After latest update, LTX image generation crashing
- Dominant language
- Python
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- 1d 7h
- Merged PRs (30d)
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Description
### Custom Node Testing
- [ ] I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
### Expected Behavior
Completes generation.
### Actual Behavior
Fails with error. This was working until I upgraded.
### Steps to Reproduce
LTX image2video generation
### Debug Logs
```powershell
[INFO] got prompt
[INFO] VAE load device: cuda:0, offload device: cpu, dtype: torch.float32
[INFO] model weight dtype torch.bfloat16, manual cast: None
[INFO] model_type FLUX
[INFO] VAE load device: cuda:0, offload device: cpu, dtype: torch.bfloat16
[WARNING] no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.
[INFO] Requested to load VideoVAE
[INFO] Model VideoVAE prepared for dynamic VRAM loading. 1384MB Staged. 0 patches attached.
[INFO] Found quantization metadata version 1
[INFO] Using MixedPrecisionOps for text encoder
[INFO] CLIP/text encoder model load device: cuda:0, offload device: cpu, current: cpu, dtype: torch.float16
[INFO] Requested to load LTXAVTEModel_
[INFO] Model LTXAVTEModel_ prepared for dynamic VRAM loading. 11200MB Staged. 0 patches attached. Force pre-loaded 400 weights: 1749 KB.
[INFO] Model LTXAVTEModel_ prepared for dynamic VRAM loading. 11200MB Staged. 0 patches attached. Force pre-loaded 400 weights: 1749 KB.
[INFO] Requested to load LTXAV
[INFO] Model LTXAV prepared for dynamic VRAM loading. 40050MB Staged. 0 patches attached. Force pre-loaded 608 weights: 3303 KB.
0%| | 0/8 [00:26
Contributor guide
Research direction
Start by reproducing the LTX image2video workflow after the upgrade, then trace the reported failure through comfy/ldm/lightricks/av_model.py, comfy/model_prefetch.py, and comfy/ops.py. Compare the failing execution with the last working version and use the traceback's hostbuf_file_reader_read failure to narrow the regression. Done means the same workflow completes generation without this exception.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 42/100