Instructions to use yitongl/sparse_quant_exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yitongl/sparse_quant_exp with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yitongl/sparse_quant_exp", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| fb13abe775d8acd0aa59ce47ebad40178e4f2604fd191b6b02c1e34dd1e95cc4 ./README.md | |
| eb151afbefca213bbf1595e94b40547e1e431e850e6fc4cd187e506eb8e25b2d ./install_overlay.py | |
| 9d1d8dc58aab529270fe31eb1735d6a1382c0c6d36fccca122a8dbffa1b714fd ./overlay_files/fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn_ours_p.py | |
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| 58f4ac013e6755336212a7a6c9948b19dab0dafc00f4a3298591598df270cb39 ./overlay_files/fastvideo/api/compat.py | |
| 2b821b0e2e7bdb3581be6312ebbece42380a6ee28a7a982f0cf2dc71fab849c8 ./overlay_files/fastvideo/attention/backends/sparse_fp4_ours_p_attn.py | |
| a97adcc52d7558c49f418c09395fd1665e988ad290d2276b95f21dfca0f8eb7d ./overlay_files/fastvideo/attention/backends/video_sparse_attn.py | |
| 79ef6f38ec0f5bfe16b2b98327ad2ccd15f3c863dd87fd03affc5dbdaa0a8224 ./overlay_files/fastvideo/configs/models/dits/base.py | |
| 4bda44746a3626551ea9a9380d890f036087092fb99fce2d302642cce14a97ed ./overlay_files/fastvideo/configs/pipelines/wan.py | |
| 5926e29a594db13b116922f131db50631bf8adbf90fe5cec00a5e2f446bfb4ca ./overlay_files/fastvideo/configs/sample/base.py | |
| d99adcf607d982b38bbb5a70be60bf87f35d0e9f6f50752f3bceb68b34ce46c2 ./overlay_files/fastvideo/configs/sample/wan.py | |
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| ae2d8309472b09927da3e450dea52d9715dcabe5d6722fc2917130ae8d85adb4 ./overlay_files/fastvideo/entrypoints/cli/generate.py | |
| d0466769626e7fd497376c544904d56ba62847745eb52527896d96b99d76ba03 ./overlay_files/fastvideo/entrypoints/video_generator.py | |
| 73afe6b2ebe0f8cfe0a8ec762a7126161621ad97a64ebad628995f4a164b8b0e ./overlay_files/fastvideo/fastvideo_args.py | |
| ddcab6f4fd33c9813840571b6bf83bbbcea164b564166951ed4301297db6cef0 ./overlay_files/fastvideo/forward_context.py | |
| e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 ./overlay_files/fastvideo/pipelines/basic/wan/__init__.py | |
| deac1e22530a6a41c501629f5e8fce47a7af4e008f321cc8a4d734c5120ef4fe ./overlay_files/fastvideo/pipelines/basic/wan/wan_pipeline.py | |
| 8908223b3ff99cdb3206148a68a730c2a13d554a2fb1316db6f2f9672efac9e8 ./overlay_files/fastvideo/pipelines/composed_pipeline_base.py | |
| 6cfd128e782b7787a27ddd28a5e2d50cb4b0e2e9425d51d9780f14c91e8206f0 ./overlay_files/fastvideo/pipelines/stages/denoising.py | |
| 489388dbdd9e5e3ad24db3012bd9b108794509a9729891d7dd315a102abba828 ./overlay_files/fastvideo/platforms/cuda.py | |
| c046b1914041b59254bcdfe577aed20d6f007a72632ea1fe1ae92fa678eca760 ./overlay_files/fastvideo/platforms/interface.py | |
| 2456d39ca28019e12bb7ab007774e86348f0582a017bf0e6c91e2a01d654a1a0 ./overlay_files/fastvideo/train/models/wan/wan.py | |
| bc46e84b732567de6c0325223405daecd1226c623e303be33c7be9b5b7fdec08 ./overlay_files/fastvideo/training/training_pipeline.py | |
| 1d3898fa37e21029df6c37e05dc34ed7805a211c2f87de6642db890e5a8c6f2e ./overlay_files/fastvideo/training/wan_training_pipeline.py | |
| 1b2addfcb414ab65e20034394ee21a8af9ada58220a680b67d3b4233a0952268 ./requirements.txt | |
| 5087bb4ffe5721c41a12d92d8dfe439cd86aa1a5d3b3d259e30ad62711d95081 ./run.sh | |
| b826c8b059a000af6054ec099c36742d01e6a329ee77bc5936ae7562e9428409 ./run_inference.py | |
| 8ddeea65247d9fa31a4a8a2a5ce5abe068a911ff4d67871453555e1355af8ecf ./training_attention_settings.json | |