datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Wan2.2-Syn-121x704x1280_32k
FastVideo Synthetic Wan2.2 720P dataset
FastVideo Team
Paper |
Github |
Project Page
Abstract
Scaling video diffusion transformers (DiTs) is limited by their quadratic 3D attention, even though most of the attention mass concentrates on a small subset of positions. We turn this observation into VSA, a trainable, hardware-efficient sparse attention that replaces full attention at \emph{both} training and inference. In VSA, a… See the full description on the dataset page: https://huggingface.co/datasets/Hahshshsshbs/Wan2.2-Syn-121x704x1280_32k.wan2.2_loraWan2.2-Syn-121x704x1280_32k
FastVideo Synthetic Wan2.2 720P dataset
FastVideo Team
Paper |
Github |
Project Page
Abstract
Scaling video diffusion transformers (DiTs) is limited by their quadratic 3D attention, even though most of the attention mass concentrates on a small subset of positions. We turn this observation into VSA, a trainable, hardware-efficient sparse attention that replaces full attention at \emph{both} training and inference. In VSA, a… See the full description on the dataset page: https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k.wan2.2-Loraswan2.2llibero4in1_wan2.2vae_latent_dataset
LIBERO 4in1 Wan2.2-VAE Latent Cache
Pre-encoded latent tensors for LIBERO 4 suites under Cosmos Wan2.2-VAE.
Skip on-the-fly VAE encoding during training — load this cache directly.
Overview
Pre-encoded latent cache for LIBERO 4in1 benchmark (libero_spatial, libero_object, libero_goal, libero_10 — 4 suites × 10 tasks, ~1700 episodes total). Each raw video frame is encoded once with Wan2.2-VAE, then saved as .pt tensors for direct loading during action-policy… See the full description on the dataset page: https://huggingface.co/datasets/MangoGoes/libero4in1_wan2.2vae_latent_dataset.Wan2.2-T2V-Activations-FP4Wan2.2-I2V-Activations-INT4Wan2.2-I2V-Activations-FP4Wan2.2-Animate-14B-COPY
Wan2.2
💜 Wan ｜ 🖥️ GitHub | 🤗 Hugging Face | 🤖 ModelScope | 📑 Paper | 📑 Blog | 💬 Discord
📕 使用指南(中文) | 📘 User Guide(English) | 💬 WeChat(微信)
Wan: Open and Advanced Large-Scale Video Generative Models
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:
👍 Effective MoE Architecture: Wan2.2… See the full description on the dataset page: https://huggingface.co/datasets/Bohdanio2408/Wan2.2-Animate-14B-COPY.Wan_2.2_I2V_10stepsWan_2.2_T2V_10steps_GGUFwan2.2-rocm-profiles
Wan2.2 Sequence Parallel ROCm Profiles (MI300X)
This dataset contains PyTorch/Perfetto traces, offline execution logs, serving benchmarks, and comparative reports for Wan2.2-T2V-A14B sequence-parallel runs on AMD ROCm (gfx942, 8x MI300X node).
Dataset Directory Structure
reports/ / Root:
wan22_rocm_sp_sweep_analysis.md: 3-way sequence parallel topology comparison report.
wan22_profile_u4_r1_analysis.md: Detailed analysis of the Ulysses-4 topology.… See the full description on the dataset page: https://huggingface.co/datasets/Akshat/wan2.2-rocm-profiles.Wan_2.2_I2V_10steps_GGUFWan_2.2_T2V_10stepswan2.2_production_values
Wan2.2 Production Values — VSA Block-Sparse Attention Inputs + Reference
Real, captured production inputs for the Video Sparse Attention (VSA) fine
block-sparse attention stage of Wan2.2 T2V-A14B, recorded from an actual
generate.py run at 832×480 / 81 frames, plus the reference Triton kernel
and the scoring rule.
This is a fixed, non-gameable benchmark for proposing a faster VSA forward
kernel: optimize on these exact tensors, score against the reference at the
tolerance below.… See the full description on the dataset page: https://huggingface.co/datasets/baseten-admin/wan2.2_production_values.wan2.2_nsfwwan2.2Wan2.2-T2V-Activations-INT4VidPair-Halluc-Wan2.2wan2.2_outputWan2.2PussyandAnusWan2.2wan2.2Wan2.2josielynn_wan2.2-datasetwan2.2_i2v_suckdildoWan2.2-Pantanos-CollectionHello there,
Heres a collection of the stuff I regulary use to create interstellar dimensions
wan_2.2Wan2.2_Checkpoints