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bdce33b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | # Restore InfiniteTalk + FP8 TensorRT on a fresh H100
## 1. Code
tar xzf code.tar.gz -C /workspace
## 2. Weights (NOT in this backup - re-download, they are public)
hf download Wan-AI/Wan2.1-I2V-14B-480P --local-dir /workspace/weights/Wan2.1-I2V-14B-480P
hf download MeiGen-AI/InfiniteTalk --include "single/*" --local-dir /workspace/weights/InfiniteTalk
hf download TencentGameMate/chinese-wav2vec2-base --local-dir /workspace/weights/chinese-wav2vec2-base
hf download TencentGameMate/chinese-wav2vec2-base --revision refs/pr/1 --include "model.safetensors" --local-dir /workspace/weights/chinese-wav2vec2-base
hf download Kijai/WanVideo_comfy Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16.safetensors --local-dir /workspace/weights/loras
## 3. Python env (pin torch, or the CUDA stack gets swapped out from under you)
uv pip install -c constraints.txt -r pip-freeze.txt
Key pins: torch==2.8.0+cu128, xformers==0.0.32.post2, transformers==4.51.3,
flash_attn 2.8.3.post1 (cu12torch2.8, cxx11abiTRUE), and
export XFORMERS_IGNORE_FLASH_VERSION_CHECK=1
## 4. TRT engines
The engines in engines/ are a CACHE, valid only on the same GPU model + TRT
version (see VERSIONS.txt). Try them first:
python -c "
import tensorrt as trt
rt = trt.Runtime(trt.Logger(trt.Logger.ERROR))
e = rt.deserialize_cuda_engine(open('/workspace/engines/block0.plan','rb').read())
print('engines OK' if e else 'ENGINES INVALID -> rebuild')"
If invalid (different GPU/TRT), rebuild - ~18 min total, fully scripted:
# a) capture calibration activations (~5 min)
CAPTURE=1 <run generate_infinitetalk.py once, 81-frame clip> # see notes below
# b) build 40 FP8 engines (~13 min)
python /workspace/trt/build_engines.py
## 5. Run
/workspace/start_server.sh # warm server, loads model once
/workspace/generate.sh <image> <audio> "<prompt>" <name>
## Gotchas
- Engines are STATIC: 81-frame windows @ 448x832 (30,576 tokens) with the 4-step
I2V LoRA baked in. Change resolution / frame count / LoRA / steps -> rebuild.
- Under WAN_TRT=1 the PyTorch blocks are DELETED (multitalk.py:689 would
otherwise drag 27GB back onto the GPU).
- All 40 engine contexts share ONE scratch buffer (5.85GB); 40 private ones
would need 234GB.
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