Instructions to use yitongl/5090_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yitongl/5090_test 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/5090_test", 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
|
Download custom_code/README.md from yitongl/5090_test: direct link, hf CLI and curl.
- Browser
- Download file 2.44 kB
-
https://huggingface.co/yitongl/5090_test/resolve/main/custom_code/README.md
- Command line
-
hf download hf://yitongl/5090_test/custom_code/README.md
-
curl -L -o README.md https://huggingface.co/yitongl/5090_test/resolve/main/custom_code/README.md
2.44 kB
| # Standalone Source Map | |
| Important files: | |
| - `fastvideo/models/dits/wanvideo.py` | |
| - Defines `WanTransformerBlock_VSA`. | |
| - Adds `to_gate_compress`. | |
| - Passes `gate_compress` into self-attention. | |
| - `fastvideo/attention/backends/sparse_fp4_compress_attn.py` | |
| - Backend name: `SPARSE_FP4_COMPRESS_ATTN`. | |
| - Sparse FP4 main branch plus high-precision block-mean compress branch. | |
| - Prints `FASTVIDEO_BACKEND_CONFIRM: SPARSE_FP4_COMPRESS_ATTN is running`. | |
| - `fastvideo/attention/backends/sparse_fp4_attn.py` | |
| - Base sparse FP4 attention without compress branch. | |
| - `fastvideo/layers/nvfp4_fake_quant_linear.py` | |
| - `NVFP4FakeQuantReplicatedLinear`. | |
| - Wan replacement helpers for normal fake-quant QAT and SVD-LoRA variants. | |
| - `fastvideo/train/models/wan/wan.py` | |
| - Training-time switches for enabling fake-quant linear and gate quantization. | |
| - `fastvideo/pipelines/basic/wan/wan_dmd_pipeline.py` | |
| - Deployment/validation pipeline used by this checkpoint. | |
| - It constructs `FlowMatchEulerDiscreteScheduler` from `flow_shift`. | |
| - `fastvideo/pipelines/stages/denoising.py` | |
| - Contains `DmdDenoisingStage`, the actual solver path for online validation. | |
| - Uses fixed DMD timesteps, predicts clean video, and re-noises to the next | |
| timestep instead of running a normal UniPC/Euler `scheduler.step` loop. | |
| - `fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py` | |
| - Flow-match scheduler implementation used for DMD conversion and re-noising. | |
| - `fastvideo/platforms/interface.py`, `fastvideo/platforms/cuda.py` | |
| - Registers `SPARSE_FP4_ATTN` and `SPARSE_FP4_COMPRESS_ATTN`. | |
| - `fastvideo-kernel/python/fastvideo_kernel/...` | |
| - Block-sparse attention and quantization kernel source used by the backend. | |
| The full source snapshot in `../repo_source/` is preferred when running the | |
| included DCP export script. This directory is meant for quick inspection and | |
| porting into another inference stack. | |
| Solver reproduction summary: | |
| - Use `WanDMDPipeline`, not the vanilla diffusers scheduler in `model_index.json`. | |
| - The copied `scheduler/scheduler_config.json` is base-model metadata | |
| (`UniPCMultistepScheduler`, `solver_type=bh2`) and is not the solver used by | |
| online validation. | |
| - Matching solver: `DmdDenoisingStage` with `FlowMatchEulerDiscreteScheduler`, | |
| `shift=8.0`, DMD timesteps `[1000, 757, 522]`, `num_inference_steps=3`, | |
| `guidance_scale=6.0`, attention backend `SPARSE_FP4_COMPRESS_ATTN`, sparsity | |
| `0.8`. | |