidance_scale": 3.0,
+      "height": 1088,
+      "null_cond_clone_zero": true,
+      "num_frames": 73,
+      "num_inference_steps": 8,
+      "shift": 3.0,
+      "sigma_tail_steps": 2,
+      "t_thresh": 0.85,
+      "width": 1920
+    },
+    "shared_initial_latent_sha256": "502b10f841d96aa101e69421b20083aeb60427c054b42fb0db29c5a6e70824cf"
+  },
+  "schema_version": 1,
+  "source_repo_id": "robbyant/lingbot-video-moe-30b-a3b",
+  "source_revision": "f2e538f64afe00cc4ae674db2aeb52e2945edfd5"
+}