Publish MicroDuck FastWAM inference weights and pinned dependencies
Browse files- .gitattributes +1 -0
- NOTICE.md +32 -0
- README.md +109 -0
- SHA256SUMS +10 -0
- licenses/Apache-2.0.txt +201 -0
- licenses/FastWAM-MIT.txt +24 -0
- licenses/Wan2.2-UPSTREAM-README.md +236 -0
- model-files.lock.json +61 -0
- models/Wan2.2-TI2V-5B/Wan2.2_VAE.pth +3 -0
- models/Wan2.2-TI2V-5B/config.json +14 -0
- models/Wan2.2-TI2V-5B/google/umt5-xxl/special_tokens_map.json +308 -0
- models/Wan2.2-TI2V-5B/google/umt5-xxl/spiece.model +3 -0
- models/Wan2.2-TI2V-5B/google/umt5-xxl/tokenizer.json +3 -0
- models/Wan2.2-TI2V-5B/google/umt5-xxl/tokenizer_config.json +2748 -0
- models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth +3 -0
- models/microduck/dataset_stats.json +605 -0
- models/microduck/model.pt +3 -0
- models/microduck/navigation_checkpoint.json +103 -0
- release-manifest.json +68 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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models/Wan2.2-TI2V-5B/google/umt5-xxl/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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NOTICE.md
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# Artifact provenance and third-party notices
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This is the inference-file release for https://github.com/fuheaven/LightX2V-microduck.
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## Project-trained navigation checkpoint
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- Original run: `clear-room-realtime-control-pilot-v1`, step 250.
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- Exact model SHA256: `8ea7793290f0a22bff56ec4e44168232564ec5d57a9e21515a8a8c4a989114ea`.
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- The original normalization and checkpoint receipt are preserved byte for byte.
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- A separate license for these project-specific weights has not been designated by the owner. The presence of third-party license texts below is not a blanket license assignment to these weights.
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- Hardware status: `hardware_unvalidated`. Published numbers are simulation development evidence, not independent full acceptance.
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## Wan inference components
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- Source: https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B/tree/921dbaf3f1674a56f47e83fb80a34bac8a8f203e
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- Files: the seven files under `models/Wan2.2-TI2V-5B/` in `model-files.lock.json`.
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- License identified by the upstream model card: Apache-2.0. A copy is provided in `licenses/Apache-2.0.txt`; the original model card is retained in `licenses/Wan2.2-UPSTREAM-README.md`.
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- UMT5 tokenizer and text-encoder components retain their upstream attribution to the UMT5 project: https://huggingface.co/google/umt5-xxl.
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- All mirrored files retain their original bytes and SHA256. The original diffusion-model shards are not redistributed in this bundle.
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## Implementation and research attribution
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- LightX2V: https://github.com/ModelTC/LightX2V, Apache-2.0.
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- FastWAM: https://github.com/yuantianyuan01/FastWAM, MIT source license retained as `licenses/FastWAM-MIT.txt`.
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- The Python inference implementation, Docker build, simulator, tests, and evidence are distributed by the linked code repository. This model repository does not ship training optimizer states or an executable Python pickle loader beyond the original model serialization.
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## Separately downloaded simulator dependencies
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- MicroDuck RL body: https://github.com/pollen-robotics/microduck_rl at `2b581c641406a48346e696212930ea881c222c52`.
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- Official policies: https://huggingface.co/pollen-robotics/microduck-policies at `088524a64e2557dc453256b6071dbb9d23888802`.
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- Robot 3D assets have separate CC BY-NC-SA terms stated upstream; this release does not replace them with the code license. They are not bundled here.
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- The code repository's pinned fetch command obtains the original assets and policy documentation.
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README.md
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---
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language:
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- en
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- zh
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tags:
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- robotics
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- microduck
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- fastwam
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- lightx2v
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- mujoco
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- simulation
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base_model: Wan-AI/Wan2.2-TI2V-5B
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---
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# LightX2V × MicroDuck — FastWAM navigation inference bundle
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Deployment files for [fuheaven/LightX2V-microduck](https://github.com/fuheaven/LightX2V-microduck), including the trained MicroDuck navigation checkpoint and its minimal Wan inference dependencies.
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**This release contains simulation development weights. It has not passed independent full capability acceptance or real-hardware validation.** It is a task-specific high-level controller, not a general-purpose autonomous robot brain.
