thu-ml / thu-ml/TurboDiffusion

I keep showing compilation errors

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Description

My CUDA is 12.6 and my graphics card is A6000 Ada. When I asked GPT, they said the problem is that my model is a full-scale model, but the model architecture is INT8 quantized architecture, which caused the problem. How should I solve it?

PyTorch version : 2.8.0+cu126
Torch CUDA version : 12.6
CUDA available : True
GPU name : NVIDIA RTX 6000 Ada Generation
Compute capability : (8, 9)

partial output:
/home/ubuntu/miniconda3/envs/turbodiffusion/lib/python3.11/site-packages/megatron/core/distributed/fsdp/src/megatron_fsdp/utils.py:109: UserWarning: Transformer Engine and Apex are not installed. Falling back to local implementations of multi_tensor_applier and multi_tensor_scale
warnings.warn(
W1218 15:30:39.892000 15829 site-packages/torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
W1218 15:30:39.892000 15829 site-packages/torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
fused_indices_to_multihot has reached end of life. Please migrate to a non-experimental function.
Warning: Sage2++ NOT enabled
[12-18 15:30:39|INFO|inference/wan2.2_i2v_infer.py:83:] Computing embedding for prompt: POV selfie video, ultra-messy and extremely fast. A white cat in sunglasses stands on a surfboard with a neutral look when the board suddenly whips sideways, throwing cat and camera into the water; the frame dives sharply downward, swallowed by violent bursts of bubbles, spinning turbulence, and smeared water streaks as the camera sinks. Shadows thicken, pressure ripples distort the edges, and loose bubbles rush upward past the lens, showing the camera is still sinking. Then the cat kicks upward with explosive speed, dragging the view through churning bubbles and rapidly brightening water as sunlight floods back in; the camera races upward, water streaming off the lens, and finally breaks the surface in a sudden blast of light and spray, snapping back into a crooked, frantic selfie as the cat resurfaces.
[12-18 15:30:39|INFO|rcm/utils/umt5.py:495:init] loading checkpoints/models_t5_umt5-xxl-enc-bf16.pth
[12-18 15:30:52|INFO|inference/wan2.2_i2v_infer.py:88:] Loading DiT models.
[12-18 15:30:53|INFO|rcm/networks/wan2pt2.py:767:enable_selective_checkpoint] Enable selective checkpoint with mm_only, for every 1 blocks. Total blocks: 40
Traceback (most recent call last):
File "/home/ubuntu/16T/lsm/TurboDiffusion/turbodiffusion/inference/wan2.2_i2v_infer.py", line 89, in
high_noise_model = create_model(dit_path=args.high_noise_model_path, args=args).cpu()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ubuntu/16T/lsm/TurboDiffusion/turbodiffusion/inference/modify_model.py", line 123, in create_model
net.load_state_dict(state_dict, assign=True)
File "/home/ubuntu/miniconda3/envs/turbodiffusion/lib/python3.11/site-packages/torch/nn/modules/module.py", line 2624, in load_state_dict
raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for WanModel:
Missing key(s) in state_dict: "blocks.0._checkpoint_wrapped_module.self_attn.q.int8_weight", "blocks.0._checkpoint_wrapped_module.self_attn.q.scale", "blocks.0._checkpoint_wrapped_module.self_attn.k.int8_weight", "blocks.0._checkpoint_wrapped_module.self_attn.k.scale", "blocks.0._checkpoint_wrapped_module.self_attn.v.int8_weight", "blocks.0._checkpoint_wrapped_module.self_attn.v.scale", "blocks.0._checkpoint_wrapped_module.self_attn.o.int8_weight", "blocks.0._checkpoint_wrapped_module.self_attn.o.scale", "blocks.0._checkpoint_wrapped_module.cross_attn.q.int8_weight", "blocks.0._checkpoint_wrapped_module.cross_attn.q.scale", "blocks.0._checkpoint_wrapped_module.cross_attn.k.int8_weight", "blocks.0._checkpoint_wrapped_module.cross_attn.k.scale", "blocks.0._checkpoint_wrapped_module.cross_attn.v.int8_weight", "blocks.0._checkpoint_wrapped_module.cross_attn.v.scale", "blocks.0._checkpoint_wrapped_module.cross_attn.o.int8_weight", "blocks.0._checkpoint_wrapped_module.cross_attn.o.scale", 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Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start at turb​​odiffusion/inference/wan2.2_i2v_infer.py line 89 and turb​​odiffusion/inference/modify_model.py line 123, where the WanModel state dict is loaded. Inspect the selected checkpoint and model configuration alongside the reported missing INT8 keys, then rerun the inference entry point. Done means the intended checkpoint loads without state-dict errors and reaches inference.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
ai, machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
30/100

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