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cbensimon/
WanTransformer3DModel-sm120-cu130-raa

Name: WanTransformer3DModel-sm120-cu130-raa
Availability: InStock

diffusers

ahead-of-time

pytorch

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[!NOTE] This README has been auto-generated by the HF Job run linked below and the whole repository is a reproducible artifact of this Job

Ahead-of-time repository

AoT repos contain pre-compiled binaries of PyTorch models, enabling:

fast startup times (no torch.compile needed)
significant speedup
ZeroGPU compatibility

How to use

python

import spaces
import torch
from diffusers import FlowMatchEulerDiscreteScheduler
from diffusers.pipelines.wan.pipeline_wan_i2v import WanImageToVideoPipeline
from diffusers.models.transformers.transformer_wan import WanTransformer3DModel
from diffusers.quantizers import PipelineQuantizationConfig
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
from torchao.quantization import quantize_
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig


pipeline = WanImageToVideoPipeline.from_pretrained(
    'Wan-AI/Wan2.2-I2V-A14B-Diffusers',
    transformer=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers',
        subfolder='transformer',
        torch_dtype=torch.bfloat16,
        device_map='cuda',
    ),
    transformer_2=WanTransformer3DModel.from_pretrained('cbensimon/Wan2.2-I2V-A14B-bf16-Diffusers',
        subfolder='transformer_2',
        torch_dtype=torch.bfloat16,
        device_map='cuda',
    ),
    torch_dtype=torch.bfloat16,
    quantization_config=PipelineQuantizationConfig(
        quant_mapping={
            "text_encoder": TransformersBitsAndBytesConfig(load_in_8bit=True),
        }
    ),
)
pipeline.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipeline.scheduler.config, shift=8.0)
pipeline.to('cuda')

quantize_(pipeline.transformer.blocks, Float8DynamicActivationFloat8WeightConfig())
quantize_(pipeline.transformer_2.blocks, Float8DynamicActivationFloat8WeightConfig())

spaces.aoti_load(
    module=pipeline.transformer,
    repo_id='cbensimon/WanTransformer3DModel-sm120-cu130-raa',
)

How to reproduce or customize

bash

# Install hf CLI
curl -LsSf https://hf.co/cli/install.sh | bash

# Login
hf auth login

# Get the job file and edit (user section) if needed
hf download cbensimon/WanTransformer3DModel-sm120-cu130-raa job.py --local-dir .

# Run the job and change flavor or image if needed
hf jobs uv run job.py \
    --flavor rtx-6000 \
    --image pytorch/pytorch:2.9.1-cuda13.0-cudnn9-devel \
    --secrets HF_TOKEN

# Or run locally with Docker
docker run --rm --gpus all \
    -v $PWD/job.py:/workspace/job.py \
    -e HF_TOKEN=$(hf auth token) \
    -e JOB_IMAGE=pytorch/pytorch:2.9.1-cuda13.0-cudnn9-devel \
    -e JOB_FLAVOR=rtx-6000 \
    pytorch/pytorch:2.9.1-cuda13.0-cudnn9-devel \
    uv run /workspace/job.py

The following job environment variables can be used to customize the repo name generation:

OUTPUT_REPO_NAMESPACE: taken from HF_TOKEN otherwise
OUTPUT_REPO_BASE_NAME: defaults to module class name
OUTPUT_REPO_ID: fully overtakes name generation

Samples

Generated as part of the compilation job: before and after compilation

Before compilation (193.05s)	After compilation (125.85s)

Speedup: 1.53x (note that this might not always reflect actual performance gain)

Environment

Click to expand

PyTorch version: 2.12.0+cu130
Is debug build: False
CUDA used to build PyTorch: 13.0
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 4.1.2
Libc version: glibc-2.35

Python version: 3.10.19 (main, Oct 31 2025, 23:02:46) [Clang 21.1.4 ] (64-bit runtime)
Python platform: Linux-6.17.0-1013-aws-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 13.0.48
CUDA_MODULE_LOADING set to: 
GPU models and configuration: GPU 0: NVIDIA RTX PRO 6000 Blackwell Server Edition
Nvidia driver version: 595.58.03
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A

CPU:
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           46 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  192
On-line CPU(s) list:                     0-191
Vendor ID:                               GenuineIntel
Model name:                              Intel(R) Xeon(R) Platinum 8559C
CPU family:                              6
Model:                                   207
Thread(s) per core:                      2
Core(s) per socket:                      48
Socket(s):                               2
Stepping:                                2
BogoMIPS:                                4800.00
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Hypervisor vendor:                       KVM
Virtualization type:                     full
L1d cache:                               4.5 MiB (96 instances)
L1i cache:                               3 MiB (96 instances)
L2 cache:                                192 MiB (96 instances)
L3 cache:                                640 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0-47,96-143
NUMA node1 CPU(s):                       48-95,144-191
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Old microcode:             Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Not affected
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Not affected
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Not affected

Versions of relevant libraries:
[pip3] Could not collect
[conda] numpy                        2.3.4            py311h2e04523_0       conda-forge
[conda] nvidia-cublas                13.0.0.19        pypi_0                pypi
[conda] nvidia-cuda-cupti            13.0.48          pypi_0                pypi
[conda] nvidia-cuda-nvrtc            13.0.48          pypi_0                pypi
[conda] nvidia-cuda-runtime          13.0.48          pypi_0                pypi
[conda] nvidia-cudnn-cu13            9.13.0.50        pypi_0                pypi
[conda] nvidia-cufft                 12.0.0.15        pypi_0                pypi
[conda] nvidia-curand                10.4.0.35        pypi_0                pypi
[conda] nvidia-cusolver              12.0.3.29        pypi_0                pypi
[conda] nvidia-cusparse              12.6.2.49        pypi_0                pypi
[conda] nvidia-cusparselt-cu13       0.8.0            pypi_0                pypi
[conda] nvidia-nccl-cu13             2.27.7           pypi_0                pypi
[conda] nvidia-nvjitlink             13.0.39          pypi_0                pypi
[conda] nvidia-nvtx                  13.0.39          pypi_0                pypi
[conda] optree                       0.17.0           pypi_0                pypi
[conda] torch                        2.9.1+cu130      pypi_0                pypi
[conda] torchaudio                   2.9.1+cu130      pypi_0                pypi
[conda] torchelastic                 0.2.2            pypi_0                pypi
[conda] torchvision                  0.24.1+cu130     pypi_0                pypi
[conda] triton                       3.5.1            pypi_0                pypi

Job run

cbensimon/None