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本仓库提供 MicroDuck 视觉寻球模型的完整推理文件。使用代码仓库的 Docker 环境即可运行推理测速、MuJoCo 仿真与独立 MicroDuck Studio 页面；无需先训练 TinyVLA、安装 ROS2 或取得作者私有权重。
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## Included model and behavior
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- Model: `clear-room-realtime-control-pilot-v1 / step 250`.
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- Model SHA256: `8ea7793290f0a22bff56ec4e44168232564ec5d57a9e21515a8a8c4a989114ea`.
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- Inputs: head-camera RGB history, a supported target instruction, and 48-dimensional robot state.
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- History: control-tick offsets `[300, 150, 50, 0]`, composed into a 512 × 512 image.
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- Output: `32 × 5` high-level action chunk: forward velocity, lateral velocity, turning, head yaw, and standing.
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- Supported instructions/tasks: `find_red_ball`, `find_blue_ball`, `find_yellow_ball`.
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- The official ONNX locomotion policy executes walking/balancing at 50 Hz; this FastWAM checkpoint does not directly generate joint angles.
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- Tested deployment family: a single NVIDIA H200, BF16, 20 flow-sampling steps. Consumer-GPU compatibility is not established by the on-disk size.
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## Files
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The ten required model files total **26,244,309,246 bytes (26.24 GB / 24.44 GiB)**. Downloading the `models/` directory directly into your deployment data directory preserves the expected paths.
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```text
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models/
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microduck/
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model.pt # 12.04 GB; complete trained task weights
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dataset_stats.json # original training-set normalization
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navigation_checkpoint.json # exact checkpoint/history/statistics binding
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Wan2.2-TI2V-5B/
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config.json
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models_t5_umt5-xxl-enc-bf16.pth # 11.36 GB
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Wan2.2_VAE.pth # 2.82 GB
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google/umt5-xxl/
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special_tokens_map.json
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spiece.model
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tokenizer.json
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tokenizer_config.json
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```
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`model-files.lock.json` and `SHA256SUMS` specify byte counts and hashes. The checkpoint receipt retains its original training provenance paths; the deployment code resolves model files from your local data directory and does not require access to those original server paths.
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Training data, optimizer/DeepSpeed states, unused Wan diffusion shards, TinyVLA checkpoints, and later experimental navigation candidates are not included or required for this inference release.
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## Download and deploy
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```bash
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git clone https://github.com/fuheaven/LightX2V-microduck.git
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cd LightX2V-microduck
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docker build -f deploy/microduck/fastwam/Dockerfile \
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-t lightx2v-microduck:fastwam-v1 .
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export FASTWAM_DATA=/absolute/path/to/microduck-data
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# Download the inference bundle; no GPU required.
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GPU_ID=none deploy/microduck/fastwam/run.sh python -c \
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'from huggingface_hub import snapshot_download; snapshot_download("lightx2v/microduck", local_dir="/data", allow_patterns=["models/**", "model-files.lock.json", "SHA256SUMS", "NOTICE.md", "licenses/**"])'
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# Fetch the pinned official robot and ONNX policies from their original sources.
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GPU_ID=none deploy/microduck/fastwam/run.sh \
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python scripts/microduck/fastwam_handoff.py fetch --component sim
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# Verify the exact checkpoint, normalizer, receipt, and backbone hashes.
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GPU_ID=none deploy/microduck/fastwam/run.sh \
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python scripts/microduck/fastwam_handoff.py verify
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# Select an available GPU and start the independent visualization service.
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export GPU_ID=0
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WEB_BIND=0.0.0.0 WEB_PORT=8770 deploy/microduck/fastwam/web.sh
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```
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Open `http://YOUR_SERVER_IP:8770/`. The gallery includes recorded successes and failures; live simulation loads the model on request. The code repository pins releases for repeatable downloads. If manually using `snapshot_download`, record and reuse the full Hugging Face commit with `revision="FULL_COMMIT_SHA"`.
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See the [deployment guide](https://github.com/fuheaven/LightX2V-microduck/blob/main/docs/microduck/deployment.md), [visualization guide](https://github.com/fuheaven/LightX2V-microduck/blob/main/docs/microduck/visualization.md), and [optimization guide](https://github.com/fuheaven/LightX2V-microduck/blob/main/docs/microduck/optimization.md).
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## Evidence and limits
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These are **historical simulation development results**, not guaranteed scores for a new machine, runtime, or driver:
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| Development condition | Result | Interpretation |
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| --- | ---: | --- |
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| Clear room, 7 layouts × 3 colors, offline | 19/21 | Development set |
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| Additional clear-room layouts, accelerated real time | 59/60 | Single H200; 500 ms observation-age limit |
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| Same historical real-time cohort, optimizations disabled | 0/60 | Stale actions caused standing fallback; not proof that the model cannot act |
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| Unaccelerated, no observation-age rejection, separate cross-host run | 17/60 | Different runtime condition; not a strict acceleration-only comparison |
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| Single static obstacle, original action range, offline recheck | 27/60 | Experimental and below target |
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The exported runtime includes subsequent execution fixes. Its new live smoke run exposed camera/latency-related fallback and failed the strict task test; historical successes must not be presented as acceptance of that deployment. Reports and failure examples are included in the [code repository evidence](https://github.com/fuheaven/LightX2V-microduck/tree/main/docs/microduck/evidence).
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Do not infer arbitrary object understanding, reliable obstacle avoidance, general language planning, real-time acceptance, or hardware readiness from these results. Model inference speed, camera delivery, control timing, and fallback all affect closed-loop behavior.
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## Sources and licensing
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Wan inference components are preserved from `Wan-AI/Wan2.2-TI2V-5B` at revision `921dbaf3f1674a56f47e83fb80a34bac8a8f203e` and retain their upstream Apache-2.0 terms. FastWAM and LightX2V attribution is retained in [NOTICE.md](NOTICE.md) and `licenses/`.
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The project-specific navigation weights do not yet have a separately designated license; publishing the files does not automatically extend the code repository's Apache-2.0 license to every model or asset. Contact the repository owner to clarify permitted uses. Official robot meshes and official ONNX policy bundles are fetched separately with their own upstream terms, including the robot asset restrictions. No simulator mesh bundle is republished here.
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SHA256SUMS
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20eb789667fa5e60e7516bf509512f6cb61f01b0aa0695eadaea930c13892b36 models/Wan2.2-TI2V-5B/Wan2.2_VAE.pth
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d1fea36899d00c2501b836c13ad65af56e2f9529ba622e50886d3f5c3e6c02bc models/Wan2.2-TI2V-5B/config.json
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+
7b8a9f5040adb67b5805abdfd42c1f8d0f3d0e711f10726580eb3789cd0ad61d models/Wan2.2-TI2V-5B/google/umt5-xxl/special_tokens_map.json
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e3909a67b780650b35cf529ac782ad2b6b26e6d1f849d3fbb6a872905f452458 models/Wan2.2-TI2V-5B/google/umt5-xxl/spiece.model
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| 5 |
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6e197b4d3dbd71da14b4eb255f4fa91c9c1f2068b20a2de2472967ca3d22602b models/Wan2.2-TI2V-5B/google/umt5-xxl/tokenizer.json
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+
ed9a3a8b0faa71a70a32847e0435fe036e6e112d4df4edb7bb48a921e344dc05 models/Wan2.2-TI2V-5B/google/umt5-xxl/tokenizer_config.json
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7cace0da2b446bbbbc57d031ab6cf163a3d59b366da94e5afe36745b746fd81d models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth
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f8805f43eeba0dc79daa30481e96e6721c41f9c5eb6e24b37798d5c0719c0b87 models/microduck/dataset_stats.json
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8ea7793290f0a22bff56ec4e44168232564ec5d57a9e21515a8a8c4a989114ea models/microduck/model.pt
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| 10 |
+
8e08e832159d58a78d88942db8b9ee6683b944d2256938d69271643f5b716d12 models/microduck/navigation_checkpoint.json
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licenses/Apache-2.0.txt
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licenses/FastWAM-MIT.txt
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
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|
|
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|
| 1 |
+
MIT License
|
| 2 |
+
|
| 3 |
+
Copyright (c) 2026 The FastWAM Authors
|
| 4 |
+
|
| 5 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
| 6 |
+
of this software and associated documentation files (the "Software"), to deal
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| 7 |
+
in the Software without restriction, including without limitation the rights
|
| 8 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
| 9 |
+
copies of the Software, and to permit persons to whom the Software is
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| 10 |
+
furnished to do so, subject to the following conditions:
|
| 11 |
+
|
| 12 |
+
The above copyright notice and this permission notice shall be included in all
|
| 13 |
+
copies or substantial portions of the Software.
|
| 14 |
+
|
| 15 |
+
Except for third-party code and files that retain their original license
|
| 16 |
+
headers, this repository is released under the MIT License.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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|
| 24 |
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|
licenses/Wan2.2-UPSTREAM-README.md
ADDED
|
@@ -0,0 +1,236 @@
|
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
- zh
|
| 6 |
+
pipeline_tag: text-to-video
|
| 7 |
+
library_name: wan2.2
|
| 8 |
+
---
|
| 9 |
+
# Wan2.2
|
| 10 |
+
|
| 11 |
+
<p align="center">
|
| 12 |
+
<img src="assets/logo.png" width="400"/>
|
| 13 |
+
<p>
|
| 14 |
+
|
| 15 |
+
<p align="center">
|
| 16 |
+
💜 <a href="https://wan.video"><b>Wan</b></a>    ｜    🖥️ <a href="https://github.com/Wan-Video/Wan2.2">GitHub</a>    |   🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>   |    📑 <a href="https://arxiv.org/abs/2503.20314">Technical Report</a>    |    📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a>    |   💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>   |    📖 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>  
|
| 17 |
+
<br>
|
| 18 |
+
|
| 19 |
+
-----
|
| 20 |
+
|
| 21 |
+
[**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be>
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations:
|
| 25 |
+
|
| 26 |
+
- 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
|
| 27 |
+
|
| 28 |
+
- 👍 **Cinematic-level Aesthetics**: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.
|
| 29 |
+
|
| 30 |
+
- 👍 **Complex Motion Generation**: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.
|
| 31 |
+
|
| 32 |
+
- 👍 **Efficient High-Definition Hybrid TI2V**: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of **16×16×4**. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest **720P@24fps** models currently available, capable of serving both the industrial and academic sectors simultaneously.
|
| 33 |
+
|
| 34 |
+
This repository contains our TI2V-5B model, built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research.
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
## Video Demos
|
| 39 |
+
|
| 40 |
+
<div align="center">
|
| 41 |
+
<video width="80%" controls>
|
| 42 |
+
<source src="https://cloud.video.taobao.com/vod/4szTT1B0LqXvJzmuEURfGRA-nllnqN_G2AT0ZWkQXoQ.mp4" type="video/mp4">
|
| 43 |
+
Your browser does not support the video tag.
|
| 44 |
+
</video>
|
| 45 |
+
</div>
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
## 🔥 Latest News!!
|
| 49 |
+
|
| 50 |
+
* Jul 28, 2025: 👋 We've released the inference code and model weights of **Wan2.2**.
|
| 51 |
+
|
| 52 |
+
## Community Works
|
| 53 |
+
If your research or project builds upon [**Wan2.1**](https://github.com/Wan-Video/Wan2.1) or Wan2.2, we welcome you to share it with us so we can highlight it for the broader community.
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
## 📑 Todo List
|
| 57 |
+
- Wan2.2 Text-to-Video
|
| 58 |
+
- [x] Multi-GPU Inference code of the A14B and 14B models
|
| 59 |
+
- [x] Checkpoints of the A14B and 14B models
|
| 60 |
+
- [x] ComfyUI integration
|
| 61 |
+
- [x] Diffusers integration
|
| 62 |
+
- Wan2.2 Image-to-Video
|
| 63 |
+
- [x] Multi-GPU Inference code of the A14B model
|
| 64 |
+
- [x] Checkpoints of the A14B model
|
| 65 |
+
- [x] ComfyUI integration
|
| 66 |
+
- [x] Diffusers integration
|
| 67 |
+
- Wan2.2 Text-Image-to-Video
|
| 68 |
+
- [x] Multi-GPU Inference code of the 5B model
|
| 69 |
+
- [x] Checkpoints of the 5B model
|
| 70 |
+
- [x] ComfyUI integration
|
| 71 |
+
- [x] Diffusers integration
|
| 72 |
+
|
| 73 |
+
## Run Wan2.2
|
| 74 |
+
|
| 75 |
+
#### Installation
|
| 76 |
+
Clone the repo:
|
| 77 |
+
```sh
|
| 78 |
+
git clone https://github.com/Wan-Video/Wan2.2.git
|
| 79 |
+
cd Wan2.2
|
| 80 |
+
```
|
| 81 |
+
|
| 82 |
+
Install dependencies:
|
| 83 |
+
```sh
|
| 84 |
+
# Ensure torch >= 2.4.0
|
| 85 |
+
pip install -r requirements.txt
|
| 86 |
+
```
|
| 87 |
+
|
| 88 |
+
|
| 89 |
+
#### Model Download
|
| 90 |
+
|
| 91 |
+
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
| Models | Download Links | Description |
|
| 95 |
+
|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------|
|
| 96 |
+
| T2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | Text-to-Video MoE model, supports 480P & 720P |
|
| 97 |
+
| I2V-A14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | Image-to-Video MoE model, supports 480P & 720P |
|
| 98 |
+
| TI2V-5B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) 🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | High-compression VAE, T2V+I2V, supports 720P |
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
> 💡Note:
|
| 102 |
+
> The TI2V-5B model supports 720P video generation at **24 FPS**.
|
| 103 |
+
|
| 104 |
+
|
| 105 |
+
Download models using huggingface-cli:
|
| 106 |
+
``` sh
|
| 107 |
+
pip install "huggingface_hub[cli]"
|
| 108 |
+
huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir ./Wan2.2-TI2V-5B
|
| 109 |
+
```
|
| 110 |
+
|
| 111 |
+
Download models using modelscope-cli:
|
| 112 |
+
``` sh
|
| 113 |
+
pip install modelscope
|
| 114 |
+
modelscope download Wan-AI/Wan2.2-TI2V-5B --local_dir ./Wan2.2-TI2V-5B
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
#### Run Text-Image-to-Video Generation
|
| 118 |
+
|
| 119 |
+
This repository supports the `Wan2.2-TI2V-5B` Text-Image-to-Video model and can support video generation at 720P resolutions.
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
- Single-GPU Text-to-Video inference
|
| 123 |
+
```sh
|
| 124 |
+
python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage"
|
| 125 |
+
```
|
| 126 |
+
|
| 127 |
+
> 💡Unlike other tasks, the 720P resolution of the Text-Image-to-Video task is `1280*704` or `704*1280`.
|
| 128 |
+
|
| 129 |
+
> This command can run on a GPU with at least 24GB VRAM (e.g, RTX 4090 GPU).
|
| 130 |
+
|
| 131 |
+
> 💡If you are running on a GPU with at least 80GB VRAM, you can remove the `--offload_model True`, `--convert_model_dtype` and `--t5_cpu` options to speed up execution.
|
| 132 |
+
|
| 133 |
+
|
| 134 |
+
- Single-GPU Image-to-Video inference
|
| 135 |
+
```sh
|
| 136 |
+
python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
|
| 137 |
+
```
|
| 138 |
+
|
| 139 |
+
> 💡If the image parameter is configured, it is an Image-to-Video generation; otherwise, it defaults to a Text-to-Video generation.
|
| 140 |
+
|
| 141 |
+
> 💡Similar to Image-to-Video, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
|
| 142 |
+
|
| 143 |
+
|
| 144 |
+
- Multi-GPU inference using FSDP + DeepSpeed Ulysses
|
| 145 |
+
|
| 146 |
+
```sh
|
| 147 |
+
torchrun --nproc_per_node=8 generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --dit_fsdp --t5_fsdp --ulysses_size 8 --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
|
| 148 |
+
```
|
| 149 |
+
|
| 150 |
+
> The process of prompt extension can be referenced [here](#2-using-prompt-extention).
|
| 151 |
+
|
| 152 |
+
|
| 153 |
+
|
| 154 |
+
|
| 155 |
+
## Computational Efficiency on Different GPUs
|
| 156 |
+
|
| 157 |
+
We test the computational efficiency of different **Wan2.2** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
|
| 158 |
+
|
| 159 |
+
|
| 160 |
+
<div align="center">
|
| 161 |
+
<img src="assets/comp_effic.png" alt="" style="width: 80%;" />
|
| 162 |
+
</div>
|
| 163 |
+
|
| 164 |
+
> The parameter settings for the tests presented in this table are as follows:
|
| 165 |
+
> (1) Multi-GPU: 14B: `--ulysses_size 4/8 --dit_fsdp --t5_fsdp`, 5B: `--ulysses_size 4/8 --offload_model True --convert_model_dtype --t5_cpu`; Single-GPU: 14B: `--offload_model True --convert_model_dtype`, 5B: `--offload_model True --convert_model_dtype --t5_cpu`
|
| 166 |
+
(--convert_model_dtype converts model parameter types to config.param_dtype);
|
| 167 |
+
> (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs;
|
| 168 |
+
> (3) Tests were run without the `--use_prompt_extend` flag;
|
| 169 |
+
> (4) Reported results are the average of multiple samples taken after the warm-up phase.
|
| 170 |
+
|
| 171 |
+
|
| 172 |
+
-------
|
| 173 |
+
|
| 174 |
+
## Introduction of Wan2.2
|
| 175 |
+
|
| 176 |
+
**Wan2.2** builds on the foundation of Wan2.1 with notable improvements in generation quality and model capability. This upgrade is driven by a series of key technical innovations, mainly including the Mixture-of-Experts (MoE) architecture, upgraded training data, and high-compression video generation.
|
| 177 |
+
|
| 178 |
+
##### (1) Mixture-of-Experts (MoE) Architecture
|
| 179 |
+
|
| 180 |
+
Wan2.2 introduces Mixture-of-Experts (MoE) architecture into the video generation diffusion model. MoE has been widely validated in large language models as an efficient approach to increase total model parameters while keeping inference cost nearly unchanged. In Wan2.2, the A14B model series adopts a two-expert design tailored to the denoising process of diffusion models: a high-noise expert for the early stages, focusing on overall layout; and a low-noise expert for the later stages, refining video details. Each expert model has about 14B parameters, resulting in a total of 27B parameters but only 14B active parameters per step, keeping inference computation and GPU memory nearly unchanged.
|
| 181 |
+
|
| 182 |
+
<div align="center">
|
| 183 |
+
<img src="assets/moe_arch.png" alt="" style="width: 90%;" />
|
| 184 |
+
</div>
|
| 185 |
+
|
| 186 |
+
The transition point between the two experts is determined by the signal-to-noise ratio (SNR), a metric that decreases monotonically as the denoising step $t$ increases. At the beginning of the denoising process, $t$ is large and the noise level is high, so the SNR is at its minimum, denoted as ${SNR}_{min}$. In this stage, the high-noise expert is activated. We define a threshold step ${t}_{moe}$ corresponding to half of the ${SNR}_{min}$, and switch to the low-noise expert when $t<{t}_{moe}$.
|
| 187 |
+
|
| 188 |
+
<div align="center">
|
| 189 |
+
<img src="assets/moe_2.png" alt="" style="width: 90%;" />
|
| 190 |
+
</div>
|
| 191 |
+
|
| 192 |
+
To validate the effectiveness of the MoE architecture, four settings are compared based on their validation loss curves. The baseline **Wan2.1** model does not employ the MoE architecture. Among the MoE-based variants, the **Wan2.1 & High-Noise Expert** reuses the Wan2.1 model as the low-noise expert while uses the Wan2.2's high-noise expert, while the **Wan2.1 & Low-Noise Expert** uses Wan2.1 as the high-noise expert and employ the Wan2.2's low-noise expert. The **Wan2.2 (MoE)** (our final version) achieves the lowest validation loss, indicating that its generated video distribution is closest to ground-truth and exhibits superior convergence.
|
| 193 |
+
|
| 194 |
+
|
| 195 |
+
##### (2) Efficient High-Definition Hybrid TI2V
|
| 196 |
+
To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released. It is supported by a high-compression Wan2.2-VAE, which achieves a $T\times H\times W$ compression ratio of $4\times16\times16$, increasing the overall compression rate to 64 while maintaining high-quality video reconstruction. With an additional patchification layer, the total compression ratio of TI2V-5B reaches $4\times32\times32$. Without specific optimization, TI2V-5B can generate a 5-second 720P video in under 9 minutes on a single consumer-grade GPU, ranking among the fastest 720P@24fps video generation models. This model also natively supports both text-to-video and image-to-video tasks within a single unified framework, covering both academic research and practical applications.
|
| 197 |
+
|
| 198 |
+
|
| 199 |
+
<div align="center">
|
| 200 |
+
<img src="assets/vae.png" alt="" style="width: 80%;" />
|
| 201 |
+
</div>
|
| 202 |
+
|
| 203 |
+
|
| 204 |
+
|
| 205 |
+
##### Comparisons to SOTAs
|
| 206 |
+
We compared Wan2.2 with leading closed-source commercial models on our new Wan-Bench 2.0, evaluating performance across multiple crucial dimensions. The results demonstrate that Wan2.2 achieves superior performance compared to these leading models.
|
| 207 |
+
|
| 208 |
+
|
| 209 |
+
<div align="center">
|
| 210 |
+
<img src="assets/performance.png" alt="" style="width: 90%;" />
|
| 211 |
+
</div>
|
| 212 |
+
|
| 213 |
+
## Citation
|
| 214 |
+
If you find our work helpful, please cite us.
|
| 215 |
+
|
| 216 |
+
```
|
| 217 |
+
@article{wan2025,
|
| 218 |
+
title={Wan: Open and Advanced Large-Scale Video Generative Models},
|
| 219 |
+
author={Team Wan and Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
|
| 220 |
+
journal = {arXiv preprint arXiv:2503.20314},
|
| 221 |
+
year={2025}
|
| 222 |
+
}
|
| 223 |
+
```
|
| 224 |
+
|
| 225 |
+
## License Agreement
|
| 226 |
+
The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
|
| 227 |
+
|
| 228 |
+
|
| 229 |
+
## Acknowledgements
|
| 230 |
+
|
| 231 |
+
We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
|
| 232 |
+
|
| 233 |
+
|
| 234 |
+
|
| 235 |
+
## Contact Us
|
| 236 |
+
If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
|
model-files.lock.json
ADDED
|
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| 1 |
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{
|
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|
| 3 |
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| 48 |
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|
| 61 |
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|
models/Wan2.2-TI2V-5B/Wan2.2_VAE.pth
ADDED
|
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version https://git-lfs.github.com/spec/v1
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size 2818839170
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models/Wan2.2-TI2V-5B/config.json
ADDED
|
@@ -0,0 +1,14 @@
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|
| 1 |
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{
|
| 2 |
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"_class_name": "WanModel",
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|
| 4 |
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|
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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"text_len": 512
|
| 14 |
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}
|
models/Wan2.2-TI2V-5B/google/umt5-xxl/special_tokens_map.json
ADDED
|
@@ -0,0 +1,308 @@
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|
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|
| 1 |
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{
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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"<extra_id_163>",
|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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|
| 181 |
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|
| 182 |
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|
| 183 |
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|
| 184 |
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|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 198 |
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| 199 |
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| 200 |
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| 201 |
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| 202 |
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| 203 |
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| 204 |
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| 205 |
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|
| 206 |
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|
| 207 |
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|
| 208 |
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|
| 209 |
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|
| 210 |
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|
| 211 |
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| 212 |
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|
| 213 |
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| 214 |
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|
| 215 |
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|
| 216 |
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|
| 217 |
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|
| 218 |
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|
| 219 |
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|
| 220 |
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|
| 221 |
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|
| 222 |
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|
| 223 |
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| 224 |
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|
| 225 |
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|
| 226 |
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|
| 227 |
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|
| 228 |
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|
| 229 |
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| 230 |
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| 231 |
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|
| 232 |
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| 233 |
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| 234 |
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| 235 |
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|
| 236 |
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|
| 237 |
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|
| 238 |
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|
| 239 |
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|
| 240 |
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| 241 |
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|
| 242 |
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|
| 243 |
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|
| 244 |
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|
| 245 |
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|
| 246 |
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|
| 247 |
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| 248 |
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|
| 249 |
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|
| 250 |
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|
| 251 |
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| 252 |
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|
| 253 |
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|
| 254 |
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|
| 255 |
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|
| 256 |
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|
| 257 |
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|
| 258 |
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|
| 259 |
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|
| 260 |
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|
| 261 |
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|
| 262 |
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"<extra_id_259>",
|
| 263 |
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"<extra_id_260>",
|
| 264 |
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"<extra_id_261>",
|
| 265 |
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"<extra_id_262>",
|
| 266 |
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|
| 267 |
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|
| 268 |
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|
| 269 |
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|
| 270 |
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|
| 271 |
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|
| 272 |
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|
| 273 |
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|
| 274 |
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|
| 275 |
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|
| 276 |
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|
| 277 |
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|
| 278 |
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"<extra_id_275>",
|
| 279 |
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"<extra_id_276>",
|
| 280 |
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|
| 281 |
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|
| 282 |
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|
| 283 |
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"<extra_id_280>",
|
| 284 |
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"<extra_id_281>",
|
| 285 |
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"<extra_id_282>",
|
| 286 |
+
"<extra_id_283>",
|
| 287 |
+
"<extra_id_284>",
|
| 288 |
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"<extra_id_285>",
|
| 289 |
+
"<extra_id_286>",
|
| 290 |
+
"<extra_id_287>",
|
| 291 |
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"<extra_id_288>",
|
| 292 |
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"<extra_id_289>",
|
| 293 |
+
"<extra_id_290>",
|
| 294 |
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"<extra_id_291>",
|
| 295 |
+
"<extra_id_292>",
|
| 296 |
+
"<extra_id_293>",
|
| 297 |
+
"<extra_id_294>",
|
| 298 |
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"<extra_id_295>",
|
| 299 |
+
"<extra_id_296>",
|
| 300 |
+
"<extra_id_297>",
|
| 301 |
+
"<extra_id_298>",
|
| 302 |
+
"<extra_id_299>"
|
| 303 |
+
],
|
| 304 |
+
"bos_token": "<s>",
|
| 305 |
+
"eos_token": "</s>",
|
| 306 |
+
"pad_token": "<pad>",
|
| 307 |
+
"unk_token": "<unk>"
|
| 308 |
+
}
|
models/Wan2.2-TI2V-5B/google/umt5-xxl/spiece.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e3909a67b780650b35cf529ac782ad2b6b26e6d1f849d3fbb6a872905f452458
|
| 3 |
+
size 4548313
|
models/Wan2.2-TI2V-5B/google/umt5-xxl/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6e197b4d3dbd71da14b4eb255f4fa91c9c1f2068b20a2de2472967ca3d22602b
|
| 3 |
+
size 16837417
|
models/Wan2.2-TI2V-5B/google/umt5-xxl/tokenizer_config.json
ADDED
|
@@ -0,0 +1,2748 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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| 1 |
+
{
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| 2 |
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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| 10 |
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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|
| 16 |
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| 17 |
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"special": true
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| 18 |
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},
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 45 |
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| 48 |
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| 68 |
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| 69 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 140 |
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| 155 |
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| 156 |
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| 164 |
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| 194 |
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|
| 2708 |
+
"<extra_id_271>",
|
| 2709 |
+
"<extra_id_272>",
|
| 2710 |
+
"<extra_id_273>",
|
| 2711 |
+
"<extra_id_274>",
|
| 2712 |
+
"<extra_id_275>",
|
| 2713 |
+
"<extra_id_276>",
|
| 2714 |
+
"<extra_id_277>",
|
| 2715 |
+
"<extra_id_278>",
|
| 2716 |
+
"<extra_id_279>",
|
| 2717 |
+
"<extra_id_280>",
|
| 2718 |
+
"<extra_id_281>",
|
| 2719 |
+
"<extra_id_282>",
|
| 2720 |
+
"<extra_id_283>",
|
| 2721 |
+
"<extra_id_284>",
|
| 2722 |
+
"<extra_id_285>",
|
| 2723 |
+
"<extra_id_286>",
|
| 2724 |
+
"<extra_id_287>",
|
| 2725 |
+
"<extra_id_288>",
|
| 2726 |
+
"<extra_id_289>",
|
| 2727 |
+
"<extra_id_290>",
|
| 2728 |
+
"<extra_id_291>",
|
| 2729 |
+
"<extra_id_292>",
|
| 2730 |
+
"<extra_id_293>",
|
| 2731 |
+
"<extra_id_294>",
|
| 2732 |
+
"<extra_id_295>",
|
| 2733 |
+
"<extra_id_296>",
|
| 2734 |
+
"<extra_id_297>",
|
| 2735 |
+
"<extra_id_298>",
|
| 2736 |
+
"<extra_id_299>"
|
| 2737 |
+
],
|
| 2738 |
+
"bos_token": "<s>",
|
| 2739 |
+
"clean_up_tokenization_spaces": true,
|
| 2740 |
+
"eos_token": "</s>",
|
| 2741 |
+
"extra_ids": 300,
|
| 2742 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 2743 |
+
"pad_token": "<pad>",
|
| 2744 |
+
"sp_model_kwargs": {},
|
| 2745 |
+
"spaces_between_special_tokens": false,
|
| 2746 |
+
"tokenizer_class": "T5Tokenizer",
|
| 2747 |
+
"unk_token": "<unk>"
|
| 2748 |
+
}
|
models/Wan2.2-TI2V-5B/models_t5_umt5-xxl-enc-bf16.pth
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7cace0da2b446bbbbc57d031ab6cf163a3d59b366da94e5afe36745b746fd81d
|
| 3 |
+
size 11361920418
|
models/microduck/dataset_stats.json
ADDED
|
@@ -0,0 +1,605 @@
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|
| 1 |
+
{
|
| 2 |
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"schema": "microduck_navigation_v1",
|
| 3 |
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"normalization_split": "train",
|
| 4 |
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| 5 |
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models/microduck/model.pt
ADDED
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ADDED
